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Globally, health information system (HIS) development projects face challenges regarding technology infrastructure, financing, user resistance, and interoperability. While these challenges are well-described in literature, most studies on HIS digitalization focus on the development of national and hospital HISs, with little focus on HISs in primary care. We described the HISs of two primary care clinics in Manila, Philippines in terms of data management procedures, governance, training and equipment, information culture [...]

Chemoprophylaxis reduces the risk of hospital-acquired venous thromboembolism (VTE), but is not reliably ordered. Our institution created a clinical decision support (CDS) interruptive alert to remind clinicians to order VTE chemoprophylaxis when it is missing for qualifying inpatients. Unfortunately, this alert has required repeated modifications to ensure accurate logic, and continues to generate negative feedback from users.This study aimed to describe multiple failures in the development and postdeployment optimization of [...]

Aging populations strain health care systems. Assisted Living Technologies (ALTs) emerge as a potential solution for promoting independent living among older adults. However, the real-world effect of ALTs remains unclear.This study explores benefits and challenges (anticipated and unanticipated) of ALTs for older adults and informal caregivers across three aged care settings (residential aged care facilities [RACFs], retirement villages [RVs], and home-dwelling communities [HDCs]) in Australia.Three ALTs (fall detection sensors, sleep [...]

In the emergency department-initiated buprenorphine for opioid use disorder (EMBED) trial, a clinical decision support (CDS) tool had no effect on rates of buprenorphine initiation in emergency department (ED) patients with opioid use disorder. The Agency for Healthcare Research and Quality (AHRQ) recently released a CDS Performance Measure Inventory to guide data-driven CDS development and evaluation. Through partner co-design, we tailored AHRQ inventory measures to evaluate EMBED CDS performance and [...]

Electronic health records (EHRs) have revolutionized clinical practice, but clinicians and institutions have not yet fully optimized their use. Inconsistent documentation of ophthalmic test results can increase potential medicolegal risks if providers bill for tests without properly documenting clinical interpretations.To address this, we developed and implemented a logic tool in Epic (Epic Systems, Verona, Wisconsin, United States) that prompts clinicians to document diagnostic test interpretations as discrete data before closing [...]

To address biased representation in Electronic Health Records (EHRs) across subpopulations (SPs), which leads to predictive models underperforming for underrepresented groups, we propose a framework to enhance equitable predictive performance.

This case series aims to evaluate several applications of inline disappearing text (DT) clinical decision support (CDS) tools within clinician documentation.DT blocks were created to prompt documentation for perioperative anticoagulation planning (scenario 1), predischarge intravenous antibiotic planning (scenario 2), and advanced care planning (ACP; scenario 3). In scenario 1, DT was the only intervention. In scenario 2, DT was paired with a documentation SmartList. In scenario 3, DT was paired [...]

School violence risk prevention in the United States relies on manual assessments that are time-consuming and subjective. We developed a machine learning algorithm named Automated RIsk Assessment (ARIA), using natural language processing (NLP) to find linguistic patterns in standardized interview questions that can predict risk of aggression. Our goal was to evaluate the incremental change in performance with the addition of each question to simulate situations where interviews cannot be [...]

To compare Observational Medical Outcomes Partnership (OMOP) Logical Observation Identifiers Names and Codes (LOINC) and Veterans Aging Cohort Study (VACS) methods for extracting laboratory chemistry data from Veterans Health Administration (VA) electronic health records (EHR).

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that disproportionately affects women and racial/ethnic minority groups. Predicting disease flares is essential for improving patient outcomes, yet few studies integrate both clinical and social determinants of health (SDoH). We therefore developed FLAME (FLAre Machine learning prediction of SLE), a machine learning pipeline that uses electronic health records (EHRs) and contextual-level SDoH to predict 3-month flare risk, emphasizing explainability and fairness.