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

David C. Chang, Jamie 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 1 2012

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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., 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; Yu, Peter T.; Cajas, Luis C.; Rogers, Selwyn O.; Talamini, Mark A.

In: Journal of the American College of Surgeons, Vol. 215, No. 1, 01.07.2012, p. 80-86.

Research output: Contribution to journalArticle

Chang, DC, Anderson, J, 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 ; 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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AB - 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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