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To develop and test a novel machine learning approach for monitoring impact of computerized clinical decision support (CDS) tools on clinicians' electronic health record (EHR) activities.

Studies on symptom concordance between patients and their caregivers often use cross-sectional designs, which may fail to capture the longitudinal, dynamic symptom experience. The Behavioral and Environmental Sensing and Intervention for Cancer (BESI-C) is a remote health monitoring system that utilizes smartwatches and ecological momentary assessments (EMAs) to empower patients and caregivers to monitor and manage cancer pain at home. BESI-C collects real-time symptom data in naturalistic settings, enabling longitudinal [...]

Our objective is to compare different pre-training strategies for electronic health record (EHR) foundation models.

Use of secure messaging through the patient portal has increased in recent years. We compile evidence of how patient-initiated secure messaging has been measured, factors associated with use, and effects on individual and organization-level outcomes.

Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide, with up to 70% of cases remaining undiagnosed. This paper proposes a COPD screening tool based on time-frequency representation features of self-recorded respiratory sounds.

To compare clinical summaries generated from simulated patient primary care electronic health records (EHRs) by GPT-4, to summaries generated by clinicians on multiple domains of quality including utility, concision, accuracy, and bias.

Digital dashboards are used to monitor patients and improve inpatient outcomes in hospital settings. A systematic review assessed the impact of dashboards across five outcomes of hospital mortality, hospital length of stay (LOS), economic impacts, harms, and patient and carer satisfaction.

Recent advancements of Artificial Intelligence (AI) are rapidly transforming clinical research. While this technology offers exciting opportunities, it amplifies existing concerns regarding the need for transparent methodology that fosters patient engagement, and introduces new challenges. PCORI's Improving Methods portfolio has invested in methodological research to enhance rigor and transparency via patient-centered approaches in AI.

Mobile and ubiquitous devices enable health data collection "in a free-living environment" to support applications such as remote patient monitoring and adaptive digital interventions using machine learning (ML). Despite their potential, significant data collection challenges persist, including issues related to user compliance with reporting data, passive data consistency, and authorization. This scoping review identifies and analyzes these challenges, focusing on barriers to effective data collection.

To describe challenges and solutions for calculating longitudinal daily opioid dose in morphine milligram equivalents from electronic health record prescriptions for a clinical trial of voluntary opioid reduction in patients with chronic non-cancer pain.