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Clinical evidence logic statements (CELS) are shareable knowledge artifacts in a semistructured "If-Then" format that can be used for clinical decision support systems. This project aimed to assess factors facilitating CELS representation.

A novel approach of department-focused electronic health record (EHR) training was implemented to improve efficiency and time management of EHR use. Based off baseline log data, 5 in-person training sessions were designed, focusing on the common inefficiencies of 6 chosen participants. Log data of 4 key metrics and 2 efficiency scores were analyzed 4 months post-training. A survey was conducted to assess self-reported EHR competence. Individually, several participants had improved [...]

Improving health data interoperability through application programming interfaces (APIs) is a focus of US policy initiatives and could have tremendous impact on many aspects of care delivery, such as innovation, operational efficiency, and patient-centered care. To better understand the landscape of API use cases, we interviewed US thought leaders involved in developing and implementing standard-based APIs.

Tobacco use/smoking for epidemiologic studies is often derived from electronic health record (EHR) data, which may be inaccurate. We previously compared smoking from the United States Veterans Health Administration (VHA) EHR clinical reminder data with survey data and found excellent agreement. However, the smoking clinical reminder items changed October 1, 2018. We sought to use the biomarker salivary cotinine (cotinine ≥30) to validate current smoking from multiple sources.

Evaluate an initiative to distribute video-enabled tablets and cell phones to individuals enrolled in Veterans Health Affairs supportive housing program during the COVID-19 pandemic.

There is a need for a systematic method to implement the World Health Organization's Clinical Progression Scale (WHO-CPS), an ordinal clinical severity score for coronavirus disease 2019 patients, to electronic health record (EHR) data. We discuss our process of developing guiding principles mapping EHR data to WHO-CPS scores across multiple institutions.

To assess whether previously observed differences in interoperable exchange by physician practice size persisted in 2019 and identify the role of 3 factors shaping interoperable exchange among physicians in practices of varying sizes: Federal incentive programs designed to encourage health IT use, value-based care, and selection of electronic health record (EHR) developer.

To develop predictive models of coronavirus disease 2019 (COVID-19) outcomes, elucidate the influence of socioeconomic factors, and assess algorithmic racial fairness using a racially diverse patient population with high social needs.

Electronic health record (EHR)-derived data are extensively used in health research. However, the pattern of patient interaction with the healthcare system can result in informative presence bias if those who have poorer health have more data recorded than healthier patients. We aimed to determine how informative presence affects bias across multiple scenarios informed by real-world healthcare utilization patterns.

The goals of this study were to harmonize data from electronic health records (EHRs) into common units, and impute units that were missing.