Covariates of Digital Technology in Providing Effective Health Care Services: A Primary Study in India
Abstract:
This
qualitative study seeks to understand the factors influencing the adoption and
effectiveness of digital health services among Indian citizens. By collecting
primary data through surveys distributed to public health staff across policy,
district, and peripheral levels, the study explores how digital technology can
be optimized to improve health outcomes in India. Descriptive and inferential
statistics, including binary logistic regression and ANOVA, were employed to analyse
the data, focusing on the digital health score and its associations with
various independent variables. Data analysis was conducted using STATA and MS
Excel. The results show varying levels of digital technology adoption for
health services across different population groups. Mobile phone use for
notifying disease outbreaks and tracking beneficiaries had the highest adoption
rates. Bivariate analysis revealed significant regional differences in digital
health scores, with central regions performing better than others. Regression
analysis indicated that district and peripheral levels had higher odds of good
digital health scores compared to the policy/program level. ANOVA confirmed
statistically significant differences between group means, with the Central
region notably differing from the national average. The adoption of digital health technologies is influenced by
regional variations, timing, and implementation levels. Mobile technologies are
prevalent, particularly for outbreak notifications and beneficiary tracking,
indicating their crucial role in healthcare. Regional differences and data
collection timing significantly impact digital health scores, with district and
peripheral levels performing better than policy levels. These findings
highlight the need for targeted, region-specific strategies to ensure equitable
and effective digital health technology adoption.
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