nach oben

International Journal of Health Economics and Management

Erschienen in:

17.04.2023 | Research article

Hospital cost efficiency: an examination of US acute care inpatient hospitals

verfasst von: Sebastian Linde, PhD

Erschienen in: International Journal of Health Economics and Management | Ausgabe 3/2023

Einloggen, um Zugang zu erhalten

Abstract

The use of stochastic frontier models for inference on hospital efficiency is complicated by the inability to fully control for quality differences across hospitals. Additionally, the potential existence of cross-sectional dependence due to the presence of unobserved common factors leads to endogeneity problems that can bias both cost function and efficiency estimates. Using a panel consisting of 1518 hospitals for the years 1996–2013 (T = 18), I adopt techniques for dealing with long, cross-sectionally dependent panel data in order to estimate cost parameters and hospital specific efficiency. In particular, I employ the estimation technique proposed by Bai (Econometrica 77(4):1229–1279, 2009), which assumes that the unobservable heterogenous effects have a factor structure. I find evidence of considerable scale economies and that hospital cost inefficiencies have been increasing during the period of 1996–2013, and that the growth in expenditures is, in part, driven by spending that increases patient satisfaction, but that does not significantly contribute to improved patient health outcomes.

Nur mit Berechtigung zugänglich

For details on hospital value based purchasing see: https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/hospital-value-based-purchasing/index.html?redirect=/Hospital-Value-Based-Purchasing/

Of particular relevance to this study are SFA studies, with a health care focus, that have used panel data; these include: Chirikos (1998), Chirikos and Sear (2000), Deily et al. (2000), Li and Rosenman (2001), Rosko (2001, 2004), Sari (2003), McKay and Deily (2005), Deily and McKay (2006), Furukawa et al. (2010), Rosko and Mutter (2010, 2014).

For excellent reviews of the SFA literature, see: Kumbhakar and Lovell (2000), Greene (2008) and Belotti (2012). For recent applications of the factor model methodology to production frontier analysis, see: Ahn et al. (2007), Mastromarco and Serlenga (2009), Kneip et al. (2012), Ahn et al. (2013), Filippini and Tosetti (2014) and Mastromarco et al. (2015).

Additional details on the potential empirical benefits of the factor model approach are provided within Appendix A.

Rather than having to imposing a set structure upon the modeled inefficiency, a factor model approach allows the researcher to use data-driven tests to determine the appropriate number of factor components to include. For details on these tests, see, e.g., Bai and Ng (2002), Bai (2004), Onatski (2009), Alessi et al. (2010), Kneip et al. (2012), Ahn and Horenstein (2013).

For additional details on the within-group interpretation of the interactive fixed effects model, please see Bai (2009, pp. 1248–1249).

In practice, this means that a interactive fixed effects approach will results in the same estimates as the fixed effects model, assuming this is indeed the true underlying model. However, in the presence of cross-sectional dependence within the data, due to latent common factors, the interactive fixed effects model is able to capture and control these latent features. As such, in motivating the use of an interactive fixed effects approach it is important to test for cross-sectional dependence using, e.g., the Pesaran (2014) cross-sectional dependence test.

An alternative example of a possible latent factor would be trends in the adoption/use of state-of-the-art medical technologies within the US health care market. For a more detailed explanation of the endogeneity problem caused due to such latent structures, please see Appendix A.

Regarding the notation throughout: the linear form is due to having taken logs. The “ln” prefix is omitted to ease the notation. That is, e.g. \(TC_{it}\) should really say ln\(TC_{it}\). Also, bold notation denotes vectors, e.g. \(x_{it}\) is not a vector, but \({\textbf{x}}_{it}\) is.

The literature on stochastic cost frontier analysis in health care has mainly consisted of working with either the Cobb-Douglas functional form or the more flexible specification of a trans-log form that nests the former as a special case (see e.g. Rosko et al., 2008 for a relevant survey of this literature). The choice to work with the Cobb-Douglas form is done for its simplicity here, however, a robustness check using the translog cost functional form is also provided within Appendix D.

For additional details on the estimation procedure see Bai (2009) and Bada and Liebl (2014); and/or Appendix A.

The notation \({\overline{z}}_{i}\) denotes the average across the time dimension. That is, \({\overline{z}}_{i}=\frac{1}{T}\left( \sum _{t}^{T}z_{it}\right)\).

The choice of taking the min(.) operator over both i, t or just i will affect the results and a discussion of this is provided within the results section.

Note that since the given Cobb-Douglas form does not allow for second-order terms, the value of scale economies is assumed to be constant irrespective of the output level (see e.g. Farsi et al. (2005b, p. 75)).

This section provides only some basic information regarding the data sources, for additional data details please see Appendix B.

For additional details, see: https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/Cost-Reports/?redirect=/CostReports/

See: https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Historical-Impact-Files-for-FY-1994-through-Present.html

The first of these comes from the Dartmouth atlas data, while the rest (including all variables in Table 3) are from the CMS’s hospital compare data.

This section draws on two computational resources for the purpose of performing estimation and various specification tests. These are: (i) the plm package by Croissant and Millo (2008), and (ii) the phtt package by Bada and Liebl (2015).

The tests for establishing the optimal number of factors vary in their recommendations from 1 to 4 factors (for details see Appendix C). Within the main analysis I employ a specification that controls for individual fixed effects and that allows for two factors.

The global CD test considers dependence across all hospitals within the whole of the US. The local CD test, on the other hand, considers dependence on a state level.

Running additional specification tests recommended by Bai (2009) and Kneip et al. (2012) I find that both of these tests reject the null hypothesis of there only being additive two-way-effects–again, lending support to a richer specification with interactive fixed effects. For additional details on these tests and the results from Augmented Dickey Fuller tests that reject the null of a unit root, see Appendix C.

This result appears robust when using even broader (25 or 50 mile) radius measures.

The finding of increasing scales within health care seems supported by the high volume of hospital mergers and system formation within the industry (again, for a nice review of this literature please see Gaynor and Town (2012)).

Ahn, Seung C., & Horenstein, Alex R. (2013). Eigenvalue ratio test for the number of factors. Econometrica, 81(3), 1203–1227.CrossRef

Aigner, Dennis, Knox Lovell, C. A., & Schmidt, Peter. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.CrossRef

Alessi, Lucia, Barigozzi, Matteo, & Capasso, Marco. (2010). Improved penalization for determining the number of factors in approximate factor models. Statistics & Probability Letters, 80(23), 1806–1813.CrossRef

Bada, Oualid, & Liebl, Dominik. (2014). phtt: Panel Data Analysis with Heterogeneous Time. Journal of Statistical Software, 59(6).

Bai, Jushan. (2004). Estimating cross-section common stochastic trends in nonstationary panel data. Journal of Econometrics, 122(1), 137–183.CrossRef

Bai, Jushan. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229–1279.CrossRef

Bai, Jushan, & Ng, Serena. (2002). Determining the number of factors in approximate factor models. Econometrica, 70(1), 191–221.CrossRef

Battese, G. E., & Coelli, T. J. (1988). Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data. Journal of Econometrics, 38(3), 387–399.CrossRef

Battese, G. E., & Coelli, T. J. (1992). Frontier production functions, technical efficiency and panel data: With application to paddy farmers in India. Journal of Productivity Analysis, 3(1–2), 153–169.CrossRef

Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332.CrossRef

Belotti, F., et al. (2012). Stochastic frontier analysis using Stata. Stata Journal, 3(ii), 1–39.

BLS. (2017). Local Area Unemployment Statistics. http://www.bls.gov/lau/%7B%5C#%7Dtables, visited on 10/01/2017

Census. (2017). SAIPE Program. https://www.census.gov/programs-surveys/saipe/data.html, visited on 10/01/2017

Chirikos, Thomas N. (1998). Further evidence that hospital production is inefficient. Inquiry, 35(4), 408–416.PubMed

Chirikos, Thomas N., & Sear, Alan M. (2000). Measuring hospital efficiency: a comparison of two approaches. Health Services Research, 34(6), 1389–1408.PubMedPubMedCentral

Chudik, Alexander, & Pesaran, M. Hashem. (2013). Large panel data models with crosssectional dependence: a survey. Mimeo.

CMS. (2017a). Cost Reports. url: https://www.cms.gov/Research-Statistics-Dataand-Systems/Downloadable-Public-Use-Files/Cost-Reports/?redirect=/costreports/02_hospitalcostreport.asp, visited on 10/01/2017

CMS. (2017b). Historical Impact Files for FY 1994 through Present. https://www.cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Historical-Impact-Files-for-FY-1994-through-Present.html, visited on 10/01/2017

CMS. (n.d). Hospital compare data archive. https://data.medicare.gov/data/archives/hospital-compare.

CMS. (2017c). Hospital Value-Based Purchasing. https://www.cms.gov/edicare/Quality-Initiatives-Patient-Assessment-Instruments/hospital-value-based-purchasing/index.html?redirect=/Hospital-Value-Based-Purchasing/, visited on 10/01/2017

Croissant, Y., & Millo, G. (2008). Panel data econometrics in R: The plm package. Journal of Statistical Software, 27(2), 1–43. issn: 1548-7660.CrossRef

Deily, Mary E., & McKay, Niccie L. (2006). Cost inefficiency and mortality rates in Florida hospitals. Health Economics, 15(4), 419–431.PubMedCrossRef

Deily, Mary E., McKay, Niccie L., & Dorner, F. (2000). Exit and inefficiency: The effects of ownership type. Journal of Human Resources, 35(4), 734–747.CrossRef

Devers, Kelly J., Brewster, Linda R., & Casalino, Lawrence P. (2003). Changes in hospital competitive strategy: A new medical arms race? Health Services Research, 38(1p2), 447–469.PubMedPubMedCentralCrossRef

Dzau, Victor J., et al. (2017). Vital directions for health and health care: priorities from a National Academy of Medicine Initiative. Jama, 317(14), 1461.PubMedCrossRef

Eberhardt, Markus, Helmers, Christian, & Strauss, Hubert. (2013). Do Spillovers matter when estimating private returns To R &D ? The Review of Economics and Statistics, 95(May), 436–448.CrossRef

Farsi, Mehdi, Filippini, Massimo, & Greene, William. (2005). Efficiency measurement in network industries: Application to the Swiss Railway companies. Journal of Regulatory Economics, 28(1), 69–90.CrossRef

Farsi, Mehdi, Filippini, Massimo, & Kuenzle, Michael. (2005). Unobserved heterogeneity in stochastic cost frontier models: An application to Swiss nursing homes. Applied Economics, 37(December), 2127–2141.CrossRef

Farsi, Mehdi, Filippini, Massimo, & Kuenzle, Michael. (2006). Cost efficiency in regional bus companies: An application of alternative stochastic frontier models. Journal of Transport Economics and Policy, 40(1), 95–118.

Ferrier, G. D. (2014). Improving the “usefulness’’ of stochastic frontier analysis for health care. Economics and Business Letters, 3(1), 27–34.CrossRef

Filippini, Massimo, & Tosetti, Elisa. (2014). Stocastic Frontier Models for Long Panel Data Sets: Measurement of the Underlying Energy Eciency for the OECD Countries. FRED, 2017, Economic Data. https://research.stlouisfed.org/fred2, visited on 10/01/2017

Furukawa, Michael F., Raghu, T. S., & Shao, Benjamin B M. (2010). Electronic medical records and cost efficiency in hospital medical-surgical units. Inquiry, 47(2), 110–123.PubMedCrossRef

Goldman, Dana P., Vaiana, Mary, & Romley, John A. (2010). The emerging importance of patient amenities in hospital care. The New England Journal of Medicine, 363(23), 2185.PubMedPubMedCentralCrossRef

Green, William. (2011). Econometric Analysis (7th ed.). Pearson.

Greene, William. (2005). Fixed and random effects in stochastic frontier models. Journal of Productivity Analysis, 23(1), 7–32.CrossRef

Green, William. (2005). Reconsidering heterogeneity in panel data estimators of the stochastic frontier model. Journal of Econometrics, 126(2), 269–303.CrossRef

Hollingsworth, Bruce. (2003). Non-parametric and parametric applications measuring efficiency in health care. Health Care Management Science, 6(4), 203–218.PubMedCrossRef

Hollingsworth, Bruce. (2008). The measurement of efficiency and productivity of health care delivery. Health Economics, 17(10), 1107–1128.PubMedCrossRef

Hussey, Peter S., et al. (2009). A systematic review of health care efficiency measures: Health care efficiency. Health Services Research, 44(3), 784–805.PubMedPubMedCentralCrossRef

IOM. (2010). The healthcare imperative: lowering costs and improving outcomes: workshop series summary. National Academies Press.

IOM. (2012). Best care at lower cost: The pathway to continuously learning health care in America. September. National Academies Press.

Jondrow, James, et al. (1982). On the estimation of technical inefficiency in the stochastic frontier production function model. Journal of Econometrics, 19(2–3), 233–238.CrossRef

Kneip, Alois, Sickles, Robin C., & Song, Wonho. (2012). A new panel data treatment for heterogeneity in time trends. 28, 03 590–628.

Kumbhakar, Subal C., & Lovell, C. A. (2012). Stochastic Frontier Analysis (2nd ed., pp. 1–204). Cambridge University Press.

Lee, Y. H., & Schmidt, Peter. (1993). A production frontier model with flexible temporal variation in technical inefficiency. In H. Fried, C. A. K. Lovell, & S. Schmidt (Eds.), The measurement of productive efficiency: techniques and applications. Oxford Newey: Oxford University Press.

Li, T., & Rosenman, R. (2001). Cost inefficiency in Washington hospitals: a stochastic frontier approach using panel data. Health Care Management Science, 4(2), 73–81.PubMedCrossRef

Mas-Colell, A., Whinston, M. D., & Green, J. R. (1995). Microeconomic Theory. Oxford University Press.

Mastromarco, Camilla, & Serlenga, Laura. (2009). Dynamic Efficiency Modelling for Technological Convergence (pp. 1–35). Mimeo.

Mastromarco, Camilla, Serlenga, Laura, & Shin, Yongcheol. (2013). Globalisation and technological convergence in the EU. Journal of Productivity Analysis, 40(1), 15–29.CrossRef

Mastromarco, Camilla, Serlenga, Laura, & Shin, Yongcheol. (2015). Modelling technical efficiency in cross sectionally dependent stochastic frontier panels. Journal of Applied Econometrics, 31(1), 281–297.CrossRef

McKay, Niccie L., & Deily, Mary E. (2005). Comparing high- and low-performing hospitals using risk-adjusted excess mortality and cost inefficiency. Health Care Management Review, 30(4), 347–360.PubMedCrossRef

Mutter, Ryan L., Rosko, Michael D., & Wong, Herbert S. (2008). Measuring hospital inefficiency: The effects of controlling for quality and patient burden of illness. Health Services Research, 43(6), 1992–2013.PubMedPubMedCentralCrossRef

Newhouse, Joseph P. (1994). Frontier estimation: How useful a tool for health economics? Journal of Health Economics, 13(3), 317–322.PubMedCrossRef

Onatski, Alexei. (2010). Determining the number of factors from empirical distribution of eigenvalues. The Review of Economics and Statistics, 92(4), 1004–1016.CrossRef

Pitt, Mark M., & Lee, Lung-fei. (1981). The measurement and sources of technical inefficiency in the Indonesian weaving industry. Journal of Development Economics, 9, 43–64.CrossRef

Rosko, Michael D. (2001). Cost efficiency of US hospitals: A stochastic frontier approach. Health Economics, 10(6), 539–551.PubMedCrossRef

Rosko, Michael D. (2004). Performance of U.S. teaching hospitals: A panel analysis of cost inefficiency. Health Care Management Science, 7(1), 7–16.PubMedCrossRef

Rosko, Michael D., & Mutter, Ryan L. (2008). Stochastic frontier analysis of hospital inefficiency. Medical Care Research and Review, 65, 131–166.PubMedCrossRef

Rosko, Michael D., & Mutter, Ryan L. (2010). Inefficiency differences between critical access hospitals and prospectively paid rural hospitals. Journal of Health Politics, Policy and Law, 35(290), 95–126.PubMedCrossRef

Rosko, Michael D., & Mutter, Ryan L. (2014). The association of hospital cost-inefficiency with certificate-of-need regulation. Medical Care Research and Review, 71(3), 280–298.PubMedCrossRef

Roth, Jean. (2017). NBER ZIP Code Distance Database. http://www.nber.org/data/zip-code-distance-database.html, visited on 10/01/2017,

Sari, Nazmi. (2003). Efficiency outcomes of market concentration and managed care. International Journal of Industrial Organization, 21(10), 1571–1589.CrossRef

Schmidt, Peter, & Sickles, Robin C. (1984). Production frontiers and panel data. Journal of Business and Economic Statistics, 2(4), 367–374.

The Dartmouth Atlas of Health Care. (2017). Hospital Discharges and Post-Acute Care. url: http://www.dartmouthatlas.org/tools/downloads.aspx?tab=41, visited on 10/01/2017,

Truven Health Analytics. (2016). 100 Top Hospitals. http://100tophospitals.com/studies-winners/100-top-hospitals/year, visited on 08/01/2016

Wang, Hung-jen. (2002). One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis, 18, 129–144.CrossRef

Titel: Hospital cost efficiency: an examination of US acute care inpatient hospitals
verfasst von: Sebastian Linde, PhD
Publikationsdatum: 17.04.2023
Verlag: Springer US
Erschienen in: International Journal of Health Economics and Management / Ausgabe 3/2023
Print ISSN: 2199-9023
Elektronische ISSN: 2199-9031
DOI: https://doi.org/10.1007/s10754-023-09356-x

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Hospital cost efficiency: an examination of US acute care inpatient hospitals

Abstract

Live-Webinar "Urologie und Sexualmedizin in der Praxis"

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 3/2023

Finding fraud: enforcement, detection, and recoveries after the ACA

How does the quality of care for type 2 diabetic patients benefit from GPs-nurses’ teamwork? A staggered difference-in-differences design based on a French pilot program

Does the market reward quality? Evidence from India

Impact of COVID-19 on hospital screening, diagnosis and treatment activities among prostate and colorectal cancer patients in Canada

Analyzing the effect of health reforms on the efficiency of Ecuadorian public hospitals

Priority setting in the German healthcare system: results from a discrete choice experiment