ArticleBMC health services research2025
Predictive power of socio-demographic attributes of healthcare workers on data quality in Tanzanian regional referral hospitals.
Article in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundHealthcare data quality is essential towards effective decision-making. Data of high quality are accurate, complete, consistent, and timely. Any mistake in patient information is likely to result in serious life-threatening issues. Monitoring and evaluation systems have been introduced to address these data quality issues. These systems are affected by human inputs which can subsequently introduce errors that lower the quality of data. This study examined the influence of socio-demographic attributes of healthcare workers on data quality in selected regional referral hospitals in Tanzania. METHODOLOGY: A cross-sectional study design was conducted from August to October 2024 across eight regional referral hospitals in Tanzania. Using simple random sampling, 336 healthcare workers involved in regular patient data entry were recruited. The sample size was determined using Cochran's formula. Data were collected through structured questionnaires, cleaned, and analyzed using IBM SPSS v27, applying both descriptive statistics and multiple linear regression to examine the relationship between socio-demographic factors and healthcare data quality.
resultsThe study identified several significant predictors of data quality. Age had a positive relationship with accuracy (β = 0.241, p <.001), completeness (β = 0.159, p <.001) and timeliness (β = 0.202, p <.001), while sex was positively associated with all outcomes, particularly completeness (β = 0.428, p <.001), and consistency (β = 0.561, p <.001). Education positively impacted accuracy (β = 0.161, p <.01), and timeliness (β = 0.240, p <.01), but negatively affected completeness (β=-0.166, p <.01), and consistency (β=-0.144, p <.01). Marital status significantly predicted completeness (β = 0.139, p =.044), while experience showed a negative relationship with timeliness (β=-0.369, p <.001). All models were statistically significant (all p <.001).
conclusionThis study highlights the significant influence of socio-demographic factors, particularly age and sex, on the accuracy and completeness of healthcare data. These findings suggest that the characteristics of patient data collectors can either enhance or hinder data quality. Therefore, it is crucial to implement targeted training programs for healthcare workers, emphasizing their role in maintaining high-quality healthcare data.
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