ROAD2H: Development and evaluation of an open-source explainable artificial intelligence approach for managing co-morbidity and clinical guidelines.

Introduction: Clinical decision support (CDS) systems (CDSSs) that integrate clinical guidelines need to reflect real-world co-morbidity. In patient-specific clinical contexts, transparent recommendations that allow for contraindications and other conflicts arising from co-morbidity are a requirement. In this work, we develop and evaluate a non-proprietary, standards-based approach to the deployment of computable guidelines with explainable argumentation, integrated with a commercial electronic health record (EHR) system in Serbia, a middle-income country in West Balkans.

Remote COVID-19 Assessment in Primary Care (RECAP) risk prediction tool: derivation and real-world validation studies.

Background: Accurate assessment of COVID-19 severity in the community is essential for patient care and requires COVID-19-specific risk prediction scores adequately validated in a community setting. Following a qualitative phase to identify signs, symptoms, and risk factors, we aimed to develop and validate two COVID-19-specific risk prediction scores. Remote COVID-19 Assessment in Primary Care-General Practice score (RECAP-GP; without peripheral oxygen saturation [SpO]) and RECAP-oxygen saturation score (RECAP-O2; with SpO).

Barriers and facilitators to incident reporting in mental healthcare settings: A qualitative study.

WHAT IS KNOWN ON THE SUBJECT?: The barriers and facilitators to incident reporting are becoming well known in general healthcare settings due to a large body of research in this area. At present, it is unknown if these factors also affect incident reporting in mental healthcare settings as the same amount of research has not been conducted in these settings. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE: Some of the barriers and facilitators to incident reporting in mental healthcare settings are the same as general healthcare settings (i.