HIMSS Analytics Adoption Model for Analytics Maturity - March 2016

HIMSS Analytics
Adoption Model for
Analytics Maturation
Copyright HIMSS Analytics 2016, some portions of this presentation are Copyright Creative Commons
https://creativecommons.org/licenses/by/4.0/legalcode

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
Copyright © HIMSS Analytics

Healthcare Analytics Maturation Model
Availability
• Basic model shared under Creative Commons copyright
– Accessible by any organization
– Freely published and available
• Derived from HAAM Analytics Maturity model shared by
Dale Sanders in 2013 under Creative Commons copyright
• Significant updates
– Refined to be internationally applicable
– Focused content around 4 key areas
– Adapted from 9 stages to 8 stages
– Standard terminology with key word references
http://www.slideshare.net/dalesanders1/analytic-adoption-model-v4

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 0%
Stage 6 4%
Stage 5 15%
Stage 4 28%
Stage 3 25%
Stage 2 75%
Stage 1 77%
Example organization…
• Achieved Stage 2 compliance
• 32% Overall compliance
• Has made progress through Stage 6

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.
Copyright Creative Commons

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
 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…
o Internal organization operations and strategic goals
o Regulatory and accreditation requirements (e.g.: Nationally sponsored programs, Governmental entities,
Accreditation commissions, tumor registry, communicable diseases tracking)
o Payer incentives (e.g.: Meaningful use of data, Physician quality reporting, Value based purchasing, readmission
reduction)
o 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

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
• Analytics Strategy initiator
– Identifies key opportunities
– Roadmap for progressing to an appropriate level
– Drives organizational strategic and tactical alignment

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

HIMSS Analytics Adoption Model for Analytics Maturity - March 2016

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