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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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Understanding the Relationship Between Data Breaches and Hospital Advertising Expenditures
Sung J. Choi, PhD; and M. Eric Johnson, PhD
Alternative Payment Models and Hospital Engagement in Health Information Exchange
Sunny C. Lin, MS; John M. Hollingsworth, MD, MS; and Julia Adler-Milstein, PhD
Drivers of Health Information Exchange Use During Postacute Care Transitions
Dori A. Cross, PhD; Jeffrey S. McCullough, PhD; and Julia Adler-Milstein, PhD

Understanding the Relationship Between Data Breaches and Hospital Advertising Expenditures

Sung J. Choi, PhD; and M. Eric Johnson, PhD
A hospital data breach was associated with a 64% increase in annual advertising expenditures.
RESULTS

A descriptive scatterplot of advertising expenditures and bed size fitted with a second-degree polynomial curve (Figure) showed a positive correlation up to about 1000 beds. A few hospitals with more than 2000 beds spent less on advertising, and those extreme cases pulled the fitted line down as bed size increased above 1000. When stratified by breach status, breached and control hospitals followed a similar concave trend. Breached hospitals spent more than nonbreached hospitals; however, the fitted lines of the 2 groups were not significantly different for most of the range of the hospital beds.

The descriptive characteristics of the full sample of hospitals are summarized by breach status in Table 1. Note that each observation in the data set was a hospital-year, as a single hospital may be repeatedly observed over time. The number of hospitals in the breached group was 75, and the number of hospitals in the control group was 3421. The breached hospitals spent nearly 3 times more on advertising than the control hospitals (approximately $688,000 vs $238,000 for annual spending; $1,713,000 vs $551,000 for 2-year spending). The breached hospitals were more likely to be larger in bed size (565.60 vs 291.49), more likely to be a teaching hospital (77.4% vs 41.7%), and higher in occupancy rate (69.11% vs 57.62%). Breached hospitals were located in counties with significantly more hospitals and Medicare enrollees, suggesting that they were in more competitive areas.

As shown in Table 2, the number of propensity score–matched hospitals in the breached group was 72; the unweighted number of matched hospitals in the control group was 915. For continuous variables, means (SDs) are shown. The breached hospitals spent $817,205.11 ($1,379,037.92) on advertising expenditures in the year of the breach, which was higher than the $568,078.12 ($1,485,531.25) spent by the matched control hospitals (SMD >0.1). The breached hospitals spent $1,753,358.75 ($2,791,376.50) on advertising over 2 years, whereas the matched control hospitals spent $1,126,682.72 ($2,813,634.41) over 2 years (SMD >0.1).

Focusing on the breached hospitals, the total revenue was $1058.77 million ($853.84 million), with a total margin of 6.75% (9.03%). The operating revenue was $843.92 million ($670.12 million), with an operating margin of –14.22% (55.71%). Total discharges were 27,876.02 (17,333.74) patients. The number of beds was 592.93 (367.47), the length of stay was 4.92 (0.86) days, and the occupancy rate was 69.28% (15.79%). For categorical variables, percentages are shown followed by counts. Ownership was mostly nonprofit (69.4% [n = 50]; investor owned: 12.5% [n = 9]; public: 18.1% [n = 13]). Teaching status was dominantly teaching (76.4%; n = 55). Most hospitals were urban (91.7%; n = 66) and meaningful users of health information technology (IT) (59.7%; n = 43). The characteristics of the matched control hospitals generally had negligible differences from the breached group, with an SMD of less than 0.1 for most of the regressors.

The GLM estimates using the matched sample are shown in Table 3. A data breach was associated with a 64% (95% CI, 7.2%-252%; P = .023) increase in annual advertising expenditures compared with the matched control group, holding observable variables constant. Similarly, the 2-year advertising expenditures were 79% (95% CI, 16.4%-274%; P = .008) higher for the breached hospitals. Nonprofit hospitals were associated with 3.7 (95% CI, 1.8-7.6; P <.001) times higher advertising expenditures than public hospitals. Two-year advertising expenditures of nonprofit hospitals were 4.9 (95% CI, 2.3-10.8; P <.001) times higher than public hospitals. Two-year advertising expenditures of investor-owned hospitals were 2.5 (95% CI, 1.2-5.3; P = .019) times higher than public hospitals. Relative to nonteaching hospitals, spending was not significantly higher in either major or minor teaching hospitals. Urban hospitals were associated with 4.0 (95% CI, 2.3-6.8; P <.001) times higher advertising expenditures than rural hospitals. The count of Medicare enrollment in a county was positively correlated (P <.001) with 2-year advertising expenditures. The number of short-term general hospitals in a county was positively correlated with both 1-year (8.1% increase; P = .047) and 2-year (17.3% increase; P <.001) advertising expenditures.


 
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