Accepted for/Published in: Online Journal of Public Health Informatics
Date Submitted: Oct 21, 2025
Open Peer Review Period: Nov 3, 2025 - Dec 29, 2025
Date Accepted: Dec 22, 2025
(closed for review but you can still tweet)
Associations Between Hospital Structural Characteristics and Adoption of Public Health Data Integration and Automation: A National Cross-Sectional Study
ABSTRACT
Background:
Public health data integration and automation systems are crucial for effective healthcare delivery and public health surveillance. However, the factors associated with hospitals' adoption and successful implementation remain inadequately explored.
Objective:
To investigate the relationship associations between public health data integration and automation on hospital characteristics.
Methods:
We analyzed data from the 2023 American Hospital Association Annual Survey and its Health Information Technology supplement, focusing on six public health reporting categories. Multivariable logistic regression models were used to examine the relationship between hospital characteristics and two primary outcomes: active electronic data submission and use of automated transmission processes.
Results:
System-affiliated and not-for-profit hospitals demonstrated significantly higher rates of electronic data submission and automated reporting across most categories (ORs ranging from 1.70-2.27, p<0.001). Rural hospitals showed lower adoption rates in immunization registry (OR=0.77, 95% CI [0.61,0.97]), public health registry (OR=0.67, 95% CI [0.46,0.97]), and clinical data registry reporting (OR=0.77, 95% CI [0.60,0.98]). Larger hospitals were more likely to implement electronic reporting, with medium and large hospitals showing stronger engagement in syndromic surveillance reporting (OR=1.52, 95% CI [1.06,2.19] and OR=2.29, 95% CI [1.17,4.46], respectively). Teaching status was significantly associated only with clinical data registry reporting (OR=2.66, 95% CI [1.56,4.52] for major teaching hospitals).
Conclusions:
Hospital characteristics, particularly system affiliation, ownership type, and geographic location, are strongly associated with public health data integration and automation capabilities. Findings suggest targeted interventions are needed to address disparities in smaller and rural facilities to ensure equitable advancement of public health reporting infrastructure.
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