Background: Clinical decision support (CDS) tools in electronic health records (EHRs) are often used as core strategies to support quality improvement programs in the clinical setting. Monitoring the impact (intended and unintended) of these tools is crucial for program evaluation and adaptation. Existing approaches for monitoring typically rely on health care providers' self-reports or direct observation of clinical workflows, which require substantial data collection efforts and are prone to reporting bias.
View Article and Find Full Text PDFIn this study, researchers reviewed electronic health record data to assess whether the coronavirus disease 2019 pandemic was associated with disruptions in diabetes care processes of A1C testing, retinal screening, and nephropathy evaluation among patients receiving care with Wake Forest Baptist Health in North Carolina. Compared with the pre-pandemic period, they found an increase of 13-21 percentage points in the proportion of patients delaying diabetes care for each measure during the pandemic. Alarmingly, delays in A1C testing were greatest for individuals with the most severe disease and may portend an increase in diabetes complications.
View Article and Find Full Text PDFElectronic health records (EHRs) were originally developed for clinical care and billing. As such, the data are not collected, organized, and curated in a fashion that is optimized for secondary use to support the Learning Health System. Population health registries provide tools to support quality improvement.
View Article and Find Full Text PDFBackground: The impact of clinician specialty on cardiovascular disease risk factor outcomes among persons with HIV (PWH) is unclear.
Methods: PWH receiving care at 3 Southeastern US academic HIV clinics between January 2014 and December 2016 were retrospectively stratified into 5 groups based on the specialty of the clinician managing their hypertension or hyperlipidemia. Patients were followed until first atherosclerotic cardiovascular disease event, death, or end of study.
Objective: The study sought to design, pilot, and evaluate a federated data completeness tracking system (CTX) for assessing completeness in research data extracted from electronic health record data across the Accessible Research Commons for Health (ARCH) Clinical Data Research Network.
Materials And Methods: The CTX applies a systems-based approach to design workflow and technology for assessing completeness across distributed electronic health record data repositories participating in a queryable, federated network. The CTX invokes 2 positive feedback loops that utilize open source tools (DQe-c and Vue) to integrate technology and human actors in a system geared for increasing capacity and taking action.