Identification of Risk Factors for Potentially Preventable Re-hospitalization: Empirical Evidence from California and Florida
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Objective: Policymakers have given substantial attention to reducing preventable readmissions because they may represent an opportunity to ensure quality of care and patient safety while lowering health care costs. The objective of this study is to examine factors associated with thirty-day potentially preventable hospital readmission (PPR). Data Sources/Study setting: This was an observational cross-sectional study of nonfederal acute-care hospital located in two states California and Florida, discharging Medicare patients with a principal discharge diagnosis of heart failure, acute myocardial infarction, or pneumonia January through December 31, 2007. Data were obtained from the Healthcare Cost and Utilization Project State Inpatient Database of the Agency for Healthcare Research and Quality. Study Design: The dependent variable of this study is condition-specific thirty-day potentially preventable hospital readmission (PPR). 3M’s PPR software was utilized to determine whether a readmission was potentially preventable. The independent variables of this study are clinical and socio-demographic characteristics of patients. We used multivariate hierarchical logistic models, clustered by hospitals, to examine factor associated with condition-specific, risk-adjusted, thirty-day PPR rates. Principal Findings: Multivariate results indicate that patients who were discharged to home health care or nursing home are more likely to be readmitted for a potentially preventable reason compared to those who were discharged to home (i.e., routine discharge) across all three conditions (p<0.001 for all). Patient with more severity, measured by history of prior admission, also had highter odds of 30-day PPR across all three conditions (p<0.001 for all). Patients with renal failure and chronic pulmonary disease as comorbid conditions had increased odds of 30-day PPR across all three conditions (p<0.001 for all). Conclusion: This work identified patient risk factors associated with the risk of PPR at patient-level analysis. Findings from this study may be served as useful sources to entities that are seeking ways to reduce the occurrences of PPRs.
Abstract
Objective: Policymakers have given substantial attention to reducing preventable readmissions because they may represent an opportunity to ensure quality of care and patient safety while lowering health care costs. The objective of this study is to examine factors associated with thirty-day potentially preventable hospital readmission (PPR). Data Sources/Study setting: This was an observational cross-sectional study of nonfederal acute-care hospital located in two states California and Florida, discharging Medicare patients with a principal discharge diagnosis of heart failure, acute myocardial infarction, or pneumonia January through December 31, 2007. Data were obtained from the Healthcare Cost and Utilization Project State Inpatient Database of the Agency for Healthcare Research and Quality. Study Design: The dependent variable of this study is condition-specific thirty-day potentially preventable hospital readmission (PPR). 3M’s PPR software was utilized to determine whether a readmission was potentially preventable. The independent variables of this study are clinical and socio-demographic characteristics of patients. We used multivariate hierarchical logistic models, clustered by hospitals, to examine factor associated with condition-specific, risk-adjusted, thirty-day PPR rates. Principal Findings: Multivariate results indicate that patients who were discharged to home health care or nursing home are more likely to be readmitted for a potentially preventable reason compared to those who were discharged to home (i.e., routine discharge) across all three conditions (p<0.001 for all). Patient with more severity, measured by history of prior admission, also had highter odds of 30-day PPR across all three conditions (p<0.001 for all). Patients with renal failure and chronic pulmonary disease as comorbid conditions had increased odds of 30-day PPR across all three conditions (p<0.001 for all). Conclusion: This work identified patient risk factors associated with the risk of PPR at patient-level analysis. Findings from this study may be served as useful sources to entities that are seeking ways to reduce the occurrences of PPRs.
참고문헌 (60)
- Centers for Medicare and Medicaid Services (CMS), "Medicare program; hospital inpatient prospective payment systems for acute care hospitals and the long-term care hospital prospective payment system and FY 2012 rates; hospitals' FTE resident caps for graduate medical education payment", Final rules. Fed Regist, 76(160), pp.51476~51846 (2011)
- Allaudeen, N., "Redefining readmission risk factors for general medicine patients", J Hosp Med, 6(2), pp.54~60 (2011)
- Anderson, G. F., "Hospital readmissions in the Medicare population", N Engl J Med, 311(21), pp.1349~1353 (1984)
- Anderson, G. F., "Predicting hospital readmissions in the Medicare population", Inquiry, 22(3), pp.251~258 (1985)
- Anderson, M. A., "Hospital readmission from home health care before and after prospective payment", J Nurs Scholarsh, 37(1), pp.73~79 (2005)
- Arbaje, A. I., "Postdischarge environmental and socioeconomic factors and the likelihood of early hospital readmission among community-dwelling Medicare beneficiaries", Gerontologist, 48(4), pp.495~504 (2008)
- Aujesky, D., "Predictors of early hospital readmission after acute pulmonary embolism", Arch Intern Med, 169(3), pp.287~293 (2009)
- Bisharat, N., "Readmissions to medical wards: Analysis of demographic and socio-medical factors", Eur J Intern Med (2012)
- Bisognano, M., "Improving transitions to reduce readmissions", Front Health Serv Manage, 25(3), pp.3~10 (2009)
- Boozary, A. S., "The Medicare Hospital Readmissions Reduction Program : Time for Reform", JAMA, 314(4), pp.347~348 (2015)
- Bottle, A., "Identifying patients at high risk of emergency hospital admissions : a logistic regression analysis", J R Soc Med, 99(8), pp.406~414 (2006)
- Bucholz, E. M., "Effect of living alone on patient outcomes after hospitalization for acute myocardial infarction", Am J Cardiol, 108(7), pp.943~948 (2011)
- Burns, R., "Factors predicting readmission of older general medicine patients", J Gen Intern Med, 6(5), pp.389~393 (1991)
- Calvillo-King, L., "Impact of social factors on risk of readmission or mortality in pneumonia and heart failure : systematic review", J Gen Intern Med, 28(2), pp.269~282 (2013)
- Corrigan, J. M., "Identification of factors associated with hospital readmission and development of a predictive model", Health Serv Res, 27(1), pp.81~101 (1992)
- El Solh, A. A., "Indicators of recurrent hospitalization for pneumonia in the elderly", J Am Geriatr Soc, 52(12), pp.2010~2015 (2004)
- Epstein, A. M., "The relationship between hospital admission rates and rehospitalizations", N Engl J Med, 365(24), pp.2287~2295 (2011)
- Ferraris, V. A., "Risk factors for early hospital readmission after cardiac operations", J Thorac Cardiovasc Surg, 122(2), pp.278~286 (2001)
- Fisher, E. S., "Hospital readmission rates for cohorts of Medicare beneficiaries in Boston and New Haven", N Engl J Med, 331(15), pp.989~995 (1994)
- Friedmann, J. M., "Predicting early nonelective hospital readmission in nutritionally compromised older adults", Am J Clin Nutr, 65(6), pp.1714~1720 (1997)
- Garcia, J. A., "Potentially avoidable rehospitalizations following acute myocardial infarction by insurance status", J Community Health, 28(3), pp.167~184 (2003)
- Goldfield, N. I., "Identifying potentially preventable readmissions", Health Care Financ Rev, 30(1), pp.75~91 (2008)
- Hannan, E. L., "Predictors of readmission for complications of coronary artery bypass graft surgery", JAMA, 290(6), pp.773~780 (2003)
- Harjai, K. J., "Simple clinical variables are markers of the propensity for readmission in patients hospitalized with heart failure", Am J Cardio, 187(2), pp.234~237 (2001)
- Hasan, O., "Hospital readmission in general medicine patients : a prediction model", J Gen Intern Med, 25(3), pp.211~219 (2010)
- "Hospital Performance Assessment" (2016)[URL]
- Holloway, J. J., "Risk factors for early readmission among veterans", Health Serv Res, 25(1 Pt 2), pp.213~237 (1990)
- Hu, J., "Socioeconomic status and readmissions: evidence from an urban teaching hospital", Health Aff (Millwood), 33(5), pp.778~785 (2014)
- Jencks, S. F., "Rehospitalizations among patients in the Medicare fee-for-service program", N Engl J Med, 360(14), pp.1418~1428 (2009)
- Joynt, K. E., "Thirty-day readmission rates for Medicare beneficiaries by race and site of care", JAMA, 305(7), pp.675~681 (2011)
- Keenan, P. S., "An administrative claims measure suitable for profiling hospital performance on the basis of 30-day all-cause readmission rates among patients with heart failure", Circ Cardiovasc Qual Outcomes, 1(1), pp.29~37 (2008)
- Kovacs, M., "A longitudinal study of biomedical and psychosocial predictors of multiple hospitalizations among young people with insulin-dependent diabetes mellitus", Diabet Med, 12(2), pp.142~148 (1995)
- Krumholz, H. M., "Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission", Circ Cardiovasc Qual Outcomes, 2(5), pp.407~413 (2009)
- Krumholz, H. M., "Readmission after hospitalization for congestive heart failure among Medicare beneficiaries", Arch Intern Med, 157(1), pp.99~104 (1997)
- Kumbhani, D. J., "Intraoperative myocardial acidosis as a risk for hospital readmission after cardiac surgery", Am J Surg, 198(3), pp.373~380 (2009)
- 이은환, "서울시 소재 한 대학병원 퇴원환자의 재입원 관련요인", 병원경영학회지, 15(4), pp.125~142 (2010), 한국병원경영학회
- Lindenauer, P. K., "Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia", J Hosp Med, 6(3), pp.142~150 (2011)
- Lu, M. L., "Marital status and living condition as predictors of mortality and readmissions among African Americans with heart failure", Int J Cardiol, 222, pp.313~318 (2016)
- Lyratzopoulos, G., "Factors influencing emergency medical readmission risk in a UK district general hospital : A prospective study", BMC Emerg Med, 5(1), pp.1~ (2005)
- Marcantonio, E. R., "Factors associated with unplanned hospital readmission among patients 65 years of age and older in a Medicare managed care plan", Am J Med, 107(1), pp.13~17 (1999)
- Mull, H. J., "Comparing 2 methods of assessing 30-day readmissions: what is the impact on hospital profiling in the veterans health administration?", Med Care, 51(7), pp.589~596 (2013)
- Nasir, K., "Is same-hospital readmission rate a good surrogate for all-hospital readmission rate?", Med Care, 48(5), pp.477~481 (2010)
- Novotny, N. L., "Prediction of early readmission in medical inpatients using the Probability of Repeated Admission instrument", Nurs Res, 57(6), pp.406~415 (2008)
- Ozturk, E., "Early readmission after ileoanal pouch surgery", Dis Colon Rectum, 52(11), pp.1848~1853 (2009)
- Parker, J. P., "Can pharmacy data improve prediction of hospital outcomes? Comparisons with a diagnosis-based comorbidity measure", Med Care, 41(3), pp.407~419 (2003)
- Philbin, E. F., "Prediction of hospital readmission for heart failure : development of a simple risk score based on administrative data", J Am Coll Cardiol, 33(6), pp.1560~1566 (1999)
- 30-Day Readmissions following Hospitalizations for Chronic vs. Acute Conditions, 2008, "Podulka, J.", Agency for Healthcare Research and Quality, ~, pp.HCUP Statistical Brief#127 (2012)
- Reddy, D. M., "Readmission after pancreatectomy for pancreatic cancer in Medicare patients", J Gastrointest Surg, 13(11), pp.1963~1974 (2009)
- Reed, R. L., "Risk factors for early unplanned hospital readmission in the elderly", J Gen Intern Med, 6(3), pp.223~228 (1991)
- Robbins, J. M., "Diagnosing diabetes and preventing rehospitalizations : the urban diabetes study", Med Care, 44(3), pp.292~296 (2006)
- Rodriguez, F., "Readmission rates for Hispanic Medicare beneficiaries with heart failure and acute myocardial infarction", Am Heart J, 162(2), pp.254~261 (2011)
- Silverstein, M. D., "Risk factors for 30-day hospital readmission in patients >", =65 years of age, 2008, pp.21(4), 363~372
- Soeken, K. L., "Predictors of hospital readmission. A meta-analysis", Eval Health Prof, 14(3), pp.262~281 (1991)
- Sullivan, D. H., "Risk factors for early hospital readmission in a select population of geriatric rehabilitation patients : the significance of nutritional status", J Am Geriatr Soc, 40(8), pp.792~798 (1992)
- Timms, J., "Documentation of characteristics of early hospital readmission of elderly patients. A challenge for inservice educators", J Nurses Staff Dev, 18(3), pp.136~143 (2002)
- Watson, J. P., "The relationship between asthma admission rates, routes of admission, and socioeconomic deprivation", Eur Respir J, 9(10), pp.2087~2093 (1996)
- Weeks, W. B., "Do older rural and urban veterans experience different rates of unplanned readmission to VA and non-VA hospitals?", J Rural Health, 25(1), pp.62~69 (2009)
- Weissman, J. S., "The impact of patient socioeconomic status and other social factors on readmission : a prospective study in four Massachusetts hospitals", Inquiry, 31(2), pp.163~172 (1994)
- Weller, W. E., "Readmissions for venous thromboembolism : expanding the definition of patient safety indicators", Jt Comm J Qual Saf, 30(9), pp.497~504 (2004)
- Woz, S., "Gender as risk factor for 30 days post-discharge hospital utilisation: a secondary data analysis", BMJ Open, 2(2), pp.e000428~ (2012)