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Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for Analytic Maturity

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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.

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Reviewing the Healthcare Analytics Adoption Model: A Roadmap and Recipe for Analytic Maturity

  1. 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. 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. 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. 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. 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. 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. 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. 8. Our Analytics Professional Accountability
  9. 9. Historical Context
  10. 10. © 2020 Health Catalyst 10 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
  11. 11. © 2020 Health Catalyst Tom Davenport’s Five Pillars Framework -- 2010 11 Industry-agnostic
  12. 12. © 2020 Health Catalyst HIMSS/Davenport Collaboration Model -- 2012 12
  13. 13. © 2020 Health Catalyst 2012 13
  14. 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. 14
  15. 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. 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. 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. 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. 19. The Details in Each Level
  20. 20. © 2020 Health Catalyst 20 – 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
  21. 21. © 2020 Health Catalyst 21 – 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
  22. 22. © 2020 Health Catalyst 22 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
  23. 23. © 2020 Health Catalyst Level 3 Automated Internal Reporting 23 – 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
  24. 24. © 2020 Health Catalyst 24 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
  25. 25. © 2020 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
  26. 26. © 2020 Health Catalyst 1,352 measures Does Anyone Really Know How Many? 26
  27. 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 27
  28. 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) 28 Level 5 Waste & Care Variability Reduction
  29. 29. © 2020 Health Catalyst • May 2018 • $210B in direct unnecessary costs per year in the US from Low Value Care 29
  30. 30. © 2020 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
  31. 31. © 2020 Health Catalyst 31 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. 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
  33. 33. © 2020 Health Catalyst 33 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. 34. © 2020 Health Catalyst Sanders’ Predictive Analytics Postulate 34 Predictions without interventions are a liability to the decision maker, not an asset.
  35. 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
  36. 36. © 2020 Health Catalyst 36 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
  37. 37. © 2020 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
  38. 38. © 2020 Health Catalyst 38 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. 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. 40. Q&A 40 Dale Sanders Chief Technology Officer, Health Catalyst
  41. 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. 42. Thank You!

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