BackgroundMobile health (mHealth) approaches for non-communicable disease (NCD) care seem particularly applicable to sub-Saharan Africa given the penetration of mobile phones in the region. The evidence to support its implementation has not been critically reviewed.MethodsWe systematically searched PubMed, Embase, Web of Science, Cochrane Central Register of Clinical Trials, a number of other databases, and grey literature for studies reported between 1992 and 2012 published in English or with an English abstract available. We extracted data using a standard form in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.ResultsOur search yielded 475 citations of which eleven were reviewed in full after applying exclusion criteria. Five of those studies met the inclusion criteria of using a mobile phone for non-communicable disease care in sub-Saharan Africa. Most studies lacked comparator arms, clinical endpoints, or were of short duration. mHealth for NCDs in sub-Saharan Africa appears feasible for follow-up and retention of patients, can support peer support networks, and uses a variety of mHealth modalities. Whether mHealth is associated with any adverse effect has not been systematically studied. Only a small number of mHealth strategies for NCDs have been studied in sub-Saharan Africa.ConclusionsThere is insufficient evidence to support the effectiveness of mHealth for NCD care in sub-Saharan Africa. We present a framework for cataloging evidence on mHealth strategies that incorporates health system challenges and stages of NCD care. This framework can guide approaches to fill evidence gaps in this area. Systematic review registration: PROSPERO CRD42014007527.

A viable, cost-effective solution at scale has been developed and implemented for collecting electronic data during household visits in a resource-constrained setting.

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BACKGROUND: Poor communication of tests whose results are pending at hospital discharge can lead to medical errors.OBJECTIVE: To determine the adequacy with which hospital discharge summaries document tests with pending results and the appropriate follow-up providers. DESIGN:Retrospective study of a randomly selected sample PATIENTS: Six hundred ninety-six patients discharged from two large academic medical centers, who had test results identified as pending at discharge through queries of electronic medical records. INTERVENTION AND MEASUREMENTS:Each patient's discharge summary was reviewed to identify whether information about pending tests and follow-up providers was mentioned. Factors associated with documentation were explored using clustered multivariable regression models.MAIN RESULTS: Discharge summaries were available for 99.2% of 668 patients whose data were analyzed. These summaries mentioned only 16% of tests with pending results (482 of 2,927). Even though all study patients had tests with pending results, only 25% of discharge summaries mentioned any pending tests, with 13% documenting all pending tests. The documentation rate for pending tests was not associated with level of experience of the provider preparing the summary, patient's age or race, length of hospitalization, or duration it took for results to return. Follow-up providers' information was documented in 67% of summaries.CONCLUSION: Discharge summaries are grossly inadequate at documenting both tests with pending results and the appropriate follow-up providers.KEY WORDS: tests with pending results; continuity of care; patient safety; discharge summary; medical errors.

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Background Mobile health (mHealth) applications have recently proliferated, especially in low- and middle-income countries, complementing task-redistribution strategies with clinical decision support. Relatively few studies address usability and feasibility issues that may impact success or failure of implementation, and few have been conducted for non-communicable diseases such as hypertension. Objective To conduct iterative usability and feasibility testing of a tablet-based Decision Support and Integrated Record-keeping (DESIRE) tool, a technology intended to assist rural clinicians taking care of hypertension patients at the community level in a resource-limited setting in western Kenya. Methods Usability testing consisted of “think aloud” exercises and “mock patient encounters” with five nurses, as well as one focus group discussion. Feasibility testing consisted of semi-structured interviews of five nurses and two members of the implementation team, and one focus group discussion with nurses. Content analysis was performed using both deductive codes and significant inductive codes. Critical incidents were identified and ranked according to severity. A cause-of-error analysis was used to develop corresponding design change suggestions. Results Fifty-seven critical incidents were identified in usability testing, 21 of which were unique. The cause-of-error analysis yielded 23 design change suggestions. Feasibility themes included barriers to implementation along both human and technical axes, facilitators to implementation, provider issues, patient issues and feature requests. Conclusions This participatory, iterative human-centered design process revealed previously unaddressed usability and feasibility issues affecting the implementation of the DESIRE tool in western Kenya. In addition to well-known technical issues, we highlight the importance of human factors that can impact implementation of mHealth interventions.

Current models for implementing electronic health records (EHRs) in resource-limited settings may not be scalable because they fail to address human-resource and cost constraints. This paper describes an implementation model which relies on shared responsibility between local sites and an external three-pronged support infrastructure consisting of: (1) a national technical expertise center, (2) an implementer's community, and (3) a developer's community. This model was used to implement an open-source EHR in three Ugandan HIV-clinics. Pre-post time-motion study at one site revealed that Primary Care Providers spent a third less time in direct and indirect care of patients (p<0.001) and 40% more time on personal activities (p=0.09) after EHRs implementation. Time spent by previously enrolled patients with non-clinician staff fell by half (p=0.004) and with pharmacy by 63% (p<0.001). Surveyed providers were highly satisfied with the EHRs and its support infrastructure. This model offers a viable approach for broadly implementing EHRs in resource-limited settings.

Evaluation of impact of potential uncontrolled confounding is an important component for causal inference based on observational studies. In this article, we introduce a general framework of sensitivity analysis that is based on inverse probability weighting. We propose a general methodology that allows both non-parametric and parametric analyses, which are driven by two parameters that govern the magnitude of the variation of the multiplicative errors of the propensity score and their correlations with the potential outcomes. We also introduce a specific parametric model that offers a mechanistic view on how the uncontrolled confounding may bias the inference through these parameters. Our method can be readily applied to both binary and continuous outcomes and depends on the covariates only through the propensity score that can be estimated by any parametric or non-parametric method. We illustrate our method with two medical data sets.