Variations in the Hospital Standardized Mortality Ratios in Korea
2
조회
0
다운로드
2
인용
0
참고문헌
키워드
초록
Objectives: The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care. Methods: All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities. Results: For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR. Conclusions: We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.
Abstract
Objectives: The hospital standardized mortality ratio (HSMR) has been widely used because it allows for robust risk adjustment using administrative data and is important for improving the quality of patient care. Methods: All inpatients discharged from hospitals with more than 700 beds (66 hospitals) in 2008 were eligible for inclusion. Using the claims data, 29 most responsible diagnosis (MRDx), accounting for 80% of all inpatient deaths among these hospitals, were identified, and inpatients with those MRDx were selected. The final study population included 703 571 inpatients including 27 718 (3.9% of all inpatients) in-hospital deaths. Using logistic regression, risk-adjusted models for predicting in-hospital mortality were created for each MRDx. The HSMR of individual hospitals was calculated for each MRDx using the model coefficients. The models included age, gender, income level, urgency of admission, diagnosis codes, disease-specific risk factors, and comorbidities. The Elixhauser comorbidity index was used to adjust for comorbidities. Results: For 26 out of 29 MRDx, the c-statistics of these mortality prediction models were higher than 0.8 indicating excellent discriminative power. The HSMR greatly varied across hospitals and disease groups. The academic status of the hospital was the only factor significantly associated with the HSMR. Conclusions: We found a large variation in HSMR among hospitals; therefore, efforts to reduce these variations including continuous monitoring and regular disclosure of the HSMR are required.
참고문헌 (35)
- Donabedian A, "Evaluating the quality of medical care", Milbank Mem Fund Q, 44(3), pp.166~206 (1966)
- Werner RM, "Relationship between Medicare’s hospital compare performance measures and mortality rates", JAMA, 296(22), pp.2694~2702 (2006)
- DesHarnais S, "Measuring outcomes of hospital care using multiple risk-adjusted indexes", Health Serv Res, 26(4), pp.425~445 (1991)
- Mant J, "Process versus outcome indicators in the assessment of quality of health care", Int J Qual Health Care, 13(6), pp.475~480 (2001)
- Iezzoni LI, "Severity measurement methods and judging hospital death rates for pneumonia", Med Care, 34(1), pp.11~28 (1996)
- Iezzoni LI, "Using risk-adjusted outcomes to assess clinical practice: an overview of issues pertaining to risk adjustment", Ann Thorac Surg, 58(6), pp.1822~1826 (1994)
- Iezzoni LI, "Predicting in-hospital mortality: a comparison of severity measurement approaches", Med Care, 30(4), pp.347~359 (1992)
- DesHarnais SI, "The Risk-Adjusted Mortality Index: a new measure of hospital performance", Med Care, 26(12), pp.1129~1148 (1988)
- Scott IA, "Using hospital standardised mortality ratios to assess quality of care: proceed with extreme caution", Med J Aust, 194(12), pp.645~648 (2011)
- Jarman B, "Explaining differences in English hospital death rates using routinely collected data", BMJ, 318(7197), pp.1515~1520 (1999)
- Richardus JH, "The perinatal mortality rate as an indicator of quality of care in international comparisons", Med Care, 36(1), pp.54~66 (1998)
- Gordon HS, "Impact of interhospital transfers on outcomes in an academic medical center. Implications for profiling hospital quality", Med Care, 34(4), pp.295~309 (1996)
- Westert GP, "An international study of hospital readmissions and related utilization in Europe and the USA", Health Policy, 61(3), pp.269~278 (2002)
- Mohammed MA, "Evidence of methodological bias in hospital standardised mortality ratios: retrospective database study of English hospitals", BMJ, 338, pp.780~ (2009)
- "HSMR: a new approach for measuring hospital mortality trends in Canada"[URL]
- "Technical notes: hospital standardized mortality ratio (HSMR)"[URL]
- Southern DA, "Comparison of the Elixhauser and Charlson", Deyo methods of comorbidity measurement in administrative data, 2004, pp.42(4), 355~360
- Elixhauser A, "Comorbidity measures for use with administrative data", Med Care, 36(1), pp.8~27 (1998)
- Li P, "Comparison of the performance of the CMS Hierarchical Condition Category (CMS-HCC) risk adjuster with the Charlson and Elixhauser comorbidity measures in predicting mortality", BMC Health Serv Res, 10, pp.245~ (2010)
- Stukenborg GJ, "Comparison of the performance of two comorbidity measures, with and without information from prior hospitalizations", Med Care, 39(7), pp.727~739 (2001)
- Hosmer DW, "Applied logistic regression", pp.147~156 (2000), Wiley
- Wright J, "Learning from death: a hospital mortality reduction programme", J R Soc Med, 99(6), pp.303~308 (2006)
- Lee EJ, "Development of hospital standardized mortality ratio index using administrative data", 박사, pp.2011
- "HSMR 2012: methodological report"[URL]
- Pouw ME, "Steyerberg E, Kalkman CJ, et al. Hospital standardized mortality ratio: consequences of adjusting hospital mortality with indirect standardization", PLoS One, 8(4), pp.e59160~ (2013)
- Ayanian JZ, "Teaching hospitals and quality of care: a review of the literature", Milbank Q, 80(3), pp.569~593 (2002)
- Allison JJ, "Relationship of hospital teaching status with quality of care and mortality for Medicare patients with acute MI", JAMA, 284(10), pp.1256~1262 (2000)
- Rosenthal GE, "Severity-adjusted mortality and length of stay in teaching and nonteaching hospitals: results of a regional study", JAMA, 278(6), pp.485~490 (1997)
- Ayanian JZ, "The relation between health insurance coverage and clinical outcomes among women with breast cancer", N Engl J Med, 329(5), pp.326~331 (1993)
- Hasan, "Insurance status and hospital care for myocardial infarction, stroke, and pneumonia", J Hosp Med, 5(8), pp.452~459 (2010)
- "Understanding HSMRs: a toolkit on hospital standardized mortality ratios"[URL]
- Jarman B, "The hospital standardised mortality ratio: a powerful tool for Dutch hospitals to assess their quality of care?", Qual Saf Health Care, 19(1) (2010)
- Bottle A, "Strengths and weaknesses of hospital standardised mortality ratios", pp.c7116~ (2010), BMJ
- Rosenthal GE, "Relationships between in-hospital and 30-day standardized hospital mortality: implications for profiling hospitals", Health Serv Res, 34(7), pp.1449~1468 (2000)
- Pouw ME, "Including post-discharge mortality in calculation of hospital standardised mortality ratios: retrospective analysis of hospital episode statistics", BMJ, 347, pp.f5913~ (2013)