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To explore home healthcare (HHC) clinicians' needs for Clinical Decision Support Systems (CDSS) information delivery for early risk warning within HHC workflows.

Unplanned readmissions following a hospitalization remain common despite significant efforts to curtail these. Wearable devices may offer help identify patients at high risk for an unplanned readmission.

Accurate record linkage (RL) enables consolidation and de-duplication of data from disparate datasets, resulting in more comprehensive and complete patient data. However, conducting RL with low quality or unfit data can waste institutional resources on poor linkage results. We aim to evaluate data linkability to enhance the effectiveness of record linkage.

Clinical Data Warehouses (CDW) are the designated infrastructures to enable access and analysis of large quantities of electronic health record data. Building and managing such systems implies extensive "data work" and coordination between multiple stakeholders. Our study focuses on the challenges these stakeholders face when designing, operating, and ensuring the durability of CDWs for research.

The objective of our research is to conduct a comprehensive review that aims to systematically map, describe, and summarize the current utilization of artificial intelligence (AI) in the recruitment and retention of participants in clinical trials.

Examine electronic health record (EHR) use and factors contributing to documentation burden in acute and critical care nurses.

To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs).

Active learning (AL) has rarely integrated diversity-based and uncertainty-based strategies into a dynamic sampling framework for clinical named entity recognition (NER). Machine-assisted annotation is becoming popular for creating gold-standard labels. This study investigated the effectiveness of dynamic AL strategies under simulated machine-assisted annotation scenarios for clinical NER.

To understand healthcare providers' experiences of using GlucoGuide, a mockup tool that integrates visual data analysis with algorithmic insights to support clinicians' use of patientgenerated data from Type 1 diabetes devices.