AMME Data → Measures → Applications Monday, June 9, 2014

1. AMME Data → Measures → Applications Monday, June 9, 2014 Michael J. Deegan, M.D.,D.M.

2. DATA

3. Health Care Data Essentials Sensitivity: the ability to identify a condition Specificity: the capacity to correctly identify a condition Timeliness: the availability of data relative to the time of the event Availability: the ease with which the data is accessed or captured for use

4. Data “Cleansing” Validate & Normalize Align data with a single condition or diagnosis; understand it’s distribution Clean & Validate Remove erroneous, incorrect data elements Extract & Validate Understand its relevance to condition

5. Pitfalls that Inhibit Using Data to Drive PI • Lack of timeliness • Reports based on readily available • non-relevant data • Failure to adequately analyze and • interpret the data • Poor assignment of accountability for • indicator performance or improvement

6. Pros & Cons of Data Types Claims Data Clinical Data Sensitive Specific Timely Longitudinal Uneven availability • Available • Untimely • Fragmented • Insensitive • Lacks specificity

7. Legitimate Uses of Claims Data •Costs associated with care provided • Categories and types of care provided • Settings where care is delivered • Demographics of those receiving care

8. Gaining Insight by Using Multiple Data Types • Clinical data • Socio-demographic data • Care management data • Claims data

9. PCMH-N Technology Support Care Management -Clinical analytics -CDS -Patient registry Population in the Community Patient Engagement -Automated outreach -Patient portals Payer Claims & Cost -Risk stratification Primary Care Office / PCMH Patient Populations Electronic Health Records Others with information or coordination needs Hospitals Specialists Lab, Radiology, Rx Distance Monitoring -Telemedicine -Remote monitoring HIE / Referral Tracking adapted from PHM Technologies for Accountable Care, Phytel, 2014

10. Uncovering Meaningful Data Patterns • Identify the most seriously ill • Appreciate the influence of socio- • demographic & clinical risk factors • Measure cost & outcomes (Value) • Discover cost & utilization drivers • Identify “deviants” ( positive & negative)

11. Applying Predictive Analytics To Achieve Greater Value • Most common conditions within • population • Patients utilizing the most resources • Most effective treatments • Most effective and efficient caregivers

12. A Contemporary Example of Predictive Analytics – VIDEO

13. QUESTIONS / COMMENTS

14. MEASURES

15. Deciding Which Measures to Select • > Balanced mix using CVC framework • Patient-oriented process & outcome metrics • Reflects population(s) at risk → practice panel • High leverage to close a care gap • Regulatory or performance requirement

16. Measure Selection – Practical Issues • Data source availability? • - administrative • - clinical • - survey • Ease of capture? • Comparative data – internal, external? • Benchmarks – risk or case mix adjust? • Inclusion – exclusion criteria defined

17. Examples of Measure Types • Process • - Health Risk Assessment • - Colorectal cancer screen • - Anti-platelet rx for CAD patient • Outcome • - Intermediate → HgbA1c • - End of episode • - Acute → post – MI return to work • - Terminal → mortality rate • Patient Experience • - Access • - Overall

18. Process of Care Measure Criteria* Sound evidence base the care process leads to improved outcomes Measure accurately captures whether the E-B care process has been provided Process measure has few intervening steps before outcome is realized Measure implementation is unlikely to have unintended consequences Chassin et al. NEJM, 23 June 2010

19. Desirable Outcome Measure Features Outcome Metrics · Condition specific · Multi-dimensional · Span full care cycle Cost · Total costs for full care cycle for condition

20. The Outcome Measures Hierarchy* Tier 1 Health Status Achieved or Retained Survival Degree of Health or Recovery Time to Recovery / Normal Activity Tier 2 Process of Recovery Disutility of care or treatment process Duration – strength of recovery / Recurrences Tier 3 Sustainability of Health Long term consequences of treatment *Porter ME: N Engl J Med 363: 2477, 2010.

21. Revised Outcome Measures Hierarchy* CARE-INDUCED ILLNESSES – Long Term Consequences of Initial Treatment Care Processes Survival Initial Patient Conditions Degree Health Recovery Sustainability Recovery Process RECURRENCES · single · multiple · related · unrelated · diagnostic accuracy · treatment choice · unintended harm OUTCOMES · clinical · functional QUALITY of LIFE *modified after Porter ME: N Engl J Med 363: 2477, 2010.

22. Outcomes Hierarchy – an Example* Hip Replacement Surgery Tier 1 – Health Status Achieved – Retained ·Survival………..Mortality rate (inpatient) ·Post-rx care…..Pain management …..Level of physical activity Tier 2 – Recovery Process *Porter & Lee: HBR, Oct 2013.

23. ·Time……..to start treatment …….to return to full physical activity …….to return to work / play (e.g., golf) ·Care Processes…..delays & anxiety …..pain during treatment …..time in hospital …..complications Tier 3 – Sustainability ·Maintenance of functional status ·Need for revision – replacement ·Long-term consequences Porter & Lee, HBR, Oct 2013.

24. QUESTIONS / COMMENTS

25. APPLICATIONS

26. Measure Selection - Overview • > Balanced mix using CVC framework • Patient-oriented process & outcome metrics • Reflects population(s) at risk → practice panel • High leverage to close a care gap • Regulatory or performance requirement

27. Clinical Value Compass* Functional - Physical³ - Mental³ - Risk status¹ ¹ practice report ² billing data ³ patient self report Patient Experience - Services -Overall satisfaction³ -Access³ - Health benefit(s)³ Clinical -Prevention¹ -Screening¹ -Diagnosis¹ -Rx Monitoring¹ - Morbidity¹ - Mortality¹ Cost to Patient - Direct medical² - Indirect personal - social³ modified from Nelson EC, et al. Measuring Outcomes & Costs: The Clinical Value Compass in Practice-Based Learning & Improvement, 2007, JCAHO .

28. Practice Metric Selection Process

29. PQRI (2007) → PQRS (2010) • Physician Quality Reporting System – Medicare • Individual & group “Eligible Providers” • · 1; 2 – 24; 25 – 99; 100+ • PQRS Metrics – 2013 • · 259 individual measures • · 22 measure groups: ex. preventive care • Program Transition: Bonus→ Penalty • · 2013, 2014….. +0.5% • · 2015…………. -1.5% [2013 baseline] • · 2016…………. -2.0%

30. IHA Value-Based P4P in CA • 8 commercial health plans • 9M commercial HMO – POS enrollees • 35,000 physicians • > 200 physician organizations • Value focus: cost + quality • Shared savings model Issue Brief: Value Based Pay for Performance in California, Sept 2013 @ www.iha.org

31. Value-Based P4P in CA – Key Steps Is PO composite quality score > threshold? 2. Is PO total cost of care < threshold? Determine PO Eligibility Calculate Shared Savings Based on Appropriate Resource Unit (ARU) measures

32. Value-Based P4P in CA – Key Steps • - Based on composite quality score • 2-fold difference between lowest • and highest performing POs • ↓ • incentivize maintenance of high • quality Adjust Shared Savings Sum Shared Savings VB P4P Incentive

33. Surgery Center Quality Reporting – 2% APU MU 1% Meaningful Use of EHR eRx 1 – 2% eRx Medicare Shared Savings ACO (MSSP) PQRS PQRS PQRS 1.5 – 2% Physician Value Modifier 1 - 2% What Will the Commercial Insurers Add to this? VOLUNTARY INCENTIVE PENALTY

34. QUESTIONS / COMMENTS