Examining the Association Between the Market Factors and Hospital Characteristics and AI Use in Hospitals, Using the Resource Dependency Theory
Abstract
Purpose: This study aims to investigate hospitals' use of artificial intelligence (AI) for scheduling and process optimization. Using the resource dependency theory as a conceptual framework, the study examines market and organizational characteristics associated with hospitals' use of AI.
Methods: The study uses a quantitative cross-sectional design, using data from a sample of hospitals in the United States responding to the 2021 American Hospital Association (AHA) Hospital Survey. Two dichotomous dependent variables were assessed: (1) AI use for scheduling and (2) AI use for process optimization. The independent variables to capture hospital characteristics included factors measuring environmental munificence (total hospital beds and total FTEs), uncertainty (rural-urban status and payer mix), and interdependence (nonprofit vs. for-profit and participation in ACOs). The models also controlled for market factors, including elderly and uninsured population rates. We performed two logistic regression models using STATA 17.0, with statistical significance assessed at the p
Results: Payer-mix (OR: 0.987, 95% CI, 0.976, 0.998), current ACO membership (OR: 2.197, 95% CI: 1.552, 3.110), and population size were significant correlates of hospitals’ AI use for scheduling. Previous (OR: 2.449, 95% CI: 1.104, 5.430), and current ACO membership (OR: 2.051 95% CI: 1.466, 2.868), and payer-mix (OR: 1.010 95% CI: 1.000, 1.019) were also statistically significant correlates of hospitals’ AI use for process optimization.
Conclusion: Our findings suggest that hospital characteristics and market factors play an important role in influencing the utilization of AI for process optimization and scheduling. The study’s findings have practice and policy implications for hospital administration.
Keywords
Artificial intelligence (AI), hospitals, process optimization, patient scheduling, and resource dependence theory.
Examining the Association Between the Market Factors and Hospital Characteristics and AI Use in Hospitals, Using the Resource Dependency Theory
Purpose: This study aims to investigate hospitals' use of artificial intelligence (AI) for scheduling and process optimization. Using the resource dependency theory as a conceptual framework, the study examines market and organizational characteristics associated with hospitals' use of AI.
Methods: The study uses a quantitative cross-sectional design, using data from a sample of hospitals in the United States responding to the 2021 American Hospital Association (AHA) Hospital Survey. Two dichotomous dependent variables were assessed: (1) AI use for scheduling and (2) AI use for process optimization. The independent variables to capture hospital characteristics included factors measuring environmental munificence (total hospital beds and total FTEs), uncertainty (rural-urban status and payer mix), and interdependence (nonprofit vs. for-profit and participation in ACOs). The models also controlled for market factors, including elderly and uninsured population rates. We performed two logistic regression models using STATA 17.0, with statistical significance assessed at the p
Results: Payer-mix (OR: 0.987, 95% CI, 0.976, 0.998), current ACO membership (OR: 2.197, 95% CI: 1.552, 3.110), and population size were significant correlates of hospitals’ AI use for scheduling. Previous (OR: 2.449, 95% CI: 1.104, 5.430), and current ACO membership (OR: 2.051 95% CI: 1.466, 2.868), and payer-mix (OR: 1.010 95% CI: 1.000, 1.019) were also statistically significant correlates of hospitals’ AI use for process optimization.
Conclusion: Our findings suggest that hospital characteristics and market factors play an important role in influencing the utilization of AI for process optimization and scheduling. The study’s findings have practice and policy implications for hospital administration.
