Overuse and Underuse of Aspirin for Primary Prevention of Cardiovascular Events in Primary Care

Background/Aims: The U.S. Preventive Services Task Force (USPSTF) currently recommends aspirin for primary prevention of coronary heart disease in men 45–79 years old and strokes in women 55–79 years old when the potential cardiovascular disease benefit outweighs the potential harm of gastrointestinal hemorrhage. The complexity and time required to assess risks and benefits for primary prevention can be a barrier for providers to giving patients USPSTF-consistent recommendations, resulting in potential overuse and underuse. Methods: As part of a National Institutes of Health-funded randomized trial to lower cardiovascular risk, we developed a sophisticated web-based electronic health record (EHR)-integrated tool to guide aspirin recommendations as determined by algorithms assessing USPSTF criteria and major bleeding risks. Baseline data was collected for whether aspirin was algorithmically indicated (or not) for all patients at their first eligible primary care encounter in 20 clinics over 18 months. The analysis included patients age 18–75 (mean 58.4) with elevated cardiovascular disease risk (mean 10-year ASCVD risk 24.7%) and excluded patients with congenital heart defects or diabetes. Aspirin overuse and underuse was determined by comparing concordance with: a) the algorithm’s aspirin recommendation, and b) EHR-medication documentation of aspirin. Results: Of the 11,682 patients meeting eligibility criteria at baseline, aspirin was indicated in 8,722 (74.7%) and not indicated in 2,960 (25.3%). Among patients with an aspirin indication, 6,493/8,722 (74.4%) did not have aspirin documented (underuse). Among patients without an aspirin indication, 1,021/2,960 (34.4%) had aspirin documented (overuse). Conclusion: Overall, 7,514/11,682 (64.3%) of patients who met study inclusion criteria for age and cardiovascular risk exhibited either potential overuse or underuse of aspirin for primary cardiovascular disease prevention. Despite expected missing documentation of aspirin due to its over-the-counter availability, which would result in measures of greater underuse and lower overuse than actuality, it is clear that patient aspirin use is very commonly inconsistent with USPSTF guidelines. The recommendation to consider colorectal benefits in the latest USPSTF draft could make decisions about aspirin appropriateness even more complex. EHR-based tools to help providers assess individualized risks and benefits of aspirin could greatly improve the quality of aspirin recommendations and potentially reduce costly cardiovascular disease events while simultaneously reducing rates of aspirin-related hazards

A Clinical Decision Support System Promotes Shared Decision-Making and Cardiovascular Risk Factor Management

Background/Aims: Cardiovascular (CV) Wizard is a web-based electronic health record-integrated point-of-care clinical decision support (CDS) system that presents personalized CV risk information to providers and patients in both a low-numeracy visual format and a high-numeracy quantitative format. We report primary care provider perspectives on how this CDS system affected shared decision-making and CV risk factor management. Methods: Twenty clinics were randomized to either usual care or use of the CDS system with diabetes, heart disease or high-reversible CV risk adults. The CDS system targeted 20% of office visits and was used at 70–80% of targeted visits over a 2-year period. Consented providers (N = 102) were surveyed at baseline and 18 months after implementation. Corrected survey response rates were 90% at baseline and 82% at follow-up. Generalized linear mixed models were used to compare usual care and CDS responses to common questions at baseline and follow-up, and CDS users were queried on their perceptions of the CDS system at follow-up only. Results: Compared to usual care providers, those in the CDS group reported increased follow-up rates of CV risk calculations while seeing patients (73% vs. 28%, P = 0.006), being better prepared to discuss CV risk reduction priorities with patients (98% vs. 78%, P = 0.03), providing accurate advice on aspirin for primary prevention (75% vs. 48%, P = 0.02) and more often discussing CV risk reduction (60% vs. 30%, P = 0.06). CDS users reported that the CDS system improved CV risk factor control (98%), saved time when talking to patients about CV risk reduction (93%), efficiently elicited patient treatment preferences (90%), was useful for shared decision-making (95%), influenced treatment recommendations (89%) and helped initiate CV risk discussions (94%); 85% of providers reported that their patients liked CV Wizard. Conclusion: The CV Wizard CDS system was successfully integrated into the workflow of primary care visits with high sustained use rates, high primary care provider satisfaction, high patient satisfaction and positive impacts on provider-reported clinical processes related to CV risk factor management

Design Features of Successful Outpatient Chronic Disease Care Clinical Decision Support Systems

Background/Aims: To identify key design features of point-of-care diabetes clinical decision support (CDS) that have high use rates and high provider satisfaction rates, and that have improved control of major cardiovascular risk factors. Methods: Based on a series of National Institutes of Health-funded projects to develop point-of-care electronic health record-linked, web-based CDS systems, we have identified design features that contribute to observed high use rates (60–80%) at targeted visits, high primary care provider satisfaction rates (94–95%) and positive effects on glucose and blood pressure control in adults with diabetes. Results: The ideal outpatient chronic disease care CDS system would include the following features: a) co-designed by primary care physicians (PCPs) and researchers, b) supported by clinic and medical group leaders, c) designed to improve publicly reported quality measures, d) introspective identification of targeted encounters, e) total target encounters limited to about 20% of all adult visits, f) rooming nurse launches CDS early in encounter workflow, g) PCP sees CDS early in workflow and uses for visit planning, h) patient reviews CDS before PCP enters room, i) simple visual display of potential benefits for patients, j) prioritization of treatment options based on potential benefit to patient, k) automated feedback to PCP and clinics on CDS use rates at targeted encounters, l) compensation to clinics to cover training costs, m) location of algorithms in web service to facilitate updates and scalability, and n) built-in SmartSet to facilitate clinical actions. Conclusion: These design features may inform future iterations of chronic disease CDS systems

C-A3-02: How Do the Best Physicians Get Diabetes Patients to Glycemic Goals?

Objective: To examine the glucose control related practice patterns of primary care physicians (PCP) and ascertain if those who provide better quality diabetes care have lower rates of clinical inertia

Evaluation of Provider Experience With an Electronic Health Record-Based Clinical Decision Support Tool

Background/Aims: Our goal was to evaluate provider experience with an electronic health record (EHR)-based clinical decision support (CDS) tool called CV Wizard implemented as part of a large randomized trial with 80% use rates for eligible patients. The tool included a quantitative “provider” form with prioritized treatment suggestions and a simpler visual companion “patient” form to efficiently elicit treatment preferences. Methods: Two focus groups were held outside of clinic hours with a meal and $250 compensation. Twelve providers participated and were asked to comment on open-ended questions including a) what goes into their decision to use the tool, b) the implementation process, c) how patients reacted to it, d) how it could be improved, and e) how effective it was. The discussions were audio-taped and transcribed verbatim and examined by the study team to identify themes. Results: Providers were enthusiastic about the tool and found it valuable. They were happy that the nurse printed it for them before visits, and commented that it helped set the visit agenda and organized cardiovascular (CV) risk information. They were more likely to discuss CV risk with patients, and indicated that they took additional time to use it with patients. There was general consensus that it was time well spent. They said the tool reinforced their treatment suggestions. Variability was noted with how nurses and providers were using the tools. For conversations with patients, some providers preferred to use the provider form over the patient form, and vice versa. The patient form was intended to be given to the patient while waiting to be seen by the provider, but this was often not happening. Providers had several suggestions for improving the use process, and asked for better documentation tools for results (smart phrases). Discussion: A clinical decision support tool designed to help providers and patients engage in shared decision-making for CV risk reduction was well received and perceived as time well spent with patients. Overcoming some problems associated with workflow and adding easier ways to document use of the tool for patient discussions would add to the existing value

Sustaining Use of a Clinical Decision Support Tool for Primary Care Providers

Background/Aims: Achieving and maintaining high rates of use of clinical decision support in primary care settings has been challenging. Our goal was to reach and maintain high use rates throughout the study period through ongoing feedback and incentives to clinics and consented providers. Methods: We conducted a clinic randomized trial of an electronic health record (EHR)-based point-of-care clinical decision support (CDS) tool (called CV Wizard) that provides prioritized treatment recommendations to optimize management of six reversible cardiovascular (CV) risk factors: lipids, blood pressure, glucose, tobacco use, aspirin use and weight. We assessed use (the number of times the tool was opened and printed) at targeted office visits for two groups of primary care providers (PCPs) at 11 intervention clinics: (a) those who provided informed consent to use and evaluate the tool (n=54), and (b) those who did not provide consent but still had access to the CDS (n=69). CDS use rate per provider was calculated in three postintervention months as the number of eligible visits at which the tool was used relative to the number of targeted outpatient visits that month. The use goal was 80% of targeted visits, and we reported monthly use rates to clinic leaders for all PCPs, with clinic compensation totaling $2,000 over the intervention period to achieve and maintain the goal. Generalized linear models tested whether PCP consent predicted use of the CDS system. Results: Among consented PCPs, average CDS use rates at 4, 8 and 12 months after full intervention implementation were 57.0%, 73.9% and 75%. Among PCPs at the same intervention clinics who did not provide consent, average use rates were 57.3%, 70.7%, and 58.9% (significant difference only at 12 months, P\u3c0.05). Discussion: We observed robust use of the CDS tool by PCPs and rooming nurses at targeted primary care visits, in the context of targeted use to high CV risk patients only, leadership support and PCP design input, implementation process measurement and feedback, and small financial incentives to clinics that achieved high use rates. Additional evaluation to explain why use rates declined at 12 months in the nonconsented PCPs is of interest

C-B4-03: EMR-based Clinical Decision Support System Improved Glucose and Blood Pressure Control in Adults With Diabetes

Context: Medical groups have invested billions of dollars in outpatient Electronic Medical Records (EMR), but few studies demonstrate a positive impact of EMR-based clinical decision support on clinically important patient outcomes

The Need for New Care Strategies to Prevent A1c Relapse

Background/Aims: The principle treatment strategy for glycemic management in most care settings is reactive; monitor A1c levels and then react with treatment intensification when the A1c exceeds the recommended optimal care goal. Our goal was to assess the potential to improve diabetes performance measures through preventive strategies directed at patients who are at A1c goal but at high risk for disease progression and A1c relapse. Methods: Patients not meeting optimal care goals were partitioned into one of three different A1c trajectories: (a) FLAT –– those who are consistently above optimal A1c goal, (b) Negative slope –– those patients who are on an improvement trajectory, and (c) Positive slope –– those who have previously been meeting A1c goals but who have relapsed (often due to medical issues, comorbidities, psychosocial stress, behavioral or medication adherence, or disease progression). We quantified the proportion of patients with diabetes who contribute to the relapse vector by identifying patients with diabetes and A1c tests in the last two years (9/1/2012–8/31/2014) and quantifying the proportion of patients who relapsed in year 2, stratified by A1c range and pharmacologic treatment in year 1. Results: We identified 29,321 patients with at least two diabetes diagnoses in years 1 and 2, with median A1c of 7.4%. Of these, 8,889 (30%) had an A1c \u3e 8% in year 2. Of 6,321 patients with A1c of 7–7.9% in year 1, 2,332 (36.9%) relapsed to \u3e 8% in year 2. Relapse was higher (43.2%) for patients medicated with sulfonylurea or insulin. Only 689/10,202 (6.7%) patients with A1c \u3c 7% in year 1 relapsed to A1c \u3e 8% in year 2. Discussion: We estimate that the phenomenon of A1c relapse accounts for one-third of all adults identified as having uncontrolled glucose on quality measures. Proactive care strategies in high-risk patients close to goal (A1c 7–7.9%) to help them sustain control could reduce the proportion of patients not meeting optimal A1c goals. More systematic use of patient-reported self-monitored blood glucose data could further help to identify patients who are relapsing or progressing. Further research is needed to test these hypotheses