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Online health communities (OHCs) have been identified as important outlets for social support and community connection for adolescents and young adults (AYAs) living with chronic illnesses. Despite evident benefits, there remains a gap in research on methods to maximize sustained patient engagement within OHCs. This study assessed per-patient daily commenting rates over time, as well as associations with program staff and volunteer-facilitated events and engagement.

Electronic health records (EHRs) are increasingly used to capture social determinants of health (SDH) data, though there are few published studies of clinicians' engagement with captured data and whether engagement influences health and healthcare utilization. We compared the relative frequency of clinician engagement with discrete SDH data to the frequency of engagement with other common types of medical history information using data from inpatient hospitalizations.

This scoping review evaluates the existing literature on clinical informatics (CI) training in medical schools. It aims to determine the essential components of a CI curriculum in medical schools, identify methods to evaluate the effectiveness of a CI-focused education, and understand its delivery modes.

SNOMED CT is the largest clinical terminology worldwide. Quality assurance of SNOMED CT is of utmost importance to ensure that it provides accurate domain knowledge to various SNOMED CT-based applications. In this work, we introduce a deep learning-based approach to uncover missing is-a relations in SNOMED CT.

Electronic health records (EHRs) offer decision support in the form of alerts, which are often though not always interruptive. These alerts, though sometimes effective, can come at the cost of high cognitive burden and workflow disruption. Less well studied is the design of the EHR itself-the ordering provider's "choice architecture"-which "nudges" users toward alternatives, sometimes unintentionally toward waste and misuse, but ideally intentionally toward better practice. We studied 3 different [...]

Combining text mining (TM) and clinical decision support (CDS) could improve diagnostic and therapeutic processes in clinical practice. This review summarizes current knowledge of the TM-CDS combination in clinical practice, including their intended purpose, implementation in clinical practice, and barriers to such implementation.

Outpatient no-shows have important implications for costs and the quality of care. Predictive models of no-shows could be used to target intervention delivery to reduce no-shows. We reviewed the effectiveness of predictive model-based interventions on outpatient no-shows, intervention costs, acceptability, and equity.

In long-term care (LTC) for older adults, interviews are used to collect client perspectives that are often recorded and transcribed verbatim, which is a time-consuming, tedious task. Automatic speech recognition (ASR) could provide a solution; however, current ASR systems are not effective for certain demographic groups. This study aims to show how data from specific groups, such as older adults or people with accents, can be used to develop an [...]

As mobile health applications continue to proliferate without clear regulation, the need for app evaluation frameworks to offer guidance to patients and clinicians also expands. However, this expanding number of app evaluation frameworks itself can be a source of confusion and often contradictory recommendations. In pursuit of better frameworks that offer innovation for app evaluation, we present 4 challenges that app evaluation frameworks must overcome as well as examples from [...]