1. Using Registries in Practice, Quality Improvement, Research, and Education Elizabeth O. Kern, MD, MS, Susan R. Kirsh, MD, and David C. Aron, MD, MS, Center for Quality Improvement Research, VA Medical Center, Cleveland, OH and QUERI-DM Objectives: To understand the link between Registry data structure and its functionality. To understand the link between Registry data structure and its functionality. To understand how a Registry can be created from the VISTA database. To understand how a Registry can be created from the VISTA database. To understand how a disease Registry can be used to in quality improvement, education, and research. To understand how a disease Registry can be used to in quality improvement, education, and research.

2. Outline Context for registry use: Chronic Care Models and Systems Redesign based on such models Context for registry use: Chronic Care Models and Systems Redesign based on such models Development of the Cleveland VAMC Diabetes Registry from the VISTA Database Development of the Cleveland VAMC Diabetes Registry from the VISTA Database Using the Diabetes Registry in Practice Using the Diabetes Registry in Practice Identification of patients at high cardiovascular risk for targeted interventions Identification of patients at high cardiovascular risk for targeted interventions Identification of patients and provision of self-management assistance. Identification of patients and provision of self-management assistance. Using the Diabetes Registry in Quality Improvement and Research Using the Diabetes Registry in Quality Improvement and Research Analyses for managers Analyses for managers Audit and feedback for staff providers Audit and feedback for staff providers Evaluation of quality improvement projects Evaluation of quality improvement projects Registry as a research data base Registry as a research data base Using the Diabetes Registry in Education Using the Diabetes Registry in Education Audit and feedback for trainees Audit and feedback for trainees

3. The Context for Registries The various models for management of chronic illness have one feature common: information Rx to care for both the sick patient and sick system The various models for management of chronic illness have one feature common: information Rx to care for both the sick patient and sick system WHO Improving Care for People with Long-Term Conditions: A Review of UK and International Frameworks. NHS Institute of Innovation, 2006

4. Shared Medical Appointments (Group Visits) Based on the Wagner Chronic Care Model

5. What are the components of Clinical Information Systems? Patient registries that are organized into a database to access important patient information easily, track individual patient outcome measures and prevention activities, and provide feedback to providers. Patient registries that are organized into a database to access important patient information easily, track individual patient outcome measures and prevention activities, and provide feedback to providers. Clinical summaries Clinical summaries Clinical reminders Clinical reminders Register recall system Register recall system

6. A ‘Flat File’ is a Roster + Information NameIDDate of Birth Site IDPrimary Provider John Doe0018/7/45541Miller Al Smith0029/5/25541Miller John Jones0031/4/52541Kern Each row represents a unique patient, plus extra information that can fit within the single row.

7. A Table is Structured by its ‘Attributes’ and its ‘Primary Key’ Patient Name Patient ID Site ID Date of Birth Primary Care Provider Primary Key ‘Attributes’ are the column headings

8. Tables are Linked to Other Tables by the Primary Key ID is the ‘Primary Key’ linking this Pharmacy Table to the Demographic Table. IDMedicationFill DatePrescribing Provider 001Metformin5/6/06Miller 001NPH insulin5/6/06Miller 001Metformin8/1/06Miller

9. Linked Tables in the Cleveland VAMC Diabetes Registry

10. Data Flow from the Database to Web Page Diabetes Registry Database Step 1: Nightly Data Pull Step 2: SQL Stored Procedures Data Warehouse VISN 10 VISTA Step 3: ASP.NET platform Step 4: Standard Queries in C# VA Intranet Web Page ‘Live’ Data Reports by User Request

11. Data Flow Software VISTA data VISN 10 SQL Data Warehouse KB-SQL in a SSIS-SQL Package SQL Data Warehouse Diabetes Registry SQL Relational Database SQL Stored Procedures (helps to run standard queries faster) Diabetes Registry Web Page ASP.NET 2.0 platform C# programming language to create standard queries Design tool is Visual Studio 2005 Web Page reports Clinical User Excel Spreadsheets Microsoft ‘Mail Merge’ generates templated letters to patients

12. Analytic Software To pull data from the Diabetes Registry for ad hoc analyses SQL ‘Query Analyzer’ To place data in analytic format Notepad.txt tab delimited file Excel spreadsheet For data management and analysis SAS statistical analytic program SAS datasets For security and confidentiality All files (including SAS working files) remain behind the VA firewall, on a server drive, in folders limited to specific users

13. Operational Definitions DEFINE patients with diabetes DEFINE patients with diabetes Had at least 3 ICD-9 codes indicating diabetes on 3 separate dates (codes are 250.xx, 357.2, 362.0, 366.41) OR Had a diabetes-specific medication* dispensed from a VISN 10 pharmacy * Diabetes-specific medication list maintained as a ‘look-up’ table in the Diabetes Registry database

14. Operational Definitions DEFINE Active versus Non-Active patients DEFINE Active versus Non-Active patients ACTIVE = Date of Death = null AND (The patient had a primary care visit within the past 18 months OR The patient had diabetes-specific medications dispensed within the past 18 months) Non-ACTIVE = conditions for ACTIVE not met

15. Operational Definitions DEFINE the clinic most responsible for diabetes care for each ACTIVE patient Find the most recent primary care type visit within past 18 months. From this visit, assign each patient to the facility site and clinic or CBOC associated with that visit (i.e., ‘follow the patient trail’) A novel system was created, mapping each visit (also called ‘encounter’) to a specific site and clinic using the ‘Hospital_Location’ variable in VISTA. The 4,200 unique Hospital_Locations were pared down to 1,792 associated with encounters in a primary care clinic, and categorized as ‘definitely indicating primary care’ (Tier 1) or ‘possible indicating primary care (Tier2).

16. Mapping 1,792 ‘Hospital Locations’ to 51 Different Clinics in VISN 10 (Hospital Location’ is a variable included in each visit or encounter) EXAMPLE: Hospital Location VariableSite IDTierMap To: A PCM/FERRIS/WHITE5411Cleveland Akron A PCM/HONG/WHITE5411Cleveland Akron A PCM/WOMENS HEALTH/HONG5411Cleveland Akron A ABI/NURSING5412Cleveland Akron A ADMN PROCESSING5412Cleveland Akron A ANTICOAG5412Cleveland Akron A ANTICOAG/LAB5412Cleveland Akron

17. Assigning the Primary Care Provider From the Primary Care Manager Module database (PCMM) most patients are assigned to a primary care provider in VISN 10. The PCMM database is up dated manually, by a person assigned to this task. The Diabetes Registry pulls the Primary Care Provider (PCP) variable from the PCMM to match with each patient in the Registry. Approximately 10% of Diabetes Registry patients are not assigned to a primary care provider, because the PCMM table has not been updated yet, or the patient is truly not assigned (e.g., ESRD patients, HIV patients, Employee Health patients) Some PCP’s cover multiple clinic sites: therefore knowing who is PCP does not necessarily mean the clinic site is known

18. Data Cleaning Problem: Text values appear in what is supposed to be a numeric result field Problem: Text values appear in what is supposed to be a numeric result field Example: LDL-c = ‘comment’ Example: LDL-c = ‘comment’ Example: HbA1c = ‘not done’ Example: HbA1c = ‘not done’ Problem: Multiple ‘names’ and ‘codes’ for the same lab test Problem: Multiple ‘names’ and ‘codes’ for the same lab test Example: 14 different ‘names’ for the A1c test in VISN 10 Example: 14 different ‘names’ for the A1c test in VISN 10 Example: 13 different ‘Test-ID’s’ for the A1c test in VISN 10 Example: 13 different ‘Test-ID’s’ for the A1c test in VISN 10 Example: 3 different ‘National VA Lab Codes’ for the A1c test in VISN 10, or a National VA Lab code is not assigned Example: 3 different ‘National VA Lab Codes’ for the A1c test in VISN 10, or a National VA Lab code is not assigned

19. How Many Ways to Name an A1c Test? Site IDTest IDName National VA Lab Code 5381751ZZHGB A1C85052 5381869~AT-HBA1CNULL 5385172HEMOGLOBIN A1C, MEASURED85052 5385414ZZZHEMOGLOBIN A1C,MEASUREDNULL 5395141HEMOGLOBIN A1C,CALC(d/c,4/17/00)NULL 5395164~HEMOGLOBIN A1C,MEASURED85053 5395490ZZHEMOGLOBIN A1C(NEW,8/06)DO NOT USE!!!!85053 5395523HBA1c-POCT82117 54197HEMOGLOBIN A1C85053 5521859HBA1C85052 75797ZZHgb A1c (no longer orderable)85052 7575122HEMOGLOBIN A1C, MEASURED85053 7575210HEMOGLOBIN A1C, IN-HOUSE85053 7575588HEMOGLOBIN A1c85053

20. Using the Diabetes Registry for Population- Based Disease Management Find the patients who are outliers in A1c LDL-c Blood pressure Foot exam Eye exam Group by clinic/provider with primary responsibility to these patients for diabetes management

21. Using the Diabetes Registry for Population- Based Disease Management Create spreadsheets for patient calls for special interventions at clinic level or provider level Merge the spreadsheets into templated letters for special interventions at clinic level or provider level Create individualized ‘Diabetes Report Cards’ containing the five parameters used for EPRP to send to patients by mail, or to use in group classes Include the Diabetes Medication Profile in order to group patients needing insulin starts or titration Example: patients with A1c > 9%, on 2 oral meds, need to start HS NPH

22. Requesting a Report from The Diabetes Registry Web Page

23. Report Result (fragment) from the Diabetes Registry Web Page

24. Cleveland VA July 27, 2007 Dear JOHN DOE, Happy Birthday! Your VA health care providers want you to have many more! We are sending you your latest diabetes test results because our VA records show that your blood test for cholesterol is either too high, or needs to be rechecked. Your LDL-cholesterol (the ‘bad’ kind of cholesterol) should be less than 100 to protect you from stroke or heart attack. Even if your last test was good, you are due to have it checked again. Your primary provider at the VA Lorain clinic would like you to call L W to go over your results, set up a fasting blood test, or set up a visit. Please call (440) 244-3833 EXT 2247 to schedule. If you come for a clinic visit, please bring in all of your medication bottles, your blood glucose meter, and any glucose records if you have them. Thanks! Templated Header to the ‘Birthday Letter’ (From the Diabetes Registry web page: patients in Lorain CBOC with high or missing LDL-C, with a birthday in July ) Underlined text is dropped in according to links and expert logic.

25. Individualized Diabetes Report Contained in the ‘Birthday Letter’ The values, messages, and smiley faces are driven by expert logic.

26. Quality Improvement How do we know a change is needed? How do we know a change is an improvement? How do we know where to put scarce resources? A Diabetes Registry can provide data to: Describe the patient population Identify patient sub-groups having the most need Identify who is in the sub-groups Show the ‘reach’ of intervention programs Show the outcomes of intervention programs

27. Growth in the Patient Population with Diabetes in VISN 10 The net growth in live patients with diabetes was 73% over the 5 year period from 2002 to 2006. By the end of 2006, there were 42,499 patients with diabetes, representing approximately 21-25% of the VISN 10 patient population. Source: VISN 10 Diabetes Registry

28. Diabetes OR Nutrition Education 17% BOTH Diabetes Education AND Nutrition Education 36% NEITHER Diabetes Education NOR Nutrition Education 47% Almost Half of Patients Do Not Receive Self-Management Education from the VA From 2002-2006 looking back for outpatient notes Diabetes Education = diabetes education class glucometer class diabetes specialty clinic diabetes team program Nutrition Education = any nutrition visit. Source: VISN 10 Diabetes Registry

29. Target Patients with Poor Glycemic Control Prioritize by the most recent HbA1c 27,031 (64%) are < 7.5% 10,131 (24%) are between 7.5-8.9% 5,278 (12%) are 9% or greater Source: VISN 10 Diabetes Registry

30. Glycemic Control Plus Medication Profiles Can Guide Interventions High A1c, on no diabetes meds from the VA, may need VA prescription. High HbA1c, on orals only, may need start of basal insulin and/or carb counting High HbA1c, on insulin, needs insulin titration and carb countingSource: VISN 10 Diabetes Registry

31. Drop in HbA1c After DSME classes in the Cleveland VAMC N= 436 patients *Results were same for a subgroup already taking insulin.Source: VISN 10 Diabetes Registry -0.1 -0.3 -0.8 -2.4 Change in HbA1c% P <.001 for all strata

32. Growth of the Nurse Diabetes Case Manager Program in Cleveland VAMC From 2003 through 2006, the Diabetes Case Manager program saw 3,886 unique patients. (~ 20% of Cleveland VA patient population with diabetes). The program grew from 3 to 10 by 2006. 7 achieved CDE after training for case management. Source: VISN 10 Diabetes Registry

33. Diabetes Case Management Resulted in Better A1c Outcomes than Usual Care Case management resulted in greater drops in A1c for patients with starting A1c < 9%, and an equivalent drop in A1c for patients with starting A1c >= 9% 0 -0.3 -0.5 -0.7 -1.3 -1.4 * * * p <.05 Change in HbA1c Source: VISN 10 Diabetes Registry

34. Dataset (from the VISN 10 Diabetes Registry) 40,632 patients receiving diabetes-specific medications in VISN 10 since Jan 2005, and who are alive. ~ 9,000 patients in VISN 10 do not receive either glucose test strips or hypoglycemic agents from the VA, but have an ICD-9 code of diabetes. These patients were excluded from this analysis

35. Thiazolidenedione (TZD) and A1c Outcomes Within VISN 10, by Site Total Patients on Diabetes Medications A1c >=9% A1c Missing in past 24 months Patients on TZD, No Insulin Patients on TZD, With Insulin 13,99210.82.5147 26,60911.35.4219 3 5,90111.72.9128 4 18,17111.73.553 5 5,95913.26.294

36. Using Registries in Practice, Quality Improvement, Research, and Education Elizabeth O. Kern, MD, MS, Susan R. Kirsh, MD, and David C. Aron, MD, MS, Center for Quality Improvement Research, VA Medical Center, Cleveland, OH and QUERI-DM Objectives: To understand the link between Registry data structure and its functionality. To understand the link between Registry data structure and its functionality. To understand how a Registry can be created from the VISTA database. To understand how a Registry can be created from the VISTA database. To understand how a disease Registry can be used to in quality improvement, education, and research. To understand how a disease Registry can be used to in quality improvement, education, and research.

37. Shared Medical Appointments (Group Visits) Based on the Wagner Chronic Care Model

38. The Patient Encounter Personnel MD, NP/CDE, RN, Pharmacist, Psychologist 8-20 patients/session 90 minutes sessions Return visit interval: 4-8 weeks or until goals achieved Group activities Education Patient Centered Discussion Review of labs/medications Individual activities Medication management Referrals Individualized plan of care outlined and give to patient

39. Evaluation of the impact of SMAs Kirsh et al. QSHC 2007; in press. Subjects: Diabetic patients with >1 of: A1c >9% SBP blood pressure >160 mmHg LDL-c >130 mg/dl Patients largely derived from registry data, few referred from pcp participated in >1 SMA from 4/05 to 9/05. Study Design: Quasi-experimental with concurrent, but non-randomized controls patients who participated in SMAs from 5/06 through 8/06. A retrospective period of observation prior to their SMA participation was used.

40. Kirsh et al. 2007; in press. Findings Levels of A1c, LDL-c, and SBP all fell significantly post-intervention Levels of A1c, LDL-c, and SBP all fell significantly post-intervention A1c decreased 1.4 (0.8, 2.1) (p<0.001) A1c decreased 1.4 (0.8, 2.1) (p<0.001) LDL-c decreased 14.8 (2.3, 27.4) (p=0.022) LDL-c decreased 14.8 (2.3, 27.4) (p=0.022) SBP decreased 16.0 (9.7, 22.3) (p<0.001). SBP decreased 16.0 (9.7, 22.3) (p<0.001). The reductions greater in the intervention group relative to the control group: The reductions greater in the intervention group relative to the control group: A1c 1.44 vs -0.30 (p=0.002) for A1c A1c 1.44 vs -0.30 (p=0.002) for A1c SBP 14.83 vs 2.54 mmHg (p=0.04) for SBP. SBP 14.83 vs 2.54 mmHg (p=0.04) for SBP. No diff. for LDL-c 16.0 vs 5.37 mg/dl (p=0.29). No diff. for LDL-c 16.0 vs 5.37 mg/dl (p=0.29).

43. Registry use in continuing care Track additional patient data hard coded in note for future reference Track additional patient data hard coded in note for future reference Monitor progress on patients and give report card to providers-pilot Monitor progress on patients and give report card to providers-pilot Birthday letters generated by registry data to engage patients in initiating SMA Birthday letters generated by registry data to engage patients in initiating SMA

44. Trainee Participation in SMA Internal Medicine residents and third year medical students on chronic disease block Internal Medicine residents and third year medical students on chronic disease block Uses of registry in general to manage population Uses of registry in general to manage population Clinical Information System module Clinical Information System module Audit and feedback of resident’s primary care panels and teams Audit and feedback of resident’s primary care panels and teams

45. Questions?

46. References 1.Gliklich RE, Dreyer NA, eds. Registries for Evaluating Patient Outcomes: A User’s Guide. (Prepared by Outcome DEcIDE Center [Outcome Sciences, Inc. dba Outcome] under Contract No. HHSA29020050035I TO1.) AHRQ Publication No. 07- EHC001-1. Rockville, MD: Agency for Healthcare Research and Quality. April 2007. 2.Bodenheimer T, Grumbach K. Electronic Technology A Spark to Revitalize Primary Care? JAMA. 2003;290:259-264