Experiences, challenges and lessons while implementing a clinical decision support system in Botswana
Department of Medical Education, University of Botswana, Gaborone (with the Botswana-Baylor Children's Clinical Center of Excellence and the University of Rochester)
How they used Delve
Digital health researchers at the University of Botswana used Delve to run a Braun and Clarke thematic analysis of interviews with 28 healthcare providers across 20 sites, with three team members coding independently, to surface the infrastructure, data protection, and sustainability barriers to rolling out a clinical decision support system in Botswana.
“For qualitative analysis, interview transcripts were uploaded into Delve software for systematic coding. Thematic analysis was conducted following Braun and Clarke’s widely recognized framework [19], allowing for the identification and organization of key themes. An iterative approach to transcript review and deductive coding [20] was independently carried out by NS, RG and MM to ensure rigor and consistency in the analytical process.”
- Field
- Digital health and mHealth implementation; a dermatology clinical decision support system in Botswana
- Data
- One semi-structured interview per participant across 28 healthcare providers and medical students at 20 clinic, hospital and university sites in six Botswana health districts, alongside three longitudinal REDCap surveys and VisualDx app usage logs
- Approach
- Explanatory sequential mixed methods feasibility study: three REDCap surveys at start, midpoint and end analysed with descriptive statistics, then thematic analysis of interview transcripts following Braun and Clarke, with an iterative approach to transcript review and deductive coding carried out independently by three team members (NS, RG, MM) for rigour and consistency; qualitative themes and survey findings integrated through a weaving narrative approach
- Data types
- Interviews, Open-ended survey responses
Abstract
The use of information and communication technologies in healthcare has given rise to mobile health applications and services. For the developing world, mobile health has been hailed as being valuable for extending access to healthcare to underserved populations. More recently, mobile health applications support clinicians to quickly navigate decision making processes. An exemplar decision support system, VisualDx, was implemented in Botswana to provide reference materials at the point of care to support early diagnosis and management of complex dermatological conditions. This study shares experiences, challenges and lessons learnt while implementing VisualDx in Botswana. An explanatory sequential mixed methods feasibility study was conducted with 28 healthcare providers stationed at 20 clinics and hospital sites across Botswana. Nine recorded training sessions were conducted via Zoom and participants thereafter interacted with VisualDx on varying use cases. Quantitative and qualitative data were collected via surveys and a semi-structured interview per participant. Standard VisualDx App usage data was also collected. Descriptive statistics were generated and analyzed. Thematic analysis of interview transcripts was performed using Delve. Experiences, challenges and lessons learned throughout VisualDx implementation in Botswana cut across; Infrastructure, Data protection compliance, Image data quality, Continuous training support, artificial intelligence regulation, Participants’ retention and Sustainable digital health funding. The implementation of VisualDx in Botswana illustrates both the value and the challenges of cross-sector and cross-border collaboration in driving adoption of eHealth tools. The lessons learned may inform future strategy for implementation of other eHealth platforms in Botswana and other similar developing countries.
Citation
Kagiso Ndlovu, Nate Stein, Ruth Gaopelo, Mosadikhumo Monkge, Laura Moen, Mmoloki Cornelius Molwantwa (2025). Experiences, challenges and lessons while implementing a clinical decision support system in Botswana. Oxford Open Digital Health. https://doi.org/10.1093/oodh/oqaf014