Health Data Management Practice and Associated Factors Among Health Professionals Working at Public Health Facilities in Resource Limited Settings

Ngusie, Habtamu Setegn; Shiferaw, Atsede Mazengia; Bogale, Adina Demissie; Ahmed, Mohammedjud Hassen

doi:10.2147/amep.s320769

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…Compared with other indicators of data management, level of 'data analysis practice score' is low (72%) which can be the reason for low 'use of data to produce narrative analytical reports' (31%), found in this study. Previous studies indicated that health data management practices of health professionals were found to be inadequate [29]. The finding indicates the need of improving data analysis skills of health care workers in the health facilities.…”

Section: Discussionmentioning

confidence: 97%

Contribution of health information system to child immunization services in Ethiopia: baseline study of 33 woredas

Worku

Alemu

Belay

et al. 2022

BMC Med Inform Decis Mak

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Background Monitoring progress using appropriate data, with a functional health information system (HIS), believed to be very crucial for success of immunization program. Baseline study was conducted to assess, immunization service coverage, HIS performance status, and their relationships. Methods A linked facility and population-based survey was conducted concurrently from September 21 to October 15, 2020. A total of 3016 households were reached to interview mothers having children aged 12–23 months in the 33 woredas. Overall 81 health posts, 71 health centers, and 15 hospitals were selected for the facility survey. The study used modified Performance of Routine Information System Management (PRISM) tools for the facility survey and a structured questionnaire for the household survey. Using STATA 14.0 software, mixed effect modeling was employed to control the effect of clustering and potential confounders. Results The proportion of fully immunized children was 58%. Coverages of measles (at least one dose) and penta3 immunization (received all 3 doses of DPT-HepB-Hib vaccine) were 86%, and 85% respectively. About 27% of mothers had missed their child immunization card mainly due to misplacing or lost. Except ‘source document completeness’ (85%) and ‘use of data for planning and target setting’ (84%), other data quality and use indicators like ‘data accuracy’ (63%), ‘data use for performance review and evidence-based decision making’ (50%), and ‘data use to produce analytical reports’ (31%) show low performance. The odds of fully immunized children is 37% lower in Muslims compared with Orthodox Christians (AOR, 0.63; 95%CI: 0.46, 0.88), higher by 42% with father’s secondary education compared with no education (AOR, 1.42; 95%CI: 1.05, 1.92), and highest wealth quintile compared with lowest quintile (AOR, 2.49; 95%CI: 1.54, 4.03). For each additional score of HIS infrastructure availability, the odds of fully immunized children increased by 22% (AOR:1.22; 95% CI: 1.03, 1.44). Conclusions Child immunization coverages are promising However, the current HIS performance is suboptimal. Both service user and HIS related factors are important for immunization service uptake. Documenting required information and advising mothers to keep immunization cards by health workers, and working to have functional HIS are recommended.

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Section: Discussionmentioning

confidence: 97%

Contribution of health information system to child immunization services in Ethiopia: baseline study of 33 woredas

Worku

Alemu

Belay

et al. 2022

BMC Med Inform Decis Mak

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“…These differences may reflect the varying levels of maturity of each health system, and differences in capacity and incentives to capture RWD, as well as lack of access to different data sources. 20 EMRs, if used properly, have the potential to collect most of the RWD we list in this paper, thereby reducing inefficiency in collecting data and eliminating the need to set up new disease specific registries, use claims databases to gather non-cost related data, or conduct ad-hoc surveys. 17 , 21 …”

Section: Discussion and Recommendationsmentioning

confidence: 99%

What, Where, and How to Collect Real-World Data and Generate Real-World Evidence to Support Drug Reimbursement Decision-Making in Asia: A reflection into the Past and A Way Forward

Lin

Bayani

et al. 2023

Int J Health Policy Manag

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Background: Globally, there is increasing interest in the use of real-world data (RWD) and real-world evidence (RWE) to inform health technology assessment (HTA) and reimbursement decision-making. Using current practices and case studies shared by eleven health systems in Asia, a non-binding guidance that seeks to align practices for generating and using RWD/RWE for decision-making in Asia was developed by the REAL World Evidence In ASia for HEalth Technology Assessment in Reimbursement (REALISE) Working Group, addressing a current gap and needs among HTA users and generators. Methods: The guidance document was developed over two face-to-face workshops, in addition to an online survey, a face-to-face interview and pragmatic search of literature. The specific focus was on what, where and how to collect RWD/ RWE. Results: All 11 REALISE member jurisdictions participated in the online survey and the first in-person workshop, 10 participated in the second in-person workshop, and 8 participated in the in-depth face-to-face interviews. The guidance document was iteratively reviewed by all working group members and the International Advisory Panel. There was substantial variation in: (a) sources and types of RWD being used in HTA; and (b) the relative importance and prioritization of RWE being used for policymaking. A list of national-level databases and other sources of RWD available in each country was compiled. A list of useful guidance on data collection, quality assurance and study design were also compiled. Conclusion: The REALISE guidance document serves to align the collection of better quality RWD and generation of reliable RWE to ultimately inform HTA in Asia.

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“…At the micro-level, factors around understanding, capability, commitment, and trust have been prominent. Many of the previous studies conducted in the country exclusively quantified quality of the routine health information system [ 9 , 10 , 12 , 14 , 15 , 17–20 , 24 , 25 ]. This particular study presented the issue of data integration as a key factor of data quality.…”

Section: Discussionmentioning

confidence: 99%

“We don’t trust all data coming from all facilities”: factors influencing the quality of care network data quality in Ethiopia

Tufa,

Gonfa,

Tesfa

et al. 2023

Global Health Action

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Background Good quality data are a key to quality health care. In 2017, WHO has launched the Quality of Care Network (QCN) to reduce maternal, newborn and stillbirth mortality via learning and sharing networks. Guided by the principle of equity and dignity, the network members agreed to implement the programme in 2017–2021. Objective This paper seeks to explore how QCN has contributed to improving data quality and to identify factors influencing quality of data in Ethiopia. Methods We conducted a qualitative study in selected QCN facilities in Ethiopia using key informant interview and observation methods. We interviewed 40 people at national, sub-national and facility levels. Non-participant observations were carried out in four purposively selected health facilities; we accessed monthly reports from 41 QCN learning facilities. A codebook was prepared following a deductive and inductive analytical approach, coded using Nvivo 12 and thematically analysed. Results There was a general perception that QCN had improved health data documentation and use in the learning facilities, achieved through coaching, learning and building from pre-existing initiatives. QCN also enhanced the data elements available by introducing a broader set of quality indicators. However, the perception of poor data quality persisted. Factors negatively affecting data quality included a lack of integration of QCN data within routine health system activities, the perception that QCN was a pilot, plus a lack of inclusive engagement at different levels. Both individual and system capabilities needed to be strengthened. Conclusion There is evidence of QCN’s contribution to improving data awareness. But a lack of inclusive engagement of actors, alignment and limited skill for data collection and analysis continued to affect data quality and use. In the absence of new resources, integration of new data activities within existing routine health information systems emerged as the most important potential action for positive change.

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Health Data Management Practice and Associated Factors Among Health Professionals Working at Public Health Facilities in Resource Limited Settings

Cited by 6 publications

References 12 publications

Contribution of health information system to child immunization services in Ethiopia: baseline study of 33 woredas

Contribution of health information system to child immunization services in Ethiopia: baseline study of 33 woredas

What, Where, and How to Collect Real-World Data and Generate Real-World Evidence to Support Drug Reimbursement Decision-Making in Asia: A reflection into the Past and A Way Forward

“We don’t trust all data coming from all facilities”: factors influencing the quality of care network data quality in Ethiopia

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