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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 [...]

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.

Among Veterans Health Administration (VHA) patients receiving internet-connected tablets, we examined (1) app usage, (2) clusters of commonly used apps and patient characteristics across clusters, and (3) the relationship between app clusters and mental health services.

We sought to characterize indicators of digital literacy among persons testing for COVID-19 and community health workers (CHWs) providing testing via a digital platform in low-income, majority-Latino communities in California.

We propose and validate a domain knowledge-driven classification model for diagnosing post-acute sequelae of SARS-CoV-2 infection (PASC), also known as Long COVID, using Electronic Health Records (EHRs) data.

This paper investigates the risk factors for wrong-patient medication orders in an emergency department (ED) by studying intercepted ordering errors identified by the "retract-and-reorder" (RaR) metric (orders that were retracted and reordered for a different patient by the same provider within 10 min).

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 [...]

This study applies predictive analytics to identify patients at risk of missing appointments at a novel post-discharge clinic (PDC) in a large academic health system. Recognizing the critical role of appointment adherence in the success of new clinical ventures, this research aims to inform future targeted interventions to increase appointment adherence.

Research on firearm violence is largely limited to people who experienced acute bodily trauma and death which is readily gathered from Inpatient and Emergency Department settings and mortality data. Exposures to firearm violence, such as witnessing firearm violence or losing a loved one to firearm violence, are not routinely collected in health care. As a result, the true public health burden of firearm violence is underestimated. Clinical notes from electronic [...]