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Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism

Author(s): Cheligeer C; Southern DA; Yan J; Wu G; Pan J; Lee S; Martin EA; Jafarpour H; Eastwood CA; Zeng Y; Quan H;

Objectives: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinical documentation, and the complexity of data formats. Pulmonary embolism as an adverse event (PEAE) is particularly difficult to identify using exi ...

Article GUID: 40105654


Identifying personalized barriers for hypertension self-management from TASKS framework

Author(s): Yang J; Zeng Y; Yang L; Khan N; Singh S; Walker RL; Eastwood R; Quan H;

Objective: Effective management of hypertension requires not only medical intervention but also significant patient self-management. The challenge, however, lies in the diversity of patients' personal barriers to managing their condition. The objective of this research is to identify and categorize personalized barriers to hypertension self-management ...

Article GUID: 39143621


Design Principles in mHealth Interventions for Sustainable Health Behavior Changes: Protocol for a Systematic Review

Author(s): Yang L; Kuang A; Xu C; Shewchuk B; Singh S; Quan H; Zeng Y;

Background: In recent years, mHealth has increasingly been used to deliver behavioral interventions for disease prevention and self-management. Computing power in mHealth tools can provide unique functions beyond conventional interventions in provisioning personalized behavior change recommendations and delivering them in real time, suppo ...

Article GUID: 36811938


Developing EMR-based algorithms to Identify hospital adverse events for health system performance evaluation and improvement: Study protocol

Author(s): Wu G; Eastwood C; Zeng Y; Quan H; Long Q; Zhang Z; Ghali WA; Bakal J; Boussat B; Flemons W; Forster A; Southern DA; Knudsen S; Popowich B; Xu Y;

Background: Measurement of care quality and safety mainly relies on abstracted administrative data. However, it is well studied that administrative data-based adverse event (AE) detection methods are suboptimal due to lack of clinical information. Electronic medical records (EMR) have been widely ...

Article GUID: 36197944


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