Dale Sanders provides an update on the Healthcare Analytics Adoption Model. Dale published the first version of this model in 2002, calling it the Analytics Capability Maturity Model. The three intentions at that time are the same as they are today: 1) Provide healthcare leaders with a clear roadmap for the progression of analytic maturity in their organization. 2) Provide vendors with a roadmap to meet the analytic needs of clients. 3) Create a common framework to benchmark the progressive adoption of analytics at the industry level. In 2012, Dale co-published a new version of the Model with Dr. Denis Protti, rebranding it the Healthcare Analytics Adoption Model and purposely borrowing from the widespread adoption of the EMR Adoption Model (EMRAM) published and supported by HIMSS. In 2015, Dale transferred the model under a creative commons copyright to HIMSS to create a vendor-independent industry standard that is now widely applied to support the original three intentions. He continues to collaborate with HIMSS to progress the Model. During this webinar, Dale: -Reviews the current state of the Health Catalyst Model, including recent changes that advocate a ninth level—direct-to-patient analytics and AI. -Shares his observations of maturity in the market. -Provides an update on the current state of the HIMSS Adoption Model for Analytic Maturity.

1. Reviewing the Healthcare Analytics Adoption Model:
A Roadmap and Recipe for Analytic Maturity
February 5, 2020
Dale Sanders
Chief Technology Officer, Health Catalyst

2. © 2020
Health
Catalyst
This is an intense, text-heavy tutorial,
more like a graduate class than a
typical vendor webinar.
If you were expecting something
different, you might not want to attend.
Fair Warning
2

3. • An Appeal for Physicians
• History of Analytics Adoption
Models in Healthcare
• The Details of the Current Model
• What About the New, 9th Level?
• Direct to Patient Analytics and AI
Agenda

4. © 2020
Health
Catalyst
• We (Health Catalyst and HIMSS) are actively
soliciting input for the next version of the
model, so think critically during the webinar
• How can we improve the model?
• Where does it need updating and
modernizing?
• Email me at dale.sanders@healthcatalyst.com
and use the subject: “Analytics Adoption
Model Webinar”
Think Critically, Offer Suggestions

5. Those of us in this
meeting are standing at
a cliff edge in US
healthcare history.
It can go either way,
good or bad.
We Are Losing Our Physicians

6. Analytics is Contributing to the EHR Problem
• The Lancet, Sep 2019
• Danielle Ofri, MD
• Bellevue Hospital, NYU School of Medicine
“There is at least one upside to this
mess, however. The aggressiveness of
the EMR’s incursion into the doctor–
patient relationship has forced us to
declare our loyalties: are we taking care
of patients or are we taking care of the
EMR?”

7. 7
NEJM, April 2018
Over- and wrong measurement of physicians is
hindering, not helping, data-driven healthcare
63% of Quality Payment Program measures categorized
as clinically invalid or of uncertain validity

8. Our Analytics Professional Accountability

9. Historical Context

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Health
Catalyst
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Level 5:
EBM
Level 4: Payer
Financial
Incentives
Level 3: Professional
Societies
Level 2: Accreditation
Level 1: Compliance &
Regulatory
Publicly
available
cohort
and metrics
definitions
Sanders’ Hierarchy of Analytic Needs -- 2002

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Health
Catalyst
Tom Davenport’s Five Pillars Framework -- 2010
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Industry-agnostic

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Health
Catalyst
HIMSS/Davenport Collaboration Model -- 2012
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Health
Catalyst
2012
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14. The Healthcare Analytics Adoption Model-- 2013
Level 8 Personalized Medicine & Prescriptive Analytics
Tailoring patient care based on population outcomes and genetic data.
Fee-for-quality rewards health maintenance.
Level 7 Clinical Risk Intervention & Predictive Analytics
Using predictive risk models to support organizational processes for
intervention. Including fixed per capita payment in fee-for-quality.
Level 6 Population Health Management & Suggestive Analytics
Tailoring patient care based upon population metrics. Including bundled
per case payment in fee-for-quality.
Level 5 Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on internal optimization
and waste reduction.
Level 4 Automated External Reporting
Ensuring efficient, consistent production of reports and adaptability to
changing requirements.
Level 3 Automated Internal Reporting
Ensuring efficient, consistent production of reports and widespread
availability in the organization.
Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data content.
Level 1 Enterprise Data Operating System Collecting and integrating the core data content.
Level 0 Fragmented Point Solutions
Tolerating inefficient, inconsistent versions of the truth and cumbersome
internal and external reporting.
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15. © 2020
Health
Catalyst
HIMSS Adoption Model for Analytics Maturity -- 2015
Collaboration with Health Catalyst
15
Dr. Anne Snowdon RN, PhD, FAAN
Director of Clinical Research
HIMSS Analytics
Anne.Snowdon@himssanalytics.org

16. The Healthcare Analytics Adoption Model-- 2019
Level 9 Direct-to-Patient Analytics & Artificial Intelligence
Putting patient data, analytics, and AI in patients’ hands so they
can own more of their health and healthcare decisions.
Level 8 Personalized Medicine & Prescriptive Analytics
Tailoring patient care based on population outcomes and genetic data.
Fee-for-quality rewards health maintenance.
Level 7 Clinical Risk Intervention & Predictive Analytics
Using predictive risk models to support organizational processes for
intervention. Including fixed per capita payment in fee-for-quality.
Level 6 Population Health Management & Suggestive Analytics
Tailoring patient care based upon population metrics. Including bundled
per case payment in fee-for-quality.
Level 5 Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on internal optimization
and waste reduction.
Level 4 Automated External Reporting
Ensuring efficient, consistent production of reports and adaptability to
changing requirements.
Level 3 Automated Internal Reporting
Ensuring efficient, consistent production of reports and widespread
availability in the organization.
Level 2 Standardized Vocabulary & Patient Registries Relating and organizing the core data content.
Level 1 Enterprise Data Operating System Collecting and integrating the core data content.
Level 0 Fragmented Point Solutions
Tolerating inefficient, inconsistent versions of the truth and cumbersome
internal and external reporting.
16

17. © 2020
Health
Catalyst
The Healthcare Analytics Adoption Model
As a Vendor Product & Evaluation Framework
17
Level 9 Direct-to-Patient Analytics & Artificial Intelligence
Level 8 Personalized Medicine & Prescriptive Analytics
Level 7 Clinical Risk Intervention & Predictive Analytics
Level 6
Population Health Management
& Suggestive Analytics
Level 5 Waste & Care Variability Reduction
Level 4 Automated External Reporting
Level 3 Automated Internal Reporting
Level 2 Standardized Vocabulary & Patient Registries
Level 1 Enterprise Data Operating System
Level 0 Fragmented Point Solutions
• Flash Data Engine: Subject Area Mart Designer
• Flash Data Engine: Source Mart Designer
• IDEA
• Atlas Data Governance
• DOS Operations Console
• Measures Manager
• Population Builder
• DOS Marts
• Standardized Terminology
• Leading Wisely
• Touchstone Suite
• CORUS Suite
• Population Health Foundations
• Community Care
• 45+ Analytic Accelerators
• Touchstone
• EHR Closed-Loop Analytics and AI/Data Science
(support all products)
• Patient Safety Monitor Suite
• Care Management Suite
• Touchstone

18. © 2020
Health
Catalyst
Level 9 Direct-to-Patient Analytics & Artificial Intelligence
Level 8 Personalized Medicine & Prescriptive Analytics
Level 7 Clinical Risk Intervention & Predictive Analytics
Level 6 Population Health Management & Suggestive Analytics
Level 5 Waste & Care Variability Reduction
Level 4 Automated External Reporting
Level 3 Automated Internal Reporting
Level 2 Standardized Vocabulary & Patient Registries
Level 1 Enterprise Data Operating System
Level 0 Fragmented Point Solutions
18
The Healthcare Analytics Adoption Model
Shifting Scarce Resources to Higher Value Levels

19. The Details in
Each Level

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Health
Catalyst
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– Fragmented “point solutions” with very focused, limited analytics capabilities, typically focused on
departmental analytics (finance, acute care nursing, pharmacy, laboratory, or physician productivity)
– New knowledge generated by these solutions tends to be isolated to one area, which may encourage
optimized sub-processes at the expense of enterprise-wide processes
– Fragmented applications not co-located in a data warehouse or otherwise architecturally integrated with one
another
– Multiple versions of the truth exist due to overlapping data content
– Labor intensive and inconsistent reports
– No formal data governance function tasked with maximizing the quality and value of data in the organization
– Point solutions are not a market differentiator and cannot scale to the more complicated analytic use cases
and business models
– Point solutions require significantly more labor from data analysts and systems administrators to use and
maintain than single, integrated data warehouses
– Inefficiencies of decentralization also apply to the fragmented costs of software licensing and vendor
contract management
Level 0 Fragmented Point Solutions

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Health
Catalyst
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– Core transaction source system data is integrated into an Enterprise Data Warehouse (EDW)
– At a minimum, the following data sources are co-located in a single local or hosted data warehouse: 1) HIMSS
EMR Stage 3 clinical data 2) Financial data, particularly costing data 3) Materials and supplies data 4) Patient
experience data and 5) Claims, if available
– A searchable metadata repository available across the enterprise
» Provides natural language descriptions of the EDW content, describes known data quality issues, and records data lineage
» Metadata repository is the single most important tool for the complete democratization of data across the enterprise
– EDW data content updated within one month of changes in the source systems
– Beginnings of an enterprise data governance function established with an initial focus on reducing
organizational and cultural barriers to data access, increasing data quality in the source systems, and master
data identification and management
– Data stewardship for the source data content areas in the EDW forming under clinical and administrative
ownership
– EDW reports organizationally to the CIO, assuming the CIO can facilitate access to and the extraction of data
from the source systems
» As the EDW evolves from the construction and early phases of adoption, the organizational alignment can shift to another
C-level executive who represents the functional use of analytics in the organization, such as the Chief Medical Officer or
Chief Quality Officer.
Level 1 Enterprise Data Operating System

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Health
Catalyst
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Level 2 Standardized Vocabulary & Patient Registries
– Master vocabularies and reference data identified and standardized across disparate source system content
in the EDW
– Master vocabularies and reference data include local master patient identity, physician identity, procedure
codes, diagnosis codes, facility codes, department codes, and others
– Data stewardship for master data functioning
– Naming, definition, and data types in the EDW data content areas standardized according to local master
reference data, enabling queries across the disparate source content areas
– Patient registries based on billing codes and defined by multidisciplinary teams available in the EDW to
support basic analytics for the most prevalent and costly chronic diseases and acute care procedures in the
local environment
– Data governance forms around the definition and evolution of patient registries and master data management

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Health
Catalyst
Level 3 Automated Internal Reporting
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– Automated internal reporting focused on consistent, effective production of reports for:
» Executive and board-level management and operation of the organization
» Self-service analytics for KPIs and interactive dashboards at the director and management levels
– Efficiency and consistency of reports necessary for effective management (but not enough to create
differentiating value in the market)
– Little or no labor to maintain these reports—nearly entirely self-service
– Reports available, consistent, and accurate, minimizing wasteful debate and redundant reports development
– Analytic services user group facilitates collaboration between corporate and business unit data analysts—
user group defines consistent data definitions and calculation standards
– Data governance includes data quality assurance and data literacy training and guides strategy to acquire
mission-critical data elements in subsequent levels of adoption

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Health
Catalyst
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Level 4 Automated External Reporting
– Consistent, efficient, and agile production of reports required for external needs, such as:
» Regulatory, accreditation, compliance, and other external bodies (e.g., tumor and communicable disease registries)
» Funding and payer requirements (e.g., commercial financial incentives and federal Meaningful Use payments)
» Specialty society databases (e.g., national cardiovascular data registry)
– Master data management requires data content in the EDW that conforms to current versions of industry-
standard vocabularies (ICD, CPT, SNOMED, RxNorm, LOINC, and others)
– Low-labor, low-maintenance production of reliable, accurate, and consistent reports
– EDW engineered for agility to respond to the constantly changing nature of external reporting requirements
– Data governance and stewardship centralized for external reporting
– Stewardship processes exist to maintain compliance with external reporting requirements and govern the
process for approving and releasing the organization’s data to external bodies
– EDW data content expanded to include text data from patient-record clinical notes and reports
– EDW-based text query tools available to support simple keyword searches within and across patient records

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Health
Catalyst
Overwhelming? Yes. Meaningful…?
25
• 1,958 quality metrics in the
National Quality Measures
Clearinghouse
• (De-funded in 2017; all work
transferred to AHRQ and CMS)
• 7% of those measure clinical
outcomes
• Less than 2% of those are based
on patient reported outcomes
N Engl J Med 2016; 374:504-506, February 11, 2016

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Health
Catalyst
1,352 measures
Does Anyone Really Know How Many?
26

27. © 2020
Health
Catalyst
• JAMA, 2011
• 212 articles reviewed in
2009 NEJM
• 35 studies suggested new
standards of care
• 16 were determined
ineffective (46%)
46% of “Evidence Based” Standards
of Care Were Later Reversed
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28. © 2020
Health
Catalyst
– Quality and cost enabled by analytics provides significant opportunity to differentiate the organization in the
market
– Data used explicitly to inform healthcare strategy and policy formulation
– Analytic motive focused on measuring adherence to clinical best practices, minimizing waste, and
reducing variability, using variability as an inverse proxy for quality
– Data governance expands to support multidisciplinary care management teams focused on improving the
health of patient populations
– Population-based analytics suggest improvements to individual patient care
– Permanent multidisciplinary teams continuously monitor opportunities that will improve quality and reduce
risk and cost across acute care processes, chronic diseases, patient safety scenarios, and internal workflows
– Precision of registries improved by inclusion of data from lab, pharmacy, and clinical observations in the
definition of the patient cohorts
– EDW content organized into evidence-based, standardized data marts that combine clinical and cost data
associated with patient registries
– The data content expands to include insurance claims (if not already included) and HIE data feeds
– EDW updated within one week of source system changes (on average)
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Level 5 Waste & Care Variability Reduction

29. © 2020
Health
Catalyst
• May 2018
• $210B in direct
unnecessary costs
per year in the US
from Low Value Care
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Health
Catalyst
• ~31 measures, 6 categories
• Cancer Screening
• Diagnostic, Preventive Testing
• Preoperative Testing
• Imaging
• Cardiovascular Testing
• Other Surgical Procedures
Medicare Definitions of LVC
30
Guided by the US Preventive Services Task Force

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Health
Catalyst
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Level 6 Population Health Management & Suggestive Analytics
– Sustainable data-drive culture
– Firm analytic environment for understanding clinical outcomes
– Financial risk and reward shared by ACO and tied to clinical outcomes
– At least 50% of acute care cases managed under bundled payments
– Analytics available at the point of care to support the Triple Aim of maximizing the quality of individual patient
care, population management, and the economics of data
– EDW data content expands to include bedside devices, home monitoring data, external pharmacy data, and
activity-based costing
– Data governance plays a major role in the accuracy of metrics supporting quality-based compensation plans
for clinicians and execs
– EDW updated within one day of source-system changes (on average)
– EDW reports organizationally to a C-level executive accountable for balancing cost and quality of care

32. © 2020
Health
Catalyst
“Despite interest in addressing social
determinants of health to improve patient
outcomes, little progress has been made in
integrating social services with medical
care.”
“…the ACOs were frequently “flying blind,”
lacking data on both their patients’ social
needs and the capabilities of potential
community partners.”
Population Health Challenges
32

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Health
Catalyst
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Level 7 Clinical Risk Intervention & Predictive Analytics
– Movement to predictive analytics enabled by expanding on optimization of the cost per capita populations and
capitated payments
– Focus expands from management of cases to collaboration with clinician and payer partners to manage
episodes of care and support outreach, triage, escalation and referrals—focus includes predictive modeling,
forecasting, and risk stratification
– Analytic motive expands to address diagnosis-based, fixed-fee per-capita reimbursement models
– Physicians, hospitals, employers, payers, and members/patients collaborate to share risk and reward (e.g.,
financial reward to patients for healthy behavior)
– Registries flag patients who are unable or unwilling to participate in care protocols due to constraints
such as cognitive disability, economic inability, geographic limitations to care access, religious restrictions, and
voluntary non-participation
– Data content expands to include home monitoring data, long-term care facility data, and protocol-specific
patient reported outcomes
– EDW updated in one hour or less of source system changes (on average)

34. © 2020
Health
Catalyst
Sanders’ Predictive Analytics Postulate
34
Predictions without interventions are a
liability to the decision maker, not an asset.

35. © 2020
Health
Catalyst
“It is generally understood that
association does not necessarily
indicate causation. However, since
causes can be used to make quality
predictions, many practitioners
take prediction accuracy as an indicator
of how likely that a predictor is a cause
of the outcome. In fact, prediction
accuracy and causal validity are
measures in two different worlds, and a
wrong link between them is very harmful
for data-driven discovery.”
35
January 2020

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Health
Catalyst
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Level 8 Personalized Medicine & Prescriptive Analytics
– Analytic motive focused on wellness management and
physical and behavioral-functional health for precise,
patient-tailored care
– Healthcare-delivery organizations transform into health-
optimization organizations under direct contracts with
patients and employers
– Patient and employer fixed-fee per-capita payment for
health optimization preferred over reimbursement for
treatment and care delivery
– Analytics expands to include natural language
processing (NLP) of text, prescriptive analytics, and
interventional decision support
– Prescriptive analytics available at the point of care
to improve patient-specific outcomes based on
population outcomes
– Data content expands to include 7x24 biometrics data,
genomic data, and familial data
– EDW updated within minutes of changes in the source
systems
– Organization completely engaged as a data-driven
culture through a shift from a fixation with care delivery
to an obsession with risk intervention, health
improvement, and preventive medicine
– New data content in the EDW is combined with not-yet-
discovered algorithms to identify relationships
between genomics, family history, and patient
environment
– Individualization of medicine enabled by smart
phones, cloud computing, gene sequencing, wireless
sensors, modernized clinical trials, internet connectivity,
advanced diagnostics, targeted therapies, and more
– Consumers have an unprecedented capacity to take
charge –it is their DNA, their cell phone, their individual
information
– Analytics applied early in the patient’s life to develop a
lifelong health optimization plan—and the patient’s
treatment protocol is tailored based on the insights
gained from these new data sources and algorithms
– Boundaries of evidence-based medicine are extended
beyond the limited applicability of randomized clinical
trials to include the quasi-experimental evidence
emerging from local and regional EDWs
– Locally-derived evidence is shared with commercial
clinical content providers to iteratively enhance the
knowledge content from randomized clinical trials

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Health
Catalyst
• 2018 Netherlands study
• Rate of patient requests for a
specific therapeutic or
diagnostic intervention, 1985-
2014
• Significant increase in
requests by patients
• Significant increase in
compliance by GPs
Owning Their Care: Patients Are Becoming More Data-Driven
37
Requests for blood tests: 2x increase
Requests for urine tests: 26x increase
Requests for radiology/imaging: 2.4x increase
Requests for medication prescription: 1.2x increase

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Health
Catalyst
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Level 9 Direct-to-Patient Analytics & Artificial Intelligence
”Hello Dale. Thank you for your data.
I am calculating a health optimization recommendation for you, informed
not only by the latest clinical trials, but also by local and regional data
about patients like you; the real-world health outcomes over time of
every patient like you; and the level of your interest and ability to
engage in your own care. I will tell you within a specified range of
confidence, which treatment or health management plan is best suited
for a patient specifically like you and how much that will cost.”*
* Inspired by the Learning Health Community, www.learninghealth.org/

39. © 2020
Health
Catalyst
If you have any suggestions for improving the next version of the model,
please feel free to email me at dale.sanders@healthcatalyst.com and
use the subject line: “Analytics Adoption Model Webinar”
In Closing…
39

40. Q&A
40
Dale Sanders
Chief Technology Officer, Health Catalyst

41. © 2020
Health
Catalyst
The Doctor’s Orders for Engaging Physicians
to Drive Improvement
Physicians drive the majority of all quality and cost decisions, yet reimbursement pressures, competing time
pressures, misaligned incentives, and a lack of credible data often make engaging clinicians in improvement work
one of the biggest challenges in healthcare.
Wednesday, February 12
2:00 - 3:00 PM ET
In this webinar you can expect to:
• Identify the levels of physician leadership in your organization you can engage to drive improvement.
• Pinpoint the types of data and information of most interest to physician leaders.
• Propose several ways data to use data to engage physicians in leading improvement work.
• Help you develop at least one mechanism you can use to better engage physicians in improvement work at your
organization.
DAVID WILD, MD, MBA
Vice President, Performance
Improvement, Assistant Professor,
Department of Anesthesiology, The
University of Kansas Hospital
JACK BEAL, JD
Vice President, Performance
Improvement and Deputy General
Counsel, The University of Kansas Health
System

42. Thank You!