Acceptance of the District Health Information System Version 2 Platform for Malaria Case-Based Surveillance By Health Care Workers in Botswana: Web-Based Survey

Abstract

Background Similar to many low- and middle-income countries, Botswana has identified eHealth as a means of improving health care service provision and delivery. The National Malaria Programme (NMP) in Botswana has implemented the District Health Information System version 2 (DHIS2) to support timely malaria case reporting across its 27 health districts; however, the implementation of an eHealth system is never without challenges. Barriers to the implementation of eHealth innovations within health care settings may arise at the individual or organizational levels. As such, the evaluation of user perceptions of the technology is an important step that can inform its sustainable implementation. The DHIS2 was implemented without evaluating user perceptions beforehand; therefore, the Botswana Ministry of Health and Wellness was uncertain about the likelihood of acceptance and use of the platform. Objective We aimed to determine the acceptance of the DHIS2 platform by the NMP in Botswana to gauge whether adoption would be successful. Methods The study’s design was informed by constructs of the technology acceptance model. A survey, with items assessed using a 7-point Likert scale, and focus group discussions were undertaken with DHIS2 core users from 27 health districts and NMP personnel at the Ministry of Health and Wellness. The web-based survey was administered from August 3, 2020 to September 30, 2020. Results Survey participants were core users (n=27). Focus group participants were NMP personnel (n=5). Overall, participants’ survey responses (frequently occurring scores of 7) showed their confidence in the DHIS2 platform for case-based surveillance of malaria; however, participants also noted some organizational issues that could compromise user acceptance of the DHIS2 platform. Conclusions Participants’ responses indicated their acceptance of the DHIS2 platform; however, the consideration of factors related to organizational readiness could further enhance successful acceptance, and consequently, successful adoption of the platform by the malaria program in Botswana.

Mapping Theories, Models, and Frameworks to Evaluate Digital Health Interventions: Scoping Review

“…DHIs targeting HCPs included (but are not limited to) mobile apps targeting patients’ smoking cessation [ 45 ] and medication adherence counseling [ 46 , 47 ], telemedicine and telehealth [ 48 , 49 ], cancer prevention decision support tools [ 50 ], e-consultation between primary care providers and specialty care expertise [ 51 , 52 ], and e-learning for dementia caregiver education [ 53 ]. Two studies targeted health system managers and an information system for case-based surveillance [ 54 ] and a patient-reported outcome data collection system [ 55 ]. Two studies focused on data services including big data analytics [ 56 ], the former being reported in combination with clients, and an electronic patient falls reporting system [ 57 ].…”

Evaluating the Feasibility and Acceptance of a Mobile Clinical Decision Support System in a Resource-Limited Country: Exploratory Study

“…In essence, the acceptance of VisualDx CDSS can be greatly influenced by its perceived ease of use and perceived usefulness. These factors have been previously documented as positive influences toward technology adoption [ 25 , 43 ].…”

“…This further suggests an ongoing need for eHealth providers to tailor solutions to the contexts in which they are being delivered, including medical content, the delivery of training resources, and offline access. These measures were also previously suggested in dealing with inadequate technology infrastructure and lack of skilled personnel to support mHealth interventions [ 25 , 41 , 46 ]. Considering that VisualDx is designed to be adaptable to specific country context, participants’ responses could directly influence further improvements to the medical content and features of the platform.…”

“…Similarly, previous studies have identified the TAM as being prominent among key theoretical approaches used to understand people’s intentions to accept various forms of ICTs [ 24 - 26 ]. In essence, most studies that focus on explaining end user acceptance and predicting successful adoption of eHealth by health care organizations use the TAM as a basis [ 25 - 28 ]. The TAM centers on three belief constructs that have been found to significantly influence an individual’s (1) acceptance of (intention to engage in) a technology, (2) perceived usefulness, and (3) perceived ease of use [ 29 ].…”

Evaluating Feasibility and Acceptance of a Mobile Clinical Decision Support System in Botswana

“…A review of technology acceptance and adoption models by Taherdoost et al, [17], identi ed several models and frameworks explaining user adoption of new technologies and presenting factors that can affect user acceptance of the technology. Similarly, previous studies have identi ed the Technology Acceptance Model (TAM) as prominent amongst key theoretical approaches used to understand people's intentions to accept various forms of ICTs [18][19][20]. The TAM centers on three belief constructs that have been found to signi cantly in uence an individual's, 1) acceptance of (intention to engage) a technology: 2) perceived usefulness; and 3) perceived ease of use [21].…”