mUzima Mobile Electronic Health Record (EHR) System: Development and Implementation at Scale.

BACKGROUND: The predominant implementation paradigm of electronic health record (EHR) systems in low- and middle-income countries (LMICs) relies on standalone system installations at facilities. This implementation approach exacerbates the digital divide, with facilities in areas with inadequate electrical and network infrastructure often left behind. Mobile health (mHealth) technologies have been implemented to extend the reach of digital health, but these systems largely add to the problem of siloed patient data, with few seamlessly interoperating with the EHR systems that are now scaled nationally in many LMICs. Robust mHealth applications that effectively extend EHR systems are needed to improve access, improve quality of care, and ameliorate the digital divide. OBJECTIVE: We report on the development and scaled implementation of mUzima, an mHealth extension of the most broadly deployed EHR system in LMICs (OpenMRS). METHODS: The "Guidelines for reporting of health interventions using mobile phones: mobile (mHealth) evidence reporting assessment (mERA)" checklist was employed to report on the mUzima application. The World Health Organization (WHO) Principles for Digital Development framework was used as a secondary reference framework. Details of mUzima's architecture, core features, functionalities, and its implementation status are provided to highlight elements that can be adapted in other systems. RESULTS: mUzima is an open-source, highly configurable Android application with robust features including offline management, deduplication, relationship management, security, cohort management, and error resolution, among many others. mUzima allows providers with lower-end Android smartphones (version 4.4 and above) who work remotely to access historical patient data, collect new data, view media, leverage decision support, conduct store-and-forward teleconsultation, and geolocate clients. The application is supported by an active community of developers and users, with feature priorities vetted by the community. mUzima has been implemented nationally in Kenya, is widely used in Rwanda, and is gaining scale in Uganda and Mozambique. It is disease-agnostic, with current use cases in HIV, cancer, chronic disease, and COVID-19 management, among other conditions. mUzima meets all WHO's Principles of Digital Development, and its scaled implementation success has led to its recognition as a digital global public good and its listing in the WHO Digital Health Atlas. CONCLUSIONS: Greater emphasis should be placed on mHealth applications that robustly extend reach of EHR systems within resource-limited settings, as opposed to siloed mHealth applications. This is particularly important given that health information exchange infrastructure is yet to mature in many LMICs. The mUzima application demonstrates how this can be done at scale, as evidenced by its adoption across multiple countries and for numerous care domains.

Creating and Evaluating a Dynamic Study Randomization and Enrollment Tool within a Robust EHRs.

Randomized trials are difficult to perform in resource-limited settings. We developed a Randomization and Enrollment Tool (RET) within a live EHRs which automated enrollment, randomization, and data-collection in support of robust EHRs-based randomized interventions. We describe an observational assessment of RET which we piloted at three Kenyan HIV clinics for a decision support trial. We manually evaluated RET's adequacy and accuracy in its core functions. RET enrolled 327/6626 patients, 100% meeting criteria based on EHRs data. Human reviews reveal that only 250 patients (76.5%) should have been enrolled as the EHRs contained inaccurate data for the other 77 (23.4%). 23 eligible patients were also missed through sole reliance on EHRs data. 18 (5.5%) RET-enrolled patients never received the intervention because of missed appointments. An automated randomization tool has potential to reduce human and financial costs of conducting EHRs-based randomized trials, but remains vulnerable to data quality and workflow limitations.