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Computational phenotyping from electronic health records (EHRs) is essential for clinical research, decision support, and quality/population health assessment, but the proliferation of algorithms for the same conditions makes it difficult to identify which algorithm is most appropriate for reuse.

To introduce a novel, standardised approach to evaluating AI prediction models in balancing effectiveness, efficiency and utility, using a sepsis prediction model case study.

To guide research data services (RDS) teams in managing researcher variability (eg, differing deadlines, funding, expertise) when honest-brokering data, we present a framework based on operations management principles and a food service analogy.

To build natural language processing (NLP) strategies to characterize measures of genetic counseling (GC) efficiency and classify measures according to phase of GC (pre- or post-genetic testing).

Stigmatizing language (SL) in Electronic Health Records (EHRs) can perpetuate biases and negatively impact patient care. This study introduces a novel method for automatically detecting such language to improve healthcare documentation practices.

Electronic health records (EHRs) lack a widely adopted standard for recording transgender and gender diverse (TGD) status, complicating research on TGD health. Computational models have been developed to identify TGD individuals in EHRs; however, gaps remain in understanding which components contribute to stronger phenotyping approaches. This scoping review evaluates EHR-based models for identifying TGD individuals, focusing on identifier types, performance, external validation, and ethical reporting to guide best practices.

To report on the feasibility of a simultaneous, enterprise-wide deployment of EHR-integrated ambient scribe technology across a large academic health system.

To inform initiatives to improve the interoperability of healthcare data, we described the experience of distinct phenotypes of physicians when obtaining information from outside sources.

To support ambulatory care innovation, we created Observer, a multimodal dataset comprising videotaped outpatient visits, electronic health record (EHR) data, and structured surveys. This paper describes the data collection procedures and summarizes the clinical and contextual features of the dataset.

To develop a transfer-learning Bayesian sparse logistic regression model that transfers information learned from one dataset to another by using an informed prior to facilitate model fitting in small-sample clinical patient-level prediction problems that suffer from a lack of available information.