Behavioral and Technical Factors Associated with Perceived Quality of HIV/AIDS Data Reported in Community Based Health Information System in Homa-Bay County, Kenya

Ogondi, Evans; Otieno, George; Mwanzo, Isaac; Koome, Gilbert

doi:10.29322/ijsrp.8.11.2018.p8373

Cited by 1 publication

(1 citation statement)

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“…Concerning data-driven decision-making, These findings resonate with those of a study done in Kenya where they identified data quality across four dimensions: completeness, accuracy, and consistency, alongside timeliness. 12 Completeness pertains to not solely the proper completion of all data facets in report forms of the facilities, but also to the percentage of facilities submitting reports within a given area of administration like a district. Timeliness evaluates whether reports are submitted according to established deadlines.…”

Section: Discussionmentioning

confidence: 99%

Determinants of data-driven decision-making among health providers: a case of Mombasa county, Kenya

Wangige Muhula,

Nyamai,

Odongo

et al. 2024

Int J Community Med Public Health

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Background: Healthcare professionals understand how important it is to turn health data into information for informed decision-making. However, a lack of trustworthy and up-to-date health information is caused by inadequate investment in infrastructure for data collection, analysis, dissemination, and use. The aim of the study was to determine data-driven decision-making among health providers, a case of Mombasa County, Kenya. Methods: The study employed an analytical cross-sectional study design where a stratified random sampling approach was utilized to recruit respondents into the study. The Yamane formula of sample size calculation was used to recruit 168 study partakers for this study. Results: The outcomes indicated that quality data-driven decision-making exhibited a substantial correlation with technical factors (r=0.642, p value=0.000). Furthermore, the findings highlighted a significant correlation between quality data-driven decision-making and behavioral factors (r=0.821, p value=0.000). Additionally, the study's results revealed a marked correlation between quality data-propelled decision-making alongside organizational factors (r=0.819, p value=0.000). Conclusions: The likelihood ratio tests demonstrated that both technical and organizational factors significantly predicted data-driven decision-making among health providers, whereas behavioral factors did not have a statistically significant impact. There is a need to provide training for health workers at the county level to enhance data utilization skills, ensure thorough data verification before submission, and promote the use of health information in decision-making.

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Section: Discussionmentioning

confidence: 99%