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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 analyze gender bias in clinical trials, to design an algorithm that mitigates the effects of biases of gender representation on natural-language (NLP) systems trained on text drawn from clinical trials, and to evaluate its performance.

The proliferation of m-health interventions has led to a growing research area of app analysis. We derived RACE (Review, Assess, Classify, and Evaluate) framework through the integration of existing methodologies for the purpose of analyzing m-health apps, and applied it to study opioid apps.

Early identification of chronic diseases is a pillar of precision medicine as it can lead to improved outcomes, reduction of disease burden, and lower healthcare costs. Predictions of a patient's health trajectory have been improved through the application of machine learning approaches to electronic health records (EHRs). However, these methods have traditionally relied on "black box" algorithms that can process large amounts of data but are unable to incorporate domain [...]

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

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 [...]

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.

This study aimed to understand the association between primary care physician (PCP) proficiency with the electronic health record (EHR) system and time spent interacting with the EHR.