Using Default Options Within the Electronic Health Record to Increase the Prescribing of Generic-Equivalent Medications

Health care costs in the United States continue to rise and now account for more than $2.8 trillion annually (1). It is estimated that as much as one third of this spending is due to unnecessary waste in the health care system (2). The Choosing Wisely campaign is a joint initiative launched in 2012 by the American Board of Internal Medicine Foundation, Consumer Reports, and several medical societies (3). Its premise is to decrease unnecessary spending by focusing on reducing the use of low-value services. Similarly, the American College of Physicians (ACP) has launched the High Value Care Initiative with the goal of helping physicians provide the best possible care for their patients while simultaneously reducing unnecessary costs to the health care system (4). Prescribing brand-name medications that have existing generic equivalents is a prime example of a low-value service. These medications are often more expensive than their generic equivalents, yet in most cases evidence suggests they are similar in effectiveness (5). A recent study of 20 popular multisource drugs found that in 2009, Medicaid spent an additional $329 million that could have been saved by using existing generic equivalents instead of brand-name medications (6). The use of default options has been recognized as a successful strategy to change behavior in many settings, including health care (710). Default options are effective because they are often viewed as an implicit recommendation and because people tend to choose the path of least resistance. If a decision maker does not opt out, the default's action takes place (7). Small changes in default settings can substantially affect medical decision making and provider behavior. In January 2012, the Division of General Internal Medicine at the University of Pennsylvania in Philadelphia implemented an intervention to change the default in the electronic health record (EHR) medication prescriber with the goal of increasing the prescribing of generic equivalents when available. Before the intervention, if a provider searched for a brand-name medication, the options for prescribing brand names were shown near the top with generic equivalents listed below. After the intervention, if a provider searched for a brand-name medication, only the generic equivalent was listed. Providers still maintained the ability to opt out and conduct a broader search that listed the brand if warranted. The objective of this study was to evaluate the effect of this intervention on physician behavior related to the ordering of brand-name medications versus existing generic equivalents. Methods The institutional review board at the University of Pennsylvania approved this study. Setting and Participants The sample comprised attending faculty and residents at 2 ambulatory clinics in the Division of General Internal Medicine (IM) and 2 ambulatory clinics in the Department of Family Medicine (FM) practicing between June 2011 and September 2012 at the University of Pennsylvania. All of these clinics were teaching practices where attending physicians practiced independently and served as preceptors for residents. The practices were located within the same ZIP code, and all providers used the same EHR. We classified providers by training level as attending physician or resident using publicly available listings of faculty and resident rosters (1114), along with special request from the Internal Medicine Residency Program, Family Medicine Residency Program, Division of General Internal Medicine, and Department of Family Medicine. Intervention During the preintervention period (June to December 2011), both IM and FM providers used the same EHR medication prescriber and were shown the same resulting options. If a provider searched for a brand-name medication, the dosing options for prescribing brand names were listed at the top, with the dosing options for generic-equivalent medications listed below (Figure 1). During the postintervention period (January to September 2012), this remained unchanged for FM providers. However, if IM providers searched for a brand-name medication, the results listed only dosing options for generic-equivalent medications (Figure 2). The IM providers had the ability to opt out (by clicking on Database Lookup or pressing F7 on the keyboard) and would then be shown the dosing options for brand names at the top, with the generic-equivalent medications listed below. Figure 1. Example of medication prescriber results before the intervention. Resulting medication dosing options before the intervention for internal medicine and family medicine providers when a search for Coreg was conducted. Brand-name options are listed first, followed by generic-equivalent options. Figure 2. Example of medication prescriber results after the intervention. Resulting medication dosing options after the intervention for internal medicine providers when a search for Coreg was conducted. Only the generic-equivalent options are listed; users have the ability to opt out and choose the brand-name if warranted. Data Provider prescription data were obtained by using EPIC's analytical reporting database, Clarity (EPIC Systems). These data reported the number of prescriptions per month by each provider that were sent electronically to the pharmacy for each medication included in the sample. When an electronic prescription is sent to the pharmacy, a patient can arrive without a paper copy and pick up the medications after presenting appropriate identification. Our reports capture all electronic prescribing from June 2011 to September 2012. Handwritten or faxed prescriptions were not captured by the reports produced for this study. Reports were produced as Excel files (Microsoft), coded, and then analyzed with Stata software, version 12 (StataCorp). Selection of Medications We evaluated 3 classes of medications: -blockers, statins, and proton-pump inhibitors (PPIs). Because we did not have access to data that allowed adjustment for patient characteristics, we chose these 3 medication classes because patients with indications for these medications are similar between the 2 specialties in our study, thus providing a better comparison between the intervention and control groups. Among these 3 classes of medications, we excluded prescriptions for which a generic equivalent did not exist (e.g., Crestor [AstraZeneca]) to remove bias due to scenarios in which it was unclear whether prescribing a nonequivalent generic medication of the same class was not feasible or had already been done but was ineffective. We excluded combination medications to remove any bias related to scenarios in which the provider was attempting to decrease pill burden for the patient (for example, niacinsimvastatin extended-release) and an equivalent generic combination did not exist. We excluded prescriptions for Lipitor (Pfizer) and atorvastatin from the analysis because the generic equivalent had become available just 1 month before the intervention (30 November 2011) and there may have been alternative motives for switching between the brand and generic other than that related to the objective of our study. While this represented a large proportion of statins in the original sample, prescribing trends in the preintervention period differed because of the lack of a generic equivalent (see Data Supplement). Data Supplement. Additional Information The following medications and their associated brand-name equivalents were included in our sample: -blockers (atenolol, carvedilol, labetalol, metoprolol, nadolol, and propranolol), statins (lovastatin, pravastatin, and simvastatin), and PPIs (lansoprazole, omeprazole, and pantoprazole). Outcome Measures To evaluate the effect of the intervention for IM providers compared with FM providers, we examined monthly prescribing trends of generic medication equivalents in the pre- and postintervention periods. Outcomes were evaluated for all providers (attending physicians and residents) for all medications in the sample and by medication class (-blockers, statins, and PPIs). Subset analyses were conducted to separately examine trends for attending physicians and residents because evidence suggests that these 2 groups may have differing practice patterns (1517). Residents are closer than attending physicians to medical school training, where generic medication names are more commonly taught and tested. Attending physicians are likely to be exposed to longer periods of industry detailing than are residents. In addition, interventions to change provider behavior may differ in effect among attending physicians, who have had longer to form practicing habits, than residents, who may be newly learning their practice style. Statistical Analysis For each measure, a multivariate logistic regression model was fit by using each prescription as the unit of analysis and clustering on provider. The effect of time was modeled by a dummy variable for each month. We evaluated the effect of the intervention by using an interaction term for provider specialty (IM vs. FM) and time to compare the proportion of generic medication prescriptions for each medication with itself before and after the intervention, contrasting the changes in IM to changes in FM. By using FM (not exposed to the intervention) as the control group in this study design, we reduce potential biases from unmeasured variables, such as general trends in prescribing behavior over time. Standard errors in the models were adjusted to account for clustering provider (18, 19). We estimated the odds of prescribing a generic medication for IM providers compared with FM providers in the postintervention period relative to the preintervention period. To assess the mean effect of the intervention in the postintervention period, we exponentiated the mean of the monthly log odds ratios (ORs) and calculated a 95% CI for this quantity (20, 21). We report the estimated probabilities of generic medic