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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 provided a longitudinal overview of how hospital characteristics and governance structure jointly affect adoption rates of tracking technology in both clinical and supply chain use contexts, which is essential for developing intelligent infrastructure for smart hospital systems. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes, locations, and governance structures.
The extensive adoption of innovative tracking technologies has left almost no industry behind. Owing to strict health care laws, regulations, and policies, the health care industry has made great strides in the area, with a growing number of hospitals in the United States and worldwide beginning to reap the benefits of tracking technologies involved in, for example, optimizing health care processes, minimizing waste and human errors, and enhancing operational efficiency [
Of several applied instances in the field of tracking technology, barcodes and radio-frequency identification (RFID) are the most widely adopted tracking technologies [
Because of the appealing potential of tracking technologies to automate data, improve security, reduce counterfeiting and theft, and expedite and optimize clinical processes and supply chain management in the health care industry, their adoption in clinical and supply chain uses has been significantly outpaced by other widely adopted health technologies such as electronic health record systems [
Within the umbrella of digital innovation, tracking technologies share some similarities, such as adopting digital features with other health information technologies (HITs). Nevertheless, they display a range of unique and distinctive characteristics that require thorough legal, clinical, and practical examination before adoption. First, unlike other HITs, concerns over privacy and security related to the use of tracking technologies are more prevalent and substantial [
One innovation in hospital management over the past few decades is strategic reconfiguration, which consolidates individual, unaffiliated hospitals into multihospital systems [
Of the very limited number of quantitative studies previously undertaken to explore factors involved in the adoption of tracking technology in hospitals in the United States, Dey et al [
This study, therefore, took the lead as the first longitudinal research study to empirically examine the different factors associated with the adoption of tracking technologies in different use contexts with more recent US hospital data sets. This was the first study to examine the impact of governance structure types on technology adoption in different use contexts in hospitals. Because of the complex nature of health care settings, we differentiate among the factors that influence the adoption of tracking technologies in the clinical and supply chain use contexts. Extant literature suggests that larger, urban, nonprofit, and teaching hospitals tend to possess more advanced resources, admit more complex patients with severe illnesses or multiple chronic conditions, and need to manage more complicated clinical workflows. When appropriate, these hospitals might implement a higher level of tracking technology to facilitate their clinical processes [
The data sets used in this study are obtained from 3 sources: the American Hospital Association’s (AHA) annual surveys, the AHA’s information technology (IT) supplemental files, and the US Bureau of Economic Analysis website. First, we collected data from the AHA’s annual surveys to identify hospital characteristics and obtain health system data. Second, we used the AHA’s IT supplemental files to capture the tracking technology implementation data. Third, we used data from the US Bureau of Economic Analysis website to obtain gross domestic product (GDP) per capita information [
The 2 dependent variables used in this study are tracking technology adoption for clinical use and supply chain use. We constructed tracking technology in a clinical use context by counting the number of technologies fully implemented and replacing paper record functionality at a hospital, an approach widely used in information systems and health care literature [
We included 3 sets of independent explanatory variables. The first set of variables was related to hospital characteristics, such as hospital size, ownership, and teaching status.
To examine the factors involved in the adoption of tracking technology in both clinical and supply chain use contexts in US hospitals, we used a mixed effects model using a population approach. This model is an extension of the simple fixed effects modeling to account for both fixed and random effects. This is particularly useful when data violate the independence assumption that arises from a hierarchical structure. For example, in this study, there were 2 levels: between hospitals (level 1) and within hospitals (level 2). As the data records for this study were collected from 3623 hospitals over 4 years, the source of variability in the observations can be attributed to either within-hospital or between-hospital effects. Repeated observations over the years from the same hospital are subject to hospital-level time-invariant unobserved effects, as within a given hospital, records are more similar. The units sampled at the highest level (ie, hospitals in this study) were independent. As our 2 dependent variables—tracking technology adoption for clinical use and tracking technology adoption for supply chain use—are binary variables, we developed a mixed effects population logistic regression model to examine the relationships among the adoption of tracking technologies (ie, clinical use vs supply chain use), hospital characteristics, and governance structure with the adjustment of time effect. Nonlinear mixed effects modeling software (NONMEM, version 7.5.0; ICON Development Solutions) was used for the modeling [
Initially, correlations among the covariates were explored. Exploratory graphical and statistical evaluations were performed to identify the relationship between estimated individual random effects and covariates. ANOVA tests for categorical covariates and linear regression for continuous covariates were used to identify possible univariate covariate relationships at
A total of 3623 hospitals in 50 states in the United States, from 2012 to 2015, were included in this study (the complete list of hospitals can be accessed in
Demographic information from the included hospitals (N=3623).
Demographics | Overall | |
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Metro | 2019 (55.72) |
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Micro | 676 (18.65) |
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Rural | 928 (25.61) |
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Not-for-profit | 3133 (86.47) |
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For-profit | 490 (13.52) |
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Yes | 223 (6.15) |
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No | 3400 (93.84) |
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Economic leveling state | 1753 (48.38) |
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Economic leading state | 1870 (51.61) |
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Centralized HS | 310 (8.55) |
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Centralized physician and insurance HS | 54 (1.49) |
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Moderately centralized HS | 276 (7.61) |
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Decentralized HS | 1419 (39.16) |
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Independent HS | 99 (2.73) |
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Within HS | 2158 (59.56) |
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Out of HS | 1465 (40.43) |
Total bed count, mean (SD) | 174 (201) |
aEconomic leading state: top 25 states in gross domestic product per capita; economic leveling state: last 25 states in gross domestic product per capita.
bHS: health system.
Adoption of tracking technologies in the United States from 2012 to 2015.
Usage | Tracking technologies year, n (%) | ||||
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2012 (N=2152) | 2013 (N=2012) | 2014 (N=2277) | 2015 (N=2409) | |
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Fully implemented | 782 (36.33) | 892 (44.33) | 1190 (52.26) | 1316 (54.62) |
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Not fully implemented | 1370 (63.66) | 1120 (55.66) | 1087 (47.73) | 1093 (45.37) |
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Fully implemented | 615 (28.57) | 746 (37.07) | 909 (39.92) | 995 (41.3) |
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Not fully implemented | 1537 (71.42) | 1266 (62.92) | 1368 (60.07) | 1414 (58.69) |
As shown in the VPC plots (
The model developed has the potential to predict the increasing trend in the implementation rate of tracking technologies in clinical use over a period of years (
Visual predictive check plots of final population logistic regression model for the adoption of tracking technologies for clinical use over time. (A) the influence of time on the implementation rate of tracking technologies for clinical use; (B) the influence of total beds on the implementation rate of tracking technologies for clinical use; (C) the influence of health system on the implementation rate of tracking technologies for clinical use; (D) the influence of location (in the rural area or not) on the implementation rate of tracking technologies for clinical use. The blue dots show observed implementation rate; the blue error bars indicate a 95% CI in the observed implementation rate; the yellow dots and yellow solid lines show the median implementation rate from model prediction; the yellow error bars and the yellow area indicate a 95% prediction interval for the implementation rate.
Parameter estimates of final population logistic regression model for the adoption of tracking technologies for clinical use.
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Estimate (relative SE; %) | ||
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Intercept | −1.08 (8) | |
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Time effect | 0.369 (8) | |
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Log total bed | 0.452 (10) | |
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Rural area | −0.535 (21) | |
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Health system | 0.79 (11) | |
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Intercept | 2.55 (8) | |
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Time effect | 0.11 (47) |
Forrest plot of covariate effects on the implementation rate of tracking technologies for clinical use. The solid vertical line corresponds to a ratio of 1 and represents a typical hospital. Points and whiskers represent the estimate and 95% CI, respectively. A typical hospital is defined as a hospital with a total of 101 beds, not part of a health system, and not in a rural area in 2012.
As shown in the VPC plots (
The model developed can also predict the increasing trend in the implementation rate of tracking technologies for supply chain use over a period of 4 years in not-for-profit hospitals, as well as stagnation in development among hospitals running for profit (
Visual predictive check plots of final population logistic regression model for the adoption of tracking technologies for supply chain use over time. (A) the influence of time on the implementation rate of tracking technologies for supply chain use; (B) the influence of total beds on the implementation rate of tracking technologies for supply chain use; (C) the influence of state economic condition on the implementation rate of tracking technologies for supply chain use; (D) the influence of health system on the implementation rate of tracking technologies for supply chain use. The blue dots show observed implementation rate; the blue error bars indicate a 95% CI in the observed implementation rate; the yellow dots and yellow solid lines show the median implementation rate from model prediction; the yellow error bars and the yellow area indicate a 95% prediction interval in the implementation rate.
Parameter estimates of final population logistic regression model for the adoption of tracking technologies for supply chain use.
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Estimate (relative SE; %) | ||
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Intercept | −1.72 (6) | |
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Time effect | 0.3 (10) | |
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Log total beds | 0.321 (12) | |
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Economic leading state | −0.428 (20) | |
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Centralized HSa | 1.57 (9) | |
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Moderately centralized HS | 1.16 (11) | |
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Decentralized or independent HS | 0.772 (13) | |
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Run for-profit effect on time effect | −1.48 (15) | |
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Intercept | 3.22 (8) | |
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Time effect | —b |
aHS: health system.
bData does not support the inclusion of random effect on time effect.
Forrest plot of covariate effects on implementation rate of tracking technologies for supply chain use. The solid vertical line corresponds to a ratio of 1 and represents a typical hospital. Points and whiskers represent the estimate and 95% CI, respectively. A typical hospital is defined as a not-for-profit hospital with a total of 101 beds, not part of a health system, and in an economic leveling state in 2012.
With a large US hospital-level longitudinal data set, we observed that, from 2012 to 2015, 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 for supply chain use increased from 28.58% (615/2152) to 41.3% (995/2409). We found that larger hospitals were more likely to fully adopt tracking technologies in both clinical and supply chain use contexts, indicating health resource disparities among hospitals of different sizes. We also discovered that adoption factors affect the clinical and supply chain use contexts differently. In the clinical use context, compared with hospitals located in urban areas, hospitals in rural areas (OR 0.68, 95% CI 0.56-0.80) are less likely to fully adopt tracking technologies, showing evidence of location disparity. 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).
Given that studies on the adoption of tracking technologies have lagged in general health technology adoption studies, and studies undertaken are either limited by a small sample size or subject to early adoption periods, we attempted to fill this gap by applying a census data set from 2012 to 2015 to examine the factors involved in tracking technology adoption in both clinical and supply chain use contexts. Using mixed effects population logistic regression models, we identified several hospital characteristics and governance structure factors associated with tracking technology adoption. Consistent with previous studies on the impact of hospital size on technology adoption [
In the context of clinical use, our results supplement existing studies with additional findings, identifying that rural hospitals are less likely to adopt tracking technologies. One possible reason is that, in contrast to hospitals located in metropolitan and micropolitan areas, those in rural areas tend to accept patients with less severe and less complicated diseases, which are more easily diagnosed and treated by local health care providers, thus requiring less sophisticated technology for clinical use [
In the context of supply chain use, our results show that compared with not-for-profit hospitals, for-profit hospitals are less likely to adopt tracking technologies. Our results, shown in
Our study also extends the current understanding of how governance structure influences technology adoption by identifying the relationship between governance structure types and tracking technology adoption. We revealed that hospitals affiliated with health systems are more likely to adopt tracking technologies for clinical use, whereas types of hospital affiliation do not affect the adoption of tracking technologies for clinical use. We also find that the type of hospital affiliation affects the adoption of tracking technologies for supply chain use—hospitals affiliated with more centralized health systems are more likely to adopt tracking technologies for supply chain use. Compared with other types of hospital affiliations (eg, decentralized or independent or moderately centralized), centralized systems provide a higher percentage of their services at the system level, making them more likely to have higher incentives to increase supply chain efficiency using tracking technologies and develop the long-term tracking technology–related infrastructure of smart hospitals [
Overall, 3 implications are set out in our study for researchers, health care stakeholders, and policy makers. First, our study indicates that the context of technology use (ie, clinical use or supply chain use) influences the tracking of technology adoption. For example, we found that for supply chain use, governance structure types are important factors in the adoption of tracking technologies, but this is not the case for clinical use. In other words, there is no one-size-fits-all solution for adopting tracking technologies in the field of health care. When examining the impact of tracking technologies, practitioners, both academic and practical, should develop a holistic view of the adoption context and cannot assume that related factors can be generalized from other contexts. Health care practitioners who aspire to establish tracking technology–enabled (eg, RFID-enabled) smart hospitals, for example, are in favor of implementing tracking technologies for clinical use, facilitating information sharing, patient identification, and medical equipment tracking, and in supply chains to avoid drug counterfeiting and to enhance supply chain operations [
Second, similar to initial studies that examine the effects of governance structure on longitudinal tracking technology adoption, our results suggest that the impact of governance structure types should be emphasized in technology adoption studies and that the underlying mechanisms require further investigation. For example, we identified that hospitals affiliated with more centralized health systems are more likely to adopt tracking technologies for supply chain use because of the centralized hospital structure settings, allowing resources to be prioritized and allocated to improve operational efficiency for more efficient and streamlined use, thus serving larger patient populations with personalized medicine. This feasibly occurs when systematic integration and synchronization for various solo practices are implemented in centralized smart hospital systems. Future studies are required to investigate the underlying mechanisms (ie, managerial support) linking technology adoption and governance structure and examine whether the findings of this study can be extended to other technology innovations.
Third, our results suggest that disparities may exist in health resources between hospitals of various sizes and governance structures. We found that larger hospitals and hospitals affiliated with health systems, especially more centralized health systems, are more likely to adopt tracking technologies. Compared with small and independent hospitals, these hospitals tend to have more human and financial resources to become the first adopters of advanced technologies. A potentially uneven distribution should be given ample attention before the trend becomes so established that it compounds the already sizable digital gap among different types of hospitals [
This study is the first longitudinal research to empirically examine the different factors associated with the adoption of tracking technologies in different use contexts. This is also the first study to examine the impact of governance structure types on technology adoption in different use contexts in hospitals. In doing so, we provided a census assessment and longitudinal overview of how hospital characteristics and governance structure are related to the adoption rates of tracking technology in both clinical and supply chain use contexts. This study informs researchers, health care providers, and policy makers that hospital characteristics, locations, and governance structures have different impacts on the adoption of tracking technologies for clinical and supply chain use and on health resource disparities among hospitals of different sizes and with different locations and governance structures. This study has important managerial implications for the development of smart hospitals using tracking technologies to establish their hospital infrastructure and practical implications for examining the impact of governance structure types on the adoption of other technologies in health contexts.
This study had some limitations. First, as comprehensive as the data set was, the timeframe was limited to the period from 2012 to 2015. Despite our rationale to address the scarcity of research into health care tracking technology by combing through details related to the issue of tracking technology adoption since its initial implementation in 2012 for the second stage of meaningful use, we caution that further development could have been in place as part of recent uptakes. Thus, it is necessary to conduct this research in conjunction with additional data. Second, we put in place 2 application scenarios to examine tracking technology in the clinical and supply chain use contexts. However, this examination has the potential for a more detailed focus on capturing additional particulars. For example, future research could examine the factors that influence the implementation of different clinical uses of tracking technologies, such as medication administration, patient verification, caregiver verification, and pharmacy verification.
This study provides a census assessment of the adoption of both clinical and supply chain tracking technologies in US hospitals and offers a comprehensive overview of the hospital characteristics and governance structure associated with tracking technology adoption. From an academic perspective, this study unearths the staggered adoption of health tracking technology in hospitals in various categories, suggesting that hospital characteristics and governance structures have a significant impact on the implementation level and rate of tracking technology in clinical and supply chain use. It expands our understanding of digital innovations in health care, providing further evidence relating to tracking technology and outlining implications that can be leveraged from a managerial point of view. This study informs health care providers and policy makers of the possible guidance references that tailored policies should be in place to further promote the ongoing digital transformation in health care, as hospital characteristics and governance structures have different influences on the digitalization process. These outcomes can facilitate both academics and practitioners in putting forward future research to further reveal the nature and scope of tracking technology in developing smart hospitals and personalized health care in general.
Complete list of hospitals applied in this study.
American Hospital Association
gross domestic product
health information technology
information technology
odds ratio
radio-frequency identification
visual predictive check
The authors thank Dr Jing Yuan from the School of Pharmacy at Fudan University for her suggestions on this manuscript. This work was funded by the Scientific Research Foundation for Talented Scholars at Fudan University (JIF301052).
The data sets analyzed during this study are not publicly available owing to the restrictions of use from the American Hospital Association data use agreement but can be accessed with permission from the American Hospital Association [
None declared.