Is there any evidence that e-health-using information technology to manage patient care-can have a positive impact in developing countries? Our systematic review of evaluations of e-health implementations in developing countries found that systems that improve communication between institutions, assist in ordering and managing medications, and help monitor and detect patients who might abandon care show promise. Evaluations of personal digital assistants and mobile devices convincingly demonstrate that such devices can be very effective in improving data collection time and quality. Donors and funders should require and sponsor outside evaluations to ensure that future e-health investments are well-targeted.

BACKGROUND Extensively drug-resistant tuberculosis has been reported in 45 countries, including countries with limited resources and a high burden of tuberculosis. We describe the management of extensively drug-resistant tuberculosis and treatment outcomes among patients who were referred for individualized outpatient therapy in Peru. METHODS A total of 810 patients were referred for free individualized therapy, including drug treatment, resective surgery, adverse-event management, and nutritional and psychosocial support. We tested isolates from 651 patients for extensively drug-resistant tuberculosis and developed regimens that included five or more drugs to which the infecting isolate was not resistant. RESULTS Of the 651 patients tested, 48 (7.4%) had extensively drug-resistant tuberculosis; the remaining 603 patients had multidrug-resistant tuberculosis. The patients with extensively drug-resistant tuberculosis had undergone more treatment than the other patients (mean [±SD] number of regimens, 4.2±1.9 vs. 3.2±1.6; P<0.001) and had isolates that were resistant to more drugs (number of drugs, 8.4±1.1 vs. 5.3±1.5; P<0.001). None of the patients with extensively drug-resistant tuberculosis were coinfected with the human immunodeficiency virus (HIV). Patients with extensively drug-resistant tuberculosis received daily, supervised therapy with an average of 5.3±1.3 drugs, including cycloserine, an injectable drug, and a fluoroquinolone. Twenty-nine of these patients (60.4%) completed treatment or were cured, as compared with 400 patients (66.3%) with multidrug-resistant tuberculosis (P=0.36). CONCLUSIONS Extensively drug-resistant tuberculosis can be cured in HIV-negative patients through outpatient treatment, even in those who have received multiple prior courses of therapy for tuberculosis.

This systematic review assesses the published literature to describe the landscape of mobile health technology (mHealth) for HIV/AIDS and the evidence supporting the use of these tools to address the HIV prevention, care, and treatment cascade. The speed of innovation, broad range of initiatives and tools, and heterogeneity in reporting have made it difficult to uncover and synthesize knowledge on how mHealth tools might be effective in addressing the HIV pandemic.To do address this gap, a team of reviewers collected literature on the use of mobile technology for HIV/AIDS among health, engineering, and social science literature databases and analyzed a final set of 62 articles. Articles were systematically coded, assessed for scientific rigor, and sorted for HIV programmatic relevance. The review revealed evidence that mHealth tools support HIV programmatic priorities, including: linkage to care, retention in care, and adherence to antiretroviral treatment. In terms of technical features, mHealth tools facilitate alerts and reminders, data collection, direct voice communication, educational messaging, information on demand, and more. Studies were mostly descriptive with a growing number of quasi-experimental and experimental designs. There was a lack of evidence around the use of mHealth tools to address the needs of key populations, including pregnant mothers, sex workers, users of injection drugs, and men who have sex with men.The science and practice of mHealth for HIV are evolving rapidly, but still in their early stages. Small-scale efforts, pilot projects, and preliminary descriptive studies are advancing and there is a promising trend toward implementing mHealth innovation that is feasible and acceptable within low-resource settings, positive program outcomes, operational improvements, and rigorous study design

ObjectivesTo compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps.DesignVignettes study.Setting200 primary care vignettes.Intervention/comparatorFor eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes’ gold-standard.Primary outcome measures(1) Proportion of conditions ‘covered’ by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of ‘safe’ urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative).ResultsCondition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs—Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs—Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs—Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10-3).ConclusionsThe utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.

No abstract