Can hospitals "game the system" by avoiding high-risk patients?

David C. Chang, Jamie E. Anderson, Peter T. Yu, Luis C. Cajas, Selwyn O. Rogers, Mark A. Talamini

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

BACKGROUND: It has been suggested that implementation of quality-improvement benchmarking programs can lead to risk-avoidance behaviors in some physicians and hospitals in an attempt to improve their rankings, potentially denying patients needed treatment. We hypothesize that avoidance of high-risk patients will not change risk-adjusted rankings. STUDY DESIGN: We conducted a simulation analysis of 6 complex operations in the Nationwide Inpatient Sample, including abdominal aortic aneurysm repair, aortic valve replacement, coronary artery bypass grafting, percutaneous coronary intervention, esophagectomy, and pancreatic resection. Primary outcomes included in-hospital mortality. Hospitals were ranked into quintiles based on observed-to-expected (O/E) mortality ratios, with their expected mortalities calculated based on models generated from the previous 3 years. Half of the hospitals were then randomly selected to undergo risk avoidance by avoiding 25% of patients with higher than median risks (ie, Charlson, Elixhauser, age, minority, or uninsured status). Their new O/E ratios and hospital-rank categories were compared with their original values. RESULTS: A total of 2,235,298 patients were analyzed, with an overall observed mortality rate of 1.9%. Median change in O/E ratios across all simulations was zero, and O/E ratios did not change in 97.5% to 99.3% of the hospitals, depending on the risk definitions. Additionally, 70.5% to 98.0% of hospital rankings remained unchanged, 1.3% to 13.1% of hospital rankings improved, and 0.7% to 14.3% of hospital rankings worsened after risk avoidance. CONCLUSIONS: Risk-adjusted rankings of hospitals likely cannot be changed by simply avoiding high-risk patients. In the minority of scenarios in which risk-adjusted rankings changed, they were as likely to improve as worsen after risk avoidance.

Original languageEnglish (US)
Pages (from-to)80-86
Number of pages7
JournalJournal of the American College of Surgeons
Volume215
Issue number1
DOIs
StatePublished - Jul 2012
Externally publishedYes

Fingerprint

Mortality
Avoidance Learning
Benchmarking
Esophagectomy
Abdominal Aortic Aneurysm
Percutaneous Coronary Intervention
Quality Improvement
Hospital Mortality
Aortic Valve
Coronary Artery Bypass
Inpatients
Physicians
Therapeutics

ASJC Scopus subject areas

  • Surgery

Cite this

Chang, D. C., Anderson, J. E., Yu, P. T., Cajas, L. C., Rogers, S. O., & Talamini, M. A. (2012). Can hospitals "game the system" by avoiding high-risk patients? Journal of the American College of Surgeons, 215(1), 80-86. https://doi.org/10.1016/j.jamcollsurg.2012.05.005

Can hospitals "game the system" by avoiding high-risk patients? / Chang, David C.; Anderson, Jamie E.; Yu, Peter T.; Cajas, Luis C.; Rogers, Selwyn O.; Talamini, Mark A.

In: Journal of the American College of Surgeons, Vol. 215, No. 1, 07.2012, p. 80-86.

Research output: Contribution to journalArticle

Chang, DC, Anderson, JE, Yu, PT, Cajas, LC, Rogers, SO & Talamini, MA 2012, 'Can hospitals "game the system" by avoiding high-risk patients?', Journal of the American College of Surgeons, vol. 215, no. 1, pp. 80-86. https://doi.org/10.1016/j.jamcollsurg.2012.05.005
Chang, David C. ; Anderson, Jamie E. ; Yu, Peter T. ; Cajas, Luis C. ; Rogers, Selwyn O. ; Talamini, Mark A. / Can hospitals "game the system" by avoiding high-risk patients?. In: Journal of the American College of Surgeons. 2012 ; Vol. 215, No. 1. pp. 80-86.
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abstract = "BACKGROUND: It has been suggested that implementation of quality-improvement benchmarking programs can lead to risk-avoidance behaviors in some physicians and hospitals in an attempt to improve their rankings, potentially denying patients needed treatment. We hypothesize that avoidance of high-risk patients will not change risk-adjusted rankings. STUDY DESIGN: We conducted a simulation analysis of 6 complex operations in the Nationwide Inpatient Sample, including abdominal aortic aneurysm repair, aortic valve replacement, coronary artery bypass grafting, percutaneous coronary intervention, esophagectomy, and pancreatic resection. Primary outcomes included in-hospital mortality. Hospitals were ranked into quintiles based on observed-to-expected (O/E) mortality ratios, with their expected mortalities calculated based on models generated from the previous 3 years. Half of the hospitals were then randomly selected to undergo risk avoidance by avoiding 25{\%} of patients with higher than median risks (ie, Charlson, Elixhauser, age, minority, or uninsured status). Their new O/E ratios and hospital-rank categories were compared with their original values. RESULTS: A total of 2,235,298 patients were analyzed, with an overall observed mortality rate of 1.9{\%}. Median change in O/E ratios across all simulations was zero, and O/E ratios did not change in 97.5{\%} to 99.3{\%} of the hospitals, depending on the risk definitions. Additionally, 70.5{\%} to 98.0{\%} of hospital rankings remained unchanged, 1.3{\%} to 13.1{\%} of hospital rankings improved, and 0.7{\%} to 14.3{\%} of hospital rankings worsened after risk avoidance. CONCLUSIONS: Risk-adjusted rankings of hospitals likely cannot be changed by simply avoiding high-risk patients. In the minority of scenarios in which risk-adjusted rankings changed, they were as likely to improve as worsen after risk avoidance.",
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