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Many routine patient care items should be reviewed at least daily for intensive care unit (ICU) patients. These items are often incompletely performed, and dynamic clinical decision support tools (CDSTs) may improve attention to these daily items. We sought to evaluate the accuracy of institutionalized electronic health record (EHR) based custom dynamic CDST to support 22 ICU rounding quality metrics across 7 categories (hypoglycemia, venothromboembolism prophylaxis, stress ulcer prophylaxis, mechanical [...]

The Integrating Clinical Trials and Real-World Endpoints (ICAREdata) project aimed to demonstrate that electronic health record (EHR) data, expressed in a standard structured format, can be extracted and transmitted to contribute to clinical research. Using the minimal Common Oncology Data Elements (mCODE), we collected standardized oncology outcome data from EHRs across 10 clinical sites and 15 trials. This report details and assesses the ICAREdata technical implementation and offers recommendations to [...]

The quality of alcohol-related prevention and treatment in US primary care is poor. The purpose of this study was to describe the extent to which Electronic Health Records (EHRs) used by 167 primary care practices across 7 states currently include the necessary prompts, clinical support, and performance reporting essential for improving alcohol-related prevention and treatment in primary care.

Pre-exposure prophylaxis (PrEP) is a key biomedical intervention for ending the HIV epidemic in the United States, but its uptake is impeded by systemic barriers, including fragmented workflows and ineffective data coordination. This study aims to design PrEPLinker, a blockchain-based, client-centered platform to enhance care to address these challenges by improving care coordination and enabling clients to securely manage their identity and PrEP-related data.

Using machine learning models to predict infants at risk of defaulting routine immunization (RI) and identify significant features for Uganda.

This study aims to investigate whether different types of electronic health record (EHR) users have distinct preferences for data quality assessment indicators (DQAI) and explore how these preferences can guide the enhancement of EHR systems and the optimization of related policies.

Population health management (PHM) is a promising data-driven approach to address the challenges faced by health care systems worldwide. Although Business Intelligence (BI) systems are known to be relevant for a data-driven approach, the usage for PHM is limited in its elaboration. To explore available scientific publications, a systematic review guided by PRISMA was conducted of mature BI initiatives to investigate their decision contexts and BI capabilities.

This study describes the development and deployment of a machine learning (ML) model called Vent.io to predict mechanical ventilation (MV).

As artificial intelligence evolves, integrating speech processing into home healthcare (HHC) workflows is increasingly feasible. Audio-recorded communications enhance risk identification models, with automatic speech recognition (ASR) systems as a key component. This study evaluates the transcription accuracy and equity of 4 ASR systems-Amazon Web Services (AWS) General, AWS Medical, Whisper, and Wave2Vec-in transcribing patient-nurse communication in US HHC, focusing on their ability in accurate transcription of speech from Black and [...]

Integrating electronic health record (EHR) data with other resources is essential in rare disease research due to low disease prevalence. Such integration is dependent on the alignment of ontologies used for data annotation. The international classification of diseases (ICD) is used to annotate clinical diagnoses, while the human phenotype ontology (HPO) is used to annotate phenotypes. Although these ontologies overlap in the biomedical entities they describe, the extent to which [...]