Cost-Effectiveness of an Electronic Medical Record Based Clinical Decision Support System

Diabetes is a common and costly chronic disease. In 2007, 17.9 million U.S. residents were diagnosed with diabetes at a cost to the economy of $174 billion (American Diabetes Association 2008).1 The Centers for Disease Control has estimated the lifetime risk of developing diabetes for individuals born in the United States in 2000 to be 32.8 percent for men and 38.5 percent for women (Narayan et al. 2003). Despite recent improvement trends, in 2008, less than 20 percent of diabetes patients reached evidence-based goals for glycated hemoglobin (A1c), blood pressure (BP), and low-density lipoprotein (LDL) cholesterol (Saydah, Fradkin, and Cowie 2004; Hoerger et al. 2008). Appropriate control of these risk factors would substantially reduce the rate of major microvascular or macrovascular diabetes-related complications and their associated costs (Patel et al. 2007; Dluhy and McMahon 2008; Gaede et al. 2008; Gerstein et al. 2008; Holman et al. 2008). One of the major barriers to improved diabetes care is the lack of timely intensification of pharmacotherapy in patients who have not achieved recommended clinical goals. Many factors contribute to this problem, including competing demands at the time of the visit (Parchman et al. 2007) and medication nonadherence (Karter et al. 2009). Rates of treatment intensification when patient are not at goal hover around 70–80 percent (Bolen et al. 2009). Studies have linked higher rates of treatment intensification by a primary care provider (PCP) to improved A1c, BP, and LDL control (McEwen et al. 2009). Interventions to improve the rates of appropriate treatment intensification include team-based case management, telephone-based management, and information technology-based interventions (Piette et al. 2001; Norris et al. 2002b; Bu et al. 2007). Integrated clinical decision support (CDS) systems have the potential to improve clinical care for millions of persons who are enrolled in health plans that have deployed electronic medical records (EMRs). EMRs can be programmed to include sophisticated algorithms that take advantage of current and past clinical information to provide detailed recommendations at the time of a clinical encounter (Von Korff et al. 1997; Wagner 1998; de Jaegher and Jegers 2001). Initial efforts at EMR-based CDS for diabetes typically improved processes of care (such as rate of A1c or LDL testing or eye exams) but failed to improve A1c, BP, or LDL control (Montori et al. 2002; Meigs et al. 2003;Crosson et al. 2005; O'Connor et al. 2005; Ziemer et al. 2006; Grant et al. 2008; Peterson et al. 2008). EMR-based CDS for other chronic conditions such as hypertension, congestive heart failure (CHF), and asthma similarly failed to improve key intermediate clinical outcomes (Montgomery and Fahey 1998; Tierney et al. 2003; Balas et al. 2004; Murray et al. 2004). A careful reading of these studies identified several possible reasons why these efforts failed: first, most CDS was limited to general prompts and reminders and did not include more detailed drug-specific advice; second, introduction of CDS was usually not accompanied by changes in staff responsibilities and clinic workflow to maximize the impact on clinical care; third, rather than being used for visit planning, CDS displays were usually provided late in the encounter and were often skipped over or not viewed by PCPs. A new generation of EMR-based CDS addressed these concerns by providing treatment recommendations including personalized and detailed drug-specific advice, and by reorganizing the workflow to incorporate recommendations into visits-planning activities. As a result, these system-wide interventions are beginning to have an impact on intermediate outcomes including A1c, BP, and LDL. However, concerns remain regarding the potential costs of these interventions, and evidence on both their costs and cost-effectiveness is limited. This study examines the cost-effectiveness of an EMR-based CDS that was implemented in a large Midwestern health plan.