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This study aimed to assess the effect of a passive, opt-in electronic health record (EHR)-based clinical decision support (CDS), the Chronic Pain OneSheet, on guideline-recommended chronic pain management in primary care.A pragmatic randomized controlled trial with a parallel group design was conducted between October 2020 and May 2022. Participants were 137 primary care clinicians (PCCs) treating qualifying patients with chronic pain at 25 primary care clinics within two academic health [...]

Digital health tools, such as artificial intelligence scribes, offer significant potential to alleviate physician burnout and reduce administrative burdens associated with electronic health records. Despite their promise, Canadian health care organizations face challenges in establishing cohesive strategies for their effective implementation and evaluation.This paper explores actionable, organizational strategies to enhance physician wellness through digital health tools. It examines systemic barriers, promising practices, and infrastructure needs, culminating in five key recommendations [...]

Falls among adults over 60 are a global health concern, including Australia.This study aimed to investigate temporospatial fall alert patterns-across time and location-detected by ambient fall detection sensors in three Australian aged care settings, to inform fall prevention strategies.A mixed-methods approach was used to analyze fall alert patterns and fall risks. Ambient fall detection sensors collected data from three care settings (residential aged care facilities [RACFs], retirement villages [RVs], and [...]

Phone surveys are crucial for collecting health data but are expensive, time-consuming, and difficult to scale. To overcome these limitations, we propose a survey collection approach powered by conversational Large Language Models (LLMs).

Clinical trial data is still predominantly manually entered by site staff into Electronic Data Capture (EDC) systems. This process of abstracting and manually transcribing patient data is time-consuming, inefficient and error prone. Use of Electronic Health Record to Electronic Data Capture (EHR-To-EDC) technologies that digitize this process would improve these inefficiencies.

Deprescribing is the clinically supervised process of stopping or reducing medications that are no longer beneficial. MedSafer is an electronic decision support tool that guides healthcare providers (HCPs) through the deprescribing process. We recently developed a novel patient-facing version of the software, allowing patients and caregivers to generate a personalized deprescribing report to bring to their prescriber.

Rare B-rapidly accelerated fibrosarcoma gene (BRAF) mutations in advanced melanoma, and other malignancies, represent a significant clinical challenge due to sparse evidence on the efficiency of targeted therapy. Conventional genomic databases do not integrate detailed outcome data on treatments for patients with these mutations, requiring innovative informatics approaches.For the use case of patients with rare BRAF-mutated melanoma, we developed a "Treatment Outcome Tool" as a web-based database on rare cancers [...]

This study evaluates the performance and deployment feasibility of a machine learning (ML) model to identify adult-onset type 1 diabetes (T1D) initially coded as type 2 on electronic medical records (EMRs) from a health information exchange (HIE). To our knowledge, this is the first evaluation of such a model on real-world HIE data.

Clinical decision support (CDS) systems have been widely adopted across clinical settings to promote evidence-based practice for clinicians. CDS malfunctions often affect the user experience and indirectly or directly interfere with patient care. To enhance optimal performance, it is critical to constantly monitor the performance of the tool and react promptly when malfunctions are identified.This study aimed to describe malfunctions identified in the development and implementation of a CDS alert [...]

Maternal harm, especially for Black women, is a significant health care issue. Unstructured clinical notes in electronic health records (EHRs) may reveal unsafe maternal care. Prior studies using natural language processing (NLP) have shown that tone and sentiment in notes contribute to preventable safety events.This study aimed to examine whether negative patient descriptors in EHR clinical notes are associated with adverse maternal outcomes and how their use varies by patient [...]