Search AMIA.org

The surge in publications increases screening time required to maintain high-quality literature reviews. One of the most time-consuming phases is title and abstract screening. Machine learning tools have semi-automated this process for systematic reviews, with limited success for scoping reviews. ChatGPT, a chatbot based on a large language model, might support scoping review screening by identifying key concepts and themes. We hypothesize that ChatGPT outperforms the semi-automated tool Rayyan, increasing [...]

To describe a health system's experience administering patient-reported outcome measures (PROMs) to patients with a non-English preferred language (NEPL).

We sought to compare and analyze the performance of a medication knowledge base (MKB) vendor's (Medi-Span) fully enabled, factory settings product to operational implementations in hospitals using the Leapfrog Group's Computerized Physician Order Entry (CPOE)/electronic health record (EHR) Evaluation Tool.

To explore the use of large language models (LLMs) to assist in developing new agent-based disease-specific patient journey models.

The increasing volume of clinical trial documents presents a significant challenge for biomedical researchers who must analyze vast amounts of unstructured and structured data. Traditional methods are no longer feasible given the scale and complexity of modern clinical trials.

To describe the process of updating the Patient Identification Safety Assurance Factors for EHR Resilience (SAFER) Guide and to review new practices and refinements to the Guide.

Management of patients with vestibular schwannoma (VS) relies on precise tumor size and growth trend evaluation. We introduce and evaluate a novel computer-assisted reporting tool for clinical decision support during multidisciplinary team meetings (MDTMs) for VS patients.

To explore the relationship between ambulatory physician electronic health record (EHR) use characteristics and proxies for physician efficiency.

To assess the interpretability and acceptance of Shapley values for making artificial intelligence/machine learning (AI/ML) tools more transparent, interpretable, and useful to clinicians.

Traditional clinical trial enrollment relies on manual screening and coordinator-led recruitment, creating scalability barriers in high-volume perioperative environments. This study evaluated whether a fully automated, electronic health record (EHR)-integrated clinical decision support (CDS) system could identify eligible patients and engage clinicians in real time without manual screening or dedicated research staff.