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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 adop-
tion 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 pro-
viders 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 correla-
tion 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 regularwork 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. |
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