A systematic review of digital health tools used for decision support by frontline health workers (FLHWs) in low- and middle- income countries (LMICs)

In in low-and middle-income countries (LMIC), where there are very few trained physicians and nurses, community health workers (CHWs) are often the only providers of healthcare to millions of people. Such LMIC are countries that are classified, based on their geographic region and Gross National Income (GNI), as low-middle income by the World Bank Group, the worlds largest development bank. Research has shown digital health tools to be an effective strategy to improve the performance of frontline line health workers. The aim of this review was to systematically examine the literature on digital health tools that are used for decision support in LMIC and describe what we can learn from studies that have used these tools. As part of a larger parent study the following databases were searched: PubMed, Embase, Scopus, CINAHL, Global Health Ovid, Cochrane and Global Idex Medicus, to find ariticles in the following domains: training tools, decision support, data capture, commodity tracking, provider to provider communication, provider to patient communication and alerts, reminders, health information content. These domains were selected based on the World Health Organisation (WHO) framework for classifying digital health interventions. Content from all seven of these domains informed a series of reviews however this review focuses on how digital tools are used to provide decision support to FLHWs. Included studies were conducted in LMIC in Africa, Asia, North America and South America with the most common users of the tools being CHWs. Most tools for FLHW decision-support used in the interventions described in included articles were in either the pilot or prototype phases, and offered maternal and child health care services. Although decision support was the primary digital health function of all these studies, there was considerable variation in the number of digital health functions of each tool with most studies reporting decision support and data capture as their primary and secondary functions respectively. All the studies found their intervention to have beneficial effects on one or more of the following outcomes: beneficiary engagement, provider engagement, health effects and process/outputs. These findings show great potential for the use of decision support digital health tools as a means of improving the outcomes of health systems through; reducing the work load of FLHWs, reducing the costs of health care, improving the efficiency of service delivery and/or improving the overall quality of care