Leveraging the Electronic Health Record for Population Decision Support and Quality Measurement. Jonathan S. Einbinder, MD, MPH

Transcription

1 Leveraging the Electronic Health Record for Population Decision Support and Quality Measurement Jonathan S. Einbinder, MD, MPH Eighth National Quality Colloquium August 18,

2 Objective Develop awareness of the potential of electronic health record for clinical quality reporting. Develop awareness of how a data warehouse may enable efficient reporting. Outline Drivers for quality measurement with clinical data. How is a data warehouse useful? Example: Partners Quality Data Warehouse Challenges and requirements for quality reporting from an EHR 2

3 Selected Meaningful Use Measures Improve quality, safety, efficiency and reduce health disparities 2011 %diabetics with A1c under control %hypertensive patients with BP under control %patients with LDL under control %smokers offered smoking cessation counseling %patients with recorded BMI %eligible surgical patients who received VTE prophylaxis %orders entered directly by physicians through CPOE Use of high-risk medications in the elderly %patients over 50 with annual colorectal cancer screenings 2013 Additional quality reports using HITenabled NQF-endorsed quality measures Potentially preventable ED visits and hospitalizations Inappropriate use of imaging, e.g. MRI for acute low back pain 2015 Clinical outcome measures Efficiency measures Safety measures

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5 A Major Driver: Pay-For-Performance PERFORMANCE TARGETS DIABETES HbA1c outcomes: 80.8% 9.0 (HEDIS 90 th %ile) Comprehensive Care ( all or none ) Screening: (HbA1c, LDL, Micro, and Eye exam) BP <130/80 LDL <100 (HEDIS 90 th ) DIABETES and CAD BP Control LDL outcomes 5

6 Measurement will require clinical (EHR) data Leveraging data in the EHR (and other systems) requires data infrastructure. At Partners HealthCare, the Quality Data Management Team is responsible for this infrastructure. The key enabling technology is a clinical data warehouse the Quality Data Warehouse 6

7 What is a data warehouse? Database Multiple sources Retrospective Optimized for population-based queries Key idea is to separate analytic systems from source systems. 7

8 Why have a data warehouse? Transaction data is poorly suited for analysis. Running reports and queries in Production database may compromise system performance. Production databases not designed/intended to support aggregate queries. May take many hours to run simple queries. Requires dedicated programmer resources, and no one programmer knows all relevant data. Hard to query across systems. e.g. Patients with diabetes and A1C>8 and no visit in past 12 months 8

9 Building a data warehouse (ETL) EXTRACT: get data out of source systems on a regular basis. TRANSFORM: clean up data, deal with codes, missing values, calculations, encryption. LOAD: move data into the warehouse database so it is available to analysts and users. 9

10 Why have a data warehouse? A new breed of company such as Amazon, Harrah s Capital One, and the Boston Red Sox have dominated their fields by deploying industrial strength analytics across a wide variety of activities crunching their way to victory. They are armed with the best evidence and the best quantitative tools. As a result, they make the best decisions: big and small, every day, over and over and over. -- TH Davenport and JG Harris. Competing on Analytics. Harvard Business School Press,

11 Quality Data Warehouse Externalize information from Partners ambulatory electronic health record Make that information available in a variety of ways Other sources Patients Colonoscopy Schedule Smoking data Census data Providers Point-of-care labs Clinics Vital signs Lab values Billing codes Allergies Med Rec Ad hoc queries Reports LMR Problems Meds Notes Health Maintenance Flow Sheets Advanced Directives Reminders DNR/DNI Asthma Action Plan End of visit Quality DWH Quality Dashboard Population Mgmt Disease Registries 11

12 Quality Data Warehouse Problems Medications Narratives Note Headers Vital Signs Health Maintenance CPM EMPI Clinic schedules BICS ADO Lab results BWH POLr Inpatient Med Rec POCT Inpatient Glucose (BWH) 400GB 4.7 Million Patients 5.2 Million Problems 5.4 Millions Meds 257 Million Lab Results Daily inpatient census Asthma Action Plans DNR/DNR End-of-Visit LMR Reminders Advanced Directives Restricted Notes (Headers) Allergies BWH Endoscopy Orders (Percipio) Colonoscopy (BWH) Smoking inpatient, LMR, OnCall Tumor Staging Inpatient admission diagnoses ECHO (for inpatients) LMR Alerts 12

13 Partners HealthCare: Report Central What is Report Central? Report Central is a reporting module that allows users to display descriptive reports and quality reports. Data for the reports comes from the Quality Data Warehouse. Who may use Report Central? More than 7000 users at 300 clinics have access to Report Central. Academic Medical Centers, Community Practices PCPs, Specialists, Adult/Pediatrics, Physicians, non- Physicians 13

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16 Report Central Usage 2008 Count of report runs or users 1,800 1,600 1,400 1,200 1, Cumulative users Monthly runs Monthly users 0 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Month 16

17 Data can be used for different purposes Externally focused (judgment) Get paid, hired/fired/promoted, publicly reported Inpatient focus, administrative data, manual abstraction Increasing ambulatory focus (pay-for-performance) Claims +/- lab results (and EHR) Internally focused (learning) Quality improvement, clinical understanding, efficiency Inpatient and outpatient, automated and manual abstraction More use of clinical data Also, patient care (populations) and research 17

18 More complex Kinds of measurement Outcomes HARDER HARDEST Process Measures Descriptive EASIEST HARDER Less complex Curiosity Operations QI Projects Public Reporting/P4P Less accountability More accountability 18

19 Measure example: CHF Percentage of Heart Failure Patients with LVSD who were prescribed ACE Inhibitor or ARB therapy with all denominator exclusions applied. 19

20 Measure example: CHF Prescribed Not prescribed (medical reasons) Not prescribed (patient reasons) 20

21 More complex Outcomes Measurement example: CHF HARDER List of CHF patients with ACE/ARB prescriptions and ECHO/LVEF HARDEST Percentage of CHF patients prescribed ACE/ARB with all denominator exclusions. Process Measures Descriptive Less complex EASIEST List of CHF patients List of CHF patients with meds Less accountability HARDER Percentage of CHF patients prescribed ACE/ARB (with some or no denominator exclusions) Curiosity Operations QI Projects Public Reporting/P4P More accountability 21

22 Examples of reports and measures in Report Central, reflecting range of measurement types. 22

23 Prescribed medications a descriptive report 23

24 Pediatric body mass index (BMI) 24

25 Pediatric Immunizations (2008 CDC Guidelines) 25

26 Diabetes report 26

27 Diabetes Metrics (provider and practice) 27

28 Pay physicians for completing notes on time Money tied to quality metrics $500, $1250 or $2500 bonus available for physicians who complete 80% of their visit notes within 120 hours of the visit. (amount of money each physician qualifies for is based on the number of clinic hours) 28

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31 Challenges Patient attribution Data understanding (a.k.a. data quality ) completeness, accuracy Focus on claims-based measures persists P4P, regulatory Measurement standards just emerging Multiple EMRs Obtaining resources 31

32 What do you have (and how are you going to get it out)? What data do you need (numerators, denominators)? What do you want? EHR + Other data Measures, Reports 1. EHR reporting utility? 2. Vendor products? 3. Data warehouse? 4. Extract, transform, and load programs 5. Analysts and developers? 1. EHR Data quality 2. Order Entry 3. Claims, billing 4. Lab 5. Schedule 6. Panels 7. Etc. 1. What measures and reports do you want to produce? Frequency? For whom? For what purpose? How distribute? 2. Will you need to produce physician-level measures? Patient attribution strategy? 32

33 Thank you! Jonathan S. Einbinder, MD, MPH 33