A quantitative meta-analysis of organizational ownership and technical efficiency: non-linear influence by facility types and time for nonprofit and for-profit healthcare providers

ABSTRACT

This study seeks to determine if for-profit healthcare organizations are more technically efficient than nonprofit organizations. We attempted to answer two questions: Does the technical efficiency of hospitals and nursing homes vary depending on ownership type? If so, how does time moderate the relationship between ownership and technical efficiency? Our findings do not show that for-profit healthcare providers are universally more technically efficient than nonprofit healthcare facilities. However, for-profit nursing homes are more technically efficient than nonprofit facilities, while there was no difference between nonprofits and for-profits in hospitals. An examination of healthcare facilities reveals that nonprofit institutions have only recently become efficient, while for-profit organizations were more efficient in the past. In conclusion, institutional changes in the healthcare delivery system of the US developed differently, depending on organization’s ownership and facility types. Theoretical and practical considerations were recommended as a policy tool in healthcare practices in terms of market and population for technical efficiency.

KEYWORDS:

• Technical efficiency
• ownership
• healthcare
• for-profit
• nonprofit
• facility types
• time

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Note that Ozcan et al. (1992) provided effect sizes directly. The data points are weighted according to their sample sizes and influence: studies with larger sample sizes have a larger circumference.

2. Some reviewers questioned how useful 14 papers are over 30 years. However, we included all relevant studies that had presented necessary indexes (e.g., mean, standard deviation, etc.) on the topic between ownership and technical efficiency including facility types of nursing home and hospitals to examine the effectiveness of ownership types on technical efficiency.

3. For the ANOVA-like models, within-group effect means, their standard errors and 95% confidence intervals were reported. In addition, two forms of chi-square statistics, $Q_b$ and $Q_w$ were reported. These two measures assess predictor-specific significance in terms of explaining systematic effect-size heterogeneity ($Q_b)$ and within-group variability of effects ($Q_w$). Also, the meta-regression results include coefficients, their standard errors and 95% confidence intervals. It also provides two chi-square statistics, $Q_m$ and $Q_e$. Both are related measures of a model fit, with larger values of $Q_m$ and smaller values of $Q_e$ corresponding to greater explanatory power of the effect-size heterogeneity by the set of model predictors.

4. For example, nursing homes may collect out-of-pocket expenses, while hospitals often rely on third-party payments.

Additional information

Notes on contributors

Amy Freeman

Amy Freeman is a doctoral student in Public Administration at Florida State University (FSU). Currently, she works in the Bureau of Workforce Statistics and Economic Research at the Florida Department of Economic Opportunity. Her research interest focuses on social service delivery and organizational collaboration.

Jiwon Nam-Speers

Jiwon Nam-Speers is an Assistant Professor of Predictive Analytics at the University of Baltimore. She teaches research method courses including causal modeling, HLM, and information resource management. Her research interests are coproduction, risk assessment, global disaster management, and methodological issues.

Umit Tokac

Umit Tokac has a PhD in Measurement and Statistics (M & S) from FSU. Currently, he is an Assistant Professor at the College of Nursing, University of Missouri – St. Louis. His primary research interests are meta-analysis, Bayesian data analysis, artificial intelligence methods, and veterans’ behavioral health.