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Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

Acceptance, Barriers, and Facilitators to Implementing Artificial Intelligence–Based Decision Support Systems in Emergency Departments: Quantitative and Qualitative Evaluation

The process domain, which describes how implementation should be enacted, was excluded because the system was in a preimplementation phase and it was thought to be irrelevant to the study. Overall, 16 constructs from 4 domains were selected for the semistructured interview (Table 1). Consolidated Framework for Implementation Research domains and constructs. a The domains and constructs selected for the semistructured interview in this study.

Ryo Fujimori, Keibun Liu, Shoko Soeno, Hiromu Naraba, Kentaro Ogura, Konan Hara, Tomohiro Sonoo, Takayuki Ogura, Kensuke Nakamura, Tadahiro Goto

JMIR Form Res 2022;6(6):e36501

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

Clinician Preimplementation Perspectives of a Decision-Support Tool for the Prediction of Cardiac Arrhythmia Based on Machine Learning: Near-Live Feasibility and Qualitative Study

The aim of this study was to explore the feasibility and clinician preimplementation perspectives of using a prediction tool for improved workflows. Therefore, this study does not provide algorithmic validation per se but instead answers questions about the clinical feasibility and workflow integration of a decision-support tool based on ML.

Stina Matthiesen, Søren Zöga Diederichsen, Mikkel Klitzing Hartmann Hansen, Christina Villumsen, Mats Christian Højbjerg Lassen, Peter Karl Jacobsen, Niels Risum, Bo Gregers Winkel, Berit T Philbert, Jesper Hastrup Svendsen, Tariq Osman Andersen

JMIR Hum Factors 2021;8(4):e26964

Intent to Adopt Video-Based Integrated Mental Health Care and the Characteristics of its Supporters: Mixed Methods Study Among General Practitioners Applying Diffusion of Innovations Theory

Intent to Adopt Video-Based Integrated Mental Health Care and the Characteristics of its Supporters: Mixed Methods Study Among General Practitioners Applying Diffusion of Innovations Theory

Specifically, we conducted a survey followed by focus groups and interviews with GPs as part of the preimplementation phase of the PROVIDE (Im PROving cross-sectoral collaboration between primary and psychosocial care: An implementation study on VIDEo consultations) project [35,36]. PROVIDE features a service model in which GPs refer patients with depression and/or anxiety to video consultations conducted in their practice with a remotely located MHS.

Markus W Haun, Isabella Stephan, Michel Wensing, Mechthild Hartmann, Mariell Hoffmann, Hans-Christoph Friederich

JMIR Ment Health 2020;7(10):e23660

Perspectives of Psychotherapists and Psychiatrists on Mental Health Care Integration Within Primary Care Via Video Consultations: Qualitative Preimplementation Study

Perspectives of Psychotherapists and Psychiatrists on Mental Health Care Integration Within Primary Care Via Video Consultations: Qualitative Preimplementation Study

The PROVIDE project consists of three stages: first, we identified barriers and facilitators of stakeholders to determine their needs and tailor the model accordingly (preimplementation phase: PROVIDE-A). Supplementing our recently published work on the perceptions of family physicians regarding mental health specialist video consultations in primary care [14], this paper presents the results for psychotherapists and psychiatrists from the initial preimplementation phase of the PROVIDE project.

Mariell Hoffmann, Michel Wensing, Frank Peters-Klimm, Joachim Szecsenyi, Mechthild Hartmann, Hans-Christoph Friederich, Markus W Haun

J Med Internet Res 2020;22(6):e17569