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Background: The government and partners have invested heavily in the health information system (HIS) for service delivery, surveillance, reporting, and monitoring. Sierra Leone’s government launched its first digital health strategy in 2018. In 2019, a broader national innovation and digital strategy was launched. The health pillar direction will use big data and artificial intelligence (AI) to improve health care in general and maternal and child health in particular. Understanding the number, distribution, and interoperability of digital health solutions is crucial for successful implementation strategies. Objective: This paper presents the state of digital health solutions in Sierra Leone and how these solutions currently interoperate. This study further presents opportunities for big data and AI applications. Methods: All the district health management teams, all digital health implementing organizations, and a stratified sample of 72 (out of 1284) health facilities were purposefully selected from all health districts and surveyed. Results: The National Health Management Information System’s (NHMIS’s) aggregate reporting solution populated by health facility forms HF1 to HF9 was, by far, the most used tool. A health facility–based weekly aggregate electronic integrated disease surveillance and response solution was also widely used. Half of the health facilities had more than 2 digital health solutions in use. The different digital health software solutions do not share data among one another, though aggregate reporting data were sent as necessary. None of the respondents use any of the health care registries for patient, provider, health facility, or terminology identification. Conclusions: Many digital health solutions are currently used at health facilities in Sierra Leone. The government can leverage current investment in HIS from surveillance and reporting for using big data and AI for care. The vision of using big data for health care is achievable if stakeholders prioritize individualized and longitudinal patient data exchange using agreed use cases from national strategies. This study has shown evidence of distribution, types, and scale of digital health solutions in health facilities and opportunities for leveraging big data to fill critical gaps necessary to achieve the national digital health vision.
For this survey, the 13 district health medical officers (DMOs) were targeted for survey. The DMOs are the health care policy implementers in their respective district and oversee district health programs at health facilities in their district. In addition, a stratified sample of 72 health facilities were also selected and visited. A separate tool was developed for digital health implementing organizations in the country. The 3 survey tools used for the survey are those shown in Table 1. Each of the questionnaires were coded into CommCare electronic mobile form [11]. Each DMO was visited, and the questionnaire was applied. Similarly, the digital health implementing organizations were visited, and a questionnaire was applied. Survey tools used for digital health mapping. aPHU: primary health care unit. For the health facilities, a stratified sampling technique that includes 72 health facilities (out of 1284) with a confidence level of 95% and 11% margin of error to ensure findings can be generalizable. Health facilities were stratified into urban and rural and into high, medium, or low digital health activity health facilities, using information from the Directorate of Policy, Planning and Information (DPPI) at the MoHS working with respective DMOs. The health facilities surveyed include 17 urban and 55 rural health facilities, as shown in Table 2. In total, 96% (n=69) are public sector health facilities. The district’s DMO determines urban-rural classification. A facility is classified as low digital health activity if no digital health solution is in use at the health facility, medium if 1 or 2 solutions are used, and high if 3 or more. Distribution of health facilities surveyed, by district (health facility survey). aR: rural. bU: urban. These classifications were in addition to their Hospital versus PHU categorizations. In order to arrive at our sample size, a minimum of 5 health facilities were purposefully targeted for selection in each district visited. Each district DMO suggested one district hospital as part of the 5 survey health facilities. One health facility with high digital health activity was prioritized, followed by one with medium activity, followed by low (or no) activity. The process outlined above is repeated until the required number of health facilities is reached. In addition to health facilities, all the DMOs and all identified digital health implementers were surveyed. Implementing partner organizations were included for the structured survey if they have an active digital health implementation at the national or district level, as determined by the DPPI at the MoHS. The implementer survey tool covered the state of their digital health solutions. In total, 15 implementing organizations reported supporting digital health solutions in Sierra Leone and were all surveyed. Each implementer had one or more digital health solutions at various degrees of implementation. Similarly, the DMO—heading the District Health Management Team (DHMT)—was surveyed for the state of digital health solutions at the health facility they oversee. Study personnel surveyed targeted respondents at the national level and then moved to the district and health facility levels. No identifiable information was collected as authorized institutional representatives were surveyed. The quantitative data collection and structured interviews were carried out using the CommCare mobile app, which facilitated automatic data transmission to the cloud for easy access. Enumerators collected data using mobile forms, which were aggregated into a Microsoft Excel spreadsheet. The aggregated data were later analyzed with “pandas” and “matplotlib” libraries of Python.
