Selecting the Best Prediction Model for Readmission
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초록
Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model. Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve. Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine,10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater. Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.
Abstract
Objectives: This study aims to determine the risk factors predicting rehospitalization by comparing three models and selecting the most successful model. Methods: In order to predict the risk of rehospitalization within 28 days after discharge, 11 951 inpatients were recruited into this study between January and December 2009. Predictive models were constructed with three methods, logistic regression analysis, a decision tree, and a neural network, and the models were compared and evaluated in light of their misclassification rate, root asymptotic standard error, lift chart, and receiver operating characteristic curve. Results: The decision tree was selected as the final model. The risk of rehospitalization was higher when the length of stay (LOS) was less than 2 days, route of admission was through the out-patient department (OPD), medical department was in internal medicine,10th revision of the International Classification of Diseases code was neoplasm, LOS was relatively shorter, and the frequency of OPD visit was greater. Conclusions: When a patient is to be discharged within 2 days, the appropriateness of discharge should be considered, with special concern of undiscovered complications and co-morbidities. In particular, if the patient is admitted through the OPD, any suspected disease should be appropriately examined and prompt outcomes of tests should be secured. Moreover, for patients of internal medicine practitioners, co-morbidity and complications caused by chronic illness should be given greater attention.
참고문헌 (37)
- Hasan M, "Readmission of patients to hospital: still ill defined and poorly understood", Int J Qual Health Care, 13(3), pp.177~179 (2001)
- Clarke A, "to hospital: a measure of quality or outcome?", Qual Saf Health Care, 13(1), pp.10~11 (2004)
- Friedman B, "The rate and cost of hospital readmissions for preventable conditions", Med Care Res Rev, 61(2), pp.225~240 (2004)
- Medicare Payment Advisory Commission, "A path to bundled payment around a rehospitalization. In: Medicare Payment Advisory Commission", Congress: reforming the delivery system, pp.83~103 (2008), Medicare Payment Advisory Commission
- Jencks SF, "Rehospitalizations among patients in the Medicare fee-for-service program", N Engl J Med, 360(14), pp.1418~1428 (2009)
- Franklin PD, "Using data to reduce hospital readmissions", J Nurs Care Qual, 14(1), pp.67~85 (1999)
- Cullen C, "Re-admission rates within 28 days of total hip replacement", Ann R Coll Surg Engl, 88(5), pp.475~478 (2006)
- Clinical indicators users’ manual: hospital wide clinical indicators, "Australian Council on Healthcare Standards", Australian Council on Healthcare Standards, 4~5 (2007)
- Lakhani A, "Compendium of clinical and health indicators: data definitions and user guide for computer files", NHS Information Centre for Health and Social Care, 97~ (2009)
- Ministry of Health & Welfare, "Guidelines for hospital evaluation programme 2009", Ministry of Health & Welfare, 254~ (2009)
- McBride S, "Parameters for the appropriate definition of hospital readmissions. In: Agency for Healthcare Research and Quality", Workshop of Agency for Healthcare Research and Quality: using administrative data to answer state policy questions, pp.4~11 (2008), Agency for Healthcare Research and Quality
- Moon OR, "An analysis on the characteristics of high cost patients in the regional medical insurance program", Korean J Health Policy Adm, 3(1), pp.53~83 (1993)
- Report to the congress: promoting greater efficiency in medicare, "Medicare Payment Advisory Commission", Medicare Payment Advisory Commission, 103~120 (2007)
- 이은환, "서울시 소재 한 대학병원 퇴원환자의 재입원 관련요인", 병원경영학회지, 15(4), pp.125~142 (2010), 한국병원경영학회
- Heggestad T, "Measuring readmissions: focus on the time factor", Int J Qual Health Car, 15(2), pp.147~154 (2003)
- Jasti H, "Causes and risk factors for rehospitalization of patients hospitalized with community-acquired pneumonia", Clin Infect Dis, 46(4), pp.550~556 (2008)
- Silverstein MD, "Risk factors for 30-day hospital readmission in patients ≥65 years of age", Proc (Bayl Univ Med Cent), 21(4), pp.363~372 (2008)
- Rumball-Smith J, "The readmission rate as an indicator of the quality of elective surgical inpatient care for the elderly in New Zealand", N Z Med J, 122(1289), pp.32~39 (2009)
- Reed RL, "Risk factors for early unplanned hospital readmission in the elderly", J Gen Intern Med, 6(3), pp.223~228 (1991)
- Corrigan JM, "Identification of factors associated with hospital readmission and development of a predictive model", Health Serv Res, 27(1), pp.81~101 (1992)
- Marcantonio ER, "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)
- Chu LW, "Risk factors for early emergency hospital readmission in elderly medical patients", Gerontology, 45(4), pp.220~226 (1999)
- Graham H, "Can readmissions to a geriatric medical unit be prevented?", Lancet, 1(8321), pp.404~406 (1983)
- Gooding J, "Hospital readmissions among the elderly", J Am Geriatr Soc, 33(9), pp.595~601 (1985)
- Fethke CC, "“Risk” factors affecting readmission of the elderly into the health care system", Med Care, 24(5), pp.429~437 (1986)
- Holloway JJ, "Clinical and sociodemographic risk factors for readmission of Medicare beneficiaries", Health Care Financ Rev, 10(1), pp.27~36 (1988)
- Oh HJ, "A case-control study of unexpected readmission in a university hospital", Korean J Prev Med, 32(3), pp.289~296 (1999)
- Smith DM, "Predicting non-elective hospital readmissions: a multi-site study. Department of Veterans Affairs Cooperative Study Group on Primary Care and Readmissions", J Clin Epidemiol, 53(11), pp.1113~1118 (2000)
- Steyerberg EW, "Validation and updating of predictive logistic regression models: a study on sample size and shrinkage", Stat Med, 23(16), pp.2567~2586 (2004)
- Bellazzi R, "Predictive data mining in clinical medicine: current issues and guidelines", Int J Med Inform, 77(2), pp.81~97 (2008)
- Shepperd S, "Early discharge hospital at home", Cochrane Database Syst Rev, (1) (2009)
- Lermitte J, "Patient selection in ambulatory surgery", Curr Opin Anaesthesiol, 18(6), pp.598~602 (2005)
- Lock M, "Higher neonatal morbidity after routine early hospital discharge: are we sending newborns home too early?", CMAJ, 161(3), pp.249~253 (1999)
- "Physicians are talking about: the culture of defensive medicine" (2010)[URL]
- Brown J, "Stemming the tide of readmissions: patient, practice or practitioner?", Rev Clin Gerontol, 8, pp.173~181 (1998)
- Burns R, "Factors predicting readmission of older general medicine patients", J Gen Intern Med, 6(5), pp.389~393 (1991)
- Lee KJ, "Increasing of revenue through reduction of length of stay", Proceedings of the 2005 Conference of Korean Society of Quality Assurance in Health Care, pp.361~362 (2005), Korean Society of Quality Assurance in Health Care