Patient-Specific Mobile Phone-Generated Reminders and Quality of Hypertension Care in Western Kenya.

To evaluate the impact of clinician-targeted mHealth-generated care suggestions on compliance with hypertension care guidelines in a resource-limited setting. This study was conducted in 10 rural health clinics in Western Kenya that offered hypertension care through nurses and clinical officers. Sites were grouped into intervention and control groups. Intervention group clinicians had patient-specific care suggestions triggered and displayed on a mobile application, mUzima, for their action. Care suggestions were also triggered in the mHealth application for control arm clinicians but were not displayed. Differences in compliance with hypertension care guidelines were evaluated. The study involved 378 patients with hypertension who had care suggestions generated during visits (217 in intervention group and 161 in control group). There was a higher proportion of adherence to hypertension care guidelines in the intervention group compared to the control group (91.1% vs. 85.7%, p=0.014). The random effects model showed significant variability in compliance rates among study clinicians (variance of 0.44, 95% CI: 0.12 -1.62). When displayed care suggestions were rejected by intervention providers, the most common reason given was 'Previously ordered' (58.8%). Clinicians felt that care suggestions improved awareness of hypertension care guidelines. The successful scaled implementation of mUzima with patient specific care suggestions led to higher adherence to hypertension care guidelines and improved quality of hypertension care. Tailormade m-Health applications in resource constrained settings for hypertension care and other chronic non-communicable diseases has the potential to lead to better adherence to care guidelines and quality of care.

Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance.

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck's framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance.

Leveraging mHealth usage logs to inform health worker performance in a Resource-Limited setting: Case example of mUzima use for a chronic disease program in Western Kenya.

BACKGROUND: Health systems in low- and middle-income countries (LMICs) can be strengthened when quality information on health worker performance is readily available. With increasing adoption of mobile health (mHealth) technologies in LMICs, there is an opportunity to improve work-performance and supportive supervision of workers. The objective of this study was to evaluate usefulness of mHealth usage logs (paradata) to inform health worker performance. METHODOLOGY: This study was conducted at a chronic disease program in Kenya. It involved 23 health providers serving 89 facilities and 24 community-based groups. Study participants, who already used an mHealth application (mUzima) during clinical care, were consented and equipped with an enhanced version of the application that captured usage logs. Three months of log data were used to determine work performance metrics, including: (a) number of patients seen; (b) days worked; (c) work hours; and (d) length of patient encounters. PRINCIPAL FINDINGS: Pearson correlation coefficient for days worked per participant as derived from logs as well as from records in the Electronic Medical Record system showed a strong positive correlation between the two data sources (r(11) = .92, p < .0005), indicating mUzima logs could be relied upon for analyses. Over the study period, only 13 (56.3%) participants used mUzima in 2,497 clinical encounters. 563 (22.5%) of encounters were entered outside of regular work hours, with five health providers working on weekends. On average, 14.5 (range 1-53) patients were seen per day by providers. CONCLUSIONS / SIGNIFICANCE: mHealth-derived usage logs can reliably inform work patterns and augment supervision mechanisms made particularly challenging during the COVID-19 pandemic. Derived metrics highlight variabilities in work performance between providers. Log data also highlight areas of suboptimal use, of the application, such as for retrospective data entry for an application meant for use during the patient encounter to best leverage built-in clinical decision support functionality.

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.

Implementation of Fingerprint Technology for Unique Patient Matching and Identification at an HIV Care and Treatment Facility in Western Kenya: Cross-sectional Study.

BACKGROUND: Unique patient identification remains a challenge in many health care settings in low- and middle-income countries (LMICs). Without national-level unique identifiers for whole populations, countries rely on demographic-based approaches that have proven suboptimal. Affordable biometrics-based approaches, implemented with consideration of contextual ethical, legal, and social implications, have the potential to address this challenge and improve patient safety and reporting accuracy. However, limited studies exist to evaluate the actual performance of biometric approaches and perceptions of these systems in LMICs. OBJECTIVE: The aim of this study is to evaluate the performance and acceptability of fingerprint technology for unique patient matching and identification in the LMIC setting of Kenya. METHODS: In this cross-sectional study conducted at an HIV care and treatment facility in Western Kenya, an open source fingerprint application was integrated within an implementation of the Open Medical Record System, an open source electronic medical record system (EMRS) that is nationally endorsed and deployed for HIV care in Kenya and in more than 40 other countries; hence, it has potential to translate the findings across multiple countries. Participants aged >18 years were conveniently sampled and enrolled into the study. Participants' left thumbprints were captured and later used to retrieve and match records. The technology's performance was evaluated using standard measures: sensitivity, false acceptance rate, false rejection rate, and failure to enroll rate. The Wald test was used to compare the accuracy of the technology with the probabilistic patient-matching technique of the EMRS. Time to retrieval and matching of records were compared using the independent samples 2-tailed t test. A survey was administered to evaluate patient acceptance and satisfaction with use of the technology. RESULTS: In all, 300 participants were enrolled; their mean age was 36.3 (SD 12.2) years, and 58% (174/300) were women. The relevant values for the technology's performance were sensitivity 89.3%, false acceptance rate 0%, false rejection rate 11%, and failure to enroll rate 2.3%. The technology's mean record retrieval speed was 3.2 (SD 1.1) seconds versus 9.5 (SD 1.9) seconds with demographic-based record retrieval in the EMRS (P<.001). The survey results revealed that 96.3% (289/300) of the participants were comfortable with the technology and 90.3% (271/300) were willing to use it. Participants who had previously used fingerprint biometric systems for identification were estimated to have more than thrice increased odds of accepting the technology (odds ratio 3.57, 95% CI 1.0-11.92). CONCLUSIONS: Fingerprint technology performed very well in identifying adult patients in an LMIC setting. Patients reported a high level of satisfaction and acceptance. Serious considerations need to be given to the use of fingerprint technology for patient identification in LMICs, but this has to be done with strong consideration of ethical, legal, and social implications as well as security issues.