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.

Usability and feasibility of a tablet-based Decision-Support and Integrated Record-keeping (DESIRE) tool in the nurse management of hypertension in rural western Kenya.

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.

Usability of implementing a tablet-based decision support and integrated record- keeping (DESIRE) tool in the nurse management of hypertension in rural Kenya.

In sub-Saharan Africa (SSA), cardiovascular disease (CVD) is the leading cause of death among individuals over the age of 30. Hypertension, a major risk factor for CVD, contributes significantly to the CVD burden in SSA. In order to address the human resource challenge of managing hypertension in low- and middle-income countries (LMICs), task-shifting hypertension care from physicians to nurses has been proposed. To support this task-shifting strategy, the Academic Partnership Providing Access to Healthcare (AMPATH) has developed an Android tablet-based electronic Decision Support and Integrated Record-Keeping (DESIRE) tool to record patient data and assist with clinical decision-making. We investigated the usability of the DESIRE tool in the setting of nurse management of hypertension in rural western Kenya through the use of "mock patient" encounters and "think aloud" exercises. Fiftyseven critical incidents were identified and twenty-three design changes were suggested. Optimization of the tool has the potential to broadly impact treatment of non-communicable diseases in LMICs by providing a model of electronic decision-support in task shifting.