Accepted for/Published in: JMIR Formative Research
Date Submitted: May 12, 2023
Open Peer Review Period: May 12, 2023 - Jul 7, 2023
Date Accepted: Aug 8, 2023
(closed for review but you can still tweet)
Evaluating Feasibility and Acceptance of a Mobile Clinical Decision Support System in Botswana – Exploratory Study
ABSTRACT
Background:
The health workforce in Botswana consists mainly of medical officers and primary care nurses working in remote areas with limited training and insufficient reference materials to support diagnosis and management of diseases in dermatology and other subspecialties. This suggests a need for clinical decision support tools for these healthcare providers. VisualDx, is among well-established mobile clinical decision support systems offering a promising solution with documented benefits. However, implementation of eHealth systems is commonly associated with challenges. To inform sustainable implementation of VisualDx in Botswana, it is important to evaluate intended users’ perceptions of the technology.
Objective:
This study aims to determine healthcare workers acceptance of VisualDx to gauge feasibility of future adoption in Botswana and other similar healthcare systems.
Methods:
The study's design was informed by constructs of the technology acceptance model. A convergent mixed methods feasibility study involving surveys and semi-structured interviews was conducted. The Research Electronic Data Capture platform supported online data capture from March 2021 through August 2021. Twenty-eight healthcare workers participated in the study. Descriptive statistics were generated and analyzed using Excel and thematic analysis of interview transcripts performed using Delve.
Results:
All survey participants (n=28) expressed interest in using mHealth technology to support their work. Prior to VisualDx, participants referenced textbooks, journal articles, and Google search engines. Overall, participants' survey responses showed their confidence on VisualDx (94.7%); however, some barriers were noted. Frequently used VisualDx features included generating a differential diagnosis through manual entry of patient symptoms (48.5%) or using the artificial intelligence feature to analyze skin conditions (22.0%). Seventeen (60.7%) survey participants were also interviewed, and 4 thematic areas derived.
Conclusions:
Participants' responses indicated acceptance of VisualDx. The ability to access information quickly without internet connection is crucial in resource constrained environments. Select enhancements to VisualDx may further increase its feasibility in Botswana.
Citation
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