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Accurate representation of clinical sex and gender identity in interoperable clinical systems is a major challenge for organizations intent on improving outcomes for sex- and gender-marginalized people. Improved data collection has been hindered by the historical approach that presumed a single, often binary, datum was sufficient. We describe the Health Level Seven International (HL7) Gender Harmony logical model that proposes an improved approach.

Most digital health systems (DHS) are unable to capture gender, sex, and sexual orientation (GSSO) data beyond a single binary attribute with female and male options. This binary system discourages access to preventative screening and gender-affirming care for sexual and gender minority (SGM) people. We conducted this 1-year multi-method project and cocreated an action plan to modernize GSSO information practices in Canadian DHS. The proposed actions are to: (1) Envisage [...]

To characterize variation in clinical documentation production patterns, how this variation relates to individual resident behavior preferences, and how these choices relate to work hours.

Frailty is a prevalent risk factor for adverse outcomes among patients with chronic lung disease. However, identifying frail patients who may benefit from interventions is challenging using standard data sources. We therefore sought to identify phrases in clinical notes in the electronic health record (EHR) that describe actionable frailty syndromes.

Chatbots are software applications to simulate a conversation with a person. The effectiveness of chatbots in facilitating the recruitment of study participants in research, specifically among racial and ethnic minorities, is unknown. The objective of this study is to compare a chatbot versus telephone-based recruitment in enrolling research participants from a predominantly minority patient population at an urban institution. We randomly allocated adults to receive either chatbot or telephone-based outreach [...]

Language status can be conceptualized as an equity-relevant variable, particularly for non-English-speaking populations. Deaf and hard-of-hearing (DHH) individuals who use American Sign Language (ASL) to communicate comprise one such group and are understudied in health services research. DHH individuals are at high-risk of receiving lower-quality care due to ineffective patient-provider communication. This perspective outlines barriers to health equity research serving DHH ASL-users due to systems developed by large-scale informatics networks [...]

Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaningful use of artificial intelligence in clinical practice. Artificial intelligence systems often involve automating cognitively challenging tasks. Therefore, previous literature on trust in automation may hold important lessons for artificial intelligence applications in healthcare. In [...]

Suicide is one of the leading causes of death worldwide, yet clinicians find it difficult to reliably identify individuals at high risk for suicide. Algorithmic approaches for suicide risk detection have been developed in recent years, mostly based on data from electronic health records (EHRs). Significant room for improvement remains in the way these models take advantage of temporal information to improve predictions.