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Child abuse and neglect are public health issues impacting communities throughout the United States. The broad adoption of electronic health records (EHR) in health care supports the development of machine learning-based models to help identify child abuse and neglect. Employing EHR data for child abuse and neglect detection raises several critical ethical considerations. This article applied a phenomenological approach to discuss and provide recommendations for key ethical issues related to [...]

The Global Digital Exemplar (GDE) Programme is a national initiative to promote digitally enabled transformation in English provider organizations. The Programme applied benefits realization management techniques to promote and demonstrate transformative outcomes. This work was part of an independent national evaluation of the GDE Programme.

To determine the effects of using unstructured clinical text in machine learning (ML) for prediction, early detection, and identification of sepsis.

We explore the telehealth experiences of adults who use augmentative and alternative communication (AAC) and clinicians who work with people using AAC.

Primary care EHR data are often of clinical importance to cohort studies however they require careful handling. Challenges include determining the periods during which EHR data were collected. Participants are typically censored when they deregister from a medical practice, however, cohort studies wish to follow participants longitudinally including those that change practice. Using UK Biobank as an exemplar, we developed methodology to infer continuous periods of data collection and maximize [...]

The COVID-19 pandemic changed clinician electronic health record (EHR) work in a multitude of ways. To evaluate how, we measure ambulatory clinician EHR use in the United States throughout the COVID-19 pandemic.

To determine factors that influence the adoption and use of patient-reported outcomes (PROs) in the electronic health record (EHR) among users.

To determine whether hospital adoption of a new electronic health record (EHR) developer increases patient sharing with hospitals using the same developer.

Reducing suicidal behavior among patients in the healthcare system requires accurate and explainable predictive models of suicide risk across diverse healthcare settings.

BaMaRa allows the secure collection and deidentified centralization of medical data from all patients followed-up in a rare disease expert network in France, based on a minimum data set (SDM-MR). The present article describes BaMaRa information system implementation and development across the whole national territory as well as data access requests through BNDMR, the data warehouse which centralizes all BaMaRa data, during the 2015-2020 period.