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Specialization and production cost efficiency: evidence from ambulatory surgery centers

Abstract

In the U.S. health care sector, the economic logic of specialization as an organizing principle has come under active debate in recent years. An understudied case is that of ambulatory surgery centers (ASCs), which recently have become the dominant provider of specific surgical procedures. While the majority of ASCs focus on a single specialty, a growing number are diversifying to offer a wide range of surgical services. We take a multiple output cost function approach to an empirical investigation that compares production economies in single specialty ASCs with those in multispecialty ASCs. We applied generalized estimating equation techniques to a sample of Pennsylvania ASCs for the period 2004–2014, including 73 ASCs that specialized in gastrointestinal procedures and 60 ASCs that performed gastrointestinal as well as other specialty procedures. Results indicated that both types of ASC had small room for expansion. In simulation analysis, production of GI services in specialized ASCs had a cost advantage over joint production of GI with other specialty procedures. Our results provide support for the focused factory model of production in the ASC sector.

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Notes

  1. 1.

    Although the number of ASCs has continued to grow, this is not the case for SSHs. Empirical studies documenting increased utilization and cherry-picking of both well-insured and less sick patients by SSHs prompted Congress to essentially halt construction of new specialty facilities and expansion of existing hospitals under Section 6001 of the ACA. Since passage of the ACA, however, Section 6001 has been challenged in numerous forums including the courts, advocacy groups, and the U.S. Congress. Most recently, H.R.2513—the Promoting Access, Competition, and Equity (PACE) Act of 2015—was introduced in the House calling for easing current ACA restrictions on existing physician-owned hospitals to expand their facilities. https://www.congress.gov/bill/114th-congress/house-bill/2513/all-info.

  2. 2.

    The number of colonoscopies performed at each ASC is identified by (CPT-4/ HCPCS codes 44388–44397; 45300–45327;45330–45345; 45355–45392; G0104, G0105, G0106, G0121). Counts of endoscopy procedures performed at each ASC are identified by (CPT-4/ HCPCS codes 43200–43272; 44360–44379; 43450–43499; 44380–44386; 46600–46615).

  3. 3.

    Other approaches have been taken to measure scale economies from short-run cost functions (Braeutigam and Daughety 1983). The theoretically faithful method is applying the envelope condition to solve for the optimal level of the fixed factor using the parameter estimates from the cost function estimates and price data on the fixed factor (in essence, deriving the long-run cost function). Vita (1990) has demonstrated that using the actual rather than the optimal level is a reliable alternative.

  4. 4.

    A third possibility is that marginal costs asymptote to some number such that increasing quantity would increase profits, but expansion is bounded by demand or limited referral patterns even when EOS are present. We are unable with the data at hand to test for this scenario.

  5. 5.

    In log cost models, retransformation of predicted values is not straightforward, because the expected value of the log of dependent variable y conditional on independent variable x is not likely to be equal to the log of the expected value of Yconditional on x. GLM has the advantage of avoiding this problem because it estimates ln(E[Y \({\vert }\) x]), not E(ln(Y \({\vert }\) x)).

  6. 6.

    The Park test assumes that:

    $$\begin{aligned} V(Expenditures|x)=\alpha (\mu (Expenditures|x))^{\lambda } \end{aligned}$$
    (4)

    for some constants \(\alpha \) and \(\lambda \) = 0, where Vis the conditional variance and the function \(\mu \) indicates the conditional mean function. We estimated (4) by regressing the log of the raw-scale residuals squared obtained from a log-linked, gamma distributed model of expenditures on the log of the raw-scale prediction. We found that \({\lambda }=2\), such that the standard deviation is proportional to the mean, as is found in a gamma distribution (Manning and Mullahy 2001).

  7. 7.

    GEE takes the dependence of observations into account by specifying a “working correlation structure”. We fit the GEE with the SAS default of an independence correlation structure, which produces sandwich (robust) standard errors. Note that GEE offers robust estimates even when the correlation structure is unknown (Diggle et al. 2002).

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Acknowledgements

The authors acknowledge the comments on earlier versions of this paper of Stephen Parente, Ellerie Weber, and Gary Young, and of participants at the International Health Economics Biennial World Congress 2015, the American Society of Health Economists Biennial Meeting 2016, and the AcademyHealth Annual Research Meeting 2016. The authors further thank Gregory Lostoski for computer programming assistance. The research is supported by the Agency for Healthcare Research and Quality, Grant #HSR01023768-01 awarded to Georgetown University, Jean M. Mitchell, Principal Investigator.

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Correspondence to Kathleen Carey.

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Carey, K., Mitchell, J.M. Specialization and production cost efficiency: evidence from ambulatory surgery centers. Int J Health Econ Manag. 18, 83–98 (2018). https://doi.org/10.1007/s10754-017-9225-9

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Keywords

  • Ambulatory surgery centers
  • Specialization
  • Cost

JEL Classification

  • I10
  • L23