Using Registries in Practice, Quality Improvement, Research, and EducationElizabeth 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 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.

Outline
        - 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
        - Using the Diabetes Registry in Practice
        - Identification of patients at high cardiovascular risk for targeted interventions
        - Identification of patients and provision of self-management assistance.
        - Using the Diabetes Registry in Quality Improvement and Research
        - Analyses for managers
        - Audit and feedback for staff providers 
        - Evaluation of quality improvement projects
        - Registry as a research data base
        - Using the Diabetes Registry in Education
        - Audit and feedback for trainees

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

WHO

Improving Care for People with Long-Term Conditions: A Review of UK and International Frameworks. NHS Institute of Innovation, 2006

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

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.
        - Clinical summaries 
        - Clinical reminders 
        - Register recall system 

A ‘Flat File’ is a Roster + Information

Each row represents a unique patient, 

plus extra information that can fit within the single row.
| Name       | ID  | Date of Birth | Site ID | Primary Provider |
|------------|-----|---------------|---------|------------------|
| John Doe   | 001 | 8/7/45        | 541     | Miller           |
| Al Smith   | 002 | 9/5/25        | 541     | Miller           |
| John Jones | 003 | 1/4/52        | 541     | Kern             |

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

Tables are Linked to Other Tables by the Primary Key
| ID is the ‘Primary Key’ linking this Pharmacy Table to the DemographicTable.     | ID          | Medication | Fill Date | Prescribing Provider |
|----------------------------------------------------------------------------------|-------------|------------|-----------|----------------------|
| 001                                                                              | Metformin   | 5/6/06     | Miller    |
| 001                                                                              | NPH insulin | 5/6/06     | Miller    |
| 001                                                                              | Metformin   | 8/1/06     | Miller    |

Linked Tables in the Cleveland VAMC 

Diabetes Registry

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

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

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 

Operational Definitions
        - 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

Operational Definitions
        - 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

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).

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 Variable | Site ID | Tier | Map To:         |
|----------------------------|---------|------|-----------------|
| A PCM/FERRIS/WHITE         | 541     | 1    | Cleveland Akron |
| A PCM/HONG/WHITE           | 541     | 1    | Cleveland Akron |
| A PCM/WOMENS HEALTH/HONG   | 541     | 1    | Cleveland Akron |
| A ABI/NURSING              | 541     | 2    | Cleveland Akron |
| A ADMN PROCESSING          | 541     | 2    | Cleveland Akron |
| A ANTICOAG                 | 541     | 2    | Cleveland Akron |
| A ANTICOAG/LAB             | 541     | 2    | Cleveland Akron |

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

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

How Many Ways to Name an A1c Test?
| Site ID | Test ID | Name                                     | National VA Lab Code |
|---------|---------|------------------------------------------|----------------------|
| 538     | 1751    | ZZHGB A1C                                | 85052                |
| 538     | 1869    | ~AT-HBA1C                                | NULL                 |
| 538     | 5172    | HEMOGLOBIN A1C, MEASURED                 | 85052                |
| 538     | 5414    | ZZZHEMOGLOBIN A1C,MEASURED               | NULL                 |
| 539     | 5141    | HEMOGLOBIN A1C,CALC(d/c,4/17/00)         | NULL                 |
| 539     | 5164    | ~HEMOGLOBIN A1C,MEASURED                 | 85053                |
| 539     | 5490    | ZZHEMOGLOBIN A1C(NEW,8/06)DO NOT USE!!!! | 85053                |
| 539     | 5523    | HBA1c-POCT                               | 82117                |
| 541     | 97      | HEMOGLOBIN A1C                           | 85053                |
| 552     | 1859    | HBA1C                                    | 85052                |
| 757     | 97      | ZZHgb A1c (no longer orderable)          | 85052                |
| 757     | 5122    | HEMOGLOBIN A1C, MEASURED                 | 85053                |
| 757     | 5210    | HEMOGLOBIN A1C, IN-HOUSE                 | 85053                |
| 757     | 5588    | HEMOGLOBIN A1c                           | 85053                |

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

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

Requesting a Report 

from The Diabetes Registry Web Page

Report Result (fragment) from the 

Diabetes Registry Web Page

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.

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

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

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

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

Diabetes

OR

Nutrition 

Education

17%

BOTH Diabetes Education 

AND Nutrition Education

36%

NEITHER Diabetes Education  NOR  Nutrition Education

47%

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

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 counting

Source: 

VISN 10 Diabetes Registry

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

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

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

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

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 |
|----------------------------------------|-------------|-------------------------------|-----------------------------|-------------------------------|
| 1                                      | 3,992       | 10.8                          | 2.5                         | 14                            | 7 |
| 2                                      | 6,609       | 11.3                          | 5.4                         | 21                            | 9 |
| 3                                      | 5,901       | 11.7                          | 2.9                         | 12                            | 8 |
| 4                                      | 18,171      | 11.7                          | 3.5                         | 5                             | 3 |
| 5                                      | 5,959       | 13.2                          | 6.2                         | 9                             | 4 |

Using Registries in Practice, Quality Improvement, Research, and EducationElizabeth 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 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.

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

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

HIPPA and patient confidentiality

Evaluation of the impact of SMAsKirsh 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.

Kirsh et al. 2007; in press. Findings
        - Levels of A1c, LDL-c, and SBP all fell significantly post-intervention
        - A1c decreased 1.4 (0.8, 2.1) (p<0.001)
        - LDL-c decreased 14.8 (2.3, 27.4) (p=0.022)
        - SBP decreased 16.0 (9.7, 22.3) (p<0.001).  
        - The reductions greater in the intervention group relative to the control group: 
        - A1c 1.44 vs -0.30 (p=0.002) for A1c
        - 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).  

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

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

Questions?

References
        - 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. 
        - Bodenheimer T, Grumbach K. Electronic Technology A Spark to  Revitalize Primary Care? JAMA. 2003;290:259-264