Comparing Outcomes and Costs of Surgical Patients Treated at Major Teaching and Nonteaching Hospitals: A National Matched Analysis.
Journal
Annals of surgery
ISSN: 1528-1140
Titre abrégé: Ann Surg
Pays: United States
ID NLM: 0372354
Informations de publication
Date de publication:
03 2020
03 2020
Historique:
pubmed:
23
10
2019
medline:
12
5
2020
entrez:
23
10
2019
Statut:
ppublish
Résumé
To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics. Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear. A study of Medicare patients at 340 teaching hospitals (resident-to-bed ratios ≥ 0.25) and matched patient controls from 2444 nonteaching hospitals (resident-to-bed ratios < 0.05).We studied 86,751 pairs admitted for general surgery (GS), 214,302 pairs of patients admitted for orthopedic surgery, and 52,025 pairs of patients admitted for vascular surgery. In GS, mortality was 4.62% in teaching hospitals versus 5.57%, (a difference of -0.95%, <0.0001), and overall paired cost difference = $915 (P < 0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P < 0.0001), and paired cost difference = $3773 (P < 0.0001), yielding $1682 per 1% mortality improvement at 30 days. Patterns for vascular surgery outcomes resembled general surgery; however, orthopedics outcomes did not show significant differences in mortality across teaching and nonteaching environments, though costs were higher at teaching hospitals. Among Medicare patients, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used in general surgery, and to some extent vascular surgery, but this was not apparent in orthopedic surgery.
Sections du résumé
OBJECTIVE
To compare outcomes and costs between major teaching and nonteaching hospitals on a national scale by closely matching on patient procedures and characteristics.
BACKGROUND
Teaching hospitals have been shown to often have better quality than nonteaching hospitals, but cost and value associated with teaching hospitals remains unclear.
METHODS
A study of Medicare patients at 340 teaching hospitals (resident-to-bed ratios ≥ 0.25) and matched patient controls from 2444 nonteaching hospitals (resident-to-bed ratios < 0.05).We studied 86,751 pairs admitted for general surgery (GS), 214,302 pairs of patients admitted for orthopedic surgery, and 52,025 pairs of patients admitted for vascular surgery.
RESULTS
In GS, mortality was 4.62% in teaching hospitals versus 5.57%, (a difference of -0.95%, <0.0001), and overall paired cost difference = $915 (P < 0.0001). For the GS quintile of pairs with highest risk on admission, mortality differences were larger (15.94% versus 18.18%, difference = -2.24%, P < 0.0001), and paired cost difference = $3773 (P < 0.0001), yielding $1682 per 1% mortality improvement at 30 days. Patterns for vascular surgery outcomes resembled general surgery; however, orthopedics outcomes did not show significant differences in mortality across teaching and nonteaching environments, though costs were higher at teaching hospitals.
CONCLUSIONS
Among Medicare patients, as admission risk of mortality increased, the absolute mortality benefit of treatment at teaching hospitals also increased, though accompanied by marginally higher cost. Major teaching hospitals appear to return good value for the extra resources used in general surgery, and to some extent vascular surgery, but this was not apparent in orthopedic surgery.
Identifiants
pubmed: 31639108
doi: 10.1097/SLA.0000000000003602
pii: 00000658-202003000-00003
doi:
Types de publication
Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
412-421Commentaires et corrections
Type : CommentIn
Type : CommentIn
Type : CommentIn
Références
Dimick JB, Cowan JA Jr, Colletti LM, et al. Hospital teaching status and outcomes of complex surgical procedures in the United States. Arch Surg 2004; 139:137–141.
Hyder O, Sachs T, Ejaz A, et al. Impact of hospital teaching status on length of stay and mortality among patients undergoing complex hepatopancreaticobiliary surgery in the USA. J Gastrointest Surg 2013; 17:2114–2122.
Khuri SF, Najjar SF, Daley J, et al. Comparison of surgical outcomes between teaching and nonteaching hospitals in the Department of Veterans Affairs. Ann Surg 2001; 234:370–382.
Zafar SN, Shah AA, Hashmi ZG, et al. Outcomes after emergency general surgery at teaching versus nonteaching hospitals. J Trauma Acute Care Surg 2015; 78:69–76.
Burke LG, Frakt AB, Khullar D, et al. Association between teaching status and mortality in US hospitals. JAMA 2017; 317:2105–2113.
Burke L, Khullar D, Orav EJ, et al. Do academic medical centers disproportionately benefit the sickest patients? Health Aff (Millwood) 2018; 37:864–872.
Burke LG, Khullar D, Zheng J, et al. Comparison of costs of care for Medicare patients hospitalized in teaching and nonteaching hospitals. JAMA Netw Open 2019; 2:e195229.
Meguid RA, Brooke BS, Perler BA, et al. Impact of hospital teaching status on survival from ruptured abdominal aortic aneurysm repair. J Vasc Surg 2009; 50:243–250.
Ayanian JZ, Weissman JS. Teaching hospitals and quality of care: a review of the literature. Milbank Q 2002; 80:569–593.
Ayanian JZ, Weissman JS, Chasan-Taber S, et al. Quality of care for two common illnesses in teaching and nonteaching hospitals. Health Aff 1998; 17:194–205.
Khullar D, Frakt AB, Burke LG. Advancing the academic medical center value debate. Are teaching hospitals worth it? JAMA 2019; 322:205–206.
Silber JH, Rosenbaum PR, Ross RN, et al. Indirect standardization matching: assessing specific advantage and risk synergy. Health Serv Res 2016; 51:2330–2357.
Silber JH, Rosenbaum PR, McHugh MD, et al. Comparison of the value of nursing work environments in hospitals across different levels of patient risk. JAMA Surg 2016; 151:527–536.
Hansen BB. The prognostic analogue of the propensity score. Biometrika 2008; 95:481–488.
CMS.gov. Cost Reports. April 2017. Available at: https://www.cms.gov/research-statistics-data-and-systems/downloadable-public-use-files/cost-reports/. Accessed June 9, 2017.
Volpp KG, Rosen AK, Rosenbaum PR, et al. Mortality among hospitalized Medicare beneficiaries in the first 2 years following ACGME resident duty hour reform. JAMA 2007; 298:975–983.
Silber JH, Rosenbaum PR, Ross RN, et al. Template matching for auditing hospital cost and quality. Health Serv Res 2014; 49:1446–1474.
Rosenbaum PR. Chapter 5: Between Observational Studies and Experiments. In. Observation and Experiment. An Introduction to Causal Inference. 2017; Cambridge, MA: Harvard University Press, 90–96.
Rosenbaum P, Rubin D. The central role of the propensity score in observational studies for causal effects. Biometrika 1983; 70:41–55.
SAS Institute. Chapter 53: The LOGISTIC Procedure. In: SAS/STAT., 9., 3 User's, Guide, Cary, NC:, SAS., Institute, Inc., 2011:4033–4267. Available at: https://support.sas.com/documentation/cdl/en/statug/63962/PDF/default/statug.pdf. Accessed February 29, 2008.
SAS Institute. Chapter 77: The ROBUSTREG Procedure. In: SAS/STAT 9.3 User's Guide. Cary, NC: SAS Institute Inc.; 2011:6531–6625. Available at: https://support.sas.com/documentation/cdl/en/statug/63962/PDF/default/statug.pdf. Accessed August 6, 2018.
Huber PJ. Robust Statistics. Hoboken, NJ: John Wiley & Sons; 1981.
Hampel FR, Ronchett EM, Rousseeuw PJ, et al. Chapter 6. Linear Models: Robust Estimation. Section 6.3. M-Estimators for Linear Models. In. Robust Statistics. The Approach Based on Influence Functions. 2nd ed. New York, NY: John Wiley & Sons; 1986:315–328.
Centers for Medicare & Medicaid Services. Provider of Services Current Files. April 2017. Available at: https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/Provider-of-Services/. Accessed June 9, 2017.
Silber JH, Arriaga AF, Niknam BA, et al. Failure-to-rescue after acute myocardial infarction. Med Care 2018; 56:416–423.
Niknam BA, Arriaga AF, Rosenbaum PR, et al. Adjustment for atherosclerosis diagnosis distorts the effects of percutaneous coronary intervention and the ranking of hospital performance. J Am Heart Assoc 2018; 7:e008366.
Silber JH, Romano PS, Rosen AK, et al. Failure-to-rescue: comparing definitions to measure quality of care. Med Care 2007; 45:918–925.
Silber JH, Rosenbaum PR, Kelz RR, et al. Medical and financial risks associated with surgery in the elderly obese. Ann Surg 2012; 256:79–86.
Halpern NA, Pastores SM. Critical care medicine in the United States 2000–2005: an analysis of bed numbers, occupancy rates, payer mix, and costs. Crit Care Med 2010; 38:65–71.
Halpern NA, Pastores SM. Critical care medicine beds, use, occupancy, and costs in the United States: a methodological review. Crit Care Med 2015; 43:2452–2459.
Bamezai A, Melnick G, Nawathe A. The cost of an emergency department visit and its relationship to emergency department volume. Ann Emerg Med 2005; 45:483–490.
Smulowitz PB, Honigman L, Landon BE. A novel approach to identifying targets for cost reduction in the emergency department. Ann Emerg Med 2013; 61:293–300.
Krinsky S, Ryan AM, Mijanovich T, et al. Variation in payment rates under Medicare's inpatient prospective payment system. Health Serv Res 2017; 52:676–696.
Pandya A. Adding cost-effectiveness to define low-value care. JAMA 2018; 319:1977–1978.
Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness—the curious resilience of the $50,000-per-QALY threshold. N Engl J Med 2014; 371:796–797.
Efron B, Gong G. A Leisurely Look at the Bootstrap, the Jackknife, and Cross-Validation. Am Stat 1983; 37:36–48.
SAS Institute. Version 9.4 of the Statistical Analytic Software System for UNIX. Cary, NC: SAS Institute, Inc; 2013.
Pimentel SD, Kelz RR, Silber JH, et al. Large, sparse optimal matching with refined covariate balance in an observational study of the health outcomes produced by new surgeons. J Am Stat Assoc 2015; 110:515–527.
Rubin DB. Bias reduction using Mahalanobis metric matching. Biometrics 1980; 36:293–298.
Rubin DB. For objective causal inference, design trumps analysis. Ann Appl Stat 2008; 2:808–840.
Rosenbaum PR, Rubin DB. Constructing a control group using multivariate matched sampling methods that incorporate the propensity score. Am Stat 1985; 39:33–38.
Cochran WG, Rubin DB. Controlling bias in observational studies. A review. Sankhya Series A 1973; 35:417–446.
Bishop YMM, Fienberg SE, Holland PW. Discrete Multivariate Analysis: Theory and Practice. Cambridge, MA: The MIT Press; 1975.
Rosenbaum PR. Sensitivity analysis for m-estimates, tests, and confidence intervals in matched observational studies. Biometrics 2007; 63:456–464.
Maritz JS. A note on exact robust confidence intervals for location. Biometrika 1979; 66:163–166.
Rosenbaum PR. Two R packages for sensitivity analysis in observational studies. Obs Stud 2015; 1:1–17.
Mantel N. Chi-square tests with one degree of freedom: extensions of the Mantel-Haenszel procedure. J Am Stat Assoc 1963; 58:690–700.
Cleveland WS. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1979; 74:829–836.
Efron B, Tibshirani R. Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. Stat Sci 1986; 1:54–75.
Rosen AK, Borzecki AM. Iezzoni LI. Chapter 4: Windows of Observation. Risk Adjustment for Measuring Health Care Outcomes 4th edChicago, IL: Health Administration Press; 2013. 77–94.