Data Quality Matters: EHR Data Quality, MACRA, and Improving Healthcare
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This document discusses data quality in electronic health records and its importance for Medicare programs and healthcare quality. It makes three key points: 1) Medicare spending is unsustainable at current rates and data quality is important for value-based programs like MACRA that tie reimbursement to quality and cost measures. 2) MACRA was introduced to replace the flawed Sustainable Growth Rate formula and moves Medicare reimbursement towards value-based payments through programs like MIPS and APMs that require accurate clinical data. 3) High quality clinical data is essential for measuring healthcare quality, costs, and outcomes required by programs like MACRA and value-based payment models. Data profiling of EHRs reveals many quality issues that can
Data Quality Matters: EHR Data Quality, MACRA, and Improving Healthcare
- 1. DATA QUALITY MATTERS: EHR DATA QUALITY, MACRA, AND IMPROVING HEALTHCARE 2017 Michael Hogarth, MD, FACP, FACMI Professor, Internal Medicine Professor and Vice Chair, Dept. of Pathology and Laboratory Medicine http://hogarth.ucdavis.edu mahogarth@ucdavis.edu
- 2. Summary Three Fundamental Questions: How is Medicare doing today? Why is MACRA here (and what is it exactly)? Why does clinical data quality matter?
- 3. Question 1 How is Medicare doing today?
- 4. US Healthcare is unsustainable In 6 years, NHE will be 19.3% of $26T!! This is a 60% increase in total expenditure 60% increase
- 6. Medicare Today -- A Runaway Train…
- 7. The widening gap between beneficiaries and contributors 2016 Medicare Trustee’s Report
- 8. 2014: 43% of all healthcare in the US paid directly by government Govt sponsor Percentage Medicare 20% Fed Medicaid 10% State Medicaid 6% VA/DOD/CHIP 4% Public Health 3%
- 9. And you are loosing money…
- 10. Medicare Projection of Cost vs. Assets (solvency) Costs exceed assets ~2022-2028
- 11. Oh by the way, US healthcare gets a ”D-”
- 13. Physician actions affect Medicare in many ways
- 14. Physicians prescribing decisions are far reaching…
- 15. Question 2 Why is MACRA here and what is it exactly?
- 16. SGR Introduced
- 17. What was wrong with the SGR? • Fundamentally flawed • Attempted to limit expenditures on physician services by restraining payments without limiting the growth in volume and complexity of the services provided • In 2015, SGR would have invoked a 24% fee reduction for Medicare providers History of the “doc fixes”
- 19. What is the scope of MACRA? The financial footprint 1,048,575 Providers Physicians, PAs, clinical nurse specialists, anesthetists The Medicare provider footprint today ~300,000 physicians (2013)
- 20. The Importance of MACRA and beyond MACRA is the ‘start’ of an evolution towards value based purchasing Value-based reimbursement requires managing patients across multiple providers -- requires data exchange between EHR systems Value-based reimbursement increases the need for your organization to know where it stands High quality clinical care data Health analytics
- 21. MACRA’s two pathways MIPS: “Merit Based Incentive Payment Program” ~90% of practices will choose this option MIPS is a Modified fee-for-service Combines meaningful use with cost, quality, and clinical practice improvement – A Composite Performance Score (CPS) APM: “Alternative Payment Model” Models that reduce costs and drive high quality Reporting is different than MIPS Incentives, NO penalties https://www.greenwayhealth.com/blog/path-macra-paved-big-decisions/
- 29. APM – Alternative Payment Model
- 30. Its not just Medicare…
- 31. ACO Lives Covered and Payer Distribution
- 32. Question 3 Why does data quality matter?
- 33. c.2017 – Is data quality important?
- 34. Clinical Data Quality: What is it? The 5 Dimensions of Clinical Data Quality 1) Completeness – is the EHR record complete? 2) Correcteness – Is an element in the EHR true? 3) Concordance – Is there agreement between elements in the EHR, or between the EHR and another data source? 4) Plausability – Does an element make sense in light of other knowledge at a given point in time? 5) Currency – Is a piece of data a relevant representation of the patient at a given point in time? 6) Relevance/Fit-for-use – Are the elements needed for a metric of high quality? Data Quality CompletenessCorrectness Concordance Plausability Relevance (fit for use) Currency
- 35. Context – A Key Factor in Data Quality
- 36. Healthcare -- ‘fit for purpose’ involving a population context (usually a specific population)
- 37. Correctness and Completeness in early EHRs
- 38. “The Tethered Meta-Registry” cohort inclusion through “tagging” with real-time rule-based algorithms The UCD Tethered Meta-Registry - “Meta-Registry” – All data for all registries is in one repository - “Tethered” – routine, automated data extraction from source systems - Computable cohorts – algorithms “tag” patients as being in one or more registries - Automated dashboards and reports
- 39. • “Meta Registry” • Shared data dimensions / standardized definitions Sepsis Registry Mobility (ICU) Registry Diabetes Registry Transfusion Registry Source Data “Tether” EMR Reporting Database Administrativ e Data Laboratory Information System TMR Patient TMR Encounters TMR Flowsheets TMR Procedures/Labs TMR Medications • Individual Registries • Leverage “Meta Registry” 39 The UC Davis Health Tethered Meta Registry (TMR) Architecture and Data Flow 2.2 Million 25 Million 100 Million 57 Million 16 Million
- 40. The UCDHS “Diabetes Registry” (4/7/2017)
- 42. Female + Prostate Cancer UC-ReX: ~14M patient records (UCLA, UCSF, UCSD, UCD, UCI) UCD: has 41 EHR records with female gender and prostate cancer diagnosis
- 43. Urine pregnancy tests from 2015-2017 17patients?
- 44. Diabetics and a glucose test UCD: 17% of diabetics do not have a glucose test result in their EHR record
- 45. Surgery and Coagulation Testing UCD: 61.1% of patients undergoing surgery did not have a coagulation test in their EHR record
- 47. Pregnancy tests in males… 24 males with urine pregnancy testing
- 49. Distributed Analytics Sending the analytics procedure to the data visualization
- 50. Clarity The Observational Medical Outcomes Partnership (OMOP) Common Data Model and Data Profiling (ACHILLES) ACHILLES
- 51. What is Data Profiling? • Systematic and generalizable method of data quality assessment • Can you answer the following questions – Does your organization have a clinical data repository? – Does the group that manages this repository implement data profiling in any way? – What kind of skill sets are required for a group to optimally perform good data profiling? Aim of Research
- 52. Using OMOP and ACHILLES – profiling your data 75% of records have unknown race? Nobody is older than 85? (1) Only have dx for pts. admitted after 1984? (2) Someone is pre-admitted for 2020.... 35 million procedures are “unknown” type? We have a procedure for someone To be admitted 12 years from now Only 659,000 records have a diagnosis!!!
- 53. Births and Deaths “en masse” • UCDHS – 2.3M patient records • Created a histogram of “deceased” across months/years • 26,000 patients “died” on Jan 1 1980... – Nobody could remember why this was the case... UCDHS pScanner data profiling with ACHILLES Over 300,000 born in 1930?
- 55. Data Density (UCDHS pScanner database)
- 56. Conditions “Heat Map” (Asthma)
- 57. Condition map – Breast Neoplasm
- 58. UC Health Patients Alive and >85 There were only 600,000 Californians over 85 in 2010! 1.8M non-deceased and over 85 across UC Health
- 59. Who should own data quality? https://gcn.com/articles/2016/07/13/data-quality-responsibility.aspx?admgarea=TC_BigData
- 60. Eastern Sierras “The Range of Light”
Editor's Notes
- #40: The TMR layer represents both raw and derived data. The derived data is the big value add. For example, each encounter is classified as a result of several raw data points. After this derivation, each patient record can be summarized with the number of classified events per time window.