Impact of an EHR-Based Diabetes Management Form on Quality and Outcomes of Diabetes Care in Primary Care Practices

Abstract

Health information technology shows promise for improving chronic disease care. This study assessed the impact of a diabetes management form (DMF), accessible within an electronic health record. From 2007 to 2009, 2108 diabetes patients were seen in 20 primary care practices; 1103 visits involved use of the DMF in 2008. The primary outcome was "optimal care": HbA1c ≤8%, low-density lipoprotein (LDL) cholesterol <100 mg/dL, blood pressure <130/80 mm Hg, not smoking, and aspirin prescription in patients ≥40 years. After adjusting for number of visits, age, sex, and insulin use, DMF-exposed patients showed less improvement in attaining "optimal care" (estimated difference-in-difference [DID] = -2.06 percentage points; P < .001), LDL cholesterol (DID = -2.30; P = .023), blood pressure (DID = -3.05; P < .001), and total cholesterol (DID = -0.47; P = .004) targets. Documented microalbumin tests, aspirin prescription, and eye and foot exams increased more. Thus, DMF use was associated with smaller gains in achieving evidence-based targets, but greater improvement in documented delivery of care.

IT‐based clinical knowledge management in primary health care: A conceptual framework

“…In terms of the dependent variables, innovation performance is conceptualized as a formative construct having both a service and a process innovation component, that is, the extent to which the medical practice has developed new health care services for its patients (e.g., OECD, ) and has adopted evidence‐based clinical practices (e.g., OECD, ). For its part, clinical performance is also modeled as a formative construct whose three components include improved quality of clinical care (e.g., Herrin et al, ), improved clinical decision making (e.g., Farmer, ), and improved clinical efficiency (e.g., Cauldwell et al, ).…”

2017 National Standards for Diabetes Self-Management Education and Support

“…Most importantly, the quality coordinator is charged with collecting and evaluating data to identify gaps in DSMES, providing feedback on the performance of the DSMES services to team members, referring practitioners, and the organization's administration. The use of EHR and person-centric software improves care (86)(87)(88)(89)(90)(91)(92) and assists the quality coordinator in evaluating the effectiveness of DSMES. The quality coordinator utilizes data mining to inform payers and members of the health care team of the clinical outcomes of DSMES.…”

Implementation of a regional quality improvement collaborative to improve care of people living with opioid use disorder in a Canadian setting

“…It has been shown that data quality can significantly influence the accuracy of quality indicators [30]; therefore, we aimed to improve the fidelity of clinical data as it was entered into the EMR. EMR forms have been found to improve the likelihood of patients receiving evidence-based care and the accuracy of both clinical and administrative data [31]. This is also in line with the CCM, which highlights the importance of decision support and information systems in the proactive care of patients with chronic conditions [8].…”