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Worldwide, there is an estimated 40.3 million victims trapped in modern day slavery, including 24.9 million in forced labor and 15.4 million in forced marriage. A majority of labor and sex trafficking survivors report at least one healthcare encounter during their victimization. An approach to an informatics technology solution for identifying trafficked persons in real time, in the hospital / emergency department settings is the primary focus of this paper.

This article provides insight into our process and considerations for selecting patient-reported outcome measures (PROMs) designed for self-reporting symptoms and quality-of-life among breast cancer (BCA) patients undergoing oral anticancer agent treatment via a patient-facing technology (PFT) platform.

Pediatric emergence delirium is an undesirable outcome that is understudied. Development of a predictive model is an initial step toward reducing its occurrence. This study aimed to apply machine learning (ML) methods to a large clinical dataset to develop a predictive model for pediatric emergence delirium.

Gun violence remains a concerning and persistent issue in our country. Novel dashboards may integrate and summarize important clinical and non-clinical data that can inform targeted interventions to address the underlying causes of gun violence.

A lack of pharmacist-specific risk-stratification scores in the electronic health record (EHR) may limit resource optimization. The medication regimen complexity-intensive care unit (MRC-ICU) score was implemented into our center's EHR for use by clinical pharmacists. The purpose of this evaluation was to evaluate MRC-ICU as a predictor of pharmacist workload and to assess its potential as an additional dimension to traditional workload measures.

To describe the reusable transformation process of electronic health records (EHR), claims, and prescriptions data into Observational Medical Outcome Partnership (OMOP) Common Data Model (CDM), together with challenges faced and solutions implemented.

Artificial intelligence (AI) holds great promise for transforming the healthcare industry. However, despite its potential, AI is yet to see widespread deployment in clinical settings in significant part due to the lack of publicly available clinical data and the lack of transparency in the published AI algorithms. There are few clinical data repositories publicly accessible to researchers to train and test AI algorithms, and even fewer that contain specialized data [...]

Changes in short-acting beta-agonist (SABA) use are an important signal of asthma control and risk of asthma exacerbations. Inhaler sensors passively capture SABA use and may provide longitudinal data to identify at-riskpatients. We evaluate the performance of several ML models in predicting daily SABA use for participants with asthma and determine relevant features for predictive accuracy.

This study aimed to evaluate women's attitudes towards artificial intelligence (AI)-based technologies used in mental health care. We conducted a cross-sectional, online survey of U.S. adults reporting female sex at birth focused on bioethical considerations for AI-based technologies in mental healthcare, stratifying by previous pregnancy. Survey respondents (n = 258) were open to AI-based technologies in mental healthcare but concerned about medical harm and inappropriate data sharing. They held clinicians [...]

To develop a standardizable, reproducible method for creating drug codelists that incorporates clinical expertise and is adaptable to other studies and databases.