Version 6.0 Release Notes PC (DOS/WIN/NT) and Unix

Transcription

The Johns Hopkins
ACG® Case-Mix System
Version 6.0 Release Notes
PC (DOS/WIN/NT) and Unix
Version 6.0 – April, 2003
(Revised June 4, 2003)
This document was produced by the Health Services Research & Development Center at
The Johns Hopkins University Bloomberg
School of Public Health
© 1991-2003 The Johns Hopkins University. All Rights Reserved
Documentation Production Staff
Editor in Chief: Jonathan P. Weiner, Dr. P.H.
Senior Editors: Chad Abrams, M.A., David Bodycombe, ScD
Major contributors: Christopher B. Forrest, MD, PhD, Thomas M. Richards, MS
Barbara Starfield, MD,MPH, Hoon Byun, M.A.
Editorial Assistance: Klaus Lemke PhD, and Tracy Lieberman.
If users have questions regarding the software and its application, they are advised to contact the
organization from which they obtained the ACG software. Comments, criticisms, or corrections related
to this document should be directed to the Johns Hopkins ACG team (see below).
Such communication is encouraged.
ACG Project Coordinator
624 N. Broadway - Room 600
Baltimore, MD 21205-1901 USA
Telephone (410) 614-3957
Fax: (410) 955-0470
E-mail: askacg@jhsph.edu
Website: http://acg.jhsph.edu
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Important Warranty Limitation and Copyright Notices
Considerable attempts have been made to ensure that the materials included in this document are
accurate and appropriate to users’ needs. However, the responsibility for the appropriate
application of the Johns Hopkins University ACG Case-Mix System, its supporting software and
this documentation rests with the end-user and not the Johns Hopkins University or its agents.
No warranty is given or implied that any of the information, methods or approaches discussed in
this document are error-free. All terms and conditions associated with the software license,
including the Johns Hopkins University and its agents not bearing any liability for actions taken
by the user on the basis of software output, are in place and should be understood by the user.
THE JOHNS HOPKINS UNIVERSITY HEREBY DISCLAIMS ALL WARRANTIES,
INCLUDING THE WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A
PARTICULAR PURPOSE.
Licensed users of the ACG software may copy and distribute this documentation within their
organization. To facilitate this process, electronic files (in .PDF format) are available for
download at http://acg.jhsph.edu. Individuals not within a licensee organization may download
one copy for personal use only, and under no circumstances may they reproduce or distribute this
documentation on paper or in any electronic format, beyond this single personal use copy. All
copies so downloaded and /or distributed shall contain this notice and the identification of the
source of the software shall not be deleted.
The terms The Johns Hopkins ACG® Case-Mix System, ACG® System, ACG®, ADG®, Adjusted
Clinical Groups®, Ambulatory Care GroupsTM , Ambulatory Diagnostic GroupsTM, Johns Hopkins
Expanded Diagnosis ClustersTM , EDCsTM , Dino-ClustersTM , ACG Predictive Model, and
acgPM, are trademarks of The Johns Hopkins University. All materials in this document are
copyrighted by the Johns Hopkins University. It is an infringement of copyright law to develop
any derivative product based on the grouping algorithm or other information presented in this
document. Copyright 2003, The Johns Hopkins University. All rights reserved.
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Preface
Welcome to ACG Version 6.0
We hope that you will share our excitement about this newest release of the Johns Hopkins ACG
System. No previous ACG software update has been more significant in terms of additional features over
the prior version. No other version has had as many practical benefits for its users.
Health care risk adjustment and predictive modeling in general are not simple processes, but the
goal of these release notes is to help ease your way into these domains and to get you started as quickly as
possible in making full use of the array of features in ACG Release Version 6.0.
How to Use This Document
This document, the ACG Version 6.0 Release Notes, augments the last comprehensive
Johns Hopkins ACG “handbook,” the Release 5.0 Documentation and Application Manual. The
chapters provide existing ACG users with all of the information needed to begin to use the new
features and capabilities of this latest release. We recommend that first-time users of ACGs also
review the Release 5.0 Manual to learn about the core features of the ACG System, including the
system’s conceptual basis. Both sets of documents are distributed with the Version 6.0 software,
but if you need a copy of the Release 5.0 documentation, it is available on our website at
http://www.acg.jhsph.edu.
The release notes are organized into six sections as follows:
1. An overview of new features in ACG Release 6.0. This will quickly introduce you to
both the new areas of functionality and the modifications and updates of previous
features.
2. A comprehensive description of the new state-of-the-art predictive model, the
acgPM, which includes performance data and application suggestions.
3. Details of the additions to and refinements of the expanded diagnosis cluster (EDC)
disease-marker methodology, including several new reports that combine EDCs and
ACGs.
4. Introduction of the new “internal” relative value weights for ACGs that are now
included in the software.
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5. Suggested guidelines on how to make the best use of the tools and methodologies
now found in the ACG System. This section will help you quickly get to the bottom
line, and thus supports mission-critical clinical and financial management decisions.
6. A detailed technical software installation guide that describes all updates and
includes operating instructions like those found in the previous ACG version. You
will need only this section (and no previous documentation) to run the software.
We hope and expect that you will find the latest ACG software and this accompanying
documentation of value to you and your organization. As always, the Johns Hopkins ACG
development team welcomes your feedback. Please forward comments, questions, or suggestions
for improvement about this release document or our software to askacg@jhsph.edu.
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Section 1
Overview of New Features in ACG Version 6.0
The Johns Hopkins University ACG development team is very pleased to distribute version 6.0 of
our ACG risk-adjustment/case-mix software. This software includes several enhancements that
significantly expand both the breadth and depth of the Johns Hopkins ACG suite of diagnosis-based
health care measurement tools.
This version reflects our ongoing commitment to continued improvement and refinement of the
ACG System. Now more than ever, the ACG toolkit can serve as the measurement engine for a full array
of health care applications, including clinical care management, resource management, health services
finance and payment, and applied evaluation and scientific research.
Some of the new features in version 6.0 will advance and expand the clinical grouper and
measurement tools available to the experienced user. Other improvements will make the ACG System
easier to use for those just starting out. Many changes and enhancements will be of value to both groups.
As noted in the Preface, the key enhancements included in Version 6.0 include:
•
predictive modeling (including new reports),
•
additions and refinements to the expanded diagnosis cluster (EDC) methodology (including new
reports),
•
updating ICD-9 codes,
•
relative risk factors based on nationally representative data, and
•
an updated installation guide.
a.
AcgPM: Predictive Modeling for High-risk Case Identification and Future
Costs
The ACG Predictive Model (acgPM) permits the rapid identification of high-risk patients
who may benefit from care management services. The acgPM is the focal point of several
notable innovations:
•
focus on individualized care management,
•
incorporation of a new hospitalization propensity index,
•
ability to employ pharmacy use data where available,
•
integration of elements of our EDC technology, and
•
incorporation of a unique regression modeling strategy.
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The acgPM remains grounded in the disease burden perspective unique to the ACG
System, one that focuses on commonly occurring patterns of morbidity and assessment of all
types of medical need. The model also shares the modest data requirements of the ACG System.
This state-of-the-art predictive modeling risk identification module builds on the many
facets of the ACG system and several years of intensive research and development at Johns
Hopkins. According to all empirical assessments to date, the accuracy of the risk prediction
measures calculated by the acgPM equals or surpasses other available methods.
One of the outputs of the acgPM model is based on a sophisticated logistic regression
equation that maximizes the tool’s ability to identify your members who will be among the very
highest cost users in the next year (or some other future period). With the same data that you use
to assign standard ACGs, the acgPM module assigns each individual within your organization a
risk Probability Score that can be employed to array the members of your population from the
lowest to the highest risk.
As described in the following section, evaluations of the acgPM Probability Score
indicate that it is more accurate than case identification derived from prior use/prior (cost)
experience. Unlike other case identification methodologies based on extended hospital stays or
repeat specialist visits, the acgPM method helps to identify many persons before they actually
become expensive.
Another set of outputs is based on linear OLS (ordinary least squares) regression and can
be used to provide an estimate of cost for each individual in the subsequent time period. Termed
the Predicted Resource Index (PRI), this index can be used to assist clinical administrators,
actuaries, and others interested in valuing future expectations of resource use. This index can be
used for a wide range of actuarial/financial applications such as setting risk-adjusted capitation
payments or setting premiums.
In addition to offering measures of risk related to overall service use, the acgPM includes
a special component for predicting an individual’s risk of using pharmaceuticals. Moreover, the
acgPM can incorporate prior pharmacy use data, when obtainable, to improve the accuracy of its
predictions. If prior pharmacy use data are available within an organization, this combination of
pharmacy and the typical ACG risk factor information derived from ICD diagnosis information
is a powerful means for identifying individuals at risk of high future resource use.
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As discussed in more detail in the main body of this document, automatically generated
acgPM reports combine the Probability Score and the PRI. This enables users to focus attention
quickly on those individuals with case-manageable conditions at greatest risk for future high
expenditures and to provide estimates of what these individuals might cost if no intervention is
taken.
b.
Additions and Refinements to the Johns Hopkins Expanded Diagnosis
Clusters
The expanded diagnosis cluster (EDC) “disease marker” system originally emphasized
commonly occurring conditions treated primarily in ambulatory settings. For this version, the
EDC mapping underwent considerable refinement. To provide a more comprehensive
categorization approach for profiling morbidity, EDCs now include less commonly occurring
conditions, many of which may require hospitalization. All in all, 40 new EDC categories have
been added and affect 16 of the 27 major EDCs (MEDCs), and several existing EDCs have been
subdivided to provide additional clinical specificity.
To facilitate quick implementation of the EDC typology, the software print file now
incorporates a series of EDC-based “standardized morbidity ratios” (prevalence of EDCs within
your population, after controlling for age and sex) as well as a series of combined ACG/EDC
tables to help support case-management and disease-management programs.
c.
Updating ICD-9 Codes
The ICD-9 mapping tables for both the EDC and ACG/ADG groupers have been updated
to include all new codes introduced in the Center for Medicare and Medicaid Services official
ICD-9-CM (Version 20). As in years past, old or retired ICD-9 codes have been retained as part
of our algorithm because they were once valid and/or their interpretation is reasonably clear.
d.
Available Relative Value Weights for ACGs and RUBs
A key strength of the ACG suite of case-mix/risk adjustment measures is that the
grouping algorithms and analytic approaches in the software are very accessible to most
organizations. The straightforward ACG actuarial cell approach readily allows for the application
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of an organization’s own information to calibrate “weights” (or coefficients) to fine-tune the
analysis to best match the local situation and context. While many users of ACGs have applied
actuarial cell-based models, there is a growing body of users who wish to implement risk
adjustment and for whom no local cost data are available
To facilitate quick implementation of ACG technologies, Version 6.0 for the first time
incorporates concurrent (i.e., same-year resource use) ACG weights based on a large research
database (more than 2 million persons) younger than 65 years of age who were enrolled in
several U.S. commercial health insurance plans.
Version6.0 also makes available individual assignment to one of six ACG categories
(from low to high) termed ACG Resource Utilization Bands, or RUBs. Relative value weights
for each RUB category can be calculated by using local cost data or by using the concurrent
ACG weights built into the software. The supplied RUB categories cover six morbidity levels:
non-users, healthy users, low morbidity, moderate morbidity, high morbidity, and very high
morbidity. Figure 1 illustrates the percentage of the population and percentage of total dollars
associated with each RUB (based on a large research population); the bottom 40% of the
population consume less than 3% of all health care dollars while the top 0.7% in the highest
RUB category consume over 15%. RUBs can be used in the same fashion as ACG assignments.
For many applications, they dramatically simplify risk adjustment computational tasks while
retaining considerable ability to explain variations in resource use that are attributable to casemix.
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Figure 1: Distribution by ACG Comorbidity RUB
Distribution of ACG Comorbidity RUB
% of P opulation
% of Dollars
50.0%
45.1%
40.0%
30.0%
28.3%
25.8%
27.6%
24.4%
20.0%
13.9%
15.2%
12.5%
10.0%
0.4%
3.7%
2.4%
0.7%
0.0%
Nonusers
Healthy Users
Low
M oderate
High
Very High
ACG Comorbidity RUB Level
The relative values and RUB assignments available within the current version of our
software are also used to generate tables that combine ACG and EDC information to develop a
series of summary reports. These reports are discussed in Section 5 of the Release Notes,
Selecting the Right Tool from the Expanding ACG “Tool Box”.
The software-provided weights may be considered viable “external” or “reference”
weights for concurrent ACG or RUB analyses. These weights can be used as substitutes for
locally calibrated weights by those organizations with no available resource-use measures, or
whenever the population may be too small to produce reliable local weights. (In addition to the
section on this topic in the Version 6.0 Release Notes, please see Chapters 6, 8, and 11 of the
Version 5.0 Documentation and Application Manual for further discussion of the advantages and
disadvantages of local versus “software-provided” reference weights).
e.
Technical Notes
Although the functionality of Version 6.0 is greater, there are only modest changes from
Version 5.0 in terms of how to load and execute the software. Those familiar with the operation
of ACG Version 5.0 will find it easy to implement 6.0. All new features follow from the
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incorporation of a few new “reserved words” in the ACG control card file (used to pass data to
the software and to control what fields are written to the software’s output file). Readers should
review the Installation and Usage section to familiarize themselves with the technical details and
new specification requirements for 6.0. The guide included in this document can stand alone, and
there should be no need for programmers or analysts running the software to refer to the previous
Version 5.0 Manual.
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Section 2
The ACG Predictive Model: Helping to Manage Care for
Persons at Risk for High Future Cost
This section introduces the ACG Predictive Model (acgPM), an advanced tool for projecting
future resource use based upon concurrent data captured largely from standard claims files. In addition to
background on predictive modeling and on the construction of the acgPM, the text also provides model
performance specifications, and concludes with a discussion of output reports and how they may be used
to support case and disease management.
a.
Introduction
This chapter describes the newest addition to the ACG System’s toolkit, the ACG
Predictive Model (acgPM). The acgPM uses sophisticated statistical techniques to project the
impact of co-morbidity and other factors on an individual’s use of health care resources in a
subsequent time period. The acgPM is designed for prospective high-risk case identification and
will be of real value for assessing both the quality and appropriateness of patient care.
We begin with an overview of the acgPM and then launch into a discussion of our
acgPM development effort, the elements of the new models, and model performance assessment.
We conclude the chapter by addressing application issues.
i.
Model Offers Fast Identification of High-risk Patients
The acgPM permits the rapid identification of high-risk patients who may benefit from
care management services. The acgPM remains grounded in the disease burden perspective
unique to the ACG System, which focuses on commonly occurring patterns of morbidity and
assessment of all types of medical need. This holistic method has repeatedly proved to have
many advantages over comparable case-mix adjustment approaches that include only a limited
set of disease or episode categories. Also, our predictive model’s straightforward approach to
integrating clinically relevant risk factors offers advantages over “black box” strategies based on
complex clinical algorithms for data-mining or on artificial intelligence.
The Venn diagram provided in Figure 1 graphically depicts the predictive modeling
challenge. Traditionally plans have made use of data on prior experiences (the Actual High-risk
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Year 1 ellipse) to project individuals likely to remain costly in the following year. This
projection represents an amalgam of patients that includes those who have continuing chronic
conditions and also those who go through acute events or injuries that do not recur. The acgPM
identifies another group of individuals (the Predicted High Risk ellipse) that overlaps the priorcost ellipse but also identifies individuals who were NOT high cost in Year 1 (the shaded area).
Thus an important attribute of predictive model performance is the size of the shaded area.
Figure 1. Identifying High-cost Persons Who Were Not Previously High Cost
ii.
Model Outputs Serve Dual Purposes
The acgPM software produces two types of predictive risk indicators: (1) a probability
score representing the likelihood that a member will be among those persons using extraordinary
health care resources in the coming year and (2) a predicted resource index that expresses
anticipated resource use as a relative value.
Probability scores are used because the clinical decision-making process is often couched
in terms of likelihood or odds. When employing the probability score, users can set their own
definition of high resource use by setting some minimum threshold. Thus, for the purposes of
case management, a health plan might choose to consider only those individuals with a
probability of .6 or more of falling within the “high-risk” group during the following year. Our
experience suggests this cut-off would identify about one half of 1% of the plan’s members.
There are performance tradeoffs to be made (e.g., increased positive predicted values with lower
sensitivities) when setting minimum thresholds. Setting higher probability thresholds permits
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prediction with greater accuracy but with a greater chance of missing potentially high-risk cases.
These tradeoffs are discussed later in this chapter. The acgPM probability score has been tailored
to case identification and thus will be especially useful to case managers in targeting patients for
intervention.
The second acgPM output, the predicted resource index, can be applied to calculate
expected resource dollars. Case managers will be able to calculate expected differences between
current and projected future costs to prioritize interventions that could have the highest impact.
Health plans and others will also find this model output to be a useful tool for rate setting and
financially related decisions.
iii.
Models Forecast Overall and Pharmacy-specific Expenses
The acgPM output provides two separate sets of probability scores and predictive
resource indexes: the first for total cost, and the second for pharmacy-specific cost. The total cost
output represents an overall measure of inpatient and outpatient resource use that is the main
focus of the prediction model. The model also provides pharmacy resource predictors because
pharmacy-specific cost has become a major component of overall expenditures and is currently
the focus of many payment and delivery organizations. Although there is a correlation between
medical care service use and pharmacy use, the relationship is not one-to-one. Disease
management evaluations are showing that total cost savings can be achieved, primarily because
of reduced inpatient care, while pharmaceutical use increases.
iv.
Identification by Prior Costs Alone Omits Many Cases
Prior cost and other utilization experience (e.g., extended hospitalization) are often used
as the basis for identifying individuals for inclusion in intensive case management. Chronically
ill individuals who have had significant health care use in the past often do continue these
patterns of high care use into the future. Prior use measures also identify patients with a highcost acute event, which may have no bearing on healthcare use in the future. Still others who had
previously been less intensive users of health care resources may enter a high-use phase. For
especially high-risk cases, reliance on prior use alone will yield a very incomplete picture. In
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Figure 2 we have pooled together those persons within a large health plan data set whom were
successfully predicted to be high use (cost) by either the acgPM or by prior cost experience.1
Figure 2. Percentage of Cases Correctly Predicted to Be at High-risk for Using
Extraordinary Health Care Resources, by Prediction Strategy
acgPM
46%
Prior
Cost
27%
acgPM
Both
27%
Prior Cost
Both
Overall, the acgPM identified 73% of the high-risk cases that were successfully captured
by either approach. The acgPM uniquely accounted for nearly half of the successfully identified
high-risk patients. These cases were not previously high resource users and would be missed if
prior cost alone were used in case identification.
v.
The acgPM Adds Another Tool to the ACG System Without
Increased Data Collection
The acgPM substantially improves high-use case identification and does so without
increasing the minimal data collection effort currently required for the ACG software. The
acgPM constructs its risk factor information from claims data streams containing only age, sex,
and diagnostic information. If a plan has historical pharmacy-cost information for each person
(the full drug claims history is not needed), this summary measure of pharmacy use can
optionally be added to the input data stream for enhanced model performance. Moreover, the
1
The high risk group in this comparison included those with an acgPM probability score of 0.6 or more of being
among those persons with the top 5% of total costs next year. An equivalent number of the highest prior cost cases
in year –1 were then identified for comparison. These two groups each account for about ½ of 1% of the test
population.
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acgPM is bundled within the current ACG System, and thus the full capabilities of ACGs,
including provider profiling and risk-adjusted capitation determination, are available as part of
the standard software package.
b.
Developing the acgPM
i.
What is a Predictive Model?
Predictive modeling in the health care management context is generally defined as a
process that applies existing patient data to identify prospectively persons with high medical
need who are “at risk” for above-average future medical service utilization. Such future resource
use is often, although not always, linked to negative health outcomes.
A predictive model can incorporate information from a wide array of sources and can rely
on many statistical approaches—from the very simple to the very complex. As noted, one simple
approach is to use contemporary cost data as a predictor of future high cost. For example, a plan
could assume that if a person is very high cost this year, he or she is likely also to have a high
care-utilization experience in the future. Other models use multivariable statistical analysis and
data available from a range of sources for their predictions. The basis for many of these efforts is
ordinary least squares regression. Some predictive models are based upon neural networks.
These are sometimes termed “artificial intelligence” approaches, given that neural networks
represent collections of mathematical models that emulate the processes observed in biological
nervous systems, including the capability to “adapt and learn.” From the perspective of either
empirical accuracy or performance, no modeling method has yet to demonstrate clear superiority.
One of the main challenges facing any modeling strategy is the limited risk factor data on
which the models can be based. Currently, the commercially available prediction tools depend
largely on standard medical care claims (age, sex, diagnoses, procedures, prescriptions, service
dates, and cost). Model coefficients are developed using one year’s data to “predict” a second
year. With the limited source of risk factor data, model predictive capabilities are limited as well.
All predictive models are tools that must be used with good clinical and managerial judgment
and other sources of information in making decisions.
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ii.
Historical Use of ACG for PM
Although a separate predictive model is new to the ACG System as of version 6.0, for
years elements of the ACG case-mix grouping algorithms have been successfully adapted by
users to apply their own customized predictive models. ACGs work very well for this purpose
because, at their core, they focus on the dimensions that help predict high risk, such as:
•
•
•
•
•
persistence of the conditions,
seriousness/severity of the conditions,
the co-morbid nature of disease,
the likelihood of a negative outcome, and
the need for high levels of medical services.
Prior work by Starfield et al. (Primary Care, Co-morbidity, and Case Management,
presented at the Conference on Health Care Risk Adjustment, Minneapolis, MN, May 2, 2001
and available in the virtual ACG library at: http://acg.jhsph.edu) suggests that the components of
the ACG System have inherent utility as predictors of high future costs. As is shown in Table 1,
the co-occurrence of ADGs and the presence of selected ACGs are superior to prior
hospitalizations in predicting high costs in subsequent years.
Table 1. Potential Influence of Prior Co-Morbidity and Hospitalization on Future Cost.
Measure
2+ Hospitalizations
12+ ADGs
4+ Major ADGs
Selected ACGs
Total
Percentage
of
Members
0.7%
0.9%
0.4%
1.7%
<3.7%
Percentage
High Cost in
Year 2
53.3%
65.6%
72.4%
60.0%
Percentage
High Cost in
Year 3
51.5%
66.1%
70.1%
52.0%
For other successful applications of the ACG System components to predict high-risk
cases, see the proceedings of the 2002 International Johns Hopkins ACG Risk Adjustment
Conference, Baltimore, MD, April 28, 2002 (available in the virtual ACG library at:
http://acg.jhsph.edu).
14
c.
The “Nuts & Bolts” of the acgPM Model
i.
Specifying the Predictor Variables
The acgPM modeling strategy makes use of the comprehensive array of morbidity
metrics that are available within ACGs. The model incorporates the morbidity-based ACGs,
selected disease-specific EDCs, and a newly developed diagnostic indicator of the likelihood that
someone will be hospitalized in an ensuing year. We have also added an indicator of the level of
prior pharmacy use.
First, a brief word on the issue of prior-use measures. Prior use is a fallible indicator of
future use because it includes many acute conditions that get resolved. The acgPM focuses on
individuals with a high morbidity burden and with high-impact chronic conditions that are likely
to continue to require significant health care resources. Prior-use measures are also not
appropriate as “risk factors” for “risk-adjusted” rate setting or profiling as they potentially could
provide incentives to overuse resources. That is, providers can readily increase the risk rating of
their patient (and potentially reimbursement) simply by ordering more services. Including prior
use is appropriate for high-risk case identification since the goal is to identify and potentially
intervene among high-cost users. Several alternative prior-use measures were assessed for
inclusion in the acgPM model, but levels of previous pharmacy expense proved to be the most
powerful resource predictor with the clearest clinical implications and with a minimal addition to
the burden of data collection.
The acgPM “risk factor” variables used in the model are as follows:
•
•
•
•
•
•
•
age (seven age groups from infants to 64 years of age),
sex,
ACGs (three broad morbidity groupings from low to high in addition to selected
individual ACGs),
a “hospital dominant” marker (reflecting diagnoses where hospital care was “dominant,”
though care could be provided in a variety of settings),
identification of pregnancy, where no delivery has yet occurred,
pharmacy expense levels, and
EDCs (a limited set that represent high impact and chronic conditions).
Specific EDC disease markers were incorporated into the model if they represented
•
•
common high-cost chronic conditions that were frequent targets for disease management
programs,
uncommon, but high impact on both cost and health, conditions,
15
•
•
•
conditions for which the evidence linking health care to outcomes is
strong,
complications that potentially signify instability in a chronic illness. (e.g., retinopathy), or
conditions that are a major biologic influence on health status (e.g., transplant status,
malignancy).
The focus of the current acgPM software release is on the non-elderly (i.e., under 65
years of age) population.
The specific ACGs and EDCs that were used in building acgPM are documented in
Appendix 1.
ii.
Defining the Model Outputs
As noted earlier, the acgPM model offers two types of outputs: a probability score of
being a member of the high-risk1 group next year and a predicted resource index reflecting
expected cumulative resource use. The two indicators are intended for different purposes (case
selection for the former and cost estimation for the latter) and benefit from somewhat different
statistical methodologies. Probability scores range between zero and one. For example,
conceptually, an individual with a probability score of .4 has a 40 in 100 chance of being in the
high-risk cohort next year. The predicted resource index ranges from zero to roughly 40 with a
population mean of 1.0. The index can be readily converted to a predicted dollar amount. These
two outputs are repeated for both total costs and for pharmacy costs only.
The Johns Hopkins ACG development team chose to use logistic regression (logit) to
develop the probability score for a patient becoming a member of the high-risk group next year.
This is an important departure from the prevailing strategies for high-risk case identification that
usually employ ordinary least squares (OLS) regression based on linear modeling strategies.
Logit models are best for predicting events (yes/no occurrences)—in this case, being a high user
of resources
In terms of estimating resource use using multivariate methods, regression is the most
effective strategy for estimating dollars. Therefore we used an OLS model for the resource-use
prediction component of our model. The acgPM’s predicted resource index is presented as a
2
Based upon repeated testing of alternative thresholds, for model development we have defined “high risk” to
represent individuals whose predicted costs for the following year are expected to fall within the top 5% of a plan’s
members. This choice of high risk definition does not preclude users from adopting other definitions, e.g., the top
1%, ½ or 1% or even top 10% of plan members.
16
relative value that can be adjusted to reflect local mean costs. This adjusted scale can be readily
converted into dollar amounts comparable to future cost estimates by a simple algebraic process.
The process for deriving dollar values is discussed later in this chapter.
d.
A Framework for Discussing the Performance of Predictive Models
There are many different ways to apply the acgPM model, and each organization will
have unique data and contextual issues. Furthermore, there are few standard approaches in the
literature for evaluating the “accuracy” of any predictive model for resource use. Thus assessing
and reporting the performance of predictive models is not straightforward. We are providing
users with several alternative ways to assess performance because ACG users tend to be
discerning with regard to appropriate methods and statistical techniques.
What follows is a summary of an evaluation that assessed the performance of our models
in helping to identify persons who are members of high-risk/high-cost cohorts in the subsequent
year. We tested the models using actual data from a very large data set, consisting of over two
million lives enrolled in several health plans. We adopted a split half approach, using a random
selection of half the observations to build the models and the other half of the observations to
validate the model. The performance figures reported in the following section are based on the
validation half of the data. Unless otherwise specified, we apply the version of our model that
includes pharmacy cost as one of the risk factor inputs.
One way to assess predictive models is to ascertain how well they classify cases as
actually being a member of a high-risk group in a future period. This yes/no accuracy assessment
is similar in many ways to the statistical/epidemiologic approaches that are used to assess the
accuracy of diagnostic screening tests or exams. However, this quantitative approach does not
tell the full story regarding model performance. Other important questions are, What are the
prediction characteristics of the model? and Can it aid in the identification of cases where
intervention is possible and where care can be improved? There is no explicit “test” to determine
this result, but our performance assessment attempts to consider this capability as well.
Epidemiologists often use sensitivity and specificity to assess the validity of screening
tests. These performance indicators are defined as follows:
•
Sensitivity is the percentage of true high-risk cases that are successfully identified:
17
Sensitivity = true positives/(true positives + false negatives).
•
Specificity is the percentage of true low-risk cases that are successfully identified:
Specificity = true negatives/(true negatives + false positives).
Specificity is not especially useful in assessing this or any other predictive model since
the focus is on only a very small subset of high-risk persons and a large number of true
negatives. Positive predictive value (PPV) represents a potentially more useful alternative to
sensitivity.
•
PPV is defined as the probability that someone predicted by the model to have high
expected Year 2 resource use does, in fact, have high Year 2 resource use.
Mathematically, this is expressed as:
PPV = true positives/(true positives + false positives).
A PPV score gives information about the likelihood that a person who tests “positive” (in
this case is predicted to be high-risk) actually will be a high-resource user in Year 2.
Finally, another widely used measure of the ability of such models to correctly classify
patients is the c-statistic. The c-statistic provides an overall measure of model performance and
represents the probability that an observation is correctly classified as a true positive or true
negative along a continuum of “test thresholds” (in this case probability score thresholds). The
closer the c-statistic is to 1.0, the better the model. For a summary of these and other
performance indicators that are often applied to diagnostic tests and predictive models, see
Appendix 2.
e.
Performance of the acgPM Model
We sought to assess our model’s performance by asking several key questions:
•
•
•
•
How well does acgPM improve upon prior cost alone in identifying high-risk cases?
Do the cases identified by the model represent a group meriting intervention?
How well does acgPM do in estimating future costs of care?
What added predictive value is gained by including the optional pharmacy cost predictor?
18
i.
How Well Does the acgPM Improve on Prior Cost Alone in
Identifying High-risk Cases?
The statistical properties of the acgPM total cost model are compared to prior cost only
predictions in Table 2. The performance of the acgPM is shown with respect to a series of
probability “thresholds” based on the prediction scores output by the model for all members of a
large health plan “test” population.
Table 2 shows the acgPM’s accuracy at six different probability score cut-offs within the
large health plan test population of about 410,000 persons (under the age of 65 years). By setting
the risk threshold low, e.g., .4 or higher, a higher percentage of cases is included (in the example,
about 1.33% of the population). Set the risk threshold higher and select a lower percentage of
cases (only 0.10% at a minimum threshold of .9 or higher). Thus certainty, expressed as the PPV,
comes at a price. By setting a high threshold, you come close to certainty that every case you
select will become high cost in Year 2. Set a lower threshold and there is less certainty but a
higher likelihood that you’re picking up all the potentially high-cost cases. Even with probability
floors as low as .4, the likelihood is still better than chance (via PPV) that an acgPM-predicted
high-risk patient will actually turn out to be a high consumer of resources.
For comparison purposes, in Table 2 we also show performance statistics associated with
using prior cost as the only predictor in an identically sized group. That is, we identified the
highest cost individuals from Year 1 on the basis of actual experience. This “prior cost” cohort
was selected to be exactly the same size as the number of individuals identified using the various
acgPM probability thresholds (see the proportion of population in column 2 of Table 2.
Table 2 indicates that the acgPM consistently outperforms prior cost in predicting actual
Year 2 cost. Sensitivity for both the acgPM and the prior cost groups is low given that these
represent very small highly targeted groups and thus do not capture many cases. However, as the
PPVs suggest, most of the identified cases turn out to be truly high cost in the following year.
19
Table 2. Model Performance (Total Cost) at Different acgPM Probability Scores vs. Samesized Prior Cost Cohort
Prior Cost*
acgPM
Probability
Score
Threshold
.4
.5
.6
.7
.8
.9
Percentage
of
Population
1.33%
0.89%
0.63%
0.42%
0.25%
0.10%
Sensitivity
PPV
Sensitivity
PPV
0.16
0.12
0.09
0.06
0.04
0.02
0.59
0.66
0.72
0.76
0.80
0.84
0.12
0.09
0.07
0.05
0.03
0.01
0.46
0.50
0.54
0.57
0.62
0.69
Based on a validation sample of approximately 410,000 covered lives.
Prior cost cohorts were chosen to the same size as acgPM high-cost cohorts, i.e., if selecting cases with an acgPM
probability score of .7 or higher yielded 100 predicted high-risk patients, the prior cost comparison would be the 100
cases at the highest Year 1 cost.
*
ii.
Do the Cases Identified by the Model Represent a Group Meriting
Intervention?
There is a clear distinction in terms of mean cost between cases identified by the acgPM
as potentially high-risk and those not so identified. As shown in Figure 3, predicted high-risk
cases proved to be nearly 13 times as expensive in actual Year 2 dollars as those not so
identified. Individuals identified as being high-risk for pharmacy service use were almost 14
times as expensive as those not so identified. The model, thus, is identifying cases that are quite
distinct in terms of very high resource use.
20
Figure 3. Mean Costs of High-risk Versus All Other Cases by Cost Category
Dollars
$15,000
$13,787
Top 5%
All Other
$10,000
$5,000
$2,532
$1,072
$189
$0
Total
Pharmacy
Type of Cost
*With an acgPM probability score of 0.4 or higher.
The model also preferentially captures cases with chronic conditions for which case
management services are often available. As shown in Table 3, the acgPM-identified “at-risk”
group includes a higher percentage of these potentially case manageable chronic conditions than
does prior cost.
Table 3. Percentage of Selected Chronic Conditions in “High-risk” Cohorts Identified by
acgPM and Prior Cost Approaches.
Condition
Hypertension
Low Back Pain
Diabetes
Ischemic Heart Disease
Arthritis
Lipoid Metabolism
Congestive Heart Failure
Asthma
COPD
Depression
Chronic Renal Failure
Percentage of
High-risk Cohort
acgPM
Prior Cost
37.91
26.96
27.42
17.19
24.00
13.39
17.83
22.72
17.26
13.33
21.49
13.30
8.45
7.42
14.03
6.87
10.01
7.05
11.00
4.85
5.66
5.40
21
iii.
How Well Does acgPM Do in Estimating Future Costs of Care?
The acgPM offers a predictive resource index that is a relative value for resource use
related to both total and pharmacy costs in Year 2. This relative value can be used for many
applications including the estimation of future expenditures for specific subgroups of patients
who are targeted for case management. The probability model results and the linear modeling
results can work in tandem. The acgPM’s probability score output is recommended for selecting
patients, while the predictive resource index is recommended for calculating expected costs (or
potential cost savings) for population subgroups.
The R-squared statistic is commonly used to assess the performance of OLS-based linear
models. The R-squared expresses the percentage of variation in the outcome variable that is
explained by the model. We believe this is an appropriate evaluative benchmark for cost
predictors calculated on a linear basis (in our case the relative weight for resource use) but not
for yes/no logistic predictions (such as our risk probability score). When assessing our predicted
resource index model, the performance characteristics of the acgPM are comparable to prior cost:
the acgPM explains 14% of the variation in total charges compared to 12% for prior cost.
iv.
What Is the Performance “Bonus” if Pharmacy Cost Data Are
Available as a Risk Factor?
When they are available, we encourage the use of pharmacy cost data as a source of riskfactor information2. The acgPM performance statistics presented in the preceding text are based
on models that include pharmacy cost as an input variable. We compare the performance of the
acgPM probability score based on models with and without pharmacy cost data in Table 4. While
there is added information if pharmacy costs are available, if they are not available, the
performance penalty is not high, especially in predicting total costs.
3
The acgPM model includes a simple five category variable based on previous pharmacy cost history. Each person
is placed into one of five groupings, from very low to very high.
22
Table 4. Performance Characteristics of acgPM with and without Inclusion of Prior
Pharmacy Cost as a Risk Factor
Model
Sensitivity
PPV
C-Statistic
3
9
6
32
66
72
60
71
0.79
0.81
0.87
0.93
acgPM Total Cost
acgPM Total Cost with Pharmacy
acgPM Pharmacy Cost
acgPM Pharmacy Cost with Pharmacy
Model performance data are based on an acgPM probability score threshold of 0.6, representing approximately
half of 1% of the population for total cost and the top 2% of the population for pharmacy costs.
The inclusion of prior pharmacy costs as a predictor variable for the predicted resource
index has little impact on the model’s R-squared associated with total cost (.14 up from .11).
However, the addition of the pharmacy categorical cost information does have a significant
impact on the R-squared associated with prediction of next year’s pharmacy use (.34 up from
.17).
Understandably, it does appear that prior pharmacy cost is an important factor in
predicting future pharmacy costs. Users who wish to focus on pharmacy cost would be advised
to incorporate prior cost data if they are available.
f.
Reports
The software currently puts two reports into the ACG output stream: and (1) High Risk
Individuals and Expected Resource Use by Disease Category and (2) Frequency and Percent
Distribution of Probability Scores. Each report is discussed below.
i.
High Risk Individuals and Expected Resource Use by Disease
Category
This report (see Figure 4) shows how your population is distributed by EDC category and
how they are predicted to consume resources among some of the “riskier” probability levels.
The columns represent a range of probability scores from a minimum value to the highest
reported value. The 11 EDCs represent chronic conditions that are often the subject of case and
disease management initiatives. For an example of how to use this report see the following
section (Application Issues).
23
Figure 4. acgPM Report One.
ii.
Frequency and Percent Distribution of Probability Scores
This report (see Figure 5) is intended to provide you with a sense of how your population
is distributed according to probability score. The information is intended to help you establish
the size of potential cohorts for case/disease management. This distribution will, of course,
change within specific EDCs.
Figure 5. acgPM Report Two.
g.
Application Issues
i.
Using the acgPM Case Management Report
As described in the section on model performance, the acgPM identifies a group of
patients distinct from those who would have been selected by prior cost alone. Further, this
group of patients appears to have a higher percentage of those who are typically case managed.
To illustrate how the model might be used to better target individualized case management
24
interventions, we provide a sample output report (Table 5) as produced by the acgPM software
(if users request this with a control card). For a series of probability thresholds, the table shows
how individuals in a large health plan are distributed within selected chronic disease groups and
how their acgPM predicted resource use varies for each risk-level cohort. The diseases represent
a sampling of some of the conditions for which “disease management” or “case management”
services are often available within an integrated delivery organization.
These chronically ill patients are all projected to use resources well above the plan’s
average (of 1.00). In a comparison of the three potential “case management” cohorts defined on
the basis of the three alternative acgPM probability score thresholds (i.e., .4 or higher, .6 or
higher, .8 or higher), the projected intensity of resource use generally doubles from the lowest to
highest risk group within each disease. Moreover, when resource use in the top acgPM risk
category (with a score of .8 or higher) is compared with the cohort of persons with the disease,
but who were not identified as being in one of the top tier risk groups (i.e., those with probability
score of less than .4), the predicted expenditure variation is dramatic—at times almost ten-fold.
Persons with chronic renal failure experience very high predicted resource use within all
of the probability score cohorts. To a lesser degree, the same is true for congestive heart failure.
For conditions such as these, projected resource use appears to be high regardless of the
probability level. Aside from employing individualized case management, it may be especially
appropriate to employ disease management programs that address the cohort of patients with
these diseases. It is evident that persons within each disease group will require a different
approach to care management, but the various predictive measures produced by the acgPM
system will provide valuable additional information to allow clinical professionals to better
design and implement these interventions.
All of the remaining diseases that are depicted in Table 5 appear to be appropriate
candidates for case management. To have the greatest impact, it would be useful to focus on
those individuals at higher predictive risk levels who are currently experiencing relatively low
resource use. Our experience suggests that at least 10% of patients will fall into this group.
Finally, it is important to consider the comorbidity profiles of these high-risk groups. It
is likely that high risk cases are affected by multiple diseases and that the condition reported in
25
Table 5 may not be the primary cost driver. Thus patterns of comorbidities should also be
assessed in the course of planning case management initiatives.
Table 5. The Number of Cases and Associated Predicted acgPM Relative Resource Use by
Alternative acgPM Risk Probability Thresholds for Selected Chronic Conditions
Number of Cases
Probability Score
Category
Disease Category
Arthritis
Asthma
Diabetes
Hypertension
Hyperlipidemia
Low Back Pain
Depression
COPD
Total
17,679
27,863
16,991
50,122
9,330
1,634
31,240
61,980
10,190
742
6,204
≥0.4
≥0.6
940
463
764
386
1,307
716
2,064 1,011
971
514
460
292
1,170
529
1,493
723
599
298
308
253
545
301
≥0.8
Mean Predicted Relative
Resource Use
Probability Score
Category
<0.4
≥0.4
≥0.6
≥0.8
172 2.18 9.22
136 1.43 9.02
345 2.67 11.03
457 2.06 10.34
242 3.27 10.70
184 5.17 13.94
186 1.97 9.15
279 1.76 8.64
113 2.09 8.82
183 13.11 22.33
147 2.58 10.84
11.69
11.25
13.87
13.57
13.64
16.90
11.59
10.89
11.03
23.60
13.38
15.71
14.85
17.36
17.57
17.33
19.61
15.46
14.27
14.30
25.21
16.68
There are trade-offs involved in setting probability threshold levels for case
identification. The higher the minimum probability, the greater the projected resource use, but
also the smaller the target group. As noted earlier, higher probabilities also improve the
likelihood that cases identified do indeed turn out to be high resource users. The performance
implications at a range of thresholds based on the acgPM probability score among a large test
population are depicted graphically in Figure 6.
26
Performance Scale
Figure 6. Trends in Model Performance, by Probability Threshold
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
Sensitivity
Positive
Predictive
Value
0.1 0.2 0.4 0.5 0.6 0.7 0.8 0.9
Probability Threshold
The user’s choice of a case identification probability threshold relates to the desired
application, availability of resources for case management, and prevailing local practices.
Depending on the intervention an organization might prefer to maximize sensitivity, while at
other times, maximization of predictive value might be the goal. For example, when
implementing a customized web-based informational campaign within a health plan, the goal
may be to contact the highest 5% or 10% of the persons “at-risk.” This goal could represent all
those persons with a probability score of .1 or higher. Based on the sensitivity of the model at
these probability levels, it may be possible to capture over 50% of those persons who will be
members of the highest resource use cohort next year. However, the PPVs for this group will be
in the .25 range, and thus most of these individuals will not actually be members of the highest
resource use group next year. But given the non-intensive nature of the intervention, that is of
small concern. Moreover, even the individuals targeted for this program who do not end up in the
highest resource group in the subsequent period (the false positives) will still have risk and
associated resource use that is far higher than the underlying population.
Many types of individualized case management can be quite resource-intensive in their
own right, and so there should be a high likelihood that persons targeted for case management
are, in fact, truly high risk. Thus for these types of programs, very high PPVs are desirable. Still,
many patients who might be appropriate candidates for case management will be excluded from
27
selection, given the corresponding low sensitivities. It should be noted that even when cases are
included that have probabilities as low as .4, the overall odds of these individuals being very high
resource users next year are still much higher than random. In this case about 60% will be high
risk next year compared to 5% of the population overall if selected at random.
For individualized case management programs, having access to current and predicted
resource use data for candidate individuals can be of real utility in setting priorities for inclusion.
When there are more persons who would likely benefit than current program resources allow,
these data could be used to help make an argument for expanding such resources on the basis of
the potential return on investment (ROI). Using the approach described below (see the section
entitled Adjusting Relative Weights to Compute Predicted Costs), you can assign a projected
cost to each individual within your targeted high-risk group. Ordering this population by the
difference between current and projected cost will quickly highlight those individuals for whom
case management has the greatest potential.
28
APPENDICES
Appendix 1. ACGs and EDCs Included in the acgPM
ACGs
4220: 4-5 Other ADG Combinations, Age 1-17, 1+ Major ADGs
4420: 4-5 Other ADG Combinations, Age >44, 1 Major ADGs
4430: 4-5 Other ADG Combinations, Age >44, 2+ Major ADGs
4510: 6-9 Other ADG Combinations, Age 1-5, No Major ADGs
4610: 6-9 Other ADG Combinations, Age 6-17, No Major ADGs
4730: 6-9 Other ADG Combinations, Male, Age 18-34, 2+ Major ADGs
4830: 6-9 Other ADG Combinations, Female, Age 18-34, 2+ Major ADGs
4910: 6-9 Other ADG Combinations, Age >34, 0-1 Major ADGs
4940: 6-9 Other ADG Combinations, Age >34, 4+ Major ADGs
5010: 10+ Other ADG Combinations, Age 1-17 No Major ADGs
5020: 10+ Other ADG Combinations, Age 1-17, 1 Major ADGs
5030: 10+ Other ADG Combinations, Age 1-17, 2+ Major ADGs
5040: 10+ Other ADG Combinations, Age 18+, 0-1 Major ADGs
5050: 10+ Other ADG Combinations, Age 18+, 2 Major ADGs
5060: 10+ Other ADG Combinations, Age 18+, 3 Major ADGs
5070: 10+ Other ADG Combinations, Age 18+, 4+ Major ADGs
5320: Infants: 0-5 ADGs, 1+ Major ADGs
5330: Infants: 6+ ADGs, No Major
5340: Infants: 6+ ADGs, 1+ Major ADG
Pregnancy w/out Delivery
EDCs
ADM04: Complications of Mechanical Devices
ALL04: Asthma, w/o status asthmaticus
ALL05: Asthma, WITH status asthmaticus
ALL06: Disorders of the Immune System
CAR03: Ischemic heart disease (excluding acute myocardial infarction)
CAR04: Congenital heart disease
CAR05: Congestive heart failure
CAR06: Cardiac valve disorders
CAR07: Cardiomyopathy
CAR09: Cardiac arrhythmia
CAR14: Hypertension, w/o major complications
CAR15: Hypertension, WITH major complications
END02: Osteoporosis
END09: Type 1 Diabetes with major complicating condition
29
END08: Type 1 Diabetes w/o major complicating condition
END07: Type 2 Diabetes with major complicating condition
END06: Type 2 Diabetes w/o major complicating condition
EYE13: Diabetic Retinopathy
GAS02: Inflammatory bowel disease
GAS05: Chronic liver diseases
GAS06: Peptic ulcer disease
GAS12: Chronic pancreatitis
GSI08: Edema
GSU11: Peripheral vascular disease
GSU13: Aortic aneurysm
GSU14: Gastrointestinal Obstruction/Perforation
GTC01: Chromosomal anomalies
GUR04: Prostatic hypertrophy
HEM01: Hemolytic anemia
HEM05: Aplastic anemia
HEM06: Deep vein thrombosis
HEM07: Hemophilia, coagulation Disorder
INF04: HIV, AIDS
MAL02: Low impact malignant neoplasms
MAL03: High impact malignant neoplasms
MAL04: Malignant neoplasms, breast
MAL06: Malignant neoplasms, ovary
MAL07: Malignant neoplasms, esophagus
MAL08: Malignant neoplasms, kidney
MAL09: Malignant neoplasms, liver and biliary tract
MAL10: Malignant neoplasms, lung
MAL11: Malignant neoplasms, lymphomas
MAL12: Malignant neoplasms, colorectal
MAL13: Malignant neoplasms, pancreas
MAL14: Malignant neoplasms, prostate
MAL15: Malignant neoplasms, stomach
MAL16: Acute Leukemias
MAL18: Malignant neoplasms, bladder
MUS10: Fracture of neck of femur (hip)
MUS14: Low back pain
NUR05: Cerebrovascular disease
NUR07: Seizure disorders
NUR08: Multiple sclerosis
NUR09: Muscular dystrophy
NUR12: Quadriplegia and Paraplegia
NUR15: Head Injury
NUR16: Spinal Cord Injury/Disorders
NUR17: Paralytic Syndromes, Other
NUR18: Cerebral Palsy
NUR19: Developmental disorders
NUT02: Nutritional deficiencies
30
PSY01: Anxiety, neuroses
PSY03: Tobacco abuse
PSY05: Attention deficit disorder
PSY07: Schizophrenia and affective psychosis
PSY08: Personality disorders
PSY09: Depression
REC01: Cleft lip and palate
REC03: Chronic ulcer of the skin
REN01: Chronic renal failure
RES03: Cystic fibrosis
RES04: COPD
RES09: Tracheostomy
ACGs Included in the Three Resource Utilization Bands (RUBs)
Reference Group
ACG 0100 0200 0300 1100 1200 1600 5100 5110
5200 9900
ACG 0400 0500 0600 0900 1000 1300 1800 1900
2000 2100 2200 2300 2400 2500 2800 2900 3000
3100 3400 3900 4000 1711 1721 1731 1741
ACG 0700 0800 1400 1500 2600 2700 3200 3300
3500 3600 3700 3800 4100 4210 4310 4320 4410
4710 4720 4810 4820 5310 1751 1761 1771
ACG RUB Level 1 (Included in Intercept)
ACG RUB Level 2
ACG RUB Level 3
31
Appendix 2. Summary of Performance Measures for Diagnostic Testing*
“True” Condition
“Screening Test”
+
-
+
-
TP
(True Positive)
FN
(False Negative)
FP
(False Positive)
TN
(True Negative)
Predictive Value +
=
TP/ TP + FP
Predictive Value -
=
TN/ FN + TN
Sensitivity
=
TP/ TP + FN
Specificity
=
TN/ FP + TN
Receiver Operating
Characteristic (ROC)
=
Function of Sensitivity vs. 1- Specificity at
different screening thresholds
(C Statistic = area under curve)
* In the context of predictive modeling, a “+” for the “true” condition reflects a person being a
member of the high-risk cohort (i.e., represents with the top 5% of costs in Year 2). The “+” for
the “screening test” in this case would represent an individual with an acgPM probability score
that is about the threshold considered actionable (e.g., a score that would place a person in the
top half of 1% of the population.
32
Section 3
Improvements to the Johns Hopkins Expanded Diagnosis
Clusters
This section discusses Release 6.0 improvements to the Johns Hopkins expanded diagnosis
clusters (EDCs) methodology, including:
• refinements of the EDC taxonomy, and
• enhanced reporting features.
New users of the software are encouraged to review Chapter 13, “ ‘Dino-Clusters’: The Johns
Hopkins Expanded Diagnosis Clusters (EDCs)” of the Version 5.0 Documentation and Application
Manual for complete details of the EDC taxonomy, a discussion of the EDC output file, and technical
specifications for customizing EDC reports.
a.
Overview
Expanded diagnosis clusters (EDCs) were added to the ACG System in 2001 to provide
the ability to partition populations into diagnosis-specific subgroups for a more complete
understanding of case mix. Our intent was to condense the unwieldy list of ICD-9 codes into a
much smaller number of clinically homogeneous clusters. As was the case for the original
“diagnosis clusters,” our system places an emphasis on commonly occurring conditions treated
primarily in ambulatory settings. The EDC system has undergone considerable refinement and
now also includes less commonly occurring conditions, many of which may be treated in the
hospital. In some instances, the original EDCs have been further subdivided to better reflect the
effect of complicated illness, examples of which are presented on Table 1.
Table 7. Subdivision of EDCs for Additional Clinical Specificity
VERSION 5 EDC
ALL02 Asthma
CAR02 Hypertension
END01 Diabetes Mellitus
REVISED VERSION 6 EDCs
ALL04 Asthma, w/o status asthmaticus
ALL05 Asthma WITH status asthmaticus
CAR14 Hypertension, w/o major complications
CAR15 Hypertension, WITH major complications
END06 Type 2 diabetes w/o major complicating conditions
END07 Type 2 diabetes WITH major complicating conditions
END08 Type 1 diabetes w/o major complicating conditions
END09 Type 1 diabetes WITH major complicating conditions
As these examples illustrate, in some instances the main refinement was to split the
original category into complicated and uncomplicated subgroups. As a case in point, the asthma
33
EDC has been divided on the basis of the presence of status asthmaticus (i.e., an acute
exacerbation of asthma). In the cardiology cluster, hypertension has been divided on the basis of
the presence of a complicating condition. In other instances, the disease itself was further
delineated, such as within the endocrine cluster where diabetes mellitus has been split into four
categories—first by categorizing patients according to type 1 versus type 2 diabetes, and second
according to the presence of a major, complicating comorbidity. (See Table A.1 in the Appendix
to this chapter for a complete listing of these complicating conditions.)
In addition to splitting existing EDCs, new categories have been added to the current
software release. Table 2 provides a summary of new EDCs. In most instances, the additions
within a given major EDC have been modest. For example, in the Administrative cluster,
categories for transplant status and complications of mechanical devices were added. In the
Cardiology cluster, a new category for acute myocardial infarction (CAR11) and Cardiac
arrest/shock (CAR12) have been added. The malignancy and neurologic MEDC categories have
been substantially expanded.
All in all, 40 new EDC categories have been added and affect 16 of the 27 major EDC
(MEDC) clusters, and several existing EDCs have been subdivided to provide additional clinical
specificity. There are now a total of 236 EDC categories in ACG Release 6.0. For a sense of
how these EDCs are distributed in the “real world,” their frequency of occurrence in a large
under-65 population (2 million covered lives) is provided in Appendix A.2.
34
Table 8. Supplemental EDCs by MEDC Category
MEDC/EDC
CATEGORY
Administrative
ADM03
DESCRIPTION
ADM04
Transplant status
Complications of
mechanical devices
ALL06
Disorders of the immune
system
MEDC/EDC
CATEGORY
Malignancies (continued)
MAL10
MAL11
Allergy
MAL12
MAL13
MAL14
MAL15
Cardiovascular
CAR11
CAR12
Disorders of lipoid
metabolism
Acute Myocardial Infarction
Eye
EYE13
GastrointestinalHepatic
GAS11
GAS12
General Surgery
GSU13
GSU14
Hematologic
HEM06
HEM07
Infections
HEM08
Malignancies
MAL04
MAL05
MAL06
MAL07
MAL08
MAL09
b.
Diabetic retinopathy
Acute pancreatitis
Chronic pancreatitis
Aortic aneurysm
Gastrointestinal
obstruction/perforation
Aplastic anemia
Deep vein thrombosis
Septicemia
Malignant neoplasm, breast
Malignant neoplasm, cervix,
uterus
Malignant neoplasm, ovary
Malignant neoplasm,
esophagus
Malignant neoplasm, kidney
Malignant neoplasm, liver
and biliary tract
MAL16
MAL18
Musculoskeletal
MUS16
Neurologic
NUR12
NUR15
NUR16
NUR17
NUR18
NUR19
Psychosocial
PSY09
Renal
REN03
REN04
Respiratory
DESCRIPTION
Malignant neoplasm, lung
Malignant neoplasm,
lymphomas
Malignant neoplasm,
colorectal
Malignant neoplasm,
pancreas
Malignant neoplasm, prostate
Malignant neoplasm, stomach
Acute leukemia
Malignant neoplasm, bladder
Amputation status
Quadriplegia and paraplegia
Head injury
Spinal cord injury/disorders
Paralytic syndromes, other
Cerebral palsy
Developmental disorder
Depression
Acute renal failure
Nephritis/nephrosis
RES08
RES09
Pulmonary embolism
Tracheostomy
RES10
Respiratory arrest
SKN18
Benign neoplasm of skin and
subcutaneous tissues
Skin
New Reporting Features
The EDC reporting capabilities of ACG Release 6.0 have been significantly enhanced to
facilitate their easy implementation within your organization. The current automatically
generated reports include EDC distributions, tables combining disease-specific EDCs and the
morbidity-class, as well as age/sex-adjusted comparison of EDC distributions across populations.
35
EDCxRUB Reports
A series of reports combining the disease-specific EDC and ACG methodologies
provides managers with information that should be useful in better targeting their care
management programs. Reports are generated first by major EDC (or MEDC) level to provide a
summary overview, and then this set of summary tables is generated for each specific diagnosis
cluster. Examples of the new reports are provided in Tables 3 and 4.
Table 3: Percent distribution of each co-morbidity level within EDC (Samples)
RUB-1
RUB-2
RUB-3
RUB-4
RUB-5
EDC
Very Low
Low
Average
High Very High
--------------------------------- --------- --------- --------- --------- --------ADM01:General medical exam
19.8
32.9
39.9
6.2
1.3
ADM02:Surgical aftercare
4.7
19.3
46.6
18.9
10.4
ADM03:Transplant status
3.8
7.7
32.9
26.6
29.1
ADM04:Complications of mechanical
0.0
10.3
32.4
25.5
31.8
ALL01:Allergic reactions
0.0
36.2
53.6
8.5
1.6
ALL03:Allergic rhinitis
0.0
34.5
56.0
8.2
1.3
ALL04:Asthma, w/o status asthmati
0.0
23.6
63.2
10.7
2.5
ALL05:Asthma, with status asthmat
0.0
20.9
58.0
15.6
5.4
ALL06:Disorders of the immune sys
0.0
6.5
47.6
25.5
20.4
CAR01:Cardiovascular signs and sy
0.0
14.5
64.2
15.2
6.1
CAR03:Ischemic heart disease (exc
0.0
0.5
55.7
27.3
16.6
CAR04:Congenital heart disease
0.0
17.9
45.9
23.9
12.4
CAR05:Congestive heart failure
0.0
0.4
36.6
31.1
31.9
CAR06:Cardiac valve disorders
0.0
7.6
59.1
22.2
11.1
CAR07:Cardiomyopathy
0.0
2.2
43.8
30.1
23.9
CAR08:Heart murmur
12.3
25.8
44.5
11.9
5.4
CAR09:Cardiac arrhythmia
0.0
3.7
58.4
24.5
13.3
CAR10:Generalized atherosclerosis
0.0
7.0
43.7
25.4
23.9
CAR11:Disorders of lipoid metabol
0.0
17.3
68.0
10.4
4.2
CAR12:Acute myocardial infarction
0.0
0.2
21.3
39.3
39.2
CAR13:Cardiac arrest, shock
0.0
5.4
19.2
31.2
44.2
CAR14:Hypertension, w/o major com
0.0
20.6
64.7
10.2
4.5
CAR15:Hypertension, with major co
0.0
4.1
55.4
24.1
16.3
Each row in the tables represents a separate MEDC (or EDC) category, and the columns
array individuals within a particular MEDC (or EDC) into five (from very low to very high)
morbidity groupings that we term resource utilization bands (RUBs). RUB categories are based
on ACG assignments (see Chapter 8, “Calibrating ACGs for Intended Use: ACG Weights,
Resource Utilization Bands, and Carve-Outs,” in the Release 5.0 Documentation and Application
Manual). The first part of this two-part table (Table 3) presents the percentage distribution for
each EDCxRUB comorbidity level, and the second (Table 4) presents an estimate of each
group’s expected relative resource use. Focusing on the EDC for general medical exam,
ADM01, you see that the first row of Table 3 shows 19.8% of users with this EDC fell into
RUB-1 or a very low resource group, 32.9% fell into the low resource RUB-2, 39.9% fell into
36
average resource RUB-3, and so on. Looking at the same row in Table 4, you see that the
anticipated resource use for such individuals is ranges from a low of 0.19 to a high of 24.76 for
those in the highest resource group. Resource estimates provided in these tables are based on
nationally representative ACG weights built into the software (see the “Using the Available
Relative Value Weights” section for additional details on concurrent weights and RUB
assignments included as part of Release 6.0). These tables help to illustrate the variability of
costs within disease categories and will be useful to managers for better understanding resource
use. Generally it is not necessarily all individuals with selected diseases who are expensive;
rather, it is individuals with multiple comorbidities who consume most of the health care
resources.
Table 4: Estimated Concurrent Resource Use by RUB by EDC (Samples)
RUB-1
RUB-2
RUB-3
RUB-4
RUB-5
EDC
Very Low
Low
Average
High Very High
0.19
0.54
1.97
7.12
24.76
0.20
0.63
2.31
7.94
27.30
0.20
0.65
2.39
8.23
29.89
0.00
0.69
2.35
7.97
29.84
0.00
0.54
2.07
7.49
25.41
0.00
0.54
2.13
7.43
25.40
0.00
0.62
2.03
7.43
26.10
0.00
0.62
2.13
7.50
28.23
0.00
0.74
2.39
7.71
29.63
0.00
0.60
2.43
7.96
26.56
0.00
0.68
2.25
8.12
25.35
0.00
0.73
2.20
7.11
25.56
0.00
0.81
2.62
8.30
28.83
0.00
0.56
2.42
7.86
27.10
0.00
0.73
2.37
8.23
28.69
CAR08:Heart murmur
0.21
0.64
2.22
7.20
23.05
0.17
0.61
2.37
8.07
25.82
0.00
0.46
2.47
8.23
27.06
0.00
0.49
2.29
8.17
25.14
0.00
0.82
1.85
7.87
26.28
0.00
0.62
2.12
7.74
27.84
0.00
0.48
2.28
8.16
25.75
0.00
0.62
2.35
8.31
27.40
Standardized Morbidity Ratios
The current release generates a series of age/sex standardized EDC-based “morbidity
ratio” tables to assist with population-level profiling based on a user-defined population stratifier
that can be provided by means of the software’s input file (see the “Installation and Usage
Guide” for the technical details of implementing this feature). Separate reports are generated for
each population group defined by the user. Such information can help practitioners and
managers understand which specific conditions within a subgroup of interest are more or less
common (beyond statistical chance) than the overall population average. As illustrated in Table
5, summary statistics generated for each group in this case, by MEDC category, include
37
•
observed prevalence rates,
•
age/sex-expected prevalence,
•
standardized morbidity ratio (SMR), as well as
•
low and high indicators for statistical significance at the 95% confidence interval.
Table 5. Age/sex-adjusted Comparison of Disease Distributions across Populations
***Observed to Expected Standardized Morbidity Ratio by MEDC***
Population: ALL
Number of persons=2,141,852
Major EDC
Administrative....................
Allergy...........................
Cardiovascular....................
Dental............................
Ears, Nose, Throat................
Endocrine.........................
Eye...............................
Female Reproductive...............
Gastrointestinal/Hepatic..........
General Signs and Symptoms........
General Surgery...................
Genetic...........................
Genito-urinary....................
Hematologic.......................
Infections........................
Malignancies......................
Musculoskeletal...................
Neurologic........................
Nutrition.........................
Psychosocial......................
Reconstructive....................
Renal.............................
Respiratory.......................
Rheumatologic.....................
Skin..............................
Toxic Effects.....................
Unassigned........................
Observed
Age-Sex
Standardized
Prevalence Expected Morbidity
95%
per 1000 Prevalence Ratio
Confidence Interval
Population per 1000
(SMR)
(Low)
(High)
--------- --------- --------- --------- --------284.43
64.19
72.79
7.69
216.82
29.74
120.49
85.97
56.26
69.65
99.12
0.25
47.42
10.41
37.95
10.21
180.73
58.19
10.87
40.73
27.35
5.16
140.57
11.67
150.22
5.52
98.88
284.43
64.19
72.79
7.69
216.82
29.74
120.49
85.97
56.26
69.65
99.12
0.25
47.42
10.41
37.95
10.21
180.73
58.19
10.87
40.73
27.35
5.16
140.57
11.67
150.22
5.52
98.88
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
1.000
0.997
0.995
0.995
0.985
0.997
0.992
0.996
0.995
0.994
0.995
0.996
0.915
0.994
0.987
0.993
0.987
0.997
0.994
0.987
0.993
0.992
0.981
0.996
0.988
0.997
0.982
0.996
1.003
1.005
1.005
1.015
1.003
1.008
1.004
1.005
1.006
1.005
1.004
1.085
1.006
1.013
1.007
1.013
1.003
1.006
1.013
1.007
1.008
1.019
1.004
1.012
1.003
1.018
1.004
A Note on Customizing These Reports
The technical specifications for creating these tables on other population subgroups
and/or based on locally calibrated cost data are provided in “Section d. Dino-Cluster
Applications/Approaches” of Chapter 13 in the Release 5.0 Documentation and Application
Guide. Although these tables are built into the software to allow quick implementation, to
maximize the usefulness of this type of information it is recommended that they be re-created
and calibrated to local data.
38
Appendix A.1
Table A.1: List of Complicating Conditions Used to Split Diabetes EDCs
ICD-9
Code
2501
25010
25011
25012
25013
2502
25020
25021
25022
25023
2503
25030
25031
25032
25033
2504
25040
25041
25042
25043
410
4100
41000
41001
41002
4101
41010
41011
41012
4102
41020
41021
41022
4103
41030
41031
41032
4104
41040
41041
41042
Description
DIABETES W KETOACIDOSIS*
DMII KETO NT ST UNCNTRLD
DMI KETO NT ST UNCNTRLD
DMII KETOACD UNCONTROLD
DMI KETOACD UNCONTROLD
DIAB W HYPEROSMOLAR COMA*
DMII HPRSM NT ST UNCNTRL
DMI HPRSM NT ST UNCNTRLD
DMII HPROSMLR UNCONTROLD
DMI HPROSMLR UNCONTROLD
DIABETES WITH COMA NEC*
DMII O CM NT ST UNCNTRLD
DMI O CM NT ST UNCNTRLD
DMII OTH COMA UNCONTROLD
DMI OTH COMA UNCONTROLD
DIAB W RENAL MANIFEST*
DMII RENL NT ST UNCNTRLD
DMI RENL NT ST UNCNTRLD
DMII RENAL UNCNTRLD
DMI RENAL UNCNTRLD
ACUTE MYOCARDIAL INFARCT*
AMI ANTEROLATERAL WALL*
AMI ANTEROLATERAL,UNSPEC
AMI ANTEROLATERAL, INIT
AMI ANTEROLATERAL,SUBSEQ
AMI ANTERIOR WALL NEC*
AMI ANTERIOR WALL,UNSPEC
AMI ANTERIOR WALL, INIT
AMI ANTERIOR WALL,SUBSEQ
AMI INFEROLATERAL WALL*
AMI INFEROLATERAL,UNSPEC
AMI INFEROLATERAL, INIT
AMI INFEROLATERAL,SUBSEQ
AMI INFEROPOSTERIOR WALL*
AMI INFEROPOST, UNSPEC
AMI INFEROPOST, INITIAL
AMI INFEROPOST, SUBSEQ
AMI INFERIOR WALL NEC*
AMI INFERIOR WALL,UNSPEC
AMI INFERIOR WALL, INIT
AMI INFERIOR WALL,SUBSEQ
39
ICD-9
Code
4105
41050
41051
41052
4106
41060
41061
41062
4107
41070
41071
41072
4108
41080
41081
41082
4109
41090
41091
41092
411
4110
4111
4118
41181
41189
412
413
4130
4131
4139
414
4140
41400
41401
41402
41403
41404
41405
4141
41410
41411
41419
4148
Description
AMI LATERAL WALL NEC*
AMI LATERAL NEC, UNSPEC
AMI LATERAL NEC, INITIAL
AMI LATERAL NEC, SUBSEQ
TRUE POSTERIOR INFARCT*
TRUE POST INFARCT,UNSPEC
TRUE POST INFARCT, INIT
TRUE POST INFARCT,SUBSEQ
SUBENDOCARDIAL INFARCT*
SUBENDO INFARCT, UNSPEC
SUBENDO INFARCT, INITIAL
SUBENDO INFARCT, SUBSEQ
MYOCARDIAL INFARCT NEC*
AMI NEC, UNSPECIFIED
AMI NEC, INITIAL
AMI NEC, SUBSEQUENT
MYOCARDIAL INFARCT NOS*
AMI NOS, UNSPECIFIED
AMI NOS, INITIAL
AMI NOS, SUBSEQUENT
OTH AC ISCHEMIC HRT DIS*
POST MI SYNDROME
INTERMED CORONARY SYND
AC ISCHEMIC HRT DIS NEC*
CORONARY OCCLSN W/O MI
AC ISCHEMIC HRT DIS NEC
OLD MYOCARDIAL INFARCT
ANGINA PECTORIS*
ANGINA DECUBITUS
PRINZMETAL ANGINA
ANGINA PECTORIS NEC/NOS
OTH CHR ISCHEMIC HRT DIS*
CORONARY ATHEROSCLEROSIS*
COR ATH UNSP VSL NTV/GFT
CRNRY ATHRSCL NATVE VSSL
CRN ATH ATLG VN BPS GRFT
CRN ATH NONATLG BLG GRFT
COR ATH ARTRY BYPAS GRFT
COR ATH BYPASS GRAFT NOS
ANEURYSM OF HEART*
ANEURYSM, HEART (WALL)
CORONARY VESSEL ANEURYSM
ANEURYSM OF HEART NEC
CHR ISCHEMIC HRT DIS NEC
40
ICD-9
Code
4149
581
5810
5811
5812
5813
5818
58181
58189
5819
582
5820
5821
5822
5824
5828
58281
58289
5829
583
5830
5831
5832
5834
5836
5837
5838
58381
58389
5839
584
5845
5846
5847
5848
5849
585
586
V56
V560
V561
V562
V568
Description
CHR ISCHEMIC HRT DIS NOS
NEPHROTIC SYNDROME*
NEPHROTIC SYN, PROLIFER
EPIMEMBRANOUS NEPHRITIS
MEMBRANOPROLIF NEPHROSIS
MINIMAL CHANGE NEPHROSIS
NEPHROTIC SYN W OTH LES*
NEPHROTIC SYN IN OTH DIS
NEPHROTIC SYNDROME NEC
NEPHROTIC SYNDROME NOS
CHRONIC NEPHRITIS*
CHR PROLIFERAT NEPHRITIS
CHR MEMBRANOUS NEPHRITIS
CHR MEMBRANOPROLIF NEPHR
CHR RAPID PROGR NEPHRIT
CHR NEPHRITIS W OTH LES*
CHR NEPHRITIS IN OTH DIS
CHRONIC NEPHRITIS NEC
CHRONIC NEPHRITIS NOS
NEPHRITIS NOS*
PROLIFERAT NEPHRITIS NOS
MEMBRANOUS NEPHRITIS NOS
MEMBRANOPROLIF NEPHR NOS
RAPIDLY PROG NEPHRIT NOS
RENAL CORT NECROSIS NOS
NEPHR NOS/MEDULL NECROS
NEPHRITIS NOS W OTH LES*
NEPHRITIS NOS IN OTH DIS
NEPHRITIS NEC
NEPHRITIS NOS
ACUTE RENAL FAILURE*
LOWER NEPHRON NEPHROSIS
AC RENAL FAIL, CORT NECR
AC REN FAIL, MEDULL NECR
AC RENAL FAILURE NEC
ACUTE RENAL FAILURE NOS
CHRONIC RENAL FAILURE
RENAL FAILURE NOS
DIALYSIS ENCOUNTER*
RENAL DIALYSIS ENCOUNTER
FT/ADJ XTRCORP DIAL CATH
FIT/ADJ PERIT DIAL CATH
DIALYSIS ENCOUNTER, NEC
41
Appendix A.2
Table A.2: Major Expanded Diagnosis Clusters and their Component Expanded Diagnosis
Clusters
Number and Prevelance per Thousand of Major Expanded Diagnosis Clusters and
their Component Expanded Diagnosis Clusters
EDC
Description
No.
No.
Persons
Persons
per 1000
Population
ADM Administrative....................................
ADM01 General medical exam
ADM02 Surgical aftercare
ADM03 Transplant status
ADM04 Complications of mechanical devices
609207
591820
33143
821
3163
284.43
276.31
15.47
0.38
1.48
ALL Allergy...........................................
ALL01 Allergic reactions
ALL03 Allergic rhinitis
ALL04 Asthma, w/o status asthmaticus
ALL05 Asthma, with status asthmaticus
ALL06 Disorders of the immune system
137496
23434
80055
49058
4303
1510
64.19
10.94
37.38
22.90
2.01
0.70
CAR Cardiovascular....................................
CAR01 Cardiovascular signs and symptoms
CAR03 Ischemic heart disease (excluding acute myocard
CAR04 Congenital heart disease
CAR05 Congestive heart failure
CAR06 Cardiac valve disorders
CAR07 Cardiomyopathy
CAR08 Heart murmur
CAR09 Cardiac arrhythmia
CAR10 Generalized atherosclerosis
CAR11 Disorders of lipoid metabolism
CAR12 Acute myocardial infarction
CAR13 Cardiac arrest, shock
CAR14 Hypertension, w/o major complications
CAR15 Hypertension, with major complications
155912
18491
16628
3192
2642
5142
1786
3113
11709
2182
49358
2692
276
82457
5674
72.79
8.63
7.76
1.49
1.23
2.40
0.83
1.45
5.47
1.02
23.04
1.26
0.13
38.50
2.65
DEN Dental............................................
DEN01 Disorders of mouth
DEN02 Disorders of teeth
DEN03 Gingivitis
DEN04 Stomatitis
16467
5599
7324
641
3349
7.69
2.61
3.42
0.30
1.56
EAR Ears, Nose, Throat................................
EAR01 Otitis media
EAR02 Tinnitus
EAR03 Temporomandibular joint disease
EAR04 Foreign body in ears, nose, or throat
EAR05 Deviated nasal septum
EAR06 Otitis externa
EAR07 Wax in ear
EAR08 Deafness, hearing loss
EAR09 Chronic pharyngitis and tonsillitis
EAR10 Epistaxis
EAR11 Acute upper respiratory tract infection
464405
174138
3029
5405
2427
4266
21013
19863
11679
8961
4477
335295
216.82
81.30
1.41
2.52
1.13
1.99
9.81
9.27
5.45
4.18
2.09
156.54
END Endocrine.........................................
END02 Osteoporosis
END03 Short stature
END04 Thyroid disease
END05 Other endocrine disorders
END06 Type 2 diabetes, w/o complication
END07 Type 2 diabetes w/complications
END08 Type 1 diabetes, w/o complication
END09 Type 1 diabetes w/complications
63700
2300
0
25859
6997
19985
2538
6948
2105
29.74
1.07
0.00
12.07
3.27
9.33
1.18
3.24
0.98
258070
48584
1133
7868
8569
116028
120.49
22.68
0.53
3.67
4.00
54.17
EYE Eye...............................................
EYE01 Ophthalmic signs and symptoms
EYE02 Blindness
EYE03 Retinal disorders (excluding diabetic retinopat
EYE04 Disorders of the eyelid and lacrimal duct
EYE05 Refractive errors
42
EYE06
EYE07
EYE08
EYE09
EYE10
EYE11
EYE12
EYE13
Cataract, aphakia
Conjunctivitis, keratitis
Glaucoma
Infections of eyelid
Foreign body in eye
Strabismus, amblyopia
Traumatic injuries of eye
Diabetic retinopathy
10316
55252
14709
9893
4990
9393
9635
3002
4.82
25.80
6.87
4.62
2.33
4.39
4.50
1.40
FRE Female Reproductive...............................
FRE01 Pregnancy and delivery, uncomplicated
FRE02 Female genital symptoms
FRE03 Endometriosis
FRE04 Pregnancy and delivery with complications
FRE05 Female infertility
FRE06 Abnormal pap smear
FRE07 Ovarian cyst
FRE08 Vaginitis, vulvitis, cervicitis
FRE09 Menstrual disorders
FRE10 Contraception
FRE11 Menopausal symptoms
FRE12 Utero-vaginal prolapse
184131
36641
24750
4517
24972
5620
19224
5972
39660
38792
33246
22311
4599
85.97
17.11
11.56
2.11
11.66
2.62
8.98
2.79
18.52
18.11
15.52
10.42
2.15
GAS Gastrointestinal/Hepatic..........................
GAS01 Gastrointestinal signs and symptoms
GAS02 Inflammatory bowel disease
GAS03 Constipation
GAS04 Acute hepatitis
GAS05 Chronic liver disease
GAS06 Peptic ulcer disease
GAS07 Diarrhea
GAS08 Gastroesophageal reflux
GAS09 Irritable bowel syndrome
GAS10 Diverticular disease of colon
GAS11 Acute pancreatitis
GAS12 Chronic pancreatitis
120499
16983
4025
8568
1940
1820
24892
45645
26499
8755
6894
855
431
56.26
7.93
1.88
4.00
0.91
0.85
11.62
21.31
12.37
4.09
3.22
0.40
0.20
GSI General Signs and Symptoms........................
GSI01 Nonspecific signs and symptoms
GSI02 Chest pain
GSI03 Fever
GSI04 Syncope
GSI05 Nausea, vomiting
GSI06 Debility and undue fatigue
GSI07 Lymphadenopathy
GSI08 Edema
149173
41431
48997
20183
6150
14094
22794
7574
5185
69.65
19.34
22.88
9.42
2.87
6.58
10.64
3.54
2.42
GSU General Surgery...................................
GSU01 Anorectal conditions
GSU02 Appendicitis
GSU03 Benign and unspecified neoplasm
GSU04 Cholelithiasis, cholecystitis
GSU05 External abdominal hernias, hydroceles
GSU06 Chronic cystic disease of the breast
GSU07 Other breast disorders
GSU08 Varicose veins of lower extremities
GSU09 Nonfungal infections of skin and subcutaneous t
GSU10 Abdominal pain
GSU11 Peripheral vascular disease
GSU12 Burns--1st degree
GSU13 Aortic aneurysm
GSU14 Gastrointestinal obstruction/perforation
212307
23172
2340
49680
6494
9671
16834
14648
3837
47125
72252
2703
792
357
4324
99.12
10.82
1.09
23.19
3.03
4.52
7.86
6.84
1.79
22.00
33.73
1.26
0.37
0.17
2.02
GTC Genetic...........................................
GTC01 Chromosomal anomalies
538
538
0.25
0.25
GUR Genito-urinary....................................
GUR01 Vesicoureteral reflux
GUR02 Undescended testes
GUR03 Hypospadias, other penile anomalies
GUR04 Prostatic hypertrophy
GUR05 Stricture of urethra
GUR06 Urinary symptoms
GUR07 Other male genital disease
GUR08 Urinary tract infections
GUR09 Renal calculi
GUR10 Prostatitis
101573
752
613
480
8538
1326
30256
11967
50656
6768
5553
47.42
0.35
0.29
0.22
3.99
0.62
14.13
5.59
23.65
3.16
2.59
HEM Hematologic.......................................
HEM01 Hemolytic anemia
22305
1007
10.41
0.47
43
HEM02
HEM03
HEM04
HEM05
HEM06
HEM07
Iron deficiency, other deficiency anemias
Thrombophlebitis
Neonatal jaundice
Aplastic anemia
Deep vein thrombosis
Hemophilia, coagulation disorder
15175
2611
2247
349
1511
968
7.08
1.22
1.05
0.16
0.71
0.45
INF Infections........................................
INF01 Tuberculosis infection
INF02 Fungal infections
INF03 Infectious mononucleosis
INF04 HIV, AIDS
INF05 Sexually transmitted diseases
INF06 Viral syndromes
INF07 Lyme disease
INF08 Septicemia
81276
523
6315
3438
826
13725
55597
819
2688
37.95
0.24
2.95
1.61
0.39
6.41
25.96
0.38
1.25
MAL Malignancies......................................
MAL01 Malignant neoplasms of the skin
MAL02 Low impact malignant neoplasms
MAL03 High impact malignant neoplasms
MAL04 Malignant neoplasms, breast
MAL05 Malignant neoplasms, cervix, uterus
MAL06 Malignant neoplasms, ovary
MAL07 Malignant neoplasms, esophagus
MAL08 Malignant neoplasms, kidney
MAL09 Malignant neoplasms, liver and biliary tract
MAL10 Malignant neoplasms, lung
MAL11 Malignant neoplasms, lymphomas
MAL12 Malignant neoplasms, colorectal
MAL13 Malignant neoplasms, pancreas
MAL14 Malignant neoplasms, prostate
MAL15 Malignant neoplasms, stomach
MAL16 Acute leukemia
MAL18 Malignant neoplasms, bladder
21861
5542
3489
2949
5040
1434
552
94
353
111
649
1750
1102
92
1265
86
390
579
10.21
2.59
1.63
1.38
2.35
0.67
0.26
0.04
0.16
0.05
0.30
0.82
0.51
0.04
0.59
0.04
0.18
0.27
MUS Musculoskeletal...................................
MUS01 Musculoskeletal signs and symptoms
MUS02 Acute sprains and strains
MUS03 Degenerative joint disease
MUS04 Fractures (excluding digits)
MUS05 Torticollis
MUS06 Kyphoscoliosis
MUS07 Congenital hip dislocation
MUS08 Fractures and dislocations/digits only
MUS09 Joint disorders, trauma related
MUS10 Fracture of neck of femur (hip)
MUS11 Congenital anomalies of limbs, hands, and feet
MUS12 Acquired foot deformities
MUS13 Cervical pain syndromes
MUS14 Low back pain
MUS15 Bursitis, synovitis, tenosynovitis
MUS16 Amputation status
387105
101099
93832
23198
31563
1948
4386
341
9461
51613
581
4273
12711
50260
109842
67999
1041
180.73
47.20
43.81
10.83
14.74
0.91
2.05
0.16
4.42
24.10
0.27
2.00
5.93
23.47
51.28
31.75
0.49
NUR Neurologic........................................
NUR01 Neurologic signs and symptoms
NUR02 Headaches
NUR03 Peripheral neuropathy, neuritis
NUR04 Vertiginous syndromes
NUR05 Cerebrovascular disease
NUR06 Parkinson's disease
NUR07 Seizure disorder
NUR08 Multiple sclerosis
NUR09 Muscular dystrophy
NUR10 Sleep problems
NUR11 Dementia and delirium
NUR12 Quadriplegia and paraplegia
NUR15 Head injury
NUR16 Spinal cord injury/disorders
NUR17 Paralytic syndromes, other
NUR18 Cerebral palsy
NUR19 Developmental disorder
124627
4697
51219
26715
20154
4141
815
9071
2387
497
7872
422
751
6388
2077
989
1108
3018
58.19
2.19
23.91
12.47
9.41
1.93
0.38
4.24
1.11
0.23
3.68
0.20
0.35
2.98
0.97
0.46
0.52
1.41
NUT Nutrition.........................................
NUT01 Failure to thrive
NUT02 Nutritional deficiencies
NUT03 Obesity
23286
4397
2327
16765
10.87
2.05
1.09
7.83
PSY Psychosocial......................................
PSY01 Anxiety, neuroses
87242
50088
40.73
23.39
44
PSY02
PSY03
PSY04
PSY05
PSY06
PSY07
PSY08
PSY09
Substance use
Tobacco abuse
Behavior problems
Attention deficit disorder
Family and social problems
Schizophrenia and affective psychosis
Personality disorders
Depression
6473
10391
2418
12015
2354
2496
785
17849
3.02
4.85
1.13
5.61
1.10
1.17
0.37
8.33
REC Reconstructive....................................
REC01 Cleft lip and palate
REC02 Lacerations
REC03 Chronic ulcer of the skin
REC04 Burns--2nd and 3rd degree
58571
393
52124
2112
4582
27.35
0.18
24.34
0.99
2.14
REN Renal.............................................
REN01 Chronic renal failure
REN02 Fluid/electrolyte disturbances
REN03 Acute renal failure
REN04 Nephritis, nephrosis
11047
1286
9136
398
1205
5.16
0.60
4.27
0.19
0.56
RES Respiratory.......................................
RES01 Respiratory signs and symptoms
RES02 Acute lower respiratory tract infection
RES03 Cystic fibrosis
RES04 Emphysema, chronic bronchitis, COPD
RES05 Cough
RES06 Sleep apnea
RES07 Sinusitis
RES08 Pulmonary embolism
RES09 Tracheostomy
RES10 Respiratory arrest
301082
24552
141625
363
10158
29636
4636
142549
494
216
4268
140.57
11.46
66.12
0.17
4.74
13.84
2.16
66.55
0.23
0.10
1.99
RHU Rheumatologic.....................................
RHU01 Autoimmune and connective tissue diseases
RHU02 Gout
RHU03 Arthropathy
RHU04 Raynaud's syndrome
24989
10331
4274
11170
908
11.67
4.82
2.00
5.22
0.42
321746
55057
69204
1289
34217
4267
14670
43120
6316
38376
16057
20999
5535
9158
1628
3247
9665
26155
43002
150.22
25.71
32.31
0.60
15.98
1.99
6.85
20.13
2.95
17.92
7.50
9.80
2.58
4.28
0.76
1.52
4.51
12.21
20.08
TOX Toxic Effects.....................................
TOX01 Toxic effects of nonmedicinal agents
TOX02 Adverse effects of medicinal agents
11831
4877
7118
5.52
2.28
3.32
UDC Unassigned........................................
UDC00 Unassigned diagnosis code
211794
211794
98.88
98.88
SKN Skin..............................................
SKN01 Contusions and abrasions
SKN02 Dermatitis and eczema
SKN03 Keloid
SKN04 Acne
SKN05 Disorders of sebaceous glands
SKN06 Sebaceous cyst
SKN07 Viral warts and molluscum contagiosum
SKN08 Other inflammatory conditions of skin
SKN09 Exanthems
SKN10 Skin keratoses
SKN11 Dermatophytoses
SKN12 Psoriasis
SKN13 Disease of hair and hair follicles
SKN14 Pigmented nevus
SKN15 Scabies and pediculosis
SKN16 Diseases of nail
SKN17 Other skin disorders
SKN18 Benign neoplasm of skin and subcutaneous tissue
45
Section 4
Using the Software-provided ACG Relative Value Weights
With Release 6.0 of the ACG software, weights are for the first time being made available as part
of the ACG output stream. Weights is the term that we have traditionally used to represent measures of
the level of resource use that are associated with an ACG assignment. Essentially they represent an
average resource use “expectation” for a particular ACG category and are generally based on local data.
Weights can be expressed as actual dollars expected to be spent over a period of time or as relative values
(the ratio of expected use in that ACG to an overall population mean). This section discusses the use of
new internal relative value weights and the conversion of these scores to dollar amounts. Readers are
especially encouraged to review the section on the “rescaling process” before using these weights.
For an extensive discussion on the computation of weights and calibration of local data, see
Chapter 8, “Calibrating ACGs for Intended Use: ACG Weights, Resource Utilization Bands, and Carveouts,” in the Release 5.0 Documentation and Application Manual).
a.
Concurrent ACG Weights
A fixed set of concurrent ACG weights is now available as part of the software output file
(see Installation and Usage Guide for instructions on how to turn on this option). These are
relative weights, i.e., relative to a population mean, and are standardized to a mean of 1.0. The
software-supplied weights may be considered a national reference or benchmark for comparisons
with locally calibrated ACG weights. However, in some instances (e.g., for those with limited or
no cost data), these weights may also be used as a reasonable proxy for local cost data. Table 2
at the end of this section is a complete listing of ACGs and their corresponding nationally
representative concurrent ACG weight. (See the additional discussion below about the
importance of rescaling so that dollars are not over- or under-predicted).
Our experience indicates that concurrent or retrospective ACG weights, especially when
expressed as relative values, have remarkable stability. Where differences in ACG weights
across plans are present, it is almost universally attributable to differences in covered services
reflected by different benefit levels. The new software-provided concurrent weights were
developed from a nationally representative database comprising approximately two million lives
with comprehensive benefit coverage.
47
Ideally, ACG weights should be calculated from plan-specific local data to account most
accurately for benefit levels and area practice patterns. The reference population (on which the
weights are developed) should be as similar as possible to the assessment population to which
the weights are applied. However, and as noted, in the absence of local cost data the softwareprovided internal weights may prove useful for calculating reasonably representative profiling
statistics (see Chapter 12, “ACG Risk Adjustment and Provider Profiling,” in the Release 5.0
Documentation and Application Manual).
b.
Converting Scores to Dollars
Both the ACG concurrent weights and the new acgPM Predicted Risk Index (PRI, see
Section 2 of this document) are expressed as relative values, where the mean is centered at 1.0.
Individuals with scores higher than 1.0 are more expensive than average whereas those with
scores less than 1.0 are less expensive than average. Such relative indices can easily be
converted to dollar amounts by multiplying by the underlying mean of the population to which
the risk adjustment values will be applied.
Before converting scores to dollar amounts, it is important to rescale the data so as to
account for differences between the reference population (in this case the Johns Hopkins
nationally representative database comprising over two million covered lives) and the population
to which the weights are applied (e.g., your population of interest). Rescaling is necessary to
assure that the underlying mean of the predictions is 1.0. A similar process is undertaken when
you use your own reference population, when it has somewhat different characteristics or
circumstances (e.g., it is from a previous time period, or benefit coverage is somewhat different).
Unless rescaling is done, resource use (or payments) may be over- or under-predicted. Table 1
below and the accompanying discussion provide a simplified example for a population with only
12 members.
c.
The Rescaling Process
Step 1: Compute population mean weight. Compute a separate grand mean for each of
the weights (either concurrent ACG weights or the acgPM PRI) generated for your population
48
(the observations represent individuals). The mean for this example is shown in Table 1 at the
bottom of column B.
Step 2: Apply weighting factor. Divide each individual weight by the rescaling factor
(i.e., the mean) that you computed in Step 1. The result is the rescaled relative weight (column
C).
Step 3: Compute population mean cost. For the same population on which the weights
were based, compute the mean cost for the current data year. For this example, the mean cost
was $1,265.11.
Step 4: Compute cost. Multiply the rescaled relative weights generated for each member
of the population (Column C) by the average population cost generated from Step 3 to calculate
an estimated individual cost (column D).
Table 1: Estimating Costs in a Sample of Cases
A
B
C
D
Relative Rescaled Estimated
Observation Weight
Weight
Cost
1
0.185
0.171
$216.36
2
0.291
0.268
$339.61
3
0.387
0.357
$451.64
4
0.457
0.422
$533.33
5
0.541
0.499
$631.33
6
0.609
0.562
$711.58
7
0.696
0.642
$812.58
8
0.842
0.777
$982.84
9
1.025
0.946 $1,196.68
10
1.293
1.194 $1,510.19
11
1.892
1.746 $2,209.38
12
4.783
4.415 $5,585.78
1.000 $1,265.11
1.083
Mean
The rescaling factor functions as a summary case-mix index for understanding how the
rating population (e.g., your local population) compares to the development data (JHU’s
nationally representative database). The interpretation of this factor is analogous to how one
interprets both relative weights and profiling indicators. If the rescaling factor is greater than 1.0
49
(as it was in the example), then your population is sicker; if the factor is less than 1.0, then your
population is healthier than the reference population.
d.
Adjustments for Inflation
If you are going to use the scores for predicting future expenditures it may be appropriate
to inflation-adjust these values. Based on Bureau of Labor Statistics results, for the calendar
year 2002 medical care costs rose by approximately 4.7% over the previous year (see
http://data.bls.gov). In the preceding example, if you were going to apply this inflation
adjustment, you would multiply the mean cost computed in Step 3 by 1.047 to reflect inflation.
For this example, the inflation-adjusted mean cost for the next year would have been $1,324.57
instead of $1,265.11. Depending on the local situation, it may also be appropriate to modify
future cost expectations for other actuarial factors such as changes in benefit structure of costsharing provisions.
Please note that the above discussion was meant to offer general instructional guidance on the
rescaling of relative values and inflation adjustment. Given that no two analytic or actuarial
applications are exactly alike, and given the potentially major impact such a process may have
on the management or financial applications within your organization, it is essential that you
seek and follow advice from experienced statistical or actuarial specialists before finalizing the
general processes described above.
50
Table 2: Relative Concurrent PMPY Weights
ACG
0100
0200
0300
0400
0500
0600
0700
0800
0900
1000
1100
1200
1300
1400
1500
1600
1710
1711
1712
1720
1721
1722
1730
1731
1732
1740
1741
1742
1750
1751
1752
1760
1761
1762
1770
1771
1772
1800
1900
2000
2100
2200
ACG Label
Acute Minor, Age 1
Acute Minor, Age 2 to 5
Acute Minor, Age > 5
Acute Major
Likely to Recur, w/o Allergies
Likely to Recur, with Allergies
Asthma
Chronic Medical, Unstable
Chronic Medical, Stable
Chronic Specialty
Eye/Dental
Chronic Specialty, Unstable
Psychosocial, w/o Psych Unstable
Psychosocial, with Psych Unstable, w/o Psych Stable
Psychosocial, with Psych Unstable, w/ Psych Stable
Preventive/Administrative
Pregnancy: 0-1 ADGs
Pregnancy: 0-1 ADGs, delivered
Pregnancy: 0-1 ADGs, not delivered
Pregnancy: 2-3 ADGs, no Major ADGs
Pregnancy: 2-3 ADGs, no Major ADGs, delivered
Pregnancy: 2-3 ADGs, no Major ADGs, not delivered
Pregnancy: 2-3 ADGs, 1+ Major ADGs
Pregnancy: 2-3 ADGs, 1+ Major ADGs, delivered
Pregnancy: 2-3 ADGs, 1+ Major ADGs, not delivered
Pregnancy: 4-5 ADGs, no Major ADGs
Pregnancy: 4-5 ADGs, no Major ADGs, delivered
Pregnancy: 4-5 ADGs, no Major ADGs, not delivered
Pregnancy: 4-5 ADGs, 1+ Major ADGs
Pregnancy: 4-5 ADGs, 1+ Major ADGs, delivered
Pregnancy: 4-5 ADGs, 1+ Major ADGs, not delivered
Pregnancy: 6+ ADGs, no Major ADGs
Pregnancy: 6+ ADGs, no Major ADGs, delivered
Pregnancy: 6+ ADGs, no Major ADGs, not delivered
Pregnancy: 6+ ADGs, 1+ Major ADGs
Pregnancy: 6+ ADGs, 1+ Major ADGs, delivered
Pregnancy: 6+ ADGs, 1+ Major ADGs, not delivered
Acute Minor and Acute Major
Acute Minor and Likely to Recur, Age 1
Acute Minor and Likely to Recur, Age 2 to 5
Acute Minor and Likely to Recur, Age > 5, w/o Allergy
Acute Minor and Likely to Recur, Age > 5, with Allergy
51
Relative
Weight
0.335
0.174
0.182
0.416
0.245
0.300
0.250
1.023
0.334
0.246
0.200
0.341
0.444
1.291
2.490
0.144
3.390
5.461
0.972
3.922
6.058
1.283
5.667
6.722
2.671
4.240
6.575
1.941
5.997
7.637
3.181
4.616
7.144
2.854
7.411
9.037
5.634
0.710
0.665
0.401
0.427
0.541
ACG
2300
2400
2500
2600
2700
2800
2900
3000
3100
3200
3300
3400
3500
3600
3700
3800
3900
4000
4100
4210
4220
4310
4320
4330
4410
4420
4430
4510
4520
4610
4620
4710
4720
4730
4810
4820
4830
4910
4920
4930
4940
5010
5020
5030
ACG Label
Acute Minor and Chronic Medical: Stable
Acute Minor and Eye/Dental
Acute Minor and Psychosocial, w/o Psych Unstable
Acute Minor and Psychosocial, with Psych Unstable, w/o Psych Stable
Acute Minor and Psychosocial, with Psych Unstable and Psych Stable
Acute Minor and Likely to Recur
Acute Minor/Acute Major/Likely to Recur, Age 1
Acute Minor/Acute Major/Likely to Recur, Age 2 to 5
Acute Minor/Acute Major/Likely to Recur, Age 6 to 11
Acute Minor/Acute Major/Likely to Recur, Age > 11, w/o Allergy
Acute Minor/Acute Major/Likely to Recur, Age > 11, with Allergy
Acute Minor/Likely to Recur/Eye & Dental
Acute Minor/Likely to Recur/Psychosocial
Acute Minor/Acute Major/Likely Recur/Eye & Dental
Acute Minor/Acute Major/Likely Recur/Psychosocial
2-3 Other ADG Combinations, Age < 18
2-3 Other ADG Combinations, Males Age 18 to 34
2-3 Other ADG Combinations, Females Age 18 to 34
2-3 Other ADG Combinations, Age > 34
4-5 Other ADG Combinations, Age < 18, no Major ADGs
4-5 Other ADG Combinations, Age < 18, 1+ Major ADGs
4-5 Other ADG Combinations, Age 18 to 44, no Major ADGs
4-5 Other ADG Combinations, Age 18 to 44, 1+ Major ADGs
4-5 Other ADG Combinations, Age > 44, no Major ADGs
4-5 Other ADG Combinations, Age > 44, 1+ Major ADGs
6-9 Other ADG Combinations, Age < 6, no Major ADGs
6-9 Other ADG Combinations, Age < 6, 1+ Major ADGs
6-9 Other ADG Combinations, Age 6 to 17, no Major ADGs
6-9 Other ADG Combinations, Males, Age 18 to 34, no Major ADGs
6-9 Other ADG Combinations, Males, Age 18 to 34, 1+ Major ADGs
6-9 Other ADG Combinations, Males, Age 18 to 34, 2+ Major ADGs
6-9 Other ADG Combinations, Females, Age 18 to 34, no Major ADGs
6-9 Other ADG Combinations, Females, Age 18 to 34, 1+ Major ADGs
6-9 Other ADG Combinations, Females, Age 18 to 34, 2+ Major ADGs
6-9 Other ADG Combinations, Age > 34, 0-1 Major ADGs
6-9 Other ADG Combinations, Age > 34, 2 Major ADGs
6-9 Other ADG Combinations, Age > 34, 3 Major ADGs
10+ Other ADG Combinations, Age 1 to 17, no Major ADGs
10+ Other ADG Combinations, Age 1 to 17, 1 Major ADGs
10+ Other ADG Combinations, Age 1 to 17, 2 Major ADGs
52
Relative
Weight
0.528
0.278
0.582
1.282
2.427
0.912
1.326
0.894
0.835
1.355
1.347
0.582
0.970
2.564
2.164
0.687
1.028
0.851
1.111
0.995
2.137
1.263
2.349
4.892
1.487
2.759
6.433
1.935
4.684
1.818
5.595
1.984
3.880
9.507
2.045
3.456
7.024
3.432
7.231
14.774
27.726
3.872
7.739
27.144
ACG
5040
5050
5060
5070
5100
5110
5200
5310
5311
5312
5320
5321
5322
5330
5331
5332
5340
5341
5342
9900
ACG Label
10+ Other ADG Combinations, Age > 17, 0-1 Major ADGs
10+ Other ADG Combinations, Age > 17, 2 Major ADGs
10+ Other ADG Combinations, Age > 17, 3 Major ADGs
10+ Other ADG Combinations, Age > 17, 4+ Major ADGs
No Diagnosis or Only Unclassified Diagnosis & Non-Users (1 input file)
No Diagnosis or Only Unclassified Diagnosis (2 input files)
Non-Users (2 input files)
Infants: 0-5 ADGs, no Major ADGs
Infants: 0-5 ADGs, no Major ADGs, low birth weight
Infants: 0-5 ADGs, no Major ADGs, normal birth weight
Infants: 0-5 ADGs, 1+ Major ADGs
Infants: 0-5 ADGs, 1+ Major ADGs, low birth weight
Infants: 0-5 ADGs, 1+ Major ADGs, normal birth weight
Infants: 6+ ADGs, no Major ADGs
Infants: 6+ ADGs, no Major ADGs, low birth weight
Infants: 6+ ADGs, no Major ADGs, normal birth weight
Infants: 6+ ADGs, 1+ Major ADGs
Infants: 6+ ADGs, 1+ Major ADGs, low birth weight
Infants: 6+ ADGs, 1+ Major ADGs, normal birth weight
Invalid Age or Date of Birth
53
Relative
Weight
5.583
8.915
15.633
35.800
0.017
0.144
0.000
1.197
7.987
1.053
5.596
23.145
2.658
2.593
8.387
2.206
17.332
42.535
8.729
0.000
Section 5
Selecting the Right Tool from the Expanding ACG “Tool
Box”
The preceding sections of this Release Document and the many sections of the comprehensive
Version 5.0 Documentation and Application Manual offer significant levels of detail on each of the
alternative applications of the various ACG measures. But even with this large mount of material to help
guide your way, we recognize that a simple overview is needed to suggest which tool you should select
from the Version 6.0 ACG “tool box” for each application within your organization. Targeted at both
new and old users alike, this section offers a quick overview of the myriad ACG applications and suggests
how the various components of the ACG tool box might be combined to maximize their usefulness to
you. In a succinct fashion, this section also attempts to summarize some material that is presented
elsewhere in our documentation. Where possible, linkages to more detailed discussion are offered to
readers.
a.
“One Size Does Not Fit All”
For over a decade the Johns Hopkins ACG risk-adjustment/case-mix methodology has
been applied by many hundreds of users to meet an extremely diverse range of health care
management and organizational needs. The ACG System represents a suite of tools that have
been used to support basic and complex applications in finance, administration, care delivery,
and evaluative research. These applications have been both real-time (concurrent) and forwardlooking (prospective). They may involve simple spreadsheet calculations or complex multivariable statistical models. No other risk adjustment method has been used for so many purposes
in so many places, while at the same time showing such high levels of quantitative and
qualitative success. The flexibility offered by our tool box means that we recognize that “one
size does not fit all.” This also means that a bit of custom tailoring may be needed to get the best
fit within your organization.
The current ACG release represents a major expansion of the array of tools available
within the Johns Hopkins ACG technology. As described earlier in this document, the acgPM
model included in this software release produces two new “predictive” measures: a probability
score indicating the likelihood that an individual will be a member of your very “high risk”
cohort next year, and a Predicted Resource Index (PRI) that reflects the likely amounts of
55
resource use by persons next year relative to the other individuals in your population. In addition
to these two brand new prospective measures of risk, this release also provides you with the
option of using software-supplied concurrent weights for ACG cells (using the same reference
population we used to develop acgPM) and provides a fixed set of ACG-based RUB categories.
With more power and functionality come more options for users to navigate. The
purpose of this section is to provide some help in making decisions about using the ACG System
to support your individual requirements.
b.
Describing a Population’s Health
The ACG System is designed as a tool for understanding and explaining population
health. The System’s various diagnosis-based risk assessment markers provide a useful means
for comparing the morbidity of different subpopulation of interest to you. Simple descriptive
analyses like those shown in the following sample tables compare the distribution of morbidity
across selected populations groupings. These are offered as models for how you may wish to
apply our system to describe the morbidity characteristics of those cared for by your
organization.
Table 1: Comparison of ADG Distribution Across Two Enrollee Groups
ADG
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Description
Time Limited: Minor
Time Limited: Minor -Primary Infections
Time Limited: Major
Time Limited: Major-Primary Infections
Allergies
Asthma
Likely to Recur: Discrete
Likely to Recur: Discrete-Infections
Likely to Recur: Progressive
Chronic Medical: Stable
Chronic Medical: UnStable
Chronic Specialty: Stable-Ortho
Chronic Specialty: Stable-ENT
Chronic Specialty: Stable-Eye
No Longer in Use
Chronic Specialty: UnStable-Ortho
56
Total
14.7%
32.2%
5.5%
6.1%
3.6%
4.4%
8.6%
20.7%
2.0%
12.9%
8.6%
0.9%
0.7%
2.6%
0.0%
0.8%
Group 1
14.8%
33.2%
4.0%
5.1%
3.6%
4.2%
6.6%
22.0%
0.8%
7.4%
4.0%
0.5%
0.6%
2.0%
0.0%
0.4%
Group 2
14.4%
27.4%
12.3%
10.6%
3.3%
5.0%
17.2%
14.9%
7.7%
37.1%
28.8%
2.8%
1.4%
5.3%
0.0%
2.4%
ADG
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
Description
continued
Chronic Specialty: UnStable-ENT
Chronic Specialty: UnStable-Eye
No Longer in Use
Dermatologic
Injuries/Adverse Effects: Minor
Injuries/Adverse Effects: Major
Psychosocial: Time Limited, Minor
Psychosoc:Recur or Persist: Stable
Psychosoc:Recur or Persist: UnStable
Signs/Symptoms: Minor
Signs/Symptoms: Uncertain
Signs/Symptoms: Major
Discretionary
See and Reassure
Prevention/Administrative
Malignancy
Pregnancy
Dental
Total
Group 1
Group 2
0.0%
1.6%
0.0%
4.5%
10.8%
9.3%
3.5%
9.8%
5.8%
16.9%
17.5%
14.8%
5.8%
1.8%
43.5%
1.0%
2.2%
1.4%
0.0%
0.8%
0.0%
4.4%
10.2%
8.1%
3.0%
7.4%
2.5%
15.3%
14.1%
11.6%
4.8%
1.3%
46.7%
0.3%
2.6%
1.4%
0.1%
5.2%
0.0%
5.0%
13.7%
14.3%
5.5%
20.3%
20.1%
24.4%
32.3%
28.9%
10.4%
3.8%
29.5%
4.0%
0.3%
1.7%
Table 1 illustrates how ADGs, the building blocks of the ACG system, can quickly
demonstrate differences in types of morbidity categories across sub-groupings within your
organization. In this example, the case-mix profile of Group 2 tends to be more complex than
that of Group 1, with the prevalence of the chronic medical and psychosocial ADGs being
especially high.
An advantage of ADGs is they can quickly identify clinically meaningful morbidity
trends that may be obscured at the disease-specific or relative morbidity index levels.
As discussed in the “Overview of New Features in ACG Release 6.0” section of this
document, the ACG software will automatically assign a six-level (Low to High) simplified
morbidity category we term RUBs (for Resource Utilization Bands). The six RUBs are formed
by combining the ACG mutually exclusive cells that measure overall morbidity burden.
Utilizing the Release 6.0 RUB categories, Table 2 demonstrates how a simple RUBbased analysis highlights differences in the distribution of morbidity of the Group 1 and Group 2
exemplary sub-populations. Confirming the impression drawn from Table 1, the Group 2
population clusters in the bands associated with higher overall morbidity burdens.
57
Table 2: Percentage Distribution of Two Sub-groups, by RUB Categories
RUB Category
1 - Non-users
2 - Healthy Users
3 - Low Morbidity
4 - Moderate
5 - High
6 - Very High
Total
Group 1
Group 2
25.8
35.6
22.5
13.9
17.5
11.1
28.3
30.1
25.0
27.6
13.8
33.5
3.7
2.5
7.4
.7
.5
1.5
As discussed earlier in this document, through use of disease-specific EDCs a
“standardized morbidity ratio” report is now included as part of the standard ACG print file.3
Based on the “Major” subheadings of Expanded Diagnosis Clusters, this report presents MEDC
level disease prevalence of a sub-population of interest after taking into account the age and
gender mix of the group relative to the underlying population. Thus, this report will assist users
in isolating statistically significant (demographically adjusted) disease category differences
within a sub-population of interest.
The diagnostic/morbidity distribution reports outlined here should be useful for many
clinically oriented applications within your organization. These could include population clinical
needs assessments and targeting where disease management or outreach programs might be
developed.
3
See “Refinements to the Johns Hopkins Expanded Diagnosis Clusters (EDCs)” in the Version 6.0 Release Notes
and Chapter 13, “Dino-Clusters: The Johns Hopkins Expanded Diagnosis Clusters (EDCs)” in the Version 5.0
Documentation and Application Manual for additional details on interpreting this table and how to generate this
table on other desired population groups.
58
Table 3. Observed to Expected Standardized Morbidity Ratio (SMR) by MEDC
Population: AW
Number of persons=167109
Observed Age/Gender
Approximate
Prevalence
Expected
95 percent
per 1000
per 1000
confidence interval
Major EDC
Population Population
SMR
(low)
(high)
--------------------------------- ---------- ---------- --------- --------- --------Administrative....................
269.87
280.93
0.961
0.952
0.969
Allergy...........................
75.56
63.50
1.190
1.169
1.211
Cardiovascular....................
86.29
79.18
1.090
1.072
1.108
Dental............................
6.65
7.60
0.876
0.824
0.927
Ears, Nose, Throat................
172.29
211.01
0.817
0.807
0.826
Endocrine.........................
40.65
31.44
1.293
1.262
1.324
Eye...............................
54.53
121.67
0.448
0.439
0.457
Female Reproductive...............
88.28
81.09
1.089
1.071
1.106
Gastrointestinal/Hepatic..........
67.47
57.13
1.181
1.159
1.203
General Signs and Symptoms........
80.15
70.37
1.139
1.120
1.158
General Surgery...................
108.65
100.40
1.082
1.066
1.098
Genetic...........................
0.25
0.24
1.045
0.729
1.360
Genito-urinary....................
50.53
48.01
1.053
1.030
1.075
Hematologic.......................
11.49
10.53
1.091
1.042
1.139
Infections........................
Malignancies......................
Musculoskeletal...................
Neurologic........................
Nutrition.........................
Psychosocial......................
Reconstructive....................
Renal.............................
Respiratory.......................
Rheumatologic.....................
Skin..............................
Toxic Effects.....................
Unassigned........................
c.
28.20
14.01
164.24
66.96
10.04
51.25
24.36
8.87
126.73
14.72
144.07
4.49
128.37
36.80
11.10
184.12
58.69
10.86
40.68
27.22
5.27
140.04
12.44
149.81
5.51
99.34
0.766
1.263
0.892
1.141
0.924
1.260
0.895
1.684
0.905
1.183
0.962
0.815
1.292
0.744
1.212
0.881
1.120
0.880
1.233
0.867
1.598
0.893
1.136
0.950
0.756
1.275
0.788
1.314
0.903
1.162
0.969
1.287
0.922
1.770
0.917
1.230
0.974
0.873
1.309
Profiling Resource Use
One of the most popular uses of the ACG software is to set risk-adjusted resource
consumption norms for sub-groups of patients/members within an organization. These norms
are compared to actual resource use in order to “profile” provider efficiency and to help suggest
where over-use and under-use may be a problem.
Profiling applications are very amenable to simple actuarial cell strategies for risk
adjustment. Most ACG users apply the ACG mutually exclusive cells for this purpose while
others have chosen to combine ACGs and use RUBs for these applications. The simpler RUB
method is sometimes selected when the population’s numbers are small or when the need to
communicate the inner-workings of the methods to a wide audience of providers is critical.
If a user has historical claims data (or other similar data sources), it is generally
preferable to calculate “expected” resource use values for each ACG (or RUB) for each resource
measure of interest (e.g., total cost, hospital use, specialist referrals, pharmacy) based on actual
59
patterns of practice within your organization. If such data are unavailable or inadequate, then the
relative weights supplied as part of this release can be used as a proxy.4
Table 4 presents a summary of the most common profiling statistics:5 1) the actual to
group average resource use (unadjusted efficiency ratio); 2) the expected to plan average (the
case-mix index or morbidity factor); and 3) the actual to expected average resource use
(efficiency ratios). The first is a measure of how the profiling group compares to the “average”
population. The second, the morbidity factor provides an indication of how “sick” the profiling
population is compared to the “average” population. The last statistic, the observed to expected
ratio (“O/E Ratio”) provides an indication of how many health care resources were consumed by
this group compared to how many resources they would have consumed had they utilized the
“average” resource use of the population based on their case-mix characteristics. All three of
these statistics are expressed as relative values with the “average” or normative value centered at
1.0. Scores greater than 1.0 indicate higher than average whereas those less than 1.0 indicate
lower than average. Tests of statistical significance can be developed to assess outlier status.
Clearly the use of risk adjustment provides a dramatically different basis for assessing the
performance of the three profiled sites.
Profiles such as those summarized above are a useful tool for evaluating performance and
allocating resources within a wide range of ACG users. The most common applications include:
•
financial exchange between MCO and providers,
•
assessing provider efficiency,
•
resource planning,
•
evaluating access to care, and
•
detecting fraud, waste, and abuse.
In the absence of local resource data that can be used to determine local weights, the
concurrent weights available within the ACG System can be used to develop summary measures
of case-mix for comparisons between groups.
4
See Chapter 8, “Calibrating ACGs for Intended Use: ACG Weights, Resource Utilization Bands, and Carve-outs,”
of the Version 5.0 Documentation and Application Manual and the “Using the Available Relative Value Weights”
section of this document for a detailed discussion of relevant methodologic issues related to weight calculation.
5
For further details see Chapter 12, “ACG Risk Adjustment and Provider Profiling,” in the Version 5.0
Documentation and Application Manual.
60
Table 4. Comparison of Observed to Expected Visits and Calculation of Three Profiling
Ratios
SITE A
SITE B
SITE C
1) Actual Visits per Person
5.35
6.10
6.90
(Observed)
2) Plan Average
5.50
5.50
5.50
3) Actual to Group Average*
0.97
1.11
1.26
(Unadjusted Efficiency Ratio)
4) Number of Expected Visits**
4.30
6.25
5.54
5) Expected to Plan Average***
.78
1.14
1.01
(Morbidity Factor)
6) Observed to Expected Ratio****
1.24
0.98
1.25
(Adjusted Efficiency Ratio)
*
**
***
****
Row 1 divided by Row 2
Expected based on ACG characteristics at each site
d.
Disease Management and Case Management Applications
As discussed previously, concurrent ACG / RUB morbidity information can be combined
with EDCs to control for morbidity differences across a given disease-specific group of interest
(e.g., diabetics enrolled in a disease management program). EDCs will be useful in portraying
the disease characteristics of a population of interest. Within disease management programs, if
significant differences in expected resource consumption exist across the morbidity sub-classes,
this analytic approach should be quite useful in better targeting interventions towards sub-groups
at higher risk.
Along these lines, two new tables (see Tables 5 and 6 for examples) are now part of the
print file that the ACG software produces. Each row of these tables represents persons falling
into EDC (or MEDC) disease-specific categories; the columns array these individuals into RUB
co-morbidity categories according to their ACG assignment. Table 5 presents the percentage
distribution for a series of selected EDCs across the five RUB categories. Table 6 presents the
expected relative resource use within each RUB. This table illustrates co-morbidity’s profound
influence on resource use within individual disease groups. The ACG-based RUBs do a very
good job of explaining variations in resource use within specific diseases. The ACG software
61
automatically generates these reports based on nationally representative weights, but such tables
are likely to become even more useful when calibrated to local cost and practice patterns.6
Table 5: Percentage Distribution of Each Co-morbidity Level within an EDC (Samples)
RUB-1
RUB-2
RUB-3
RUB-4
RUB-5
EDC
Very Low
Low
Average
High Very High
19.8
32.9
39.9
6.2
1.3
4.7
19.3
46.6
18.9
10.4
3.8
7.7
32.9
26.6
29.1
0.0
10.3
32.4
25.5
31.8
0.0
36.2
53.6
8.5
1.6
0.0
34.5
56.0
8.2
1.3
0.0
23.6
63.2
10.7
2.5
0.0
20.9
58.0
15.6
5.4
0.0
6.5
47.6
25.5
20.4
0.0
14.5
64.2
15.2
6.1
0.0
0.5
55.7
27.3
16.6
0.0
17.9
45.9
23.9
12.4
0.0
0.4
36.6
31.1
31.9
0.0
7.6
59.1
22.2
11.1
0.0
2.2
43.8
30.1
23.9
CAR08:Heart murmur
12.3
25.8
44.5
11.9
5.4
0.0
3.7
58.4
24.5
13.3
0.0
7.0
43.7
25.4
23.9
0.0
17.3
68.0
10.4
4.2
0.0
0.2
21.3
39.3
39.2
0.0
5.4
19.2
31.2
44.2
0.0
20.6
64.7
10.2
4.5
0.0
4.1
55.4
24.1
16.3
As discussed elsewhere, EDCs are very useful for many purposes. If users so choose,
they can develop their own reports, and the EDCs that define the rows in Tables 5 and 6 could be
replaced by episodes of illness categories that an organization may obtain from other sources.
ACG-based RUBs are equally effective in explaining variations in resource use within episodes
of care.
6
See Chapter 13, “Dino-Clusters: The Johns Hopkins Expanded Diagnosis Clusters (EDCs),” in the Version 5.0
Documentation and Application Guide for detailed instructions on how to create these tables calibrated to your own
data.
62
Table 6: Estimated Concurrent Resource Use by RUB by MEDC (Samples)
RUB-1
RUB-2
RUB-3
RUB-4
RUB-5
EDC
Very Low
Low
Average
High Very High
0.19
0.54
1.97
7.12
24.76
0.20
0.63
2.31
7.94
27.30
0.20
0.65
2.39
8.23
29.89
0.00
0.69
2.35
7.97
29.84
0.00
0.54
2.07
7.49
25.41
0.00
0.54
2.13
7.43
25.40
0.00
0.62
2.03
7.43
26.10
0.00
0.62
2.13
7.50
28.23
0.00
0.74
2.39
7.71
29.63
0.00
0.60
2.43
7.96
26.56
0.00
0.68
2.25
8.12
25.35
0.00
0.73
2.20
7.11
25.56
0.00
0.81
2.62
8.30
28.83
0.00
0.56
2.42
7.86
27.10
0.00
0.73
2.37
8.23
28.69
CAR08:Heart murmur
0.21
0.64
2.22
7.20
23.05
0.17
0.61
2.37
8.07
25.82
0.00
0.46
2.47
8.23
27.06
0.00
0.49
2.29
8.17
25.14
0.00
0.82
1.85
7.87
26.28
0.00
0.62
2.12
7.74
27.84
0.00
0.48
2.28
8.16
25.75
0.00
0.62
2.35
8.31
27.40
e.
High-risk Case Identification for Case Management
The various components of the “acgPM”—the new ACG predictive modeling module—
represent a real advance for users wishing to establish or augment care management programs
within their organization. Furthermore, existing ACG measures have many applications in this
domain as well.
There are many ways to adapt the ACG suite of tools in the pursuit of improved patient
care. This sub-section provides a summary and overview of some of the recommended
approaches that an organization may wish to consider in the care-management and quality
improvement (QI) domains.
As discussed in some detail earlier in this document, the new acgPM risk measurement
tools provide information at the individual patient level to help identify persons who potentially
would be well served by special attention from the organization’s care management
infrastructure. This “high-risk case identification” process could be used to target a person for
interventions such as a referral to a case-manager, special communication with the patient’s
physician, structured disease management programs, or educational outreach.
As part of the new acgPM module the Release 6.0 software now includes a report that
provides a disease-specific (based on selected individual and aggregated EDCs) distribution of
63
risk probability scores and average expected resource use for different risk cohorts. This latter
report, shown here as Table 7, will be potentially be useful in helping to frame a strategy for
targeting various risk cohorts within disease management programs.
Table 7. Number of Cases and acgPM Predicted Relative Resource Use, by Risk
Probability Thresholds for Selected Chronic Conditions
Number of Cases
Disease Category (EDC)
Arthritis
Asthma
Diabetes
Hypertension
Hyperlipidemia
Low Back Pain
Depression
COPD
Predicted Relative
Resource Use
Probability Score Category Probability Score Category
≥0.4
≥0.6
≥0.8
<0.4 ≥0.4 ≥0.6 ≥0.8
17,679
940
463
172 2.18 6.82 9.31 15.71
27,863
764
386
136 1.43 6.75 9.29 14.85
16,991
1,307
716
345 2.67 7.59 10.62 17.36
50,122
2,064
1,011
457 2.06 7.25 10.27 17.57
9,330
971
514
242 3.27 7.40 10.35 17.33
1,634
460
292
184 5.17 8.81 12.26 19.61
31,240
1,170
529
186 1.97 7.13 9.49 15.46
61,980
1,493
723
279 1.76 6.53 8.77 14.27
10,190
599
298
113 2.09 6.63 9.03 14.30
742
308
253
183 13.11 16.48 19.40 25.21
6,204
545
301
147 2.58 7.71 10.24 16.68
Total
The acgPM’s probability score was fine-tuned to identify persons who will likely be the
ones in your organization who would most benefit from special attention. To capitalize on this
new method, an organization will want to develop periodic reports of members with high acgPM
scores who also meet other organizational criteria, such as
•
enrollment with certain providers,
•
falling into certain eligibility categories,
•
residing in certain geographic areas, or
•
meeting previous patterns of utilization.
After these other stratifiers are taken into consideration as appropriate, a “case finding”
report should list all in-scope individuals arrayed from highest to lowest based on the overall
acgPM high-risk probability score within your organization.
In addition to running the report automatically generated by the software, users are
encouraged to develop their own “individual risk summary” reports on each potential case over a
certain threshold (say the top 1% of individuals). This target group can be winnowed further by
64
case managers on the basis of various sources of information available from the ACG software
and elsewhere. These additional data might include primary care provider information, service
history, history of prior inclusion in care management programs, and results from any ongoing
surveys (such as health-risk appraisals). Please see the “The ACG Predictive Model: Helping to
Manage Care for Persons at Risk for High Future Cost” section of this document for a
comprehensive discussion of the new acgPM module and its applications.
f.
Capitation, Actuarial Underwriting, and Rate Setting
ACGs have been successful for so long because they do a good job at capturing the
complex interplay of co-morbidities that explain the impact of case-mix on resource use. This
factor clearly distinguishes ACGs from other risk-adjustment strategies that treat diseases
individually, as if they each have a completely independent effect on health. The validity of the
ACG view of illness has been borne out through the successful application of this riskadjustment strategy across a range of applications for over a decade.
i.
Using ACGs as “Actuarial Cells”
The ACG System has made it possible to accomplish risk adjustment with fairly simple
and straightforward analytic strategies. For example, ACGs can readily be used as actuarial
cells, which have long been the primary actuarial method for both capitation rate setting and
underwriting. Actuarial cells represent a fixed number of discrete categories into which
individuals are placed based on their expected use of resources. ACGs are very well suited for
assigning individuals into these types of actuarial cells.
There are a number of advantages associated with using an actuarial cell-based approach
to risk adjustment for capitation and underwriting:
•
Simplicity. Once the population has been classified into around 100 ACG “cells,” it
is possible to risk-adjust the population by using a spreadsheet. Some users have
chosen to simplify this approach even further by collapsing the ACGs into smaller
homogeneous groupings, resource utilization bands (RUBs). Even when grouped into
RUBs, studies indicate that ACGs retain much of their explanatory power.
65
•
Less prone to gaming or manipulation. Particularly in applications involving rate
setting, there could be incentives to “game” risk-adjustment strategies to increase
payment. Unlike some other disease-specific risk adjusters, aggressive efforts to
capture additional diagnostic codes on the part of providers will have a more limited
impact on ACG assignments. Where “code creep” associated with general increases
in completeness and accuracy of coding exists, the simplicity of the ACG system
makes it very easy to identify this trend and to implement appropriate action, such as
re-calibration of weights.
•
Stability. The conceptual elegance and underlying simplicity of ACGs have made
the system very stable over long periods. The underlying clinical “truth” captured by
ACGs does not change dramatically with each new data set and each new application.
•
Ease of making local calibrations. It is very easy to recalibrate ACG-based
actuarial cells to reflect local differences in patterns of practice, benefit structure, and
provider fees. Especially for capitation and rate-setting tasks, we encourage users to
calibrate the ACG output to reflect the unique nature of the local cost structure. The
same simplicity that makes it possible to risk-adjust using a spreadsheet makes it
equally possible to accomplish recalibration using the same types of simple tools.
The ultimate testimony to the value of ACGs used as the basis of actuarial cells is the fact
that for almost a decade they have been used to facilitate the exchange of many billions of
dollars within numerous private and public health plans in both the United States and Canada.
ii.
ACGs in Multivariate Models
Multivariate regression for risk adjustment has been used for many years by some of the
more sophisticated users of ACGs. If additional risk descriptors are available beyond diagnosis,
age, and sex, this approach has the potential for improved predictive models.
The strength of regression-based strategies is the ease with which additional risk factor
information can be incorporated and thereby introduce better control for the effects of case-mix.
This ease is also a potential drawback since regression may introduce some assumptions and
statistical pitfalls that can be troublesome without seasoned analytical support. Their inherent
66
complexity makes them difficult to calibrate to local cost patterns, and regression models are
also potentially easier to game because more factors can be manipulated. Finally, while it is
possible to introduce a wide range of variables that improve the model’s explanatory power, this
explanatory power is often confined to the data set and time period on which the model is based.
The model’s results may end up differing significantly from year to year depending on the interrelations of the myriad risk factors that have been included, a phenomenon referred to as “overfitting.”
To address some of these analytic challenges, the acgPM provided as part of this release
represents a regression-based strategy that can be applied for prospective financial applications.
As discussed, one acgPM output, the probability score, has been specifically tailored for case and
disease-management applications. The other acgPM output, the predicted resource index (PRI),
assigns a relative value that can be readily converted to dollars. This PRI output is most relevant
for financial risk-adjustment applications and can be considered a substitute for ACG cells for
prospective rate setting or payment.
One important caveat is worth noting here. Prior pharmacy cost has been made an
optional “risk factor” variable in the new acgPM. Although it is useful for calculating the most
accurate predictions for future costs, we do NOT recommend that models using the optional
pharmacy cost predictor be applied to capitation rate setting. Instead, we suggest that the acgPM
model relying only on ICD input variables be used for such a purpose. We take this position for
the same reason we believe that episode groupers that rely on procedure codes (such as CPT) and
“Rx-groupers” based on use of specific medications (as defined by NDC codes) should not be
used for rate-setting purposes or efficiency profiles. Risk factor variables of this type, which are
directly defined by the providers’ clinical practices, are potentially intertwined with patterns of
“over use” or “under use.” Risk-adjusted rates based on these factors may, in a circular manner
(termed endogoneity by the economists), lead to setting rates that are inappropriate—either too
high or too low. Moreover, when risk factors are determined by such drug use (or procedural)
delivery patterns, providers who practice “efficiently” could potentially be penalized for their
efficiency. This circularity issue is not a major concern when only diagnostic information (not
linked to specific types or settings of service) is used as the main source of information on risk
factors.
67
iii.
To Regress or Not to Regress: That Is the Question
One of the key decision points in using risk adjustment for financial applications is
whether to use a simple actuarial cell approach or a more complex multivariate model. If you
have been applying ACG-based actuarial cells successfully for some time, there may be little
incentive to change since ACGs alone remain a highly effective case-mix adjustment tool. "If it
ain’t broke, don’t fix it." If you are just starting out in your selection of methods, you will need
to balance the stability and ease of use of ACG-based actuarial cells against the potentially
enhanced ability to explain variations in resource use by applying regression modeling
strategies. If you have access to additional well-validated risk factor data and if you have
previous experience using regression models within your organization, then you should consider
using regression. In regression strategies, ACGs, ADGs, and EDCs remain valuable as distinct
risk factors to be supplemented by additional data. Although EDCs are useful for identifying
individuals with specific high impact diseases, it is important to note that they do not account for
burden of co-morbidity as do ACGs. Therefore, we do not generally recommend that EDCs be
used as the only means of controlling for case-mix in regression analysis.
The ultimate choice of risk-adjustment approach depends on the specific application, and
it is prudent to compare both actuarial cell and regression approaches over a span of several
years before making a final decision. For multivariate models, the R-squared statistic is often
used as an indicator of performance. In fact, extreme caution is recommended when evaluating
models based on R-squared values. The R-squared statistic is very sensitive to outlier
individuals. Aside from considering measures of model fit, such as the R-squared value, you
should consider whether the results are reasonably stable over time. It is also advisable to
simulate the degree to which each approach results in over- or under-payment to key segments of
your population.
iv.
Concurrent versus Prospective Applications
The time frame used for most rate setting and other financial analyses is a “prospective”
or predictive one. That is, this year’s diagnostic information is used to determine risk factors and
expected resource consumption in some future period. Thus the weights associated with each risk
factor are calibrated to that future period. But this is not the only temporal approach that
organizations can use for rate setting. Some ACG users have implemented concurrent rating
processes for financial exchanges. In such cases, this year’s expected resource use among the
68
benchmark population is attached to each ACG cell as a relative value rather than next year’s
resource use. While we do encourage experienced actuaries and financial analysts to learn more
about the advantages and challenges of these innovative concurrent approaches, we do not
recommend that organizations apply concurrent approaches to payment without first simulating
the impact that these methods might have on the rate-setting process.
A real-world example of a concurrent approach to rate setting is one being implemented
in Minnesota Medicaid where plan-level payments are based on concurrent ACG-adjusted
profiles of the plan. Under this scenario, payment to a health plan is the same for each individual
enrollee within a particular plan; however, the amount paid is case-mix adjusted by the plan’s
overall morbidity burden (relative to an average, across the population, of 1.0). This approach
assumes that the morbidity burden of large groups (i.e., any individual health plan) is fairly
stable and that the group’s overall morbidity does not change much by the addition/exit of any
one individual.
For additional discussion on this and related issues related to risk adjustment as applied to
financial exchanges, we encourage readers to review our chapter incorporated into Charles
Wrightson’s recently published book Financial Strategy for Managed Care Organizations: Rate
Setting, Risk Adjustment, and Competitive Advantage (see
http://www.ache.org/pubs/wrightson.cfm for ordering details). Our chapter is available online at
http://www.acg.jhsph.edu. Readers are also encouraged to review the ACG bibliography at that
site for a variety of articles illustrating ACGs used for capitation.
g.
In Closing
As part of our ongoing commitment to furthering the international state-of-the-art of riskadjustment methodology and supporting ACG users worldwide, we will continue to perform
evaluation, research, and development. We will look forward to sharing the results of this work
with our user-base via white papers, our web site, peer-reviewed articles, and in-person
presentations. After you have carefully reviewed the documentation supplied with this software
release, we would welcome your inquiries on any topic of relevance to your use of ACGs within
your organization. (Contact us at askacg@jhsph.edu.) We thank you for using ACGs and for
69
helping us to work toward meeting the Johns Hopkins University’s ultimate goal of improving
the quality, efficiency, and equity of health care across the United States and around the globe.
70
Section 6
Installation and Usage
Intended for old and new users alike, this chapter is written for the programmer/analyst who will
be using the software. This chapter begins with an overview of the technical enhancements new to
Release 6.0 and is required reading for all users of the software. The remainder of the chapter is divided
into the following main sections:
•
installing the software,
•
using the software,
•
required components of the input files and how to pass data to the software, and
•
output files.
Readers are referred to the relevant sections of the ACG Version 6.0 Release Notes and the
Version 5.0 Documentation and Application Manual for additional details on calculating or using ACG
weights, discussions of the built-in reporting features and how to customize these reports, and other
relevant criteria for implementing ACG technologies within your organization.
a.
For Users Already Familiar with the ACG Software
Although the functionality of Release 6.0 has been greatly enhanced, these improvements
only slightly affect the installation and use of the software. This section highlights the few
important changes so that current ACG System users will be able to get up and running quickly.
A full set of current installation and usage instructions follows so that new (and current) users
will have all the information that they need readily at hand.
What is needed to take advantage of ACG System enhancements involves two new
“control card keywords” and four “names” that have been added to the OUTREC control card.
The control card keywords allow for additional (and optional) data to be passed to the grouper
from the input file; the four names accommodate additional (and optional) output fields produced
by the software.
71
i.
New Control Card Keywords
Two new record layout control card keywords permit the input of two new data fields:
1. POP is used for a group membership identifier such as underwriting group or PCP
assignment; and
2. PCOST is used for pharmacy cost data.
The group identifier, called POP, serves as a stratifier for producing age/sex-adjusted
prevalence rates and standard morbidity ratio reports using the Johns Hopkins Expanded
Diagnosis Clusters (EDCs) typology.7
The optional pharmacy cost information, called PCOST, is a useful adjunct that improves
performance of the acgPM (see description of the HRCI card below). If pharmacy cost
information is available, we recommend its inclusion.
ii.
New OUTREC Features
Four new names have been added to the control card that controls the output file.
OUTREC additions are as follows:
1. CWT, to output a set of fixed concurrent ACG weights based on our nationally
representative database (written to the output file as ##.###).
2. RUB, to output six resource use levels (Resource Utilization Bands, or RUBs for short)
expressed as an ordinal number with values between zero and five as follows:
0 = nonusers
1 = healthy users
2 = low morbidity
3 = moderate morbidity
4 = high morbidity
5 = very high morbidity
3. HOS, to output a Boolean indicator (e.g., a value of zero or one) for the presence of
conditions likely to lead to a hospitalization.
4. HRCI, to output the four scores for predictive modeling (written to the output file as
###.###) in the following order:
7
Note: Please refer to Section 2 of this Addenda Additions and Refinements to the Expanded Diagnosis Cluster
(EDC) Methodology (including new reports) and ICD-9 Coding Updates and Chapter 13, “ ‘Dino-Clusters’: The
Johns Hopkins Expanded Diagnosis Clusters (EDCs),” of the Version 5.0 Documentation and Application Manual
for technical specifications necessary to customize these tables to your application.
72
a. Total cost predicted resource index – an estimate for Year 2 total expenditures
(including pharmacy charges) expressed as a relative weight;
b. Pharmacy cost predicted resource index – an estimate for Year 2 predicted
pharmacy expenditures also expressed as a relative weight;
c. Probability of being in the high total cost cohort – a probability score with values
between zero and one, indicating the likelihood that a person will have high cost
in the subsequent time period; and
d. Probability of being in the high pharmacy cost cohort – a probability score
indicating the likelihood that a person will have high pharmacy cost in the
subsequent time period.
Also note that users may include the POP and PCOST control cards in the OUTREC
statement if they are interested in having this information appear in the output (as well as the
input) file produced by the software.
iii.
Processing Speed and Space Requirements
Unlike prior versions of the software that allocated memory dynamically for each
individual, the expanded reporting features and modeling components of Release 6.0 require
more processing time. Processing time depends on a variety of factors including but not limited
to CPU speed, disk read-write speed, available memory and disk space, as well as the size of the
input files. As a general guideline the software can be expected to take two to three times longer
than previous versions. For groups that are fewer than 100,000 the increase in processing speed
is negligible, but for very large populations the software may take substantially longer than prior
versions to process similarly sized input files. Note that processing time may be reduced if
duplicate diagnoses cards are removed from the input data stream.
The expanded reports and model-building component of Release 6.0 now also necessitate
the use of temporary files. While the ultimate output file(s) produced by the software will be
similar in size to those produced by prior versions of the software (with the addition of space
needed to accommodate new OUTREC fields), sufficient disk space must be available for the
writing of temporary files. At a minimum the software will need 1 MB of available disk space
for general overhead. On top of this, an estimated additional 600 bytes per person for member
identifier, age, and sex fields plus an additional 51 bytes per each unique ICD per person are
73
required. To simplify things somewhat, as a general guideline Release 6.0 requires temporary
disk space approximately five to six times the size of the input file.
Because of the modeling component of the software, there is now for the first time a limit
to the maximum number of people that can be processed in one run. We estimate the upper limit
to be approximately 3.5 million individuals. The limit is imposed not on the maximum number
of individuals, but rather on the maximum allowable temporary file size, which has been set at 2
GB in most operating systems. If you have membership in excess of 3.5 million, please contact
your software distributor for further guidance on how best to divide your input file into smaller
population subgroupings.
iv.
The Print File
The number of fixed reports has increased in this release, and so the size of the software’s
print file can be expected to be larger. The size of the print file will be at least partially
determined by the number of levels provided in the population stratifier control card (e.g., POP
discussed in the preceding paragraphs) because these reports are generated for each individual
stratifier. If hundreds of stratifiers are used, the software will generate hundreds of pages of
printed reports. Even if hundreds of stratifiers are included, the print file can be easily managed
by loading it into text processing software and globally changing the print font to Courier 8.
With this simple adjustment, reports should be legible and page breaks should appear in logical
places. Extracts of this file can easily be extracted by using the cut and paste feature to separate
files as desired.
v.
Other
The input file or files no longer need to be sorted. Consequently, the NOSORT card is
not needed (and is no longer provided). The age reference date must be provided on the DOB
card and must be of the form CCYYMMDD. Previously this information could be left blank, and
the system date would be used, or it could be provided as YYMMDD. The format of the EDC
output file has changed, so current users must change their programs to read data from these
files. Although the EDCs are still written from columns one through five, the unique member ID
does not begin until column 12 so as to allow for future expanded EDC categories.
74
b.
Installing the Software
The ACG grouper software is supplied on a diskette or CD. Installation involves copying
all files from this disk to your hard disk; other installation steps depend on the platform. If you
are upgrading from an earlier release and want to maintain the prior release for comparison
purposes, then rename the old executable file before installing the new version. This can be done
by using the DOS rename command or the UNIX mv command. Consult your system
documentation for more information on renaming files. Alternatively, you could create separate
directories for each version of the software, although you should be sure to use the appropriate
version. The version of the software used at any one time is listed in the heading of the print file
output.
i.
UNIX Platforms
The file supplied on the disk is named ACGGROUP. Copy this file to your UNIX
partition. You may then need to change the file mode to allow “execute permission.” This is
usually done with the chmod command, e.g., chmod +x acggroup. The software can then be
invoked by merely entering acggroup controlcardfilename. By pressing the break key
combination for your system (e.g., Ctrl-Break or Ctrl-C), the system can be halted. That
completes the UNIX installation.
ii.
PC Platforms
The file supplied on the disk is named ACGGROUP.EXE. Copy this file to your hard
disk for the PC-DOS installation.
75
c.
Using the Software
Figure 1. Opening Screen for ACG Version 6.0.
After copying the file to the appropriate subdirectory, to access the software
(acggroup.exe) simply double click8 on the file listing or icon using Windows Explorer or File
Manager. Figure 1 is a screen capture of what should appear. The software prompts for the
controlcardfilename. The controlcardfilename specifies the location of a file containing a series
of ACG control cards that communicate to the software a) the location of the input and output
files and what fields to read from the input file, b) which ACG branching options are to be used,
and c) what ACG-based risk assessment variables are to be written to the output file(s). (See
Table 1. More detail to follow.) After you type in the full filename and hit return, the software
executes and a series of progress bars will appear at the bottom of the screen indicating the
percentage of data processed. When 100% of the data is processed, the window automatically
closes, and files created by the software will reside in the appropriate directories (as indicated by
the control card). If a problem is found with any of the control cards, then an error message is
written to the screen and the user must press enter to halt execution of the program (at which
time viewing the print file may help users to better ascertain where the problem resides and/or at
what point the software stopped executing).
8
Alternatively, type the following from the command line: ACGGROUP CONTROLCARDFILENAME (where
CONTROLCARDFILENAME is the filename that contains all of the control cards)
76
Control Card File
Table 1: Sample Control Card File
*********************************************************************
* CONTROLFILESAMPLE.TXT
* FILE DEFINITIONS
INPUT1=sample1.txt
INPUT2=
OUTPUT=sample_acg.txt
PRINT=sample_prn.txt
EDC=sample_edc.txt
* ACG BRANCHING SPECIFICATIONS
DELIVERED
* INPUT FILE LAYOUT
ID=1,11
AGE=12,2
SEX=14,1
PCOST=23,5
POP=28,1
ICD=29,5,3
* OUTPUT FILE LAYOUT
OUTREC=ID,ACG,HRCI,CWT
The format of the input and output records is controlled through the use of control cards.
Table 1 provides an example of a “control card file.” Although, strictly speaking, the order of the
control cards does not matter, it may be helpful to think of this file as having four main
77
components. The first part, *FILE DEFINITIONS, specifies the location of the input and output
files. The second part, *ACG BRANCHING SPECIFICATIONS, provides specifications on
optional branching algorithms (e.g., DELIVERED, LOWWEIGHT discussed in more detail
later). The third part, * INPUT FILE LAYOUT, provides additional detail about the positioning
of data in the input files. The final component of the control card file, *
OUTPUT FILE LAYOUT, specifies the output(s) produced by the software.
A few simple rules apply to all control cards. Most have the same general form9:
Keyword=parameter1,parameter2,parameter3
Each control card or keyword must start with a valid keyword and must be contained entirely on
one line. Control card keywords are not case sensitive, and the order of the control cards in the
control file does not affect the program. Comment lines, starting with either an asterisk (*), a
forward slash followed by an asterisk (/*), or two forward slashes (//) are ignored.
The parameters of the card depend on which keyword is being used, and all three
parameters are not always required. The number required depends on the keyword. For example,
on the ID control card, only two parameters are allowed: the starting column and the length. On
the ICD card, three parameters are allowed: the starting column, the length, and the number of
occurrences. On the DELIVERED card, no parameters are required. Most parameters are simply
integers defining the starting column and length of an item. Some parameters, such as on the
FEMALE card, must be enclosed in quotation marks. Table 2 presents all valid control card
keywords followed by a detailed description of each.
Control cards may also be used to control certain user-defined options. Specifically, EDC
reporting, the splitting of pregnancy into “delivered” and “not delivered” categories, and the
optional bifurcation of newborns into “low” and “normal” birth weight categories are all
controlled by the appropriate control card(s).
9
OUTREC=field, field, … is one exception.
78
Control Card Keywords
Table 2: Control Card Keywords
(1)
Filenames
EDC
INPUT1
INPUT2
NONMATCH
OUTPUT
PRINT
(1)
(2)
Record Layout
AGE
DOB
FEMALE
ICD
ID
ID2
OUTREC
SEX
(3)
Optional Fields
DELIVERED
DELIVERED2
FIELD
LOWWEIGHT
LOWWEIGHT2
POP
PCOST
PREGNANT
PREGNANT2
(4)
Miscellaneous
LRECL1
LRECL2
NOCRLF
NOWARN
Filenames
(1) EDC
This card invokes the optional Dino-Cluster algorithm and instructs the system to output
a Dino-Cluster file. The format of this card is as follows:
EDC=filename
where filename is the complete path and filename for the Dino-Cluster output file. The use of
this card is optional. In addition to providing a file of individual level EDC assignment, inclusion
of this card produces a series of additional descriptive tables in the print file.
(2) INPUT1
This file is used to specify the location of the input file for the software as follows:
INPUT1= filename
where filename is the complete path and filename for the input record. The input data must
consist of records that contain a person’s ID, age or date of birth, sex, and ICD-9 code(s) of the
patient. The user may also provide additional information about pregnancy, delivery status, birth
weight, population grouping variable (e.g., PCP assignment) and pharmacy cost. General
considerations of the components, structure, and format of this file are discussed in more detail
79
in the next section of this chapter. Control card keywords are used to indicate the starting and
ending columns of the various pieces of data required in the input file.
(3) INPUT2
The control card for INPUT2 is functionally identical to INPUT1 and is invoked as
follows:
INPUT2=filename
where filename is the complete path and filename for the second input file. The second input file,
while similar to the first, is typically limited to diagnosis information only (but occasionally is
used to carry user-defined flags for pregnancy, delivery, and low birth weight status).
(4) NONMATCH
The NONMATCH card is used to specify the file to which unclassifiable diagnoses are to
be written. Analysis of this file, consisting of unique combinations of member ID and diagnosis
codes not found in the ICD-9 to ADG mapping, is often useful for evaluating the quality of the
input diagnosis and/or effectiveness of the assignment algorithm. The format of this control card
is as follows:
NONMATCH=filename
where filename is the complete path and filename for the nonmatch file.
(5) OUTPUT
The OUTPUT card is used to specify the file into which ACG assignments (and, at the
user’s discretion, ADG and other assignments) are to be written and is of the form:
OUTPUT= filename
where filename is the full path/directory and name into which this file is to be written.
(6) PRINT
The general summaries of the run, warnings, and error messages are written to the
designated print file. The format of this control card is as follows:
PRINT=filename
80
where filename is the complete path and filename. Discussed in more detail in a subsequent
section, the print file includes summary statistics and simple statistical reports on the ADG,
ACG, and EDC distributions of this population.
(2)
Record Layout
(1)
AGE
This card is used to specify the position and length of the age on the INPUT file
(discussed in more detail later in this document) as follows:
AGE=start,length
where start and length indicate starting columns and number of columns to read from the input.
The age must be right justified or incorrect results may occur. Note that either an AGE or a DOB
card can be specified, but not both. If two input files are used, the AGE code must appear on the
first file.
(2) DOB
This card is used as an alternative to the AGE card. It specifies that the date of birth is to
be used in the age calculation as follows:
DOB=start,format,agedate
where start indicates the starting column from the input, and format indicates the format of the
date as follows:
1=YYMMDD
2=CCYYMMDD (CCYY=century & year, e.g., 1990)
3=MMDDYY
4=MM/DD/YY
5=MM/DD/CCYY
6=YYDDD (Julian date)
7=YYMM (a day of 01 is assumed)
8=CCYYDDD (Julian date)
If the DOB card is used then an agedate, indicating the date from which the age is to be
computed, must be provided and the format must be CCYYMMDD. Note: Either an AGE or a
DOB card can be specified, but not both. If two input files are used, the DOB code must appear
on the first file.
81
(3) FEMALE
This card is used to recode the sex for females as follows:
FEMALE=“code”
The optional code indicates the code on the input to consider female. This card is used to
override the default assumption of 2 or F for female. All other codes are considered male. To
recode blanks, insert the proper number of blanks between the two quotation marks. There may
be more than one FEMALE card to supply additional codes. This code is found on the first file,
if two input files are used.
(4) ICD
This card is used to control the position and length of the ICD-9 codes as follows:
ICD=start,length,number
where start is the starting column of the input, length is the length of each field, and number is
the number of consecutive codes. If number is not specified, then the software assumes there is
only one diagnosis code per line. The user can specify more than one ICD card, a useful option if
the diagnosis codes are in different positions on the same record. If two files are used, ICD codes
should always be on the second file.
(5) ID
This card is used to specify the position and length of the person ID as follows:
ID=start,length
where start and length indicate starting columns and number of columns to pick up from the
input. Note that the ID fields can exist in multiple columns and do not have to be contiguous on a
record. Multiple ID cards would be used to define the position and lengths of the parts of a
noncontiguous ID.
(6) ID2
This card is used to specify the position and length of the person ID on input file 2 as
follows:
ID2=start,length
82
where start and length indicate starting columns and number of columns to pick up from the
input. Note that the ID does not have to be contiguous on a record. If only one input file is used,
then the ID2 card is not needed. If an ID2 card is specified, but no second input file is specified,
then a warning message is given. If a second input file is specified but no ID2 card is included,
then the ID position in file 2 is assumed to be the same as the ID position in file 1.
(7) OUTREC
This card is used to control the format of the OUTPUT record as follows:
OUTREC=field,field,field,...
where the fields are used to specify the order of the output. If no OUTREC control card is used,
the default is to write out only the ID and ACG. Any of 14 predefined fields or any user-defined
field (see the FIELD control card) may be specified in any order. The predefined field names are:
ACG, ADG, AGE, CWT, DEL, DOB, HOS, HRCI, ICD, ID, LOW, POP, PCOST PRE, RUB,
and SEX. Except for the ICD-9 codes, the fields will be written out in the order specified. If
ICD-9 codes are indicated for output, they will always be written at the end of the output record
regardless of where they are specified on the OUTREC card. Please see the separate section
subsequent in this document entitled “Output Files” for additional details on what is written to
the output files produced by the software.
(8) SEX
This card controls the position and length of the sex field as follows:
SEX=start,length
where start and length indicate starting columns and number of columns. Note: Sex codes are
always found in the first input file.
(3)
Optional Fields
(9) DELIVERED
This card instructs the system to classify patients who are associated with a pregnancy
according to whether they delivered during the period of observation. If the DELIVERED card is
used, ACGs associated with pregnancy (those beginning with ‘17’) will end with ‘1’ (delivered)
or ‘2’ (no delivery). The format is:
DELIVERED
83
If the DELIVERED card is not used, the software will not assign an ACG that specifies delivery
status, and the ACGs associated with pregnancy (those beginning with ‘17’) will end in ‘0’.
The user may also use this control card to specify the location of a user-supplied flag on the first
input file (INPUT1) indicating that the woman has delivered a baby during the period of
observation. The format is:
DELIVERED=start,"code"
where start is the starting column number on the input record of the data used to indicate a
delivery, and "code" is the value of the data that indicates a delivery. For example,
DELIVERED=106,"1" indicates that if column 106 in the input data contains “1”, the patient
delivered a baby. There is no limit to the number of DELIVERED cards that can be used.
Note: If the user-supplied flags indicate a woman delivered, but there is no other indication that
she was pregnant, then a warning message is printed, and the woman is automatically considered
to have been pregnant.
(10)
DELIVERED2
This card is identical to the DELIVERED control card, except that it is used to identify
values in data file 2, when two input data files are used.
(11)
FIELD
This card specifies a user-defined field as follows:
FIELD=field name,start,length
where field name is a user-defined name for this field. The user-defined name cannot be one of
the predefined names (see OUTREC card description). Start and length indicate the starting
column and number of columns for the field. A field name can be defined only once. Userdefined fields are picked up from the INPUT1 file only. Field names may then be used on the
OUTREC card to write those fields to the output record. Examples of using the field card include
picking up and carrying through to the output record a population stratifier variable or some
resource measure, such as total charges, thereby enabling further analysis of the output file
without having to re-merge these data. Note a few restrictions on the field name: 1) it must be 10
84
characters or fewer, 2) it must start with an alphabetic letter, and 3) it can contain only letters and
numbers.
(12)
LOWWEIGHT
This control card instructs the system to classify infant patients according to whether they
had low birth weight, i.e., less than 2,500 grams. To do this, the user must specify the location of
a user-supplied flag on the first input data file that indicates that a newborn had a low birth
weight. This flag should only be used for patients who are infants (1 year or younger) during the
observation period. The format is:
LOWWEIGHT=start,"code"
where start is the starting column number on the input record and "code” is the value of the data
used to indicate low birth weight. For example, LOWWEIGHT=107,"1" indicates that if column
107 is “1”, the infant should be considered to have had low birth weight. There is no limit to the
number of possible LOWWEIGHT cards. Please note that the use of the LOWWEIGHT card is
optional. If the LOWWEIGHT card is not used, the software will not assign an ACG that
specifies birth weight status.
(13)
LOWWEIGHT2
This control card is identical to the LOWWEIGHT control card, except that it is used to
identify the low birth weight flags on the second input file, when two input data files are used.
(14)
POP
This card is used to specify the position and length of the population stratifier field as
follows:
POP=start,length
where start and length indicate starting columns and length. This field is treated as a character
field and may contain any data.
(15)
PCOST
This card is used to specify the position and length of pharmacy cost field as follows:
PCOST=start,length
85
where start and length indicate starting columns and number of columns. The PCOST must be
all numeric (leading blanks are allowed). It is assumed to be a whole dollar amount (no cents). If
it does not meet these conditions, it is set to zero and a WARNING09 will appear in the warning
message section of the print file.
(16)
PREGNANT
This control card specifies the location of a flag on the first input file indicating that a
woman is pregnant during the period of observation. This flag would be necessary only if
comprehensive pregnancy diagnosis data were not available during the period of analysis. For
example, some health plans using a global fee for all perinatal services may not have pregnancy
diagnoses in their claims data until after delivery has occurred. The format is:
PREGNANT=start,"code"
where start represents the starting column number on the input record of the data used to indicate
a pregnancy and "code” is the value of the data that indicates pregnancy. For example,
PREGNANT=105,"1" specifies that if column 105 is “1”, the woman should be considered
pregnant. Any other entry indicates that the woman is not pregnant. There is no limit to the
number of PREGNANT= cards that can be used at one time. If this card is not used, and no
PREGNANT2= card is used (see below), then the software will, by default, use the ICD-9 codes
alone to classify patients according to pregnancy status.
(17)
PREGNANT2
This control card is identical to the PREGNANT= control card, except that it is used to
identify the pregnancy values from data file 2, when two input data files are used.
(4)
Miscellaneous
(18)
LRECL1
This card is used to specify the record length for input file 1 in the case of fixed-length
input records. It is needed only if the input is a fixed length with no end-of-record mark—
normally a carriage return and line feed—at the end of each record. It is useful for inputting data
directly from database files, which usually store data as fixed length records, or after a header
with no end-of-record mark. The format of this card is:
86
LRECL1=length,skip
where length is the length of each record, and skip is the number of bytes to skip at the start of
the file. At the end of the file, if a complete record cannot be read, then the trailing bytes are
ignored.
(19)
LRECL2
This card is used to specify the record length for input file 2 in the case of fixed-length
input records. Its format is identical to that of the LRECL1 card described above.
(20)
NOCRLF
This card is used to specify that output records be written without an end-of-record mark.
The format for this card is:
NOCRLF
(21)
NOWARN
This card is used to adjust the warning threshold. Normally, the software will abort
execution if warnings are produced for more than 20 consecutive people in the first half of the
input file. If a NOWARN card is included in the control card file, then although warnings will
still be generated, execution will not abort if this threshold is reached. Its format is:
NOWARN
d.
Required Components of the Input File
The following are general considerations:
•
The software accepts only flat ASCII (or .txt) files.
•
A record may contain more than one diagnosis code. Blank diagnosis codes are
simply ignored.
•
If only one input file is specified, then it is assumed to contain the ID, age or DOB,
sex, and ICD-9 codes. This file may contain multiple records per ID. The age and sex
are picked up from the first record for a particular ID. When two input files are
specified, the first must contain the ID, age or DOB, and sex, and the second must
contain the ICD-9 codes and an ID. If two input files are used, then the first should
87
Chapter 9
contain only one record per unique ID, whereas the second can contain multiple
records per ID.
•
The system can use either the age or the date of birth. If the date of birth is supplied,
then a date (the AGEDATE) from which to compute the age MUST be supplied on
the DOB control card.
•
The age/DOB and sex are extracted from the first record for each individual in the
data set. If a person has more than one record, the age/DOB and sex on subsequent
records are ignored. Preliminary validation of age, or the month and day of a date of
birth code, should be performed. The software will stop execution if more than 20
consecutive people are identified as having no demographic information or being
"older" than 107 years of age in the first half of the input data file.
•
Special sex codes can be indicated on a control card.
i.
Augmenting Diagnosis Data
Certain aspects of the ACG sorting algorithm are optional and are intended to help the
user make a better assessment of the case-mix patterns of the study population. These options are
initiated by user-supplied flags for pregnancy, delivery, and/or low birth weight, which serve to
augment the diagnosis information typically found in claims or encounter data. Please note that
the use of user-supplied data for classifying pregnant women and infants is optional. The
discussion here complements that outlined in the Version 5.0 Documentation and Application
Manual, Chapter 7, “Basic Data Requirements for ACG Categorization and Analysis,” and
discusses the technical ‘how to’ of this process.
ii.
Pregnancy and Optional User-supplied Information on Pregnancy
Status
By default, the ACG software searches for a defined set of ICD-9 codes to determine
pregnancy status. The diagnosis codes used to indicate a pregnancy include: 640xx-677xx, V22x,
V23x, V24x, V27x, and V28x. Individuals with a diagnosis in this range, and who are not
otherwise excluded on the basis of age or sex, are assigned an ACG beginning with ‘17.’ If an
individual’s diagnoses indicate pregnancy but she is younger than 10 years or older than 55, a
warning is issued that the patient is of a suspicious age to be pregnant. Similarly, if an
individual’s diagnoses indicate pregnancy but that person is coded as male, a warning is issued
of suspicious sex to be pregnant. Those who are coded as male and those who are younger than 5
88
years or older than 60 (regardless of sex) are assumed to have received a pregnancy diagnosis in
error and will not be assigned an ACG beginning with ’17.’
Alternatively, the user may decide that available ICD-9 code data are insufficient
indicators of pregnancy status. That is, in some cases patients may actually be pregnant when no
ICD-9 code is present. User-supplied ‘flags’ can be defined by using control cards (designated
PREGNANT or PREGNANT2 and discussed later in this chapter) that serve to augment the
ICD-9 diagnosis data. If a user-supplied ‘flag’ indicates pregnancy on the input file, the ACG
software will classify such a patient as pregnant regardless of any other indication of pregnancy
(including typical exclusions due to inappropriate age and sex). Warnings of suspicious age
and/or sex may be issued but the user-defined information will be assumed to be correct. If the
ICD-9 data indicate pregnancy when a user-supplied flag does not, the individual will also be
assumed to be pregnant as long as she meets the default criteria for age and sex (i.e., female
between the ages of 5 and 55). In other words, a user-supplied indication of pregnancy serves as
a supplement to the diagnostic data, rather than as a strict replacement.
iii.
Delivery Status and Optional User-supplied Information on Delivery
Status
Because of the significant increase in resources used in association with delivery, the user
may want to specify that the ACG system categorize pregnant women as either having delivered
or not having delivered during the period of observation. Unless otherwise instructed by the user,
the ACG system will not automatically attempt to classify pregnant women by delivery status.
However, two control cards, DELIVERED and DELIVERED2, can be used to instruct the
software to search for either ICD-9 diagnosis codes or a user-supplied flag indicating delivery
status. As with the classification of pregnancy status, the user-supplied data serves as a
supplement to ICD-9 coding indicating delivery. The ICD-9 codes that the software uses to
subdivide these ACG categories include: 664.0, 664.01, 664.11, 664.21, 664.31, 650, 654.21, 656.31,
658.11, 658.21, 658.31, 661.01, 661.11, 661.21, 661.31, 663.11, 663.31, 669.5, 669.7, 669.70, V27*
(where * = 0-9).
iv.
User-supplied Information on Birth Weight Status (Optional)
When adequate information is available, the user may also want to specify that the ACG
software categorize infants as having either low or normal birth weight, in order to enhance the
case-mix adjustment process. Unless otherwise instructed by the user, the ACG system will not
89
automatically attempt to classify infants by birth weight status. However, either one of two
control cards, LOWWEIGHT and LOWWEIGHT2, can be used to instruct the software to
search for a user-supplied flag indicating birth weight status. Unlike the DELIVERED control
card discussed above, the software will not classify patients as low or normal birth weight on the
basis of ICD-9 codes.
v.
ICD-9 Codes and Decimal Points
The recoding and handling of explicit or implicit decimal points is automatic and cannot
be modified by the user. If no decimal point is detected in the ICD-9 code, then the software
assumes that a) the decimal point is implied after the third character, b) the first three characters
of the diagnosis code are right justified, and c) the fourth and fifth characters are left justified.
For example, "4011 " is assumed to be 401.1, "0112 " is assumed to be 011.2, and "112 " is
assumed to be 112. If the input ICD-9 code does contain a decimal point, then the software will
take up to three characters to the left of the decimal point and right justify them with leading
zeros. It will then take up to two characters to the right of the decimal point and left justify them.
For example, “401.1" will be recoded to 4011, “11.2" will be recoded as 0112, and “112.” will
be recoded as 112.
e.
Output Files
There are potentially four output files produced by the software, including 1) a print file
containing summary statistics of the run; 2) an output file containing (at a minimum) individual
level ACG assignment; 3) an optional Expanded Diagnosis Clusters output file containing each
unique combination of member IDs and EDC assignment; and 4) a nonmatched file containing
unique combinations of member IDs and diagnosis codes not identified by the software.
i.
Print File
The print file of the software provides the following:
·
A copy of the control cards used for that run.
·
A listing of the input and output files defined for that run.
·
Selected summaries of input and output data including:
-- the number of input records on the first input file;
-- the number of input records on the second input file;
-- the number of output records (this is equivalent to the number of persons
90
appearing in either file one or file two);
-- the sum of unique diagnosis codes across all input records;
-- the sum of unique diagnosis codes across member IDs;
-- the sum of unique non-grouped diagnosis codes across member IDs;
-- non-grouped diagnosis code percentage; and,
-- number of people with non-grouped diagnosis codes.
·
Frequency and percentage distributions of ACGs and ADGs.
·
Selected summary statistics of the distribution of ADGs including:
-- the number and percent distribution of people with numbers of ADGs
ranging from zero to 10 or more per person;
-- the number and percent distribution of people with numbers of ACGs
ranging from 1 to 4 or more per person;
-- the average number of ADGs per person;
-- the average number of major ADGs per person;
-- the average number of ADGs for those who have an ADG;
-- the average number of major ADGs;
-- the average number of ADGs for those with an ADG;
-- the average number of major ADGs for those with an ADG;
-- the average number of ADGs for those with a major ADG; and
-- the average number of major ADGs for those with a major ADG.
·
Selected summary statistics and distribution of unique diagnoses including:
-- the average number of unique diagnosis codes per person;
-- the average number of unique diagnosis codes per person for those
with diagnoses; and
-- the number and percent distribution of people with unique diagnosis
codes ranging from zero to 10 or more per person;
·
Age/Sex Distribution.
· Frequency distribution of EDCs and MEDCs and prevalence per 1,000 persons.
· Average numbers of EDCs per person overall and for those with EDCs.
· Percentage distribution of EDC and MEDC in 5 resource utilization bands (RUBs)10.
·
Estimated concurrent resource use by EDC and MEDC in 5 RUBs.
·
Observed and age/sex-adjusted prevalence, standardized morbidity ratios (SMRs), and
SMR confidence intervals by MEDC.
· Frequency and percent distribution of acgPM probability scores.
·
10
Number of cases and associated predicted acgPM relative resource use by
Note: the non-user RUB is excluded.
91
alternative acgPM risk probability thresholds for selected chronic conditions.
·
Warning Messages
-Warnings associated with each individual who generates a warning are
listed on one line per person at the end of the print file. The warning lines
include the ID, ACG assignment, age, sex, date of birth (if entered as
input), and a list of warnings numbers. The warning numbers can be
compared to the text for each warning printed above the individual-level
warning data.
ii.
Output File
The output record produced by ACGGROUP is controlled through the OUTREC control
card (discussed previously). The fields will be written out only if specified, and in the order
specified. The only exception to this rule of order is that ICD-9 codes will be written at the end
of the record. The field lengths are as follows:
Name Field
ACG ACG code
Length
4
ADG ADG vector
34 one-character fields for ADG 1–34
(where a 1=the person had this ADG; 0=otherwise)
(Please note: Because they are no longer in use, the values
for ADG 15 and ADG 19 will always be 0.)
AGE Age
3 (right justified, leading zeros)
CWT Concurrent weight
Based on a nationally representative database.
Written to the output file as ##.###.
DEL Delivery status
1=person delivered; 2=person did not deliver;
9=optional branching turned off
DOB Date of birth
8 in CCYYMMDD format if possible
ICD
ICD-9 codes
6 characters each (where the first 5 are the ICD-9 code, and
a mismatch flag is in the sixth position [1=no ADG code
and blank=successfully matched])
ID
ID code
Same as designated for input
HRCI acgPM scores
All written to the output file as ###.### in the following
order:
1) Total cost predicted resource index – an estimate for
Year 2 total expenditures (including pharmacy
charges) expressed as a relative weight;
92
2) Pharmacy cost predicted resource index – an estimate
for Year 2 predicted pharmacy expenditures also
expressed as a relative weight;
3) Probability of being in the high total cost cohort – a
probability score with values between zero and one
indicating the likelihood that a person will have high
cost in the subsequent time period; and
4) Probability of being in the high pharmacy cost cohort
– a probability score indicating the likelihood that a
person will have high pharmacy cost in the
subsequent time period.
HOS Hospital Dominant
A Boolean indicator (e.g., a value of zero or one) for the
presence of conditions likely to lead to a hospitalization.
LOW Low birth weight
1=low birth weight; 2=normal birth weight;
9=optional branching turned off
PRE
1=person is pregnant; 2=person is not pregnant;
Pregnancy status
RUB Resource utilization 0 = nonusers
band
1 = healthy users
2 = low morbidity
3 = moderate morbidity
4 = high morbidity
5 = very high morbidity
SEX
Sex code
1 (F=female, M=male)
User-defined fields specified on the FIELD card can also be specified for output. The
length of user-defined fields is the same as specified by the user on the FIELD cards. If no
OUTREC card is included, then the software automatically writes the unique member ID and
ACG to the output file.
Because the software is not limited to a maximum number of unique ICD-9 codes per
person, the number of codes on the output will depend on the number of unique ICD-9 codes for
any particular patient. Only unique codes are written to the output. Even if codes are duplicated
on the input, only one occurrence of each code is written to the output in the order read. If ICD-9
codes are indicated for output, they will always be written at the end of the output record
regardless of where they are specified on the OUTREC card. There is one output record
produced for each unique ID.
93
iii.
Differentiating Nonusers from Those with Only One Input File
If only one input file is used to enter both demographic and diagnostic data, all patients
without associated diagnoses or without groupable diagnoses are assigned to ACG 5100. To
more cleanly separate nonusers from those who used services but had no classifiable diagnoses,
it is recommended that individuals classified into ACG 5100 be subdivided into two groups
according to the following criteria. Nonusers, defined as individuals with no claims or
encounters, should be reassigned to ACG 5200. This will leave only those individuals in ACG
5100 (which can be renamed 5110) who, although they are users of services, have no diagnosis
and/or only unclassified diagnoses.
iv.
The EDC File
The Dino-Cluster output file is optional and will be produced by the software only if the
EDC control card is present. The format of this file is as follows:
Columns
1-5
6-11
12-n
v.
Field
EDC code
space
Person ID where n depends on the length of the ID
n=11+length of ID
Mismatched ICD-9 List
All input ICD-9 codes without a corresponding ADG code will be written to an output file
called the mismatched ICD-9 file. Because all currently defined ICD-9-CM codes are now included
in the ICD to ADG mapping, this mismatch file will typically be limited to: 1) ‘E’ codes that,
because they typically indicate the cause of an injury rather than an underlying morbidity, are
excluded from the mapping; 2) local coding that is not otherwise included; and 3) genuine errors in
coding. Mismatched codes are written out one time for each person who has that code. In this way,
the file can be used to get a listing of codes and/or a listing of people who have mismatched codes. It
can also be used to count the number of patients with each mismatched code. The format of this file
is as follows:
Columns
1-5
6-n
Field
ICD-9 code
Person ID where n depends on the length of the ID
n=5+length of ID
94
This file should be analyzed carefully. Briefly, using this file to create a frequency
distribution of non-grouped ICD-9 codes can help identify diagnosis codes that are not in standard
format. Historically, the five most frequently occurring codes will account for more than 50% of all
codes not identified by the software. How the codes are justified and/or padded and whether decimal
places are explicit or implied are the two leading causes of problems pertaining to mismatched
codes. For a more theoretical discussion of ICD-9 coding considerations, see Chapter 7, “Basic Data
Requirements for ACG Categorization and Analysis,” of the Version 5.0 Documentation and
Application Manual.
The software will assign ADGs to "legal" ICD-9-CM and ICD-9 (non-CM) diagnostic codes.
In certain circumstances, ADGs will also be assigned to "illegal" codes, where the intent of the codes
was clear (for example, a three-digit code that is unambiguous, but where only a four- or five-digit
code is considered valid according to official ICD-9 publication vendors).
vi.
Error Messages
Error messages generated by ACGGROUP are either written to the print file or displayed on
the screen. Errors are basically of two types: those that involve the control cards and those that
involve the data. Each message is numbered. The following sections describe the error messages in
order by message number for each of the two types of errors/warnings. Most messages are selfexplanatory and can be resolved by checking the documentation for the specific control card in error.
Further clarification is supplied below, as appropriate.
vii.
Control Card Errors and Warnings
The control cards are written to the print file in a numbered list. Control card error messages
will, many times, contain a number that refers to the specific control card in that list. While many
error messages are generic to multiple control cards, by using the control card number and referring
to the list of control cards in the print file, you can easily identify the specific control card in error.
For example, given the following control cards:
id=x,11
age=12,3
sex=15,1
icd=21,6
The print file would list the control cards as follows:
1: id=x,11
95
2: age=12,3
3: sex=15,1
4: icd=21,6
The following error message would also be printed:
ERROR02: Parameter 1 is missing or non-numeric on control card: 1
This message indicates that on control card #1, which is the ID card, the first parameter,
which is an “x,” is non-numeric. It should indicate the starting column of the patient ID.
The following is a complete list of the control card error messages
1. ERROR01: Two control cards were found and only one is allowed: #
Certain control cards are allowed to be used only once per run. The software found more than
one of these cards. The number given in the error message points to the control card in error.
2. ERROR02: Parameter 1 is missing or non-numeric on control card: #
The control card referenced has an invalid first parameter, which is usually the starting
column number.
3. ERROR03: Parameter 1 was not specified on control card: #
4. ERROR04: Parameter 2 is missing or non-numeric on control card: #
The control card referenced has an invalid second parameter, which is usually the length.
5. ERROR05: Parameter 2 was not specified or is invalid on control card: #
6. ERROR06: Value must be enclosed in quotes on control card: #
7. ERROR07: Could not obtain memory to hold control cards.
Your system does not have sufficient free RAM to read in all of the control cards.
Eliminating other programs that are running at the same time as ACGGROUP or adding
more system memory will clear up this problem.
8. ERROR08: Not enough memory to continue program:
Your system does not have sufficient free RAM. Eliminating other programs that are
running at the same time as ACGGROUP or adding more system memory will clear up
this problem.
9. ERROR09: Both AGE and DOB control cards were found and only one is allowed:
10.
ERROR10: Parameter 3 is non-numeric or not specified on control card: #
11.
ERROR11: The date format code specified on the DOB card is invalid: #
12.
ERROR12: Age date is non-numeric on control card: #
96
The agedate parameter on the DOB card provides a date upon which to calculate the age.
If provided, it must be a numeric field.
13.
ERROR13: The month of the age date on the DOB card is invalid: #
14.
ERROR14: The day of the age date on the DOB card is invalid: #
15.
ERROR15: An undefined field name was specified on the OUTREC card: fieldname
The OUTREC card contains a field name that is undefined. It is not one of the predefined
names, and it is not a user-defined field name. The field name in question is shown on the
card.
16.
WARNING16: Field specified at least twice on the OUTREC card: fieldname
17.
ERROR17: Unidentifiable keyword on control card: keyword
The control file contains a line that cannot be understood by ACGGROUP. Check the
proper spelling of the keyword on the control card shown in the message.
18.
WARNING18: An ID2 card was used, but no second input file was specified.
If no second input file is specified, then an ID2 card is not needed. One was specified
that will be ignored.
19.
ERROR19: Duplicate, missing, or reserved user field name on FIELD card: #
The field card specified by # contains a field name that is already defined, either as a
predefined field or on another FIELD card, or it contains no field name.
20.
ERROR20: Parameter 3 is not allowed on control card: #
The control card specified by # contains something where a third parameter would be
specified, but a third parameter is not allowed on this control card. In particular, if this is
an ID card, note that previous versions would allow multiple, noncontiguous pieces of the
ID to be defined on a single ID card, whereas this version of the software only allows one
piece of the ID to be defined per ID card. Multiple ID cards can be used to define other
parts of the ID. As an example, in previous versions, the card ID=1,10,20,5 would be
acceptable. In this version, this input would generate the error message. The proper
coding would be with two ID cards: ID=1,10 and ID=20,5.
97
Appendix A
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A-1
“Characteristics of Maryland Residents Who Obtain Health Insurance from the Small and Large
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ADifferences in Case Mix Between CHC Users and Non Users: Washington and Missouri, 1992
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Behavioral Health Care Under Medicaid.@ Health Services Research. August 2001; 36(4): 793811.
“The Faces of Medicaid” Publication for the Center of Health Care Strategies, Inc., Finance.
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Fakhraei SH, Kaelin J, Conviser R. “Comorbidity-Based Payment Methodology for Medicaid
Enrollees with HIV/AIDS.” Health Care Financing Review. Winter 2001; 23(2): 53-68
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Emergency Visits: Experiences of Medicaid Patients Using Federally Qualified Health Centers.”
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A-2
Fishman PA, Shay DK. ADevelopment and Estimation of a Pediatric Chronic Disease Score
Using Automated Pharmacy Data.@ Medical Care. 1999; 37(9): 874-883.
FitzHenry F, Shultz EK. AHealth-Risk-Assessment Tools Used to Predict Costs in Defined
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Forrest CB, Majeed A, Weiner J, Carroll K, Bindman A. “Comparison of Specialty Referral
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Forrest CB, Reid RJ. APrevalence of Health Problems and Primary Care Physicians= Specialty
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