Himss analytics + capsite = your source for market

Transcription

AMAM Overview
PREPARED FOR
COACH & HIMSS
Ontario Chapter
April 2016
© HIMSS Analytics 2016
Adoption Model for Analytics
Maturity
• James E. Gaston, MBA, FHIMSS
• HIMSS Analytics
• James.Gaston@HIMSSAnalytics.org
What is “Analytics”?
Analytics is the discovery and communication of
meaningful
patterns in data.
Analytic Value Estimator
Transformation
A1C Test
Results
1-15-2016
7.3%
2-15-2016
6.5%
Etc…
A1C level of 6.5%+
on two occasions
indicates diabetes.
Diabetes
treatment and
counseling
specifically to
Results from 5.7 address
6.4% are considered patients’
pre-diabetes
lifestyle
Q: Why develop Analytics?
•
•
•
•
Increased performance and meet competitive demands
Business insights and market understanding
Support regulatory requirements
Move from “What do I need to do?” to answering the
more complex, nuanced question of, “What do I need
to know?”
Data Driven Decision (DDD) making empowers consistently better
decisions, moves beyond intuition based decision making, and
incorporates the ability to make effective decisions in a changing
environment where experience is limited.
A: To enable informed decision making
Balance
People
Process
Technology
Why Use a Maturity Model?
• Learn from others experiences
• Provides a roadmap
• Helps convey a vision of target (end?)
state
• Encourages everyone to work
collectively for the same goal
• Many options and approaches…
Identifying the best approach
Adoption Model
for Analytics
Maturity
How hospitals
leverage data for
better care and
process
improvement.
Adoption Model for Analytics Maturation
Model Overview
• Capability oriented approach (not technology oriented)
• Healthcare industry specific, internationally applicable
• Leverages an 8 stage maturity model, like EMR Adoption
– 4 key focus areas theme for each stage, across entire
model
• Prescriptive
– Each stage has specific compliance goals
– Bullet point description of compliance requirements
– Clearly defined requirements, industry standard
terminology
• Simple assessment survey
• Outlines a clear path to analytics maturity
Key Focus Areas Across All Stages
• Data Content growth
– Basic data to advanced data
– Aligned with clinical, financial, and operational analytics activities
• Analytics competency growth
– Start simple and work to master specific competencies
– Enhance performance tracking / clinical decision support
– Appropriate analytics maturation for individual parts of the
organization
• Infrastructure growth
– Flexible approaches to accommodate a wide variety of situations
– Vendor neutral
– Timely data, centrally accessible
• Data Governance growth
– Quality data and resource management
– Executive suite and strategic alignment
Survey Approach & Achievement
•
Compliance statements for each stage in each key focus category
– Lowest is Stage 0, highest Stage 7
– Compliance measured using a Likert Scale
• Overall and stage level achievement presented as a percentage
– Color and % conveys overall progress against compliance
– Identifies areas of strength as well as opportunity
• Achieving a stage requires 70% or > stage compliance
– On that stage and all previous stages
– Your “Stage” standing is the highest stage achieved
– Accommodates different approaches in priorities,
resources types, and execution
Stage Achievement
2
Overall Compliance
32%
Stage 7
Stage 6
Stage 5
Stage 4
Stage 3
Stage 2
Stage 1
0%
4%
15%
28%
25%
75%
77%
Example organization…
• Achieved Stage 2 compliance
• 32% Overall compliance
• Has made progress through Stage 6
Analytics
Data
Data Content Infrastructure Governance Competancy
Focus Area Stage Achievement
Focus Area Compliance
Stage 7
Stage 6
Stage 5
Stage 4
Stage 3
Stage 2
Stage 1
3
52%
0%
25%
25%
25%
71%
88%
100%
3
66%
0%
0%
0%
63%
100%
100%
75%
1
76%
75%
50%
75%
63%
94%
69%
88%
3
41%
5%
13%
38%
44%
81%
92%
100%
Stage 0 – Fragmented Point Solutions
Stage Descriptive Bullets
 Specific analytics needs as they arise are addressed by individual
and segregated applications.
 Multiple fragmented business and clinical data presentation and
management solutions are not architecturally integrated.
 Overlapping ungoverned data content leads to significant
discrepancies in versions of the derived “truth”, resulting in a lack
of confidence in the underlying data and resulting potential
conclusions.
 Report development is labor intensive and inconsistent.
 Data governance is non-existent.
Achievement Statements
There are no achievement statements for stage 0; all organizations
begin their analytics journey here.
Stage 1 – Foundation Building: Data
Aggregation and Initial Data Governance
Data Content

Foundational data includes
o HIMSS EMR Stage 3 data
o Clinical Electronic Medical record (EMR) data
o Revenue Cycle data
o Financial/General Ledger (GL) accounting data
o Patient level financial data
o Cost data
o Supply Chain data
o Patient Experience data

Searchable metadata repository is available across the enterprise
Infrastructure

An operational data store of managed and integrated data from one or more disparate sources is
in place. This single accumulation and management location stores current and historical data

Primary data sources are updated within one month of system of record changes
Data Governance

Data governance is forming around development of an analytics strategy

Data governance is focused on the data quality of source systems

Data management and data governance activities reports organizationally to a chief executive
demonstrating executive level program support
Analytics Competency

Analytics resources are inventoried and profiled
Example: Stage Level 1 Key Terminology
Data Governance: A set of processes that ensures that important data assets are formally managed
throughout the enterprise.
Metadata: Data and information that explains details about the data of interest. Two types of metadata
exist: structural metadata and descriptive metadata. Structural metadata is data about the containers of
data, such as date formatting
Operational data store (ODS): The general purpose of an ODS is to integrate data from disparate
source systems in a single structure, using data integration technologies like data virtualization, data
federation, or extract, transform, and load. This will allow operational access to the data for operational
reporting, master data or reference data management.
Data warehouse: Central repositories of integrated data from one or more disparate sources. They
store current and historical data and are used for creating analytical reports for knowledge workers
throughout the enterprise
System of Record: The authoritative data source for a given data element or piece of information
Analytics strategy: A formal document presenting an organizational plan that outlines the goals,
methods, and responsibilities for achieving analytics maturation.
Wikipedia, https://en.wikipedia.org/wiki/Data_governance
Wikipedia, https://en.wikipedia.org/wiki/Metadata
Wikipedia, https://en.wikipedia.org/wiki/Operational_data_store
Wikipedia, https://en.wikipedia.org/wiki/Data_warehouse
Wikipedia, https://en.wikipedia.org/wiki/System_of_record
Stage 2 – Core Data Warehouse Workout
Data Content

Data content includes patient health insurance claim data
Infrastructure

A centralized formal primary database is acting as an enterprise wide data warehouse, a
repository of centralized and managed data

The data warehouse is dedicated to storing historical, integrated data while supporting ad-hoc
query and reporting solutions
Data Governance

Master data management is practiced so that vocabulary and reference data are identified and
standardized across disparate source system content in the data warehouse

Naming, definition, and data types are consistent with local standards

Data governance supports the design and evolution of patient registries

Data governance is thoroughly engaged in management of the entire set of data in the data
warehouse

Data governance expands to raise the data literacy of the organization and develop a data
acquisition, stewardship, and management strategy

Corporate and business unit data analysts and Subject Matter Experts (SMEs) meet regularly to
collaborate and steer data warehouse activities, managing them in a manner that benefits the
entire enterprise

Patient registries are defined at least by ICD billing data

An analytics competency center is used to profile and track analytics resources, collectively
manage their training and education, and coordinate analytical skills development as well as
standard methodology
Stage 3 – Efficient, Consistent Internal /
External Report Production and Agility
Data Content
•
The data warehouse represents a strong cross section of critical internal (clinical, financial, operational) data
and critical external data sources, representing an enterprise wide perspective
Infrastructure
•
There is an enterprise oriented data warehouse with a wide reaching database schema and data
orientation
•
Key performance indicators (KPIs) tracked in the data warehouse and are easily accessible from the
executive level to the front-line staff
Data Governance
•
Adherence to industry-standard vocabularies is required, such as ICD and SNOMED-CT
•
Centralized data governance has documented standard process(s) for review, approval/denial, and delivery
procedure to manage all externally released data
•
Clinical text data content (if available) can be searched using simple key word searches and basic text
searching
•
Analytic motive is focused on consistent, efficient production of reports supporting basic management and
operation of the healthcare organization (historical / retrospective reporting)
•
Analytic efforts are focused on consistent, efficient production of KPI reports required for…
•
Internal organization operations and strategic goals
•
Regulatory and accreditation requirements (e.g.: Nationally sponsored programs, Governmental
entities, Accreditation commissions, tumor registry, communicable diseases tracking)
•
Payer incentives (e.g.: Meaningful use of data, Physician quality reporting, Value based purchasing,
readmission reduction)
•
Specialty society databases
Stage 4 – Measuring & Managing Evidence
Based Care, Care Variability, & Waste
Reduction
Data Content
•
Clinical, financial, and operational data content of the enterprise oriented data warehouse are
presented in standardized data marts
•
Data content expands to include insurance eligibility, claims, and payments (if not already included)
•
Data content expands to include external feeds such as those from Health Information Exchanges
(HIE) in order to provide a complete and holistic view of the patient
Infrastructure
•
Primary data sources are updated more frequently than monthly from when there are system of
record changes
Data Governance
•
Governance supports special analytical expertise needed by dedicated teams that are focused on
improving the health of patient populations as well as organizational process improvement
•
Data governance links business owners of data with analytics capabilities
•
Analytic activities are focused on measuring adherence to best practices, minimizing waste, and
reducing variability across clinical, operational, and financial practice areas
Stage 5 – Enhancing Quality of Care, Population
Health, and Understanding the Economics of Care
Data Content
•
Data content expands to include provider based bedside devices, monitoring data
originating in the home care setting, external pharmacy data, and detailed activity
based costing
Infrastructure
•
Primary data sources are updated less than 2 weeks from when there are system of
record changes
Data Governance
•
Data governance oversees the quality of data and accuracy of metrics supporting
quality-based performance measurement for clinicians, executives, and other staff
•
Analytics are significantly enabled at the point of care
•
Population-based analytics are used to suggest improvements in support of an
individual patients’ care
•
Permanent multidisciplinary teams are in-place that continuously monitor
opportunities to improve quality, and reduce risk and cost across acute care
processes, chronic diseases, patient safety scenarios, and internal workflows
•
Precision of registries is improved by including data from lab, pharmacy, and clinical
observations in the definition of the patient cohorts
Stage 6 – Clinical Risk Intervention & Predictive
Analytics
Data Content
•
Data warehouse content expands to include population census data, some social determinants
of health, long term care facility data, and protocol-specific patient reported outcomes
Infrastructure
•
Primary data sources are updated less than 1 week from when there are system of record
changes
Data Governance
•
Data governance activities are directed by executive oversight that is accountable for managing
the economics of care (cost of care and quality of care)
•
Analytic motive expands to address high volume diagnosis-based per-capita cohorts
•
Focus expands from management of cases to collaboration between clinician and payer partners,
government or otherwise, to manage episodes of care, using predictive modeling, forecasting,
and risk stratification to support outreach, education, population health, triage, escalation and
referrals
•
Patient engagement is profiled and patients are flagged in registries that are unable or unwilling to
participate in care protocols
•
The financial risk and reward of healthcare influencing behavior and treatments are clearly
presented for care providers and the patient. The benefit of healthy behavior(s) and the costs of
treatment(s) are presented for citizen/patient consideration.
Stage 7 – Personalized Medicine &
Prescriptive Analytics
Data Content
•
Data warehouse content expands to include 7x24 biometrics data and genomic data
•
Data warehouse content expands to include behavioral health outcomes management
Infrastructure
•
Primary data sources are updated less than 24 hours from when there are system of record
changes
Data Governance
•
Data governance is tightly aligned with organizational strategic, financial, and clinical
leadership
•
Analytic motive expands to wellness management, physical and mental health, and the mass
customization of care through personalized medicine
•
Analytics expands to include patient specific prescriptive analytics and interventional
decision support, available at the point of care to improve patient specific outcomes based
upon related population outcomes
Analytics
Analytics is the identification of meaningful patterns in data
used to empower Data Driven Decision making (DDD)
•
Analytical maturity…
• Is a corporate competency and cannot be purchased
• Must be nurtured and developed over time
• Is best leveraged as a organizational cultural trait
AMAM Value Propositions
•
•
•
•
Healthcare specific
Vendor neutral
Capability oriented (not technology oriented)
Prescriptive, clear, and informative
– Simply stated compliance requirements
– Industry standard terminology and detailed references
The HIMSS AMAM analytics maturity model is an analytics maturity
framework for assessing and benchmarking healthcare organizations and
provides a road map to analytics maturity
Adoption Model for Analytics
Maturity
Questions and comments?
• James E. Gaston, MBA, FHIMSS
• HIMSS Analytics
• James.Gaston@HIMSSAnalytics.org
WANT TO LEARN MORE?
Provider Engagement and Educational Services
Jessica Daley
Provider Consulting and Engagement
540-433-1422
Jessica.Daley@HIMSSAnalytics.org
Vendor Engagement and Certified Educator Opportunities
Bryan Fiekers
Vendor Client Relations
312-497-6617
Bryan.Fiekers@HIMSSAnalytics.org
WEB: www.himssanalytics.org
TWITTER: @himssanalytics
Thank You

Himss analytics + capsite = your source for market

Transcription

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