Heart disease is the leading cause of death in. Impact of an EMR Clinical Decision Support Tool on Lipid Management.

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1 Impact of an EMR Clinical Decision Support Tool on Lipid Management Erik Kelly, BA, Thomas Wasser, PhD, MEd, Julian Diaz Fraga, MD, Jorge J. Scheirer, MD, and Richard L. Alweis, MD ABSTRACT Objective: To determine if an electronic medical record (EMR) clinical decision support tool was effective as a method for improving the management of hyperlipidemia. Methods: Retrospective chart review in randomly selected patients aged 50 to 75 years old with lipid measurements, stratified by whether or not they had been assigned an LDL goal using the EMR tool. Changes in patients lipid values were assessed as follows: For the tool group (n = 100), lipid results immediately prior to setting lipid goals and the subsequent lipid results were used. For the no-tool group (n = 100), the first and second lipid results available in the EMR were used. Paired t tests were used to determine the impact of the tool on achieving lipid goals. Group t tests were performed to compare baseline characteristics. Results: Patients who were not at their LDL goal at baseline had significant reductions in both total cholesterol and LDL levels, regardless of tool use (both p < 0.05) and improvement in achievement of LDL goals (72% to 77% in no-tool group; 41% to 57% in tool group; p < 0.001). 50.0% (no-tool group) and 40.7% (tool group) of patients not at goal initially achieved target LDL, but the difference was nonsignificant (p = 0.413). Conclusions: Use of the EMR tool did not lead to an improvement in achieving lipid goals as compared with not using the tool. Since this echoes other recent findings, the cost and effectiveness of this tool should be re-evaluated. Heart disease is the leading cause of death in the United States and is a major contributor to disability [1]. Low-density lipoprotein cholesterol (LDL) is a major contributor to the development of atherosclerosis, resulting in increased cardiovascular risk [2]. For this reason, interventions aimed at lowering LDL are a major focus of preventive medicine. The Third Report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III) [3] offers guidelines on cholesterol management that advise physicians to assess patients level of risk for a CHD event based on medical history and presence of CHD risk factors. Each of 3 levels of risk are associated with an LDL goal as well as suggested treatment based on the patient s current LDL level. The recent advent of electronic medical records (EMRs) has brought new avenues for LDL management. Currently, it is believed that approximately 17% of U.S. physicians and 8% to 10% of U.S. hospitals have some form of electronic health record system [4]. Common components of many EMRs are integrated clinical decision support systems, which are designed to improve clinical decision-making. These systems match characteristics of individual patients to a computer knowledge base, allowing computer algorithms to generate specific recommendations [5]. Health care providers can enter patient characteristics manually, or the EMR can be queried to retrieve patient data needed for a decision support calculation. Once a recommendation has been calculated, it can be delivered to a patient s EMR, a physician s pager, or a printer for print out [5]. These systems have been developed for a variety of clinical issues including alerts of critical values, reminders of overdue preventive health tasks, advice for drug prescribing, and critiques of existing health care orders [5]. While EMRs have been touted as a tool for improving compliance with clinical guidelines, studies have not shown that EMRs consistently improve care [5,6]. In fact, one analysis revealed that the use of an EMR compared From the Reading Hospital and Medical Center, West Reading, PA. Vol. 18, No. 12 December 2011 JCOM 551

2 Clinical Decision Support to a paper record was associated with a lower likelihood of appropriately prescribing statins for hypercholesterolemia [6]. Other research has indicated that computerized clinical decision support may improve practitioner performance on quality measures, but the data was mixed with regard to the systems ability to improve care [5,7]. Other research showed that multifaceted EMR tools did accelerate the rates of improvement for multiple measures [8]. Unfortunately, the results and recommendations of these support systems were also shown to be ignored by physicians, leading to decreased quality of care [9]. In 2009, the U.S. government pledged $19.2 billion for health information technology improvements. These funds will be used largely for the rapid expansion of EMR implementation [10]. In 2011, Medicare and Medicaid began multiyear financial incentives of up to $65,000 per eligible provider and up to $11 million per hospital for meaningful use of health information technology, such as electronic exchange of health information and reporting of clinical quality measures. After 2015, physicians and hospitals not using certified products will be penalized [10]. This recent dedication of funding and energy towards the rapid expansion of EMR systems warrants further investigation into the effectiveness of these systems and their support tools, especially given their mixed usefulness in the literature. One clinical decision making tool for the management of LDL cholesterol is an LDL goal calculator. We hypothesized that the use of the Lipid Q & E by Clinical Content Consultants (Concord, NH) clinical decision support tool in our Centricity EMR (GE Healthcare, UK) to calculate LDL goals would increase the success rate of patients reaching their designated goal. Methods The study was conducted at the Reading Professional Services Internal Medicine faculty practice at The Reading Hospital and Medical Center, which is staffed by 4 general internists and 1 nurse practitioner. The practice serves a predominantly geriatric population, with 74% of the patients aged 65 and over, of whom 95% have health insurance. The study was exempted by our institutional review board. The EMR was implemented in 2005 with an embedded suite of decision support tools as well as an interface with the health system laboratory and radiology departments. At the time of implementation, each provider received a total of 8 hours of training, which included introduction to the disease-specific templates. No specific requirement was made to utilize the templates. The Lipid Q&E template determines a patient s LDL goal based on the NCEP criteria: age, diabetes status, HDL level, family history of cardiovascular disease, smoking status, and history of hypertension, coronary artery bypass graft surgery, stroke, peripheral vascular disease, or abdominal aortic aneurysm. The tool then displays the LDL and other lipid goals plus the NCEP treatment recommendations. All active patients in the practice aged 50 to 75 with a baseline LDL measurement and at least 1 follow-up measure were stratified according to whether they had ever received an LDL goal from the EMR clinical decision support tool (tool group, n = 823) or whether they had not received a goal from the EMR clinical decision support tool (no-tool group, n = 579). This age-range was selected because it represented those with a high likelihood of need for cardiovascular risk modification via management of cholesterol levels. Patients were also excluded if they did not have follow-up lipid levels ever documented in the system or came to the practice with a diagnosis of hyperlipidemia or dyslipidemia from a previous provider. A total of 100 patients were randomly selected from each of these 2 groups for analysis, as this was a sample size large enough to yield a power of 0.8 with a two-tailed P value of 0.05 required for significance using a 1 standard deviation difference between mean LDL values. Data was collected by chart review, the follow-up lipid level from baseline being defined as the next lipid result in the interfaced system. We used the chi-square goodness of fit test for discrete variables and group tests for continuous variables. The pre-post comparison of lipid values were performed by paired tests using an overall p value of 0.05 considered significant. The global comparisons of efficacy for situations where patients were stratified according to whether they were at goal pre- and post-intervention were compared with the chi-square test of association. We included patients at goal at baseline in the analysis as maintenance of baseline is also a goal of care. Again the p value considered significant was Due to the exploratory nature of this research there were no corrections applied to the data for multiple comparisons. Results There were more women in the no-tool group as compared with the tool group (P = 0.033). Additionally, 552 JCOM December 2011 Vol. 18, No. 12

3 Table 1. Baseline Characteristics Variable No Tool (n = 100) Tool (n = 100) P Value Gender* n n Male Female n Mean SD n Mean SD Age TC LDL HDL TG A1C AST ALT *Test is chi-square; all others are group (independent) t tests. Table 2. Pre-Post Comparisons for No-Tool Group* Variable Sample Size Pre Mean SD Post Mean SD P Value NOT at goal prior At goal prior TC LDL < HDL TG A1C AST ALT TC LDL v HDL TG A1C N/A 6.7 N/A N/A AST ALT *Paired t test. the no-tool group had a younger mean age and better mean cholesterol values (total, LDL, HDL, P = 0.005; P = 0.011; P = 0.001; P = 0.003, respectively). There was no significant difference in mean triglyceride values between the 2 groups (Table 1). Patients in the no-tool group who were not at goal at the beginning of the study saw significant reductions in both total cholesterol (P = 0.001) and LDL (P < 0.001) measurements between their initial and final blood readings (Table 2). Patients in the tool group who were not at goal at the beginning of the study also showed significant reductions in both total cholesterol (P < 0.001) and LDL (P < 0.001) levels (Table 3). A significant number of patients from both groups achieved their goal by the end of Vol. 18, No. 12 December 2011 JCOM 553

4 Clinical Decision Support Table 3. Pre-Post Comparisons for Tool Group* Variable Sample Size Pre Mean SD Post Mean SD P Value NOT at goal prior At goal prior TC < LDL < HDL TG A1C AST ALT TC LDL HDL TG A1C AST ALT *Paired t test. Table 4. Comparison of Patients by Use and No Use of Tool At Goal At Baseline No Yes P Value No-Tool cohort Goal met No 14 9 Yes < Tool cohort Goal met No 35 8 Yes < the study (P < 0.001) (Table 4). Half of the no-tool group and 40.7% of the tool group achieved target LDL but the difference was not significant (P = 0.413) (Table 5). No significant reductions in any lipid measurements were observed in patients who were at goal at the beginning of the study, regardless of EMR tool use (Tables 2 and 3). Discussion We studied the use of a clinical decision support tool for the management of lipids in a community hospital ambulatory faculty practice. Overall, the use of the LDL calculator tool did not improve patients rate of achieving their LDL goals or improve lipid measures compared with not using the tool. The decision support tool was most often used for patients with the most significant cardiovascular risk, and it may have been felt by providers that use of the tool allowed for more aggressive management. This observation is supported by evidence showing that physicians are more aggressive with cardiovascular health management and treatment in more severely at-risk patients [11]. Another possibility is that tool use would have been associated with significant improvement in LDL lowering if the desired effect size was smaller, perhaps less than 1 standard deviation between LDL mean values. For example, LDL reduction in the tool arm was greater than that in the no tool arm, though not significantly so. One potential limitation of this study is caregiver heterogeneity with regards to practice style and comfort with the EMR system. It has been shown that practitioners have significant variation in cardiovascular health 554 JCOM December 2011 Vol. 18, No. 12

5 treatment and management strategies [9,11,12]. Within our study, different providers undoubtedly have different decision making strategies and treatment practices, preventing any true standardization of care within the practice. These differences may obscure the results of this study, as no standard protocol was followed that outlined when a patient needed an EMR-calculated LDL goal or required medical intervention. Another potential limitation of the study is that we were unable to determine if a provider had used another tool, eg, an online LDL calculator, to determine goal and communicate this with a patient, but did not document either doing so or setting of a goal. While this was a retrospective study, measures were taken to prevent selection bias through randomization of patients. In summary, use of the LDL goal calculator in our practice did not lead to improvement in patients obtaining their LDL goal. These results underscore the fact that while EMR technology can be beneficial, it will not necessarily lead to improvement of patient care. Corresponding author: Richard Alweis, MD, The Reading Hospital and Medical Center, Sixth Ave and Spruce St, West Reading, PA Financial disclosures: None. Author contributions: conception and design, EK, TW, JDF, RA, JJS; analysis and interpretation of data, EK, TW, JDF, RA; drafting of article, EK, TW, JDF, RA, JJS; critical revision of the article, EK, TW, RA, JJS; provision of study materials or patients, RA, JJS; statistical expertise, TW; administrative or technical support, RA, JJS; collection and assembly of data, EK, RA. References 1. Centers for Disease Control and Prevention. Heart disease is the number one cause of death Accessed 13 July 2011 at 2. Schaefer JR. Lipid management for the prevention of cardiovascular disease. Curr Pharm Des 2011;17: Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults: Executive Summary of Table 5. Efficacy Analysis for Groups That Were Not at Goal at Baseline (n = 87) Goal met No-Tool Cohort Tool Cohort P Value No Yes the Third Report on the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285: Blumenthal D. The federal role in promoting health information technology. New York: The Commonwealth Fund Perspectives on Health Reform, Jan Accessed 13 Jul 2011 at Jan/The-Federal-Role-in-Promoting-Health-Information- Technology.aspx. 5. Garg AX, Adhikari NKJ, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293: Linder JA, Ma J, Bates DW, et al. Electronic health record use and the quality of ambulatory care in the United States. Arch Intern Med 2007;167: Nemeth LS, Wessell AM. Improving medication safety in primary care using electronic health records. J Patient Saf 2010;6: Persell SD, Kaiser D, Dolan NC, et al. Changes in performance after implementation of a multifaceted electronic health record based quality improvement system. Med Care 2011;49: Sposito AC, Ramires JA, Jukema JW, et al. Physicians attitudes and adherence to use of risk scores for primary prevention of CV disease. Curr Med Res Opin 2009;25: The American Recovery and Reinvestment Act of H.R.1. The Library of Congress Accessed 13 Jul 2011 at thomas.loc.gov/cgi-bin/bdquery/z?d111: h.r.00001:. 11. Foley KA, Denke MA, Kamal-Bahl S, et al. The impact of physician attitudes and beliefs on treatment decisions and lipid therapy in high risk patients. Med Care 2006;44: Evans JS, Harries C, Dennis I, Dean J. General practitioners traits and stated policies in the prescription of lipid lowering agents. Br J Gen Pract 1995;45:15 8. Copyright 2011 by Turner White Communications Inc., Wayne, PA. All rights reserved. Vol. 18, No. 12 December 2011 JCOM 555