scholarly journals The Role of Organizational Factors in Acceptance of Health Management Information Systems

2019 ◽  
Vol 3 (5) ◽  
pp. 242-246
Author(s):  
Joel Rey U. Acob ◽  
Heru Santoso Wahito Nugroho
Keyword(s):  
Information Systems ◽  
Health Management ◽  
Management Information Systems ◽  
Organizational Factors ◽  
Management Information ◽  
Health Management Information Systems

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scholarly journals Estimating population-based coverage of reproductive, maternal, newborn, and child health (RMNCH) interventions from health management information systems: a comprehensive review

2021 ◽  
Vol 21 (S2) ◽  
Author(s):  
George Mwinnyaa ◽  
Elizabeth Hazel ◽  
Abdoulaye Maïga ◽  
Agbessi Amouzou
Keyword(s):  
Information Systems ◽  
Child Health ◽  
Data Quality ◽  
Health Management ◽  
Management Information Systems ◽  
Management Information ◽  
Intervention Coverage ◽  
Comprehensive Review ◽  
Health Management Information Systems ◽  
And Child Health

Abstract Background Routinely collected health facility data usually captured and stored in Health Management Information Systems (HMIS) are potential sources of data for frequent and local disaggregated estimation of the coverage of reproductive, maternal, newborn, and child health interventions (RMNCH), but have been under-utilized due to concerns over data quality. We reviewed methods for estimation of national or subnational coverage of RMNCH interventions using HMIS data exclusively or in conjunction with survey data from low- and middle-income countries (LMICs). Methods We conducted a comprehensive review of studies indexed in PubMed and Scopus to identify potential papers based on predefined search terms. Two reviewers screened the papers using defined inclusion and exclusion criteria. Following sequences of title, abstract and full paper reviews, we retained 18 relevant papers. Results 12 papers used only HMIS data and 6 used both HMIS and survey data. There is enormous lack of standards in the existing methods for estimating RMNCH intervention coverage; all appearing to be highly author dependent. The denominators for coverage measures were estimated using census, non-census and combined projection-based methods. No satisfactory methods were found for treatment-based coverage indicators for which the estimation of target population requires the population prevalence of underlying conditions. The estimates of numerators for the coverage measures were obtained from the count of users or visits and in some cases correction for completeness of reporting in the HMIS following an assessment of data quality. Conclusions Standard methods for correcting numerators from HMIS data for accurate estimation of coverage of RMNCH interventions are needed to expand the use of these data. More research and investments are required to improve denominators for health facility-derived statistics. Improvement in routine data quality and analytical methods would allow for timely estimation of RMNCH intervention coverage at the national and subnational levels.

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