Medical care for patients with diabetes accounts for. Introduction of an Electronic Registry to Improve Diabetes Outcomes in a Primary Care Network

Transcription

1 REPORTS FROM THE FIELD DISEASE MANAGEMENT Introduction of an Electronic Registry to Improve Diabetes Outcomes in a Primary Care Network Jeffrey Hummel, MD, MPH, Thomas E. Norris, MD, CPE, and Kathy Gibbs, RN, CDE Abstract Objective: To describe a diabetes management program developed around an electronic registry reporting system and evaluate its impact on selected outcomes. Design: Before-and-after evaluation of a quality improvement project. Setting: An 8-clinic primary care delivery system affiliated with the University of Washington in Seattle. Methods: Patients with diabetes were identified and tracked using an electronic registry. System providers received periodic reports generated from the registry identifying all patients with diabetes and highlighting the date and value of each patient s most recent hemoglobin A 1c ( ) and blood pressure measurement. A nurse manager maintained the registry, coached providers and their support staff on diabetes care follow-up tasks, and provided self-management counseling and telephone followup to patients. Results: The number of patients with greater than 8.0% dropped from 333 to 305 (P < 0.05), the number of patients with systolic pressure 135 mm Hg dropped from 515 to 478 (P < 0.05), and the number of patients with diastolic pressure 85 mm Hg dropped from 328 to 270 (P < 0.01). Conclusion: Clinically significant reductions in blood pressure and improved glycemic control were seen across the study population following implementation of the registry-based intervention. Medical care for patients with diabetes accounts for approximately 9.5% of health care costs in the United States. [1]. Strict adherence to current diabetes care guidelines, including tight glycemic control, aggressive management of blood pressure and lipids, and the use of low-dose aspirin and angiotensin-converting enzyme (ACE) inhibitors, has been shown to significantly lower rates of cardiac, renal, and other end organ complications among patients with diabetes [2 5]. Population-based interventions implemented to improve diabetes management have resulted in clinical benefits, including reduced mean hemoglobin A 1c ( ) levels [6 9]. The success of diabetes management programs at improving clinical outcomes appears to be dependent on the presence of 3 essential components: a registry that allows all patients to be tracked and scheduled for needed testing and intervention; decision support systems that allow providers and patients to share a common understanding of the goals for managing diabetes; and a designated team of trained individuals (nurses, pharmacists, nurse practitioners, or physician assistants) to be responsible for following through on clinical tasks for patients in the registry using evidence-based guidelines and standardized treatment protocols, including coaching them in self-management activities [10]. The cost of developing a diabetes program with these components, however, constitutes a significant barrier to smaller provider groups with limited resources. Many physicians practice in an environment in which they are reimbursed only for patient visits, often at a rate that does not cover the true cost of the office encounter. Third-party payors frequently demand evidence of actual cost savings prior to consideration of sharing the cost of innovative programs that have been shown in a research setting to improve clinical outcomes. We hypothesized that the creation of a track record of improved clinical outcomes for diabetes was a precondition for negotiating adequate reimbursement for disease management activities. Thus, we sought to develop an affordable first step toward a disease management model for primary care providers who practice in a mixed-payor environment with very limited staffing resources. In the setting of an 8-clinic network of neighborhood primary care clinics, we constructed a registry reporting system to identify and track a population of patients with diabetes. Our goal was to develop a diabetes management program that could be financially supported within a mid-sized primary care network but be able to produce measurable improvements in physiologic outcomes. From the University of Washington Physicians Network, Seattle, WA. Vol. 10, No. 10 October 2003 JCOM 541

2 DIABETES CARE Diabetes Registry Project Setting This project was conducted at the University of Washington Physicians Network (UWPN), a not-for-profit, primary care network affiliated with the University of Washington academic medical center. The provider composition of the network is 33 family physicians (28.3 FTEs), 18 internal medicine physicians (12.4 FTEs), 8 pediatricians (6.7 FTEs), and 6 full-time mid-level providers. Network physicians see approximately 20,000 patients per month. The demographic composition of UPWN patients is representative of King County, Washington. An electronic medical record (EpiCare/Epic Systems) links all of the UWPN clinics utilizing a shared database. Networked computers in all exam rooms and physicians offices provide information access, chart notes, order entry, and results reporting. The network operates in a mixedpayor environment with approximately 71% commercial insurance, 13% Medicaid, 11% Medicare, and 5% self pay. The proportion of at-risk or capitated care in the local market is minimal. The program began as a quality improvement pilot project in a single clinic in 1998 and expanded to 3 additional clinics over the next 2 years. In December 2000, the reporting system was expanded to the remaining 4 clinics in the network. Diabetes Registry The UWPN diabetes registry was structured as a series of queries using Crystal Reports (Seagate, Inc.) analysis software in standard query language from a relational database (Clarity/Epic Systems). Patients with an ICD-9 code for diabetes ( ) on the problem list in the electronic medical record were included in the registry. The database was automatically updated nightly from the electronic medical record. On a monthly basis, the nurse certified diabetes educator (CDE) for the project reviewed the charts of patients who had encounter diagnoses for diabetes but did not have diabetes on their problem lists and entered an ICD-9 code in their problem lists if appropriate. Physician Reports Registry reports were distributed to the physicians on a regular basis. The report listed the names of every patient with diabetes in the physician s practice, the date and value of the patient s most recent result, and the average of the patient s last 2 systolic and diastolic blood pressures. On the reports, a double asterisk was used to denote if the most recent or blood pressure measurement was more than 6 months old. values below 8.0% and diastolic and systolic pressures less than 80 mm Hg and 130 mm Hg, respectively, were presented in normal or nonemphasized font. values between 8.0% and 9.4%, diastolic pressure between 80 and 89 mm Hg, and systolic pressure between 130 and 139 mm Hg were presented in bold font for emphasis. values of 9.5% or greater, diastolic pressure 90 mm Hg or greater, and systolic pressure 140 mm Hg or greater were formatted in bold red font with a yellow background for greater emphasis. A quarterly newsletter that included suggestions for how to interpret the reports and instructions for specific steps to take in response to the reports was sent to the physicians. The newsletter suggested diabetes management tasks that could be delegated to medical assistants, including the printing of standard letters and the use of scripted phone calls to contact patients. No specific instructions were given regarding what medical therapy should be used in the care of specific patients other than general reminders about the benefits of ACE inhibitors and aspirin [11]. Physicians were encouraged to report data problems and suggest ways that the diabetes reports could be improved. Feedback from physicians was used to modify the structure and content of the report. Physicians were also given feedback on a quarterly basis comparing their management practices with the practices of other providers in the network (Figure). This feedback was accompanied by reviews of the evidence-based guidelines for diabetes management [11] and assurances that the data on individual physician practices was not being used for judging physicians clinical competence. The message at all times was that the report was a quality improvement tool to help them in their efforts to achieve optimal outcomes for their patients. Decision Support In addition to information on current standards of care from the American Diabetes Association that accompanied the reports to physicians, a set of system alerts that popped up on the computer monitor screen was implemented in all 8 clinics early in At the time of the patient s office visit, the provider would be alerted if the patient was overdue for measurement of, albumin/creatinine ratio, or lipids or for an eye examination. The alerts included an option for the provider to place an order for testing or to bypass the alert. Nurse CDE The program nurse CDE spent 70% of her time directly involved in activities for the diabetes initiative. Her responsibilities included providing self-management counseling during face-to-face patient visits, telephone follow-up of these patients, and 3 specific activities related to the information system: updating the patient records to eliminate inaccuracies in the reports (eg, adding diabetes to the problem list of patients with diabetes or inactivating charts of patients who had left the system or were deceased); distributing the 542 JCOM October 2003 Vol. 10, No. 10

3 REPORTS FROM THE FIELD DISEASE MANAGEMENT Providers, n Target Patients overdue for, % You are here > 60 Figure. Graph comparing the provider with colleagues. monthly reports to providers and creating the quarterly comparison charts for each provider; and reviewing the reports with providers and helping them delegate to their medical assistants the tasks of bringing patients in for needed changes in their therapy. A pharmacist from the University of Washington working on a part-time basis supplemented her efforts. Project Assessment To assess the impact of the registry project, we measured changes in blood pressure and levels for patients older than 18 years of age continuously enrolled as patients at UWPN and included in the registry between 21 January 2001 and 20 January On 21 January 2001, a total of 1765 patients with diabetes were identified by the registry (Table 1). Of these, 526 patients were found to have had no contact with their physician for 2 years or left the system prior to 1 July 2001; these patients were dropped from the registry and not included in the analysis. Another 25 patients died during the 1-year period and also were dropped, leaving a population of For blood pressure analysis, we required 2 preregistry blood pressure readings on different dates and 2 postimplementation blood pressure readings and calculated an average for each. The preimplementation readings had to have been done within the 12-month period prior to 21 January The postimplementation readings had to have been done within the 12-month period prior to 20 January patients did not have the requisite number of readings during this 2-year period, leaving a blood pressure analysis cohort of Table 1. Demographics of Diabetes Population Identified by the Registry (n = 1765) Mean age, yr (range) 54.7 (6 99) Female, n (%) 870 (49.3) Insurance status, n (%) Commercial 884 (50.1) Medicaid/state assistance 204 (11.6) Medicare 508 (28.8) Military 68 (3.9) None 101 (5.7) For the glycemic control analysis, we omitted from analysis 114 patients who were followed by a specialist and did not have their regularly monitored by a primary care provider. We also omitted 79 patients who did not have an measurement in their chart for the 12-month preimplementation period as well as another 199 patients who did not have a test performed during the intervention period. This left a glycemic control cohort of 752 patients. We used 1-sample paired t-testing to compare the preimplementation and postimplementation mean and blood pressure levels. Changes in the number of patients with systolic pressure of 135 mm Hg or higher, diastolic pressure 85 mm Hg or higher, and patients with an over 8.0% were evaluated for statistical significance using a chisquare test. Results The mean systolic and diastolic blood pressures decreased by 1.01 mm Hg (P = 0.180) and 1.18 mm Hg (P = 0.005), respectively (Table 2). The number of patients with systolic pressure of 135 mm Hg or greater fell from 515 (48.5%) to 478 (44.9%) (P = 0.04). Patients with a starting systolic pressure of 140 mm Hg or greater had a mean reduction in systolic pressure of 9.61 mm Hg (P < 0.001). The number of patients with a diastolic pressure of 85 mm Hg or greater fell from 328 (30.8%) to 270 (25.4%) (P < 0.001), while those starting with an average diastolic pressure of 90 mm Hg or greater had a mean decrease of 13.4 mm Hg (P < 0.001). Changes in glycemic control are shown in Table 3. The number of patients with greater than 9.5% decreased from 126 to 113. For the 126 patients starting with an greater than 9.5%, the mean value decreased 1.79 percentage points, from 11.16% to 9.37% (95% confidence interval [CI], ). For the 207 patients starting with an between 8.0% and 9.4%, the mean value fell 0.38% from 8.60% to 8.22% (95% CI, ). The number of patients with a of 8.0 or greater fell from 333 to 305 (P < 0.05). Vol. 10, No. 10 October 2003 JCOM 543

4 DIABETES CARE Table 2. Blood Pressure Levels Blood Pressure, mm Hg Confidence n Starting Ending Change Interval P Value SBP All patients to Starting SBP 140 mm Hg to 7.58 < Starting SBP mm Hg to DBP All patients to Starting DBP 90 mm Hg to < Starting DBP mm Hg to 2.96 < DBP = diastolic blood pressure; SBP = systolic blood pressure. Discussion An electronic registry is a prerequisite building block for a population-based program for achieving improved outcomes in diabetes. Smith and others have shown that the use of a registry increases the number of patients who receive recommended monitoring in a diabetes specialty clinic [12]. One challenge was to determine which patients were active. We found that almost 30% of the patients identified as active on the registry when it started had had no contact with the system for more than 2 years, demonstrating the difficulty in keeping an accurate census of active patients in a noncapitated system. It is also common for patients with diabetes to receive at least some of their care from either a general endocrinologist or a diabetologist. Although we tracked results ordered by the primary care physicians, we had no mechanism to include results ordered by a specialist using a different information system. As a result, the primary care physicians requested that the statistics on glycemic control for their panels not include data for those patients whose glycemic control was followed by a specialist. The same was not true of blood pressures, since we felt the primary care physician was responsible for blood pressure control regardless of where the patient received diabetes care. A number of recently published studies have reported reductions in on smaller numbers of subjects through interventions based on grant-funded programs [13 15]. The greatest improvements in these studies have been seen in the patients with the worst initial glycemic control [16]. Our findings demonstrate similar improvements, with the greatest reductions in both blood pressure and occurring in those patients who started with the worst control. A possible explanation for these findings is that there was regression to the mean. In a similar population, Hirsch showed that patients with a starting greater than 8.0% experienced a total decline in the average from 9.21% to 9.07% receiving usual care, while those with a starting between 7.0% and 8.0% experienced a mean rise of 0.76 percentage points [17]. Patients with the worst blood pressure and glycemic control are likely to be more motivated to take their disease seriously and to adhere to medication and behavioral recommendations than patients who perceive themselves to be doing fine. Disease management companies have used this strategy to limit expenditures by stratifying patients with diabetes according to risk and focusing attention on those with the worst control. Physicians are also trained to respond to patients with the worst clinical parameters. The clinical improvement in our study may be attributable to this natural reaction of both patients and their physicians to information. In this sense, the registry proved to be of major value by alerting physicians about patients who were overdue for monitoring and whose blood pressure and glycemic control needed more aggressive intervention. The nurse CDE operated the registry and distributed the reports to the physicians as well as visited each clinic for at least 1 day every 2 weeks to see a small number of patients referred for case management. The majority of the work for these case management activities was done as a single in-clinic visit for 1 hour, followed by short telephone follow-up contact at intervals from 1 week to 1 month. The efficiency of our system program could be improved if reimbursement were to cover practice-based chronic illness care. Many of the data management and scheduling tasks performed by the nurse CDE could be delegated to a clerical person supervised by a nurse. Awelltrained registered nurse could do many of the clinical tasks without certification as a diabetes educator. A grant for $25,000 from Merck Pharmaceuticals was used to purchase the relational database and server to start the registry reporting system. Thereafter, the total cost of the intervention, not including costs associated with normal operations of the electronic medical record, was under $75,000 per year. This consisted of programming time to 544 JCOM October 2003 Vol. 10, No. 10

5 REPORTS FROM THE FIELD DISEASE MANAGEMENT Table 3. Glycemic Control,% Confidence n Starting Ending Change Interval P Value All patients to < Starting 9.5% to < Starting 8.0% 9.4% to < = hemoglobin A 1c. build and maintain the reporting system and the salary and indirect costs associated with a nurse and minimal physician oversight. We did not attempt an economic analysis of the benefits of improved clinical parameters but relied on recently published reports showing interventions to improve glycemic control and reduce nephropathy, including control of blood pressure, to be clearly cost effective [18]. Feedback from patients about the program was overwhelmingly positive. The reaction of providers was largely supportive, although initially several physicians expressed distrust of the data. We used physicians feedback following report distribution to improve data quality, thereby increasing the confidence of providers. We also used frequent positive messaging to address concerns of some physicians that outcomes would be used to judge clinical competence. Although a survey of physicians about the program had a response rate too low to be valid, the responses we received were all positive, and yearly provider satisfaction surveys conducted by UWPN showed consistently high rates of satisfaction with the medical information system in general. This study has several weaknesses. There were some patients with diabetes who did not have an ICD-9 code for diabetes on their problem list and did not show up on the registry until the appropriate code was added to the problem list. Although these patients received the same intervention as those in this study, they were not included due to the analysis methodology. This program was designed as a clinical quality improvement initiative, not a clinical trial, and as a result it contained no control group. The mean at UWPN was already significantly lower than the national average prior to the start of the observation period. This may have been due to the quality of usual care in an electronic medical record environment, the socioeconomic cross-section of patients, referral of sicker patients to specialists, and the fact that in half of the clinics many components of the intervention had been functioning for more than 2 years at the point that the baseline measurements were taken. These factors made clinically significant improvement in the study population harder to detect and reduced the ability to generalize these findings to other delivery systems; however, this error would underestimate the magnitude of improvement that might be found in other populations exposed to a similar intervention. The patients who had sufficient data to be included in the analysis may not be representative of the entire diabetic population, and it may be unwise to generalize from this report to a larger dynamic population of patients with diabetes. If there were reasons that those patients with sufficient data to be included were more motivated patients, we would expect to see better glycemic control at baseline and better endpoint values a year later. It is possible that among the total population the sickest patients either left or were referred to an endocrinologist, thereby excluding them at least from the glycemic control portion of the analysis. Conclusion Providing the primary care physician with accurate, timely data about the physiologic parameters in their patients with diabetes can result in significant improvements in blood pressure and moderate improvement in. This effect is enhanced when physicians are coached on how to respond to the information in a setting that provides decision support and delivery system redesign. The registry reporting system represents a potentially affordable first step for health care delivery systems with access to an electronic registry, which seeks to show physiologic improvements in their management of diabetes in order to generate additional funding for a full-scale disease management program using the chronic illness care model. The authors thank Tabitha Lieberman for informatics support and problem solving; Barb Williams, PhD, for informatics support and query writing; and James LoGerfo, MD, MPH, for support and consultation. Corresponding author: Jeffrey Hummel, MD, MPH, University of Washington Physicians Network, nd Ave., Ste. 200, Seattle, WA Funding/support: Merck Pharmaceuticals provided an initial grant for informatics start-up costs in 1998 and a second grant in 2000 to study the impact of this program on blood pressure. Vol. 10, No. 10 October 2003 JCOM 545

6 DIABETES CARE References 1. Laditka SB, Mastanduno MP, Laditka JN. Health care use of individuals with diabetes in an employer-based insurance population. Arch Intern Med 2001;161: Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). UK Prospective Diabetes Study (UKPDS) Group [published erratum in Lancet 1999;354:602].Lancet 1998;352: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. UK Prospective Diabetes Study Group [published erratum in BMJ 1999;318:29]. BMJ 1998;317: Fuller J, Stevens LK, Chaturvedi N, Holloway JF. Antihypertensive therapy for preventing cardiovascular complications in people with diabetes mellitus. Cochrane Database Syst Rev 2000;(2):CD Gerstein HC, Bosch J, Pogue J, et al. Rationale and design of a large study to evaluate the renal and cardiovascular effects of an ACE inhibitor and vitamin E in high-risk patients with diabetes. The MICRO-HOPE Study. Microalbuminuria, cardiovascular, and renal outcomes. Heart Outcomes Prevention Evaluation. Diabetes Care 1996;19: McCulloch DK, Price MJ, Hindmarsh M, Wagner EH. A population-based approach to diabetes management in a primary care setting: early results and lessons learned. Eff Clin Pract 1998;1: Sadur CN, Moline N, Costa M, et al. Diabetes management in a health maintenance organization. Efficacy of care management using cluster visits. Diabetes Care 1999;22: Aubert RE, Herman WH, Waters J, et al. Nurse case management to improve glycemic control in diabetic patients in a health maintenance organization. A randomized, controlled trial. Ann Intern Med 1998;129: Sidorov J, Gabbay R, Harris R, et al. Disease management for diabetes mellitus: impact on hemoglobin A1c. Am J Manag Care 2000;6: Wagner EH, Glasgow RE, Davis C, et al. Quality improvement in chronic illness care: a collaborative approach. J Qual Improvement. 2001;27: American Diabetes Association: clinical practice recommendations Diabetes Care 2000;23(Suppl 1):S Smith SA, Murphy ME, Huschka TR, et al. Impact of a diabetes electronic management system on the care of patients seen in a subspecialty diabetes clinic. Diabetes Care 1998;21: Mazze RS, Etzwiler DD, Strock E, et al. Staged diabetes management. Toward an integrated model of diabetes care. Diabetes Care 1994;17(Suppl 1): Stein GH. The use of a nurse practitioner in management of patients with diabetes mellitus. Med Care 1974;12: Sullivan FM, Menzies A. The costs and benefits of introducing a nurse-run diabetic review service into general practice. Pract Diabetes 1991;8: Rothman AA, Wagner EH. Chronic illness management: what is the role of primary care? Ann Intern Med 2003;138: Hirsch IB, Goldberg HI, Ellsworth A, et al. A multifaceted intervention in support of diabetes treatment guidelines: a cont trial. Diabetes Res Clin Pract 2002;58: Klonoff DC, Schwartz DM. An economic analysis of interventions for diabetes. Diabetes Care 2000;23: Copyright 2003 by Turner White Communications Inc., Wayne, PA. All rights reserved. 546 JCOM October 2003 Vol. 10, No. 10