Browsing by Author "Ahimbisibwe, Julius"

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    Effect of capacity building, staffing levels and technology on quality of health management information system data on maternal deliveries at Arua referral hospital, Uganda
    (Kyambogo University (Unpublished work), 2024-09) Ahimbisibwe, Julius
    This study aimed to investigate the impact of capacity building, staffing levels, and technology on the Quality of Health Management Information System (HMIS) Data concerning maternal deliveries at Arua Referral Hospital (ARRH) in Uganda. The research focused on three specific objectives: evaluating how capacity building influences the completeness of HMIS data for deliveries, assessing the effect of staffing levels on the timeliness of HMIS data related to deliveries, and examining the impact of technology on the accuracy of HMIS data concerning deliveries. Using a descriptive cross-sectional design incorporating quantitative and qualitative methodologies, the study involved a population of 120 hospital staff members (6 administrators, 6 records officers, 58 midwives, and 50 nurses). Respondents were selected through a combination of random and purposive sampling methods. Quantitative data was analyzed using SPSS and R software, while qualitative data utilized Nvivo V14 for thematic analysis. Findings revealed a predominantly female workforce in departments critical to maternal health, aligning with national nursing demographics. Notably, capacity building initiatives showed a strong positive correlation (r=0.6) with the completeness of HMIS data for maternal deliveries. Staffing levels also correlated positively (r=0.5) with timeliness, particularly influenced by the presence of Records Officers. Technology showed a weaker positive correlation (r=0.4) with data accuracy, access to DHIS2 and computers contributing mildly, while internet connectivity showed limited impact. Recommendations from the study emphasize clear responsibilities in data management to address issues like incorrect register filling. It advocates for inclusive training sessions involving junior staff and promotes enhanced data sharing and dissemination practices to bolster data quality assurance processes. In conclusion, this study highlights the pivotal roles of capacity building, staffing adequacy, and appropriate technology utilization in enhancing quality of HMIS data for maternal deliveries.
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    Effects of capacity building interventions on Health Management Information System data completeness for maternal delivery services at Arua regional referral hospital Uganda
    (Discover Health Systems, 2026-08-12) Ahimbisibwe, Julius; Nakayinga, Ritah; Ekakoro, Newton
    Background Reliable HMIS data are vital for maternal health planning, yet completeness remains low. This study examines how capacity-building interventions improve HMIS data completeness for maternal deliveries at Arua Regional Referral Hospital. Methods A mixed-methods study was conducted in 2023 at Arua Regional Referral Hospital. A total of 92 participants were selected through both purposive and simple random sampling. Quantitative data were gathered using a structured mobile questionnaire administered via Kobo Collect, while qualitative data were obtained from key informant interviews and a review of HMIS registers, monthly reports, and DHIS2 records. Quantitative analysis was performed in SPSS v26 and R v4.5.1 using descriptive statistics and Spearman’s correlation, with significance set at P < 0.05. Qualitative data were analysed through thematic content analysis using NVivo software. Results The majority of the enrolled participants were female and predominantly midwives. Supportive supervision and data quality assessments were commonly conducted quarterly, while HMIS training occurred less frequently. Half of the reviewed monthly delivery reports indicated consistency between on-site counts and HMIS submissions. Supportive supervision showed a moderate, significant positive correlation with data completeness (ρ = 0.598, p = 0.040), and data quality assessment also showed a positive significant correlation (ρ = 0.714, p = 0.009) with data completeness. HMIS training (ρ = 0.488, p = 0.108) and capacity building (ρ = 0.600, p = 0.453) revealed a positive correlation with completeness. The correlation between capacity building and data completeness was not statistically significant. Conclusion Supportive supervision and routine data quality assessments are key contributors to improved HMIS data completeness. Strengthening the consistency, coverage, and feedback mechanisms of capacity-building interventions may enhance routine data quality and support evidence-based decision-making in maternal health services.