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Intrahospital patient transport is pivotal in enabling hospital operations and facilitating safe and efficient patient movement. However, transport delays are common in hospitals, signaling a need for improvement. This study develops, implements, and evaluates a proximity-based transporter-to-request assignment system aimed at improving transport service system efficiency.

Electronic health records (EHRs) contain valuable patient information, yet certain aspects of care remain infrequently documented and difficult to extract. Identifying these rarely documented elements requires advanced informatics approaches to uncover clinical documentation patterns that would otherwise remain inaccessible for research and quality improvement.This study developed and validated an informatics approach using natural language processing (NLP) to detect and characterize rarely documented elements in EHRs, using spiritual care documentation as [...]

Artificial intelligence (AI) scribes use advanced speech recognition and natural language processing to automate clinical documentation and ease administrative burden. However, little is known about the effect of AI scribes on clinicians, patients, and organizations.This study aimed to (1) propose an evaluation framework to guide future AI scribe implementations, (2) describe the effect of AI scribes along the domains proposed in the developed evaluation framework, and (3) identify gaps in [...]

To examine the discrimination, calibration, and algorithmic fairness of the Epic End of Life Care Index (EOL-CI).

Primary graft dysfunction (PGD) is an essential outcome after the heart transplant, which causes severe complications and symptoms for recipients. The in advance prediction of PGD can help the transplant physician better manage the risks of PGD occurrence for patients. Domain experts have identified some important risk factors leading to PGD. However, a widely accepted PGD prediction method is lacking from a computational perspective. In this work, we focus on [...]

To characterize and demonstrate how to reduce the administrative burden experienced by patients when navigating medication affordability resources in the United States.

To synthesize knowledge on tensions characterizing large-scale electronic health record (EHR) implementations.

Machine learning algorithms can advance clinical care, including identifying mental health conditions. These algorithms are often developed without considering the perspectives of the affected populations. This study describes the process of incorporating end-user perspectives into the development and implementation planning of a prediction algorithm for new perinatal depression onset.