Natalia Olchanski, MS, Paige Lin, PhD, Aaron Winn, MPP. Center for Evaluation of Value and Risk in Health, Tufts Medical Center.

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1 ISPOR 2013, New Orleans, LA Using EMR data for conducting retrospective studies: Opportunities and Pitfalls Natalia Olchanski, MS, Paige Lin, PhD, Aaron Winn, MPP Center for Evaluation of Value and Risk in Health, Tufts Medical Center Kathy Lang, PhD Boston Health Economics Who we are 2 Natalia Olchanski, MS Project Director Paige Lin, PhD Assistant Professor Aaron Winn, MPP Research Associate Kathy Lang, PhD Senior Director Center for Evaluation of Value and Risk in Health, Tufts Medical Center An academic research center focusing on issues pertaining to value, cost-effectiveness, and risk tradeoffs in healthcare Boston Health Economics BHE is an independent, full-service outcomes research company providing clear, objective, rigorous data to support better healthcare decisions 1

2 Disclaimer 3 Workshop discussion leaders are not affiliated with any data vendor, and do not favor any specific data source Outline 4 Overview understanding data Framework for data decisions The power of EMR, a case study Summary the good, the bad, and the reality Q & A 2

3 Perspectives on EMRs 5 The Believer Patients Like Me Continuous Improvement The Skeptic Rapid Misinformation Patient Privacy Rapid Learning Drug Safety Payers Can Keep Score Precision Medicine Patient Centered Outcomes Disorganized Systems Biased Results Off-Label Use Learning Networks Understanding data 6 Why was the data created? How do patients enter and leave the data? How was the data aggregated? 3

4 Why was the data created? 7 Claims=Payment EMRs=Patient Care Registry=Patient Outcomes Clinical Trials=Efficacy How do patients enter and leave the data? 8 Claims Registry Clinical Trials EMRs Entry: Start Insurance Exit: Stop Insurance Entry: Broad - Having Disease Exit: Death or Cure, Loss of follow-up Entry: Narrow - Fit desired profile Exit: Trial has been completed Entry: Receive services from provider Exit: Not clear 4

5 How was the data aggregated? 9 Claims Registry Clinical Trials EMRs Payer aggregated information to pay providers Researchers aggregated information for research Researchers aggregated information for research Data vendors or large providers aggregated information for purchase Types of EMR sources 10 Single Provider Collection of Providers Geography Utilization Examples MedMining (Geisinger) Kaiser Humedica Premier 5

6 11 Framework for data decisions Important factors in data decisions 12 Research question Required variables Data coverage Project timeline Available budget 6

7 Deciding on secondary data source 13 CLAIMS CHART REVIEW EMR Access/Delivery? Size Acquisition Cost?? Clinical/Lab Data? Generalizability?? Economic Data Integrated Data?? Lag Time?? Incomplete Data? Example data decision 14 Research Question Data and Design Considerations Claims Chart Review EMR Other 7

8 Case example: blood products study 15 Research Question: Mortality and time to death in patients receiving specific blood products Required variables: Demographic, clinical characteristics, treatment, INR test results Claims Case example: blood products study 16 Research Question: Mortality and time to death in patients receiving specific blood products Required variables: Demographic, clinical characteristics, treatment, INR test results Claims 8

9 Case example: blood products study 17 Research Question: Mortality and time to death in patients receiving specific blood products Required variables: Demographic, clinical characteristics, treatment, INR test results Claims Chart Review Case example: blood products study 18 Research Question: Mortality and time to death in patients receiving specific blood products Required variables: Demographic, clinical characteristics, treatment, INR test results Claims Chart Review 9

10 Case example: blood products study 19 Research Question: Mortality and time to death in patients receiving specific blood products Required variables: Demographic, clinical characteristics, treatment, INR test results Claims Chart Review EMR Case example: Blood products study 20 Research Question: Mortality and time to death in patients receiving specific blood products Required variables: Demographic, clinical characteristics, treatment, INR test results Claims Chart Review EMR Uncorrected INR was not associated with higher risk of mortality, but was statistically significant for the intracranial hemorrhage (ICH) subgroup 10

11 What to ask the data vendor 21 Comprehensiveness and Generalizability Patient and Services Inclusion/Exclusion Factors Appropriateness for study design Sample Size and Missing Data Accuracy and Validation Access and Cost Key questions for vendors (see handout for more) 22 Comprehensiveness and Generalizability Patient and Services Inclusion/Exclusion Factors Appropriateness for study design Sample Size and Missing Data Accuracy and Validation Access and Cost Are data provider-centric or insurer-centric? Geographic distribution of the data How are patients selected for inclusion in the data? Are relevant settings/types of care included? Are required variables available? Is the length of enrollment or follow up appropriate? Sample counts for population of interest Prevalence of missing or incomplete details How are data collected, entered, and cleaned? Can data be validated against medical charts? How much will the data cost? Time frame for receiving and how current the data? 11

12 8% 7% 7% 6% 6% 5% 4% 2% 2% 1% 1% 23 Case Study A fresh look at comorbidities in diabetes using EMRs Funding source: West Health Institute Comorbidities in type 2 diabetes patients: Prevalence rates 24 77% 65% 49% 11% 12

13 Why use EMR? 25 EMR data allow us to better understand comorbidities Humedica data 26 Provider data Ambulatory groups Hospital systems Integrated delivery networks (IDNs) Demographics Medical Rx Labs 13

14 Prescription Labs 27 Hospitalizations Encounter ID Visit ID Encounter ID Date Admitting Spec Attending Spec Procedures Prescription Procedures Demographics Hospitalizations Diagnosis Labs Diagnosis Demographics 27 Prescription ID Date Drug Description NDC Provider spec. Hospitalizations ID Visit ID Visit type Date - Start Date - End Attending Spec Encounter ID Visit ID Encounter ID Date Admitting Spec Attending Spec Labs ID Encounter ID Test code Test date Test result 28 Procedures ID Encounter ID Date Procedure code Code type Demographics ID Age group Gender Race Diagnosis ID Encounter ID Date Diagnosis code Primary diag flag 28 14

15 Identifying hypertension among type 2 diabetes patients 29 Diagnosis Source % of all hypertension patients Diagnosis 55% Lab Rx 29 Identifying hypertension among type 2 diabetes patients 30 Rx Source % of all hypertension patients Diagnosis 55% Rx 64% 30 15

16 Identifying hypertension among type 2 diabetes patients 31 Source % of all hypertension patients Diagnosis 55% Rx 64% Lab Lab 32% 31 Identifying hypertension among type 2 diabetes patients 32 Lab 24% Diagnosis 10% 2% 40% 3% 3% Rx 18% Source % of all hypertension patients Diagnosis 55% Rx 64% Lab 32% 16

17 Comorbidity clusters (Poster #PDB2) 33 21% 18% 13% 6% 5% 5% 3% 3% 2% 2% 2% 1% 1% 1% 1% 33 Summary 34 17

18 35 The good, the bad and the reality 35 The good 36 Lab results & clinical details Large sample Inexpensive (compared with RCTs or registries) Near real time analytics Natural language processing to extract information from physician notes 18

19 The bad 37 Missing data Incomplete variables HbA1c Out-of-network utilization? Sample attrition over time? % Missing 18% % % The bad 38 More expensive (compared with claims data) Natural Language Processing (NLP) not validated Prescriptions written, not filled (cannot measure adherence) Economic variables unavailable Payer-specific analyses not possible 19

20 The reality 39 EMRs are new tools Understanding heterogeneity (and messiness) of real-world practice Still a long way to link data from different EMR systems and other sources Select a data source that best meets your needs Think about trade-offs 40 Q & A 20

21 Q & A 41 Discuss your research questions Share your experiences with EMR data Natalia Olchanski, MS Paige Lin, PhD Aaron Winn, MPP Kathy Lang, PhD 21

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