Big Data Strategies for Mitigating Malpractice Risk

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1 Big Data Strategies for Mitigating Malpractice Risk John Birkmeyer, MD MCIC Patient Safety Symposium June 10, 2015

2 Big Data Won t Eliminate Malpractice Exposure and Claims

3 The Northern New England Cardiovascular Disease Study Group, Fletcher Allen Health Care Eastern Maine Medical Center Dartmouth-Hitchcock Medical Center Maine Medical Center Catholic Medical Center

4 O Connor et al., JAMA, 1991

5 Mortality Rate (%) 6 5 CABG Mortality in Northern New England Data feedback to surgeons Clinicians learning from data and each other Year

6 MI CQI PROGRAM HIGHLIGHTS Supported by BCBSM / BCN Coordinating centers (mainly) at University of Michigan Started with pilot test with PCI in 1998, broad implementation Now 15 specialty-specific programs: Cardiac surgery and PCI, medical oncology, bariatrics, breast cancer, prostate cancer, trauma/ acute care, joint replacement, spine surgery and medical admissions 50+ hospitals 200,000+ pts / year 6

7 About ArborMetrix FOCUS Cloud-based technology for performance measurement and quality improvement in acute & specialty care CLIENTS Hospitals, health systems & accountable care organizations Payers & purchasers MY ROLE / FINANCIAL DISCLOSURE Founder, Chief Scientific Officer, Board member Equity interest

8

9

10 Overview of Presentation Reducing adverse events Lessons from Michigan How data technology can help Mitigating risk in an integrated care delivery system Challenges Dartmouth-Hitchcock strategy Approach to data & analytics

11 Michigan Value Partnerships Partnership between BCBSM, Michigan hospitals, and clinician scientists Pilot test in1998, broad implementation $160 million annual investment from BCBSM 15+ collaborative quality improvement programs Cardiac care, cancer surgery, bariatrics, breast cancer, trauma/ acute care, joint replacement, spine surgery, prostate cancer and medical admissions 50+ hospitals 200,000+ pts / year

12 Improvement Targets Tailored to Clinical Context

13 Commonalities Clinically credible registry Timely feedback to clinicians about comparative practice & outcomes Systematic approach to improvement Physicians learning from their data Physicians learning from each other Continuous implementation and evaluation of best practices and improvement interventions Consistently outperform national benchmarks on specialty-specific quality measures - see Share et al., Health Affairs, 2011

14 1. Beaumont Grosse Pointe 2. Borgess Medical Center 3. Bronson Medical Center 4. Crittenton Hospital and Medical Center 5. Forest Health Medical Center 6. Gratiot Medical Center 7. Harper University Hospital 8. Henry Ford Macomb Hospital 9. Henry Ford Hospital 10. Henry Ford Wyandotte 11. Hurley Medical Center 12. Lakeland Community Hospital 13. Marquette General Hospital 14. McLaren Regional Medical Center 15. Mercy General Health Partners 16. Metro Health in Wyoming 17. Munson Medical Center 18. Oakwood Hospital 19. Port Huron Hospital 20. Sparrow Health System 21. Spectrum Health System 22. St. John Hospital and Medical Center 23. St. John Oakland 24. St. Mary Mercy Hospital 25. St. Mary's Grand Rapids 26. University of MI Health System 27. Beaumont Troy 28. Beaumont Royal Oak 29. Huron Valley Sinai 30. Henry Ford West Bloomfield 31. St. Joseph Mercy Oakland 32. North Ottawa Community Hospital

15

16 Use of Pre-Operative Heparin, 2008 None UF LMW 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Site

17 Use of Post-Operative Heparin, 2008 None UF LMW 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Site

18 Use of Post-Discharge Heparin, % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% Site

19 Use of Prophylactic IVC Filters, % 30% 25% 20% 15% 10% 5% 0% Site

20 What We Did Developed prediction rule for stratifying baseline risk of VTE by patient factors Finks et al., Ann Surg, 2011 Identified optimal medical prophylaxis for each patient risk group

21 VTE Rates by Type of Heparin Used Pre-Operative Post-Operative UF LMW UF LMW 12% 12% 10% 10% 8% 8% 6% 6% 4% 4% 2% 2% 0% Low Medium High VTE Risk Category 0% Low Medium High VTE Risk Category Birkmeyer NJO et al., Arch Surg, in press

22 Point-of-Care Clinical Decision Support HIGHLIGHTS Prognostic models for patient-specific VTE risk Identified optimal medical prophylaxis by risk group Statewide practice guidelines

23 Risk-Adjusted Outcomes Before & After VTE Improvement Intervention Outcome OR LB UB p-value VTE 0.50% 0.27% Hemorrhage 1.5% 2.0% Serious hemorrhage* 0.37% 0.33% Death/permanent disability ~ 0.54% 0.24% * requiring transfusion > 4 units, splenectomy, or reoperation. ~ including myocardial infarction, cardiac arrest, renal failure requiring long-term dialysis, respiratory failure requiring > 7 days intubation or tracheostomy, and death.

24

25 3 Components of Successful Quality Improvement Motivated & engaged MDs ~ Timely feedback of clinically meaningful, actionable performance data Help in translating data into practice change

26 How Data Technology has Helped in Michigan More effective and efficient electronic data integration (including EHRs) Combining cost and quality information More efficiency technology for performance measurement

27 ˆ i Y Z ˆ Y Z measurement V Var Y Mean V N 1 N ˆ i i i i i 1 i ˆ YW i ( Z W ˆ i V i Embedded Statistical Engines i ˆ)( I 1 ˆ W i ) Software-generated risk adjustment & i i Regression-based and machine learning algorithms Real-time execution & measurement updates Reliability adjustment Ad hoc statistical analysis Point-of-care decision support i

28 How Data Technology has Helped in Michigan More effective and efficient electronic data integration (including EHRs) Combining cost and quality information More efficiency technology for performance measurement Better tools for helping clinicians understand their data Flexible user-driven analytics Specialty-specific configuration

29

30

31 Average of Six Ratings of Technical Skill Average Rating Video # = N Raters = Note: represents the mean; bars extend from mean ± standard error.

32 Complication Rate P< Surgeon Skill Rating

33 Overview of Presentation Reducing adverse events Lessons from Michigan How data technology can help Mitigating risk in an integrated care delivery system Challenges Dartmouth-Hitchcock strategy Approach to data & analytics

34 Reimbursement Reform Status quo / Fee for service Pay for performance Episodebased bundled payment ACOs, Shared Savings models Global payment (capitation) PAYERS FINANCIAL RISK DARTMOUTH- HITCHCOCK

35

36 New Approach to Credentialing System-wide credentialing Minimum volume standards for selected procedures (next) Video-based peer review

37 Specialty-Specific Service Lines Change in organizational structure and reporting relationships One D-H care Regional IT integration EHR-embedded, evidence-based protocols across system Michigan-like, data-driven collaborative improvement Financial incentives aligned with safe, high value care

38 Linking Value to MD Compensation Value Domains Quality 5% Access 5% Benchmark Compensation (paid at 100%) 15% Value Patient Experience 5% 85% Productivity for Attribution to Compensation, these need to be Applicable, Measurable, Manageable and where appropriate, consistent with how we are Reimbursed *Consumer Assessment of Healthcare Providers and Systems

39 Performance Measurement - Challenges PAIN POINT Data integration; data warehouse development Analytics Performance measurement & management Physician engagement & improvement ISSUES Slow, expensive, and painful Expensive infrastructure More questions than analysts Slow turnaround on BI Data all over the place Long data lags Opaque measures, pushback High level measures with limited clinical relevance (particularly to specialists) Limited insights about how to improve

40 Analytics Institute Population Management Clinical departments & Service Lines Research Project management, configuration, and improvement Vendor/ partners Analytic Application Suite Single Source Datamart DH Databases EHR Data Elements Cost Accounting Abstracted Data Patient- Reported Data Clinical Registries

41 Department Quality/Financial

42 Physician Summary View

43 Reducing Malpractice Exposure

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