Measuring value for money: a scoping review on economic evaluation of health information systems.

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.

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

Background: Diet is a critical element of diabetes self-management. An emerging area of research is the use of images for dietary records using mobile telephones with embedded cameras. These tools are being designed to reduce user burden and to improve accuracy of portion-size estimation through automation. The objectives of this study were to (1) assess the error of automatically determined portion weights compared to known portion weights of foods and (2) to compare the error between automation and human. Methods: Adolescents (n = 15) captured images of their eating occasions over a 24 h period. All foods and beverages served were weighed. Adolescents self-reported portion sizes for one meal. Image analysis was used to estimate portion weights. Data analysis compared known weights, automated weights, and self-reported portions. Results: For the 19 foods, the mean ratio of automated weight estimate to known weight ranged from 0.89 to 4.61, and 9 foods were within 0.80 to 1.20. The largest error was for lettuce and the most accurate was strawberry jam. The children were fairly accurate with portion estimates for two foods (sausage links, toast) using one type of estimation aid and two foods (sausage links, scrambled eggs) using another aid. The automated method was fairly accurate for two foods (sausage links, jam); however, the 95% confidence intervals for the automated estimates were consistently narrower than human estimates. Conclusions:

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Comparison of known food weights with image-based portion-size automated estimation and adolescents' self-reported portion size.

Consumer health technologies can educate patients about diabetes and support their self-management, yet usability evidence is rarely published even though it determines patient engagement, optimal benefit of any intervention, and an understanding of generalizability. Therefore, we conducted a narrative review of peer-reviewed articles published from 2009 to 2013 that tested the usability of a web- or mobile-delivered system/application designed to educate and support patients with diabetes. Overall, the 23 papers included in our review used mixed (n = 11), descriptive quantitative (n = 9), and qualitative methods (n = 3) to assess usability, such as documenting which features performed as intended and how patients rated their experiences. More sophisticated usability evaluations combined several complementary approaches to elucidate more aspects of functionality. Future work pertaining to the design and evaluation of technology-delivered diabetes education/support interventions should aim to standardize the usability testing processes and publish usability findings to inform interpretation of why an intervention succeeded or failed and for whom.

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Getting a technology-based diabetes intervention ready for prime time: a review of usability testing studies.

The aim was to evaluate the effect of a 12-month individualized health coaching intervention by telephony on clinical outcomes. An open-label cluster-randomized parallel groups trial. Pre- and post-intervention anthropometric and blood pressure measurements by trained nurses, laboratory measures from electronic medical records (EMR). A total of 2594 patients filling inclusion criteria (age 45 years or older, with type 2 diabetes, coronary artery disease or congestive heart failure, and unmet treatment goals) were identified from EMRs, and 1535 patients (59%) gave consent and were randomized into intervention or control arm. Final analysis included 1221 (80%) participants with data on primary end-points both at entry and at end. Primary outcomes were systolic and diastolic blood pressure, serum total and LDL cholesterol concentration, waist circumference for all patients, glycated hemoglobin (HbA1c) for diabetics and NYHA class in patients with congestive heart failure. The target effect was defined as a 10-percentage point increase in the proportion of patients reaching the treatment goal in the intervention arm. The proportion of patients with diastolic blood pressure initially above the target level decreasing to 85 mmHg or lower was 48% in the intervention arm and 37% in the control arm (difference 10.8%, 95% confidence interval 1.5–19.7%). No significant differences emerged between the arms in the other primary end-points. However, the target levels of systolic blood pressure and waist circumference were reached non-significantly more frequently in the intervention arm. Individualized health coaching by telephony, as implemented in the trial was unable to achieve majority of the disease management clinical measures. To provide substantial benefits, interventions may need to be more intensive, target specific sub-groups, and/or to be fully integrated into local health care. ClinicalTrials.gov Identifier: NCT00552903

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T. Saric

Papers

Improving Performance in Diabetes Care: Benefits of Information Technology Enabled Diabetes Management.