Impact of Hospital Characteristics and Governance Structure on the Adoption of Tracking Technologies for Clinical and Supply Chain Use: Longitudinal Study of US Hospitals

• Published in: Journal of medical Internet research 2022-05, Vol.24 (5), p.e33742-e33742
• Main Authors: Zhu, Xiao, Tao, Youyou, Zhu, Ruilin, Wu, Dezhi, Ming, Wai-Kit
• Format: Article
• Language: English
• Subjects: Automation; Censuses; Centralization; Clinical governance; Comparative analysis; Decentralization; Delivery of Health Care; Efficiency; Forecasts and trends; Health care industry; Health care policy; Health disparities; Hospital systems; Hospitals; Human error; Humans; Infrastructure; Innovations; Intelligence; Logistics; Longitudinal Studies; Medical personnel; Medical records; Older people; Original Paper; Patients; Policy making; Radio frequency identification; Rural areas; Rural communities; Supply; Supply chain management; Technological change; Technology; Technology adoption; Tracking; United States; Urban areas
• ISSN: 1438-8871; 1439-4456; 1438-8871
• DOI: 10.2196/33742

Despite the increasing adoption rate of tracking technologies in hospitals in the United States, few empirical studies have examined the factors involved in such adoption within different use contexts (eg, clinical and supply chain use contexts). To date, no study has systematically examined how governance structures impact technology adoption in different use contexts in hospitals. Given that the hospital governance structure fundamentally governs health care workflows and operations, understanding its critical role provides a solid foundation from which to explore factors involved in the adoption of tracking technologies in hospitals. This study aims to compare critical factors associated with the adoption of tracking technologies for clinical and supply chain uses and examine how governance structure types affect the adoption of tracking technologies in hospitals. This study was conducted based on a comprehensive and longitudinal national census data set comprising 3623 unique hospitals across 50 states in the United States from 2012 to 2015. Using mixed effects population logistic regression models to account for the effects within and between hospitals, we captured and examined the effects of hospital characteristics, locations, and governance structure on adjustments to the innate development of tracking technology over time. From 2012 to 2015, we discovered that the proportion of hospitals in which tracking technologies were fully implemented for clinical use increased from 36.34% (782/2152) to 54.63% (1316/2409), and that for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We also discovered that adoption factors impact the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (odds ratio [OR] 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies. In the context of supply chain use, the type of governance structure influences tracking technology adoption. Compared with hospitals not affiliated with a health system, implementation rates increased as hospitals affiliated with a more centralized health system-1.9-fold increase (OR 1.87, 95% CI 1.60-2.13) for decentralized or independent hospitals, 2.4-fold increase (OR 2.40, 95% CI 2.07-2.80) for moderately centralized health systems, and 3.1-fold increase for centralized health systems (OR 3.07, 95% CI 2.67-3.53). As the first of such type of studies, we pr