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 Commonshttps://creativecommons.org/licenses/by/4.0/legalcode

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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


Adoption Model for Analytics MaturationSurvey 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


Adoption Model for Analytics MaturationAdoption Model for Analytics Maturation



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 StatementsThere are no achievement statements for stage 0; all organizations begin their analytics journey here. Copyright Creative Commons




Adoption Model for Analytics MaturationStage 1 – Foundation Building: Data Aggregation and Initial Data Governance Data Content Foundational data includes

o HIMSS EMR Stage 3 datao Clinical Electronic Medical record (EMR) datao Revenue Cycle datao Financial/General Ledger (GL) accounting datao Patient level financial datao Cost datao Supply Chain datao 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

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Adoption Model for Analytics MaturationExample: Stage Level 1 Key TerminologyData 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

https://en.wikipedia.org/wiki/Data_governance

https://en.wikipedia.org/wiki/Metadata

https://en.wikipedia.org/wiki/Operational_data_store

https://en.wikipedia.org/wiki/Data_warehouse

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 Analytics Competency 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 methodologyCopyright Creative Commonshttps://creativecommons.org/licenses/by/4.0/legalcode



Adoption Model for Analytics MaturationStage 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

Analytics Competency 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 goalso 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




Adoption Model for Analytics MaturationStage 4 – Measuring & Managing Evidence Based Care, Care Variability, & Waste ReductionData 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 Analytics Competency Analytic activities are focused on measuring adherence to best practices, minimizing waste, and

reducing variability across clinical, operational, and financial practice areasCopyright Creative Commonshttps://creativecommons.org/licenses/by/4.0/legalcode




Stage 5 – Enhancing Quality of Care, Population Health, and Understanding the Economics of CareData 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 Competency 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




Adoption Model for Analytics MaturationStage 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) Analytics Competency 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.




Adoption Model for Analytics MaturationStage 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 Analytics Competency 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 DaleyProvider Consulting and [email protected] Vendor Engagement and Certified Educator OpportunitiesBryan FiekersVendor Client [email protected]


mailto:[email protected]

mailto:[email protected]