Mobile and wireless technology for health (mHealth) has the potential to improve health outcomes by addressing critical health systems constraints that impede coverage, utilization, and effectiveness of health services. To date, few mHealth programs have been implemented at scale and there remains a paucity of evidence on their effectiveness and value for money. This paper aims to improve understanding among mHealth program managers and key stakeholders of how to select methods for economic evaluation (comparative analysis for determining value for money) and financial evaluation (determination of the cost of implementing an intervention, estimation of costs for sustaining or expanding an intervention, and assessment of its affordability). We outline a 6 stage-based process for selecting and integrating economic and financial evaluation methods into the monitoring and evaluation of mHealth solutions including (1) defining the program strategy and linkages with key outcomes, (2) assessment of effectiveness, (3) full economic evaluation or partial evaluation, (4) sub-group analyses, (5) estimating resource requirements for expansion, (6) affordability assessment and identification of models for financial sustainability. While application of these stages optimally occurs linearly, finite resources, limited technical expertise, and the timing of evaluation initiation may impede this. We recommend that analysts prioritize economic and financial evaluation methods based on programmatic linkages with health outcomes; alignment with an mHealth solution’s broader stage of maturity and stage of evaluation; overarching monitoring and evaluation activities; stakeholder evidence needs; time point of initiation; and available resources for evaluations.

The addition of electronic treatment adherence support technology can help to improve TB outcomes and lower average cost per patient by reducing treatment failure and the associated higher cost and burden on limited resources. CMA is an appropriate initial analysis for health planners to highlight options that may justify more sophisticated methods such as cost effectiveness analysis or full cost benefit analysis where a preferred option is immediately revealed. CMA is proposed as a tool for use by public health planners in low-resource settings to evaluate the ROI of treatment adherence technology postpilot and prior to implementation.

BackgroundThe historically marginalized Platfontein San youths have a high rate of teenage pregnancies and sexually transmitted infections (STIs). The aim of the study was to assess the knowledge and perception of male and female school-going youths in Platfontein of STIs and HIV/AIDS, and the health care services that are available to them.Participants and methodsA descriptive cross-sectional survey with a sample of 201 learners in grades 6–12 at the !Xunkwesa Combined School in Platfontein was conducted in July 2007. A pretested self-administered questionnaire was used for data collection.ResultsThe study found that STI knowledge was 70.1% and HIV and AIDS was 11.9%. Perceptions of risk among the learners were uniformly low; 24% for contracting a STI and 26% for HIV. About 59% (n=119) of the respondents were either unaware or not sure of the primary health care (PHC) services within the community. Overall, 65% of the students reported using PHC services while 35% exclusively used traditional healers. Slightly less than half (43%) of the learners acquired information about sexual and reproductive health through the Life Skills curriculum at school.ConclusionThe study highlights the importance of increasing HIV awareness and inculcating sexual and reproductive health into the school curriculum. The study further shows the imperative need to recognize the role of traditional medicine in the health care choices of this community. Traditional value systems need to be incorporated into the way that education and health care is proposed to the community leaders, to increase acceptance and utilization of health services.

A conceptual artificial intelligence (AI)-enabled framework is presented in this study involving triangulation of various diagnostic methods for management of coronavirus disease 2019 (COVID-19) and its associated comorbidities in resource-limited settings (RLS). The proposed AI-enabled framework will afford capabilities to harness low-cost polymerase chain reaction (PCR)-based molecular diagnostics, radiological image-based assessments, and end-user provided information for the detection of COVID-19 cases and management of symptomatic patients. It will support self-data capture, clinical risk stratification, explanation-based intelligent recommendations for patient triage, disease diagnosis, patient treatment, contact tracing, and case management. This will enable communication with end-users in local languages through cheap and accessible means, such as WhatsApp/Telegram, social media, and SMS, with careful consideration of the need for personal data protection. The objective of the AI-enabled framework is to leverage multimodal diagnostics of COVID-19 and associated comorbidities in RLS for the diagnosis and management of COVID-19 cases and general support for pandemic recovery. We intend to test the feasibility of implementing the proposed framework through community engagement in sub-Saharan African (SSA) countries where many people are living with pre-existing comorbidities. A multimodal approach to disease diagnostics enabling access to point-of-care testing is required to reduce fragmentation of essential services across the continuum of COVID-19 care.

Background: Diagnostic support for clinicians is a domain of application of mHealth technologies with a slow uptake despite promising opportunities, such as image-based clinical support. The absence of a roadmap for the adoption and implementation of these types of applications is a further obstacle. Objectives: This article provides the groundwork for a roadmap to implement image-based support for clinicians, focusing on how to overcome potential barriers affecting front-line users, the health-care organization and the technical system. Methods: A consensual approach was used during a two-day roundtable meeting gathering a convenience sample of stakeholders (n = 50) from clinical, research, policymaking and business fields and from different countries. A series of sessions was held including small group discussions followed by reports to the plenary. Session moderators synthesized the reports in a number of theme-specific strategies that were presented to the participants again at the end of the meeting for them to determine their individual priority. Results: There were four to seven strategies derived from the thematic sessions. Once reviewed and prioritized by the participants some received greater priorities than others. As an example, of the seven strategies related to the front-line users, three received greater priority: the need for any system to significantly add value to the users; the usability of mHealth apps; and the goodness-of-fit into the work flow. Further, three aspects cut across the themes: ease of integration of the mHealth applications; solid ICT infrastructure and support network; and interoperability. Conclusions: Research and development in image-based diagnostic pave the way to making health care more accessible and more equitable. The successful implementation of those solutions will necessitate a seamless introduction into routines, adequate technical support and significant added value.