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The American Journal of Managed Care January 2019
The Gamification of Healthcare: Emergence of the Digital Practitioner?
Eli G. Phillips Jr, PharmD, JD; Chadi Nabhan, MD, MBA; and Bruce A. Feinberg, DO
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Eric D. Newman, MD; Paul F. Simonelli, MD, PhD; Shelly M. Vezendy, BS; Chelsea M. Cedeno, BS; and Daniel D. Maeng, PhD
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Karen Donelan, ScD, EdM; Esteban A. Barreto, MA; Sarah Sossong, MPH; Carie Michael, SM; Juan J. Estrada, MSc, MBA; Adam B. Cohen, MD; Janet Wozniak, MD; and Lee H. Schwamm, MD
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Sung J. Choi, PhD; and M. Eric Johnson, PhD
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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
Drivers of Health Information Exchange Use During Postacute Care Transitions
Dori A. Cross, PhD; Jeffrey S. McCullough, PhD; and Julia Adler-Milstein, PhD

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.
Control variables. Previous research has found that alternative payment model (APM) participation is correlated with both more integrated organizational structures17 and higher levels of HIT adoption.18 We controlled for APM experience, which was measured by the total number of APMs in which most or all of the system’s hospitals and medical groups were participating. We also controlled for relative size (standardized to a mean of 0 and SD of 1), as measured by the number of physicians in the system, and whether respondents perceived there to be high levels of competition in either inpatient or outpatient settings. We also controlled for each system’s geographic region based on the US Census, which was categorized as South, Midwest, Northeast, West, or Multiregion based on the states in which they operated.


Descriptive analyses of all key study variables were conducted, including a breakdown of advanced HIT adoption, organizational structure, EHR standardization, and resource allocation practices by number and percentage of systems. All 3 hypotheses were tested simultaneously using multivariate regression models that examined the association of organizational structure, resource centralization, and EHR standardization with advanced HIT adoption, controlling for system size, perceived competition, region, corporate parent status, and APM experience. Results were weighted to account for differential selection probabilities resulting from probability sampling of systems based on organizational complexity, including having a subsidiary or not.


Table 1 provides descriptive statistics for key study variables. Adoption of advanced HIT capabilities was inconsistent, with a mean of 2.4 capabilities adopted and most (80.5%) systems in our sample adopting between 1 and 4 features. Only 8.4% of systems in our sample reported adopting all 5 advanced HIT capabilities. Adoption rates varied widely by feature, with more than 75% of systems reporting the ability of patients to access their medical records but less than 32% reporting the ability of physicians to know when patients fill prescriptions and of patients to comment on their medical records.

Just under half (48.8%) of the systems in our sample owned and managed their hospitals and medical groups. They also allocated resources centrally; 67.3% reported conducting capital budgeting at the system level and 78.9% reported conducting vendor selection at the system level. Most systems used a single EHR (69.0%) and standardized EHR data elements (58.6%), although just under half reported using both a single EHR and standardized data elements (48.1%). APM experience varied, with the lowest rate of participation being in risk-bearing ACOs (21.5%) and the highest rate of participation being in primary care improvement and pay-for-performance programs (60.4%).

Multivariate regression results indicate that the degree of EHR standardization (β = 0.76; P = .001) was the strongest predictor of advanced HIT adoption, supporting our third hypothesis, and system management and ownership of hospitals and medical groups was also statistically significant (β = 0.32; P = .04), supporting our first hypothesis. The degree of centralized resource allocation was not a significant predictor of advanced HIT adoption (β = 0.42; P = .13). Of our control variables, APM experience was a significant predictor of advanced HIT adoption (β = 0.10; P = .03), as was size (β = 0.13; P = .006), but other variables were not significant. Full regression results can be found in Table 2.

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