A Case Study of Real World Data in Health Economics and Outcomes Research. Presented by: Joe Menzin Boston Health Economics June 25, 2014
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1 A Case Study of Real World Data in Health Economics and Outcomes Research Presented by: Joe Menzin Boston Health Economics June 25, 2014
2 Overview Introduction Selected EHR Studies Key Lessons 2014 Boston Health Economics, Inc. Confidential and proprietary. 1
3 BHE Overview Independent, full-service research organization Specializing in data analytic solutions and custom research Interdisciplinary staff Customer-focused Proven track record over the past 18 years Substantial experience with novel data sources, including EMR 2014 Boston Health Economics, Inc. Confidential and proprietary. 2
4 What Does Emergence of EMR Data Mean for HEOR? Richer information than claims data alone EMR data have similar, but also different challenges Possibilities to collaborate with provider groups, IDNs, and ACOs 2014 Boston Health Economics, Inc. Confidential and proprietary. 3
5 Richer Data Clinical details Treatments provided Populations served 2014 Boston Health Economics, Inc. Confidential and proprietary. 4
6 Clinical Details Labs PLUS physiologic measures o Blood pressure, BMI, smoking, mortality in most systems Notes available in all systems, but not always readily-accessible (may need manual review) Identify poorly-coded conditions Document disease severity 2014 Boston Health Economics, Inc. Confidential and proprietary. 5
7 Treatments Provided By day in hospital (sometimes hour : minute) Bridging between inpatient and outpatient care In various care units (ER, CCU, others) 2014 Boston Health Economics, Inc. Confidential and proprietary. 6
8 Populations Insured and non-insured Additional sociodemographics (possibly) 2014 Boston Health Economics, Inc. Confidential and proprietary. 7
9 EMR has Similar Challenges to Claims Data Heavy reliance on ICD-9 coding Not all medical encounters are captured Lack of standardized data Can be expensive to access Potentially large (big) data 2014 Boston Health Economics, Inc. Confidential and proprietary. 8
10 But Perhaps More Challenging to Address Leakage (don t know what you don t know) Patients not typically enrolled in a network Unpopulated/underpopulated/non-structured fields make things big and messy Somewhat new ( emerging source ) 2014 Boston Health Economics, Inc. Confidential and proprietary. 9
11 Case Study 1: INR and Mortality 2014 Boston Health Economics, Inc. Confidential and proprietary. 10
12 Highlights: Study Case 1 Data from regional, integrated network used to explore relation between reversal of INR and outcomes for patients with anticoagulation-related bleeding About 400 cases were identified, one-third of whom had their INR corrected (<1.3) within 2 days ICH patients without early reversal had significantly higher 30-day mortality Relatively small sample size, limited data on bleed severity Lessons: Complex data to work with, allow proper schedule/budget 2014 Boston Health Economics, Inc. Confidential and proprietary. 11
13 Patient Selection: Case Study Boston Health Economics, Inc. Confidential and proprietary. 12
14 Findings: Case Study Boston Health Economics, Inc. Confidential and proprietary. 13
15 Case Study 2: PAD Re-Intervention 2014 Boston Health Economics, Inc. Confidential and proprietary. 14
16 Study Highlights Data (claims, manual review of EMR) from regional, integrated network used to explore factors associated with re-intervention for treatment of PAD About 250 cases were identified, 20% of whom had a second PAD-related procedure on the same lower limb or had amputation over 2 years Few factors were predictive of procedure failure for PAD Relatively small sample size, potential confounding Lessons: Interesting to compare claims and EMR encounters for consistency; need EMR data for sided-ness 2014 Boston Health Economics, Inc. Confidential and proprietary. 15
17 Findings: Case Study Boston Health Economics, Inc. Confidential and proprietary. 16
18 Findings: Case Study 2 (continued) 2014 Boston Health Economics, Inc. Confidential and proprietary. 17
19 Key Lessons Knowing when EMR data add value Understanding differences among data sources claims are pretty uniform, not EMR Takes time to learn how to use EMR data Need more published examples on methods and results 2014 Boston Health Economics, Inc. Confidential and proprietary. 18
20 Much Needed Methods Paper 2014 Boston Health Economics, Inc. Confidential and proprietary. 19
21 The Big Data Problem Facing Pharma Data Multiple providers of data with varying degrees of quality Varied and proprietary data models and formats Technology Costly and time consuming to set up and maintain in-house IT Inability to rapidly scale to meet growing data processing needs Slow to adopt innovative technologies Expertise Strained internal capacity and budgets Industry-wide shortage of skilled resources Reliance on outside experts! Complex, costly and time consuming 2014 Boston Health Economics, Inc. Confidential and proprietary. 20
22 2014 Boston Health Economics, Inc. Confidential and proprietary. 21
23 Challenges in executing database studies Finding appropriate resources Acquiring data in a timely fashion Long feedback loop Ever-changing priorities Scope creep Slow contracting processes 2014 Boston Health Economics, Inc. Confidential and proprietary. 22
24 Opportunities for HEOR groups Gain experience with latest (big) data sources Engage with IDNs and ACOs on comparative effectiveness and riskbased contracting Others? 2014 Boston Health Economics, Inc. Confidential and proprietary. 23
25 Conclusions EMR data can add value when applied to the right questions EMR data availability presents significant opportunities for learning However, more research is needed to help us understand the reliability and validity of EMR data 2014 Boston Health Economics, Inc. Confidential and proprietary. 24
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