Determinants of data use for programmatic evidence-based decision making at peripheral public health care centres in Haryana, India

Background

Health policies and programs in India are put into practice at the local level, where the frontline managers -Medical Officers in Charges (MOICs) use data for evidence-based decision-making (EBDM) and implementing these programs. However, there are various organizational, technical, and individual determinants that can impact data use. The study aims to recognize the determinants of data-driven decision-making at the grassroots level.

Methods

The cross-sectional study collected primary empirical data from 120 MOICs from six identified districts in Haryana, India. Data utilization was the variable of interest and was measured through Data Utilization Score (DUS). Determinants affecting DUS were extracted through Principal Component Analysis (PCA). Hierarchical multiple regression analysis was used to identify predictors of data utilization from the extracted factors.

Results

MOICs used routine data to plan, implement, manage, and monitor health programs, and administrative activities. Actual skill for data usage (65 %) was less than the anticipated skill (82 %). Twenty-seven reliable organizational, technical, and individual factors were generated from the 154 variables explaining 57.7 %–68 % of the total variance. Regression analysis showed that management meetings with superiors/subordinates, data-conducive and promotive culture, perceived data quality, incentivization, basic software knowledge/skills, and training needs were among the most significant predictors of data usage.

Conclusion

Although a disparity exists between the expected and actual data utilization skills of MOICs, still data-based decisions can be enhanced by effective management meetings, fostering a robust data culture, prioritizing skill development, and incentivizing data use.