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Organizational Influences on Healthcare System Adoption and Use of Advanced Health Information Technology Capabilities
Paul T. Norton, MPH, MBA; Hector P. Rodriguez, PhD, MPH; Stephen M. Shortell, PhD, MPH, MBA; and Valerie A. Lewis, PhD, MA
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Organizational Influences on Healthcare System Adoption and Use of Advanced Health Information Technology Capabilities

Paul T. Norton, MPH, MBA; Hector P. Rodriguez, PhD, MPH; Stephen M. Shortell, PhD, MPH, MBA; and Valerie A. Lewis, PhD, MA
This is the first national study to examine the relationship between healthcare system organizational characteristics and adoption of advanced health information technology capabilities.
DISCUSSION

Our findings have several implications for the further adoption of advanced HIT capabilities by health systems. First, the strongest predictor of advanced HIT adoption in a health system is the extent to which EHR systems are standardized. The second is that ownership and management of hospitals and medical groups is also a significant predictor of advanced HIT adoption. The third is that system resource allocation practices are less significant predictors of advanced HIT adoption when controlling for other organizational characteristics. Whereas previous studies’ findings suggest that resources and economies of scale are the primary drivers of HIT adoption among medical groups and hospitals, we found that EHR standardization, rather than centralized resource allocation, was the strongest predictor of advanced HIT adoption across a healthcare system.

There are several reasons why standardization may have a much stronger association with advanced HIT adoption than resource centralization in our study. The first is that our focus was specifically on health systems, which, by nature, are more centralized and well-resourced organizations than are independent hospitals and medical groups. The second is that the adoption of advanced EHR capabilities may represent more a challenge of change management than of resource allocation. All of the systems in our analytic sample had already acquired EHRs, and 4 of the 5 capabilities in question were required capabilities of EHR systems for CEHRT designation. Third, health systems may decide to standardize their EHR systems so they can adopt advanced capabilities in the future, which the cross-sectional nature of our study could not address.

Our findings indicate that health systems that aim to accelerate the adoption of advanced HIT capabilities may benefit from standardizing their EHR systems across hospitals and medical groups within the system. The results related to resource allocation indicate that organizations with more distributed forms of resource allocation, but high levels of standardization, may achieve similar levels of advanced technology adoption. The findings also suggest that payers can assist health systems with EHR adoption by targeting technical assistance toward health systems with lower levels of EHR standardization. Moreover, HIT vendors may consider developing ways to standardize use of their products across systems to ensure greater adoption of new and beneficial features.

Limitations

The results should be considered in light of some limitations. First, the cross-sectional nature of the NSHOS cannot establish the temporal ordering of any associations found. Second, NSHOS is a single-informant survey, which may affect the internal validity of the study. Self-reported data are sometimes inaccurate, and it is possible that the reliability with which systemwide standardization, resource allocation, and HIT capabilities are reported varied by respondent. Third, data limitations prevented us from controlling for additional factors that may influence advanced HIT adoption, such as patient mix. Finally, we included measures of centralized resource allocation, but the specific resources available for investment in training and software upgrades were not assessed. Questions about targeted HIT investments would be useful to include in future research.

CONCLUSIONS

The degree of EHR standardization within health systems, as measured by the degree of uniformity of technology systems and data elements across hospitals and medical groups, is a stronger predictor of advanced HIT adoption than the system’s ownership and management structure, resource allocation practices, or APM participation. Health system leaders looking to improve the diffusion of new technologies should consider ways to better standardize their implementation and use of EHRs to drive widespread adoption of and benefit from new features. Further research should assess the impact of healthcare system resources for training and software upgrades on the adoption of advanced HIT and determine the drivers of wide variability in the adoption of individual advanced HIT capabilities. 

Acknowledgments

The statements, findings, conclusions, views, and opinions contained and expressed in this article are based in part on data obtained under license from IQVIA information services: OneKey subscription information services 2010-2017, IQVIA Incorporated, all rights reserved. The statements, findings, conclusions, views, and opinions contained and expressed herein are not necessarily those of IQVIA Incorporated or any of its affiliated or subsidiary entities.

The American Medical Association (AMA) is the source for the raw physician data; statistics, tables, or tabulations were prepared by Paul Norton using AMA Masterfile data.

Author Affiliations: School of Public Health, University of California Berkeley (PTN, HPR, SMS), Berkeley, CA; Gillings School of Global Public Health, University of North Carolina Chapel Hill (VAL), Chapel Hill, NC.

Source of Funding: This study was supported by the Agency for Healthcare Research and Quality Comparative Health System Performance Initiative under grant #1U 19HS024075, which studies how healthcare delivery systems promote evidence-based practices and patient-centered outcomes research in delivering care.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (PTN, HPR, SMS, VAL); acquisition of data (HPR, SMS, VAL); analysis and interpretation of data (PTN, HPR, VAL); drafting of the manuscript (PTN); critical revision of the manuscript for important intellectual content (PTN, HPR, SMS, VAL); statistical analysis (PTN); obtaining funding (HPR, SMS, VAL); administrative, technical, or logistic support (PTN); and supervision (HPR, SMS).

Address Correspondence to: Paul T. Norton, MPH, MBA, School of Public Health, University of California Berkeley, 2121 Berkeley Way, Berkeley, CA 94704. Email: paul_norton@berkeley.edu.
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