PS2-20: Racial Disparities in A1c Change and Medication Intensification

Introduction: While it is well documented that disparities exist across racial groups of diabetes patients relative to glucose control, the underlying causative factors are not well understood. The purpose of this study was to examine differences in physician orders for adjustments of glucose control medications in diabetes patients between African Americans and Caucasians

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

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

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

Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial

PURPOSE We wanted to assess the impact of an electronic health record–based diabetes clinical decision support system on control of hemoglobin A1c (glycated hemoglobin), blood pressure, and low-density lipoprotein (LDL) cholesterol levels in adults with diabetes