Health Spring Meeting June 2009

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1 Health Spring Meeting June 2009 Session # 76 PD: Health Outcomes: The Use of Electronic Medical Records in the ID and Management of Patients with Diabetes Grace Flood, MD Bruce S. Pyenson, FSA, MAAA Marjorie Rosenberg, Ph.D., FSA, MAAA Moderator: Marjorie Rosenberg, Ph.D., FSA, MAAA

2 Health Outcomes: The Use of Electronic Medical Records in the Identification and Management of Patients with Diabetes Society of Actuaries 2009 Health Spring Meeting Session 76 Objectives of Session To focus on the identification and management of patients with diabetes by: Provide background on Electronic Medical Record and application to chronic disease (diabetes) Provide perspective on actuarial work in this area Provide clinical perspective on work in this area Discuss pathways for future opportunities

3 Expert Panelists Grace Flood, MD, MPH Bruce Pyenson, FSA, MAAA Marjorie Rosenberg, PhD, FSA, MAAA Introduction Institute of Medicine advocated use of computerbased patient medical records in 1997 (IOM Committee on Improving the Patient Record) Obama plan to invest $10 billion a year over the next five years to promote broad adoption of standardsbased electronic health information systems, including electronic health records (www.healthleadersmedia.com ) Electronic health information provides incentives to different stakeholders to develop approaches to use this information to improve health outcomes through the management of patient care

4 Electronic Health Information Push to HIT due to current state of health care quality in delivery of care, outcomes, and patient safety (Burstin 2008, Diamond 2009) Emerging work from AHRQ suggest that we not ask is health IT worth it? but how assure promise of health IT is translated to into improved patient care (Clancy 2008) Challenges (Diamond 2009) : Development and Maintenance Costs National Quality Metrics Regional public health efforts Lack of appropriate feedback loops to providers of data Types of Electronic Health Information Electronic Medical Records Disease Registries Personal Health Records Administrative Data

5 Electronic Medical Record (EMR) Also know as (Dean 2009): Electronic Health Record (EHR) Computerized Medical Record Electronic Medical Record Automated Medical Record Electronic Patient Record Electronic Medical Record Data Provider Notes Electronic viewing of lab and radiology results Computerized Physician Order Entry As part of system: Electronic Reminder System

6 Theoretical Advantages of Electronic Medical Record Just in time patient care (Clancy 2009) Fewer medical errors More efficient delivery of care Reduced costs Streamlined clinical workflow Reminders for providers and patients Potential data available for research Parente (Health Affairs 2009) used sample of Medicare data to measure averted inpatient infections Disadvantages of Electronic Medical Record No standard definitions of what is in EMR and how data are stored (Dean 2009, Ashish 2009) No linkages across systems (like administrative or billing, pharmacy, lab and patient reported) EMR adoption rate is low: inpatient 17% 32% of hospitals; 24% physicians (size of practice influences adoption) (Ashish 2009) Data accuracy ( copy and paste ) (Hirschtick 2006; Yackel and Embi 2006; Hammond et al. 2003) Implementation costs HIPAA concerns (Greenberg 2009) Keyhani 2008 failed to find any relationship between use of EMR and receipt of appropriate therapy or appropriate screening tool

7 Why do this session? Because EMR will be more prevalent Data can help in prediction of utilization Transparency in performance measurement (Burstin 2008) Allow better quality measures Achievement of evidence based care Link to pay for performance Link of EMR to demographics for reporting Diabetes and EMR Studies Mangione 2006: TRIAD (Translating Research into Action for Diabetes) examined processes of care, intermediate outcomes and clinical management of outcomes Maclean 2006: VDIS (Vermont Diabetes Information System) used NHANES data Weber 2007: Used NHANES data to determine compliance with practice guidelines for those who used EMRs Peterson 2008: TRANSLATE (Target high risk, Registry, Administration, Notify and remind, Site coordinator, Local physician champion, Audit and feedback, Track, Education) used the Chronic Care Model

8 Dr. Flood: Diabetes Quality Improvement: A Provider Group s Perspective Mr. Pyenson: Actuarial Challenges with Diabetes in a Commercial Population

9 Diabetes Quality Improvement: A Provider Group s Perspective Grace Flood, MD MPH Society of Actuaries 2009 Health Spring Meeting June 10, 2009 Presentation Goals Describe practice setting Review diabetes quality measures Discuss challenges in designing a diabetes registry Describe quality improvement efforts all with the backdrop of an EMR

10 A Prediction EMRs will greatly facilitate disease and population health management, but it will take time. UW Health An academic health system based in Madison, Wisconsin One of the 10 largest multi-disciplinary physician groups in the US 1,070 physicians (~250 primary care providers) 41,000 inpatient admissions/year 1.7 million ambulatory patient visits/year Diverse ambulatory practices Large, multi-specialty clinics Community-based primary care clinics

11 Our EMR Epic Systems Health Link Founded in 1979 in Verona, Wisconsin A private, employee-owned software company Focused solely on developing and supporting integrated, patient-centric applications Used by 175 large healthcare organizations 70,000,000 patients (~22% of the US population) has an EpiCare record Implementation Takes Time

12 Our EMR Implementation Slow! Staggered Primary care clinics ( ongoing) Specialty care clinics ( ongoing) UW Hospital ( ongoing) Variable by clinic Phased-in (scheduling only, then ordering added, then full phase ) Big-bang Pros Pros and Cons of the EMR: Provider s Perspective Improved legibility Ability to work off-site and during off-hours Improved access to past medical records Access to notes from specialists/from inpatient stays Improved quality of care -?? A lot of potential

13 Cons Pros and Cons of the EMR: Provider s Perspective User learning curve System designed for the masses Implementation vs. Enhancement vs. Maintenance vs. Upgrading Difficult to interface in some databases Understanding the Data Takes Time

14 Pros and Cons of the EMR: Data Perspective Pros: A lot of data (50,000 fields in 1,000 tables) Scheduling Patient Demographics and History Encounter Information Outpatient and Inpatient Non-billed encounters Telephone and letter encounters Visits with non-billing providers Clinic notes easily accessible (via chart review) Clinical Information - Vital Signs, Medications, Procedure Orders and Results, etc. Pros and Cons of the EMR: Data Perspective Cons Difficult to interface in some databases Staggered implementation Some important fields aren t discrete Data quality sometimes poor Database extremely complex

15 Web of Woes Diabetes Definition: Disease where body does not produce or properly use insulin, a hormone needed to convert sugar, starches and other food into energy. Complications Microvascular disease: retinopathy, nephropathy, neuropathy, lower extremity amputation Macrovascular disease: coronary artery disease, peripheral vascular disease, stroke Reduced life expectancy of 7 8 years

16 Diabetes Statistics Prevalence of diabetes in US population ( ) 7.7% of US population diagnosed (24 million) 5.1% of US population undiagnosed diabetes 29.5% of US population with pre-diabetes Total annual economic cost of diabetes in 2007 was estimated at $174 billion $116 billion for medical expenditures (direct diabetes care, chronic diabetes-related complications, and excess general medical costs) $58 billion was estimated due to indirect costs of the disease (increased absenteeism, reduced productivity, disease-related unemployment disability, and loss of productive capacity due to early mortality) Cowie et al 2009 Diabetes Quality Improvement Goal: Ensure our diabetes patients receive quality care Reasons we care: Patient Care to reduce symptoms and complications of diabetes Financial Pay-for-performance (PQRI) Our Reputation Wisconsin Collaborative for Healthcare Quality

17 Wisconsin Collaborative for Healthcare Quality (WCHQ) A voluntary consortium of 29 healthcare organizations (physician groups, hospitals, health plans) in Wisconsin that has led the US in measuring and reporting the quality of care in physician groups since Has developed specific criteria to identify patients who are currently managed by a physician group Organization-level performance is publicly reported 45 outpatient and inpatient measures 6 of the outpatient measures pertain to diabetes 1 Hatahet MA, Bowhan J, Clough EA. Wisconsin Collaborative for Healthcare Quality (WCHQ): Lessons learned. WMJ. 2004;103(3): Website: WCHQ Diabetes Measures (for year olds with Diabetes) Process Measures Outcome Measures A1c Blood Sugar 2 tests/year <7.0% LDL Cholesterol 1 test/year <100mg/dl Kidney Function >1 microalbumin test/year Blood Pressure < 130/80

18 Diabetes Population Management Designing an Effective System for Population Management Takes Time

19 Identifying Patients With Diabetes (i.e., creating a registry) Establishing a gold standard of diabetes is challenging even with chart review Diabetes is a progressive disease Diagnosis is based upon lab thresholds - somewhat arbitrary Patients may not have received 2 of these labs Possible methodologies 1. WCHQ methodology: Billing data 2. Our Algorithm: Billing + EMR data Identifying Patients with Diabetes: Billing Data Challenges Diabetes may be addressed during a visit, but not be coded Diabetes may be inappropriately coded while screening for disease Patients with diabetes may not routinely come in Must define qualifying criteria How many encounters? With which codes? What type of encounters? With what type of provider? In what timeframe?

20 Identifying Patients with Diabetes: Billing Data* WCHQ Q1 Is this a patient with the disease? WCHQ Q2 Is this a patient whose care is managed within the physician group? WCHQ Q3 Is this a patient currently managed by the physician group? Our Question Do we have their data? * This approach uses the Wisconsin Collaborative for Healthcare Quality (WCHQ) definition of a "currently managed" population. This definition is the property of WCHQ and our use of it is with their permission. Identifying Patients with Diabetes: Billing + EMR Data Denominator Active Patients Patients with Diabetes Specifications Patients must satisfy all requirements: 1 E&M visit in past 2 years at UW clinic with UW physician PCP = Primary Care (IM, Fam Med, Geriatrics, Peds) 18 years old as of run date Patients must satisfy any 1 of the following in past 2 years: Diagnosis (per outpatient billing data) >1 physician encounter with ICD-9 code like 250.xx + other discrete values Problem List Active outpatient diagnosis = 250.xx Outpatient Medications Pharmacy class = Antidiabetic, excluding pure Metformin Outpatient Lab Results A1C > 6.5 or Random Glucose > 199

21 Algorithm Frequencies Key D = Diagnosis P = Problem List L = Lab Result M = Meds List 1,888 patients with Problem List (83.7%) D, L 2.6% Only Problem List 2.9% Only Diagnosis 3.2% Diab. Diagnosis & Prob. List Diab. Diagnosis & Meds List 7.4% D, P, M 0.6% 5.2% D, P, L 18.4% Prob. List & Lab Results 1.9% 1,919 patients with Diagnosis (85.1%) D, P, M. L 44.7% P, M, L 1.2% Only Lab Result 4.1% D, M, L 3.0% Meds List & Lab Results 0.4% Only Meds List 2.4% N = 2,256 1,343 patients with Med List (59.5%) P, M 2.0% 1,723 patients with Lab Result (76.4%) Refining the Denominator Within the EMR 1. Tag diabetes patients in EMR 2. Provide reports of diabetes patients within EMR 3. Allow providers to mask patients from the registry Patients who don t have diabetes Patients who should not be contacted

22 Actionable Report Listing Diabetes Patients and Information Within EMR Identifying Patients With Diabetes

23 Diabetes Population Management Improving Rates of Testing: Overdue Reminders Epic s Health Maintenance Flags patients overdue for procedures Provider notified at point of care Automatic actions can occur if overdue Overdue patient list (report) generated within Epic Patient reminder letters generated message sent to care manager

24 Health Maintenance Screenshot Use of Information Technology for Precision Performance Measurement and Focused Quality Improvement David W. Baker, MD MPH Chief, General Internal Medicine Feinberg School of Medicine, Northwestern University AHRQ Annual Conference September 9 th, 2008

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26 Improving Rates of Control: Patient Education + Medication Management Our Pilot (modeled after UNC) Patient education at primary care clinic Titration of diabetes medications via telephone by RN care managers per protocol

27 Ed + Med Pilot Results (N = 67 Willing & Able Patients) 70% 60% 50% 40% 30% 20% 10% 28% 34% 37% 64% 36% 0% Pre-Intervention Post-Intervention A1c Category: Good (<7%) Fair (7%-8.99%) Poor ( 9%) Diabetes Population Management

28 Conclusions Implementation of an EMR has many challenges Using billing + EMR data to identify patients with chronic diseases is probably better than using billing data alone Using the EMR for clinical quality improvement is challenging, but has tremendous potential

29 Actuarial Challenges with Diabetes in a Commercial Population Presented by Bruce Pyenson Principal New York SOA Spring Meeting June 2009 Source for Charts: Value-Based Insurance Designs for Diabetes Drug Therapy: Actuarial and Implementation Considerations Available at, Diabetes has High Prevalence in the Working Age Population. Prediabetes is Higher Prevalence and HbA 1c control for diabetics Adults Ages Prevalence Among Adults Number of People* Controlled 3.1% 451 Uncontrolled 2.6% 389 Undiagnosed 2.1% 314 Diabetics 7.8% 1,154 Prediabetics 25.3% 3,730 *Among adult health plan members aged 20 to 69 years per employees. Which Category Needs the Most Attention? 2 July 17, 2009 July 17, 2009 [Enter presentation title in footer] Copyright 2007

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33 Will EMRs Improve Compliance? Co-pay, $ 9 July 17, 2009 Copyright 1996 by European Society of Endocrinology The Research 10 July 17, 2009 July 17, 2009 [Enter presentation title in footer] Copyright 2007

34 An unscientific selection of results Conclusion: Participants experiences support further study (BMJ 2004; 328; 1159) 11 July 17, 2009 The Promise 12 Hillstead R, Can Electronic Medical Record Systems Transform Health Affairs, Sep-Oct 2005 July 17, 2009 July 17, 2009 [Enter presentation title in footer] Copyright 2007

35 Choices for Investment High Value Health/Disease Management High Performance Provider Network Weight Loss Programs DM, Wellness Smoking Cessation Breast and colon cancer screening Flu Shots Treatment Option Support Statins and ACE Inhibitors for target populations Value Total Body Scan for Low Risk Back Surgery for Some Back Pain Cosmetic Surgery Low Value 13 Anything in an Informercial? Weak Evidence for Benefit or Strong Evidence for non-benefit Evidence Base for Efficacy Strong Evidence for Benefit Summary: Challenges for Diabetes & EMR Will EMR do a better job than Disease Management? Which diabetics to focus on: pre-diabetics, undiagnosed, undertreated or controlled? CBO report expects minimal savings from EMR 14 July 17, 2009 July 17, 2009 [Enter presentation title in footer] Copyright 2007

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