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http://dx.doi.org/10.5762/KAIS.2019.20.4.435

A Study on the Development of Readmission Predictive Model  

Cho, Yun-Jung (Dept. of Biomedical Science Graduate School, Kyung Hee University)
Kim, Yoo-Mi (Dept. of Health Policy & Management, Sangji University)
Han, Seung-Woo (Korea Cancer Center Hospital)
Choe, Jun-Yeong (Dept. of Health Information Management, Wonkwang Health Science University)
Baek, Seol-Gyeong (Ajou University Hospital)
Kang, Sung-Hong (Dept. of Health Policy & Management, Inje University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.4, 2019 , pp. 435-447 More about this Journal
Abstract
In order to prevent unnecessary re-admission, it is necessary to intensively manage the groups with high probability of re-admission. For this, it is necessary to develop a re-admission prediction model. Two - year discharge summary data of one university hospital were collected from 2016 to 2017 to develop a predictive model of re-admission. In this case, the re-admitted patients were defined as those who were discharged more than once during the study period. We conducted descriptive statistics and crosstab analysis to identify the characteristics of rehospitalized patients. The re-admission prediction model was developed using logistic regression, neural network, and decision tree. AUC (Area Under Curve) was used for model evaluation. The logistic regression model was selected as the final re-admission predictive model because the AUC was the best at 0.81. The main variables affecting the selected rehospitalization in the logistic regression model were Residental regions, Age, CCS, Charlson Index Score, Discharge Dept., Via ER, LOS, Operation, Sex, Total payment, and Insurance. The model developed in this study was limited to generalization because it was two years data of one hospital. It is necessary to develop a model that can collect and generalize long-term data from various hospitals in the future. Furthermore, it is necessary to develop a model that can predict the re-admission that was not planned.
Keywords
Readmission; Predictive Modeling; Data Mining; Quality Improvement;
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1 M. Charlson, P. Pompei, K. Ales, C. MacKenzie, "A new method of classifying prognostic comorbidity in longitudinal studies: development and validation", J Chronic Dis, 40, pp. 373-383, 1987.   DOI
2 V. Sundararajan, T. Henderson, C. Perry, A. Muggivan, H. Quan, V.A. Ghali, "New ICD-10 version of the Charlson comorbidity index predicted in-hospital mortality," J Clin Epidemiol, vol. 57, pp. 1288-94, 2004. DOI: https://doi.org/10.1016/j.jclinepi.2004.03.012   DOI
3 C.Y. Wang, Y.S. Lin, C. Tzao, H.C. Lee, M.H. Huang, W.H. Hsu, H.S. Hsu, "Comparison of Charlson comorbidity index and Kaplan-Feinstein index in patients with stage I lung cancer after surgical resection", European Journal of ardio-Thoracic Surgery, vol. 32, no. 1 pp. 877-881, Dec. 2007. DOI: https://doi.org/10.1016/j.ejcts.2007.09.008   DOI
4 C.N. Klabunde, J.M. Legler, J.L. Warren, L.M. Baldwin, D. Schrg, "A refined comorbidity measurement algorithm for claims-based studies of breast, prostate, colorectal, and lung cancer patients," Ann Epidemiol, vol. 17, pp. 584-90, 2007. DOI: https://doi.org/10.1016/j.annepidem.2007.03.011   DOI
5 M. Kim, H. Kim, S.H. Hwang, "Developing a Hospital-Wide All-Cause Risk-Standardized Readmission Measure Using Administrative Claims Data in Korea: Methodological Explorations and Implications," Health Policy and Management, vol. 25, no. 3, pp. 197-206, 2015. DOI: https://doi.org/10.4332/KJHPA.2015.25.3.197   DOI
6 Tabak YP1, Sun X, Nunez CM, Gupta V, Johannes RS., Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score., 2017 Mar;55(3):267-275. DOI: https://doi.org/10.1097/MLR.0000000000000654   DOI
7 S.F. Jencks, M.V. Williams, E.A. Coleman, "Rehospitalizations among patients in the Medicare fee-for-service program," New England Journal of Medicine, vol. 360, no. 14, pp. 1418-1428, 2009. DOI: https://doi.org/10.1056/NEJMsa0803563   DOI
8 M.R. Chassin, J.M. Loeb, S.P. Schmaltz, R.M. Wachter, "Accountability measures using measurement to promote quality improvement," New England Journal of Medicine, vol. 363, no. 7, pp. 683-688, 2010. DOI: https://doi.org/10.1056/NEJMsb1002320   DOI
9 I. Shams, S. Ajorlou, K. Yang, "A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD," Health Care Manag Sci., vol. 18, no. 1, pp. 19-34, Mar. 2015. DOI: https://doi.org/10.1007/s10729-014-9278-y   DOI
10 Health Insurance Review & Assessment Service, "Evaluation of Hospital-Wide All-Cause Quality Measures," July. 2015.
11 Medicare Payment Advisory Commission (MedPAC), "Promoting Greater Efficiency in Medicare," pp. 103-20, 2007.
12 R.B. Zuckerman, S.H. Sheingold, E.J. Orav, J. Ruhter, A.M. Epstein, "Readmissions, observation, and the hospital readmissions reduction program," New England Journal of Medicine, vol. 374, no. 16, pp. 1543-1551, 2016. DOI: https://doi.org/10.1056/NEJMsa1513024   DOI
13 Health Insurance Review & Assessment Service, "Results for Risk-Standardized Readmission Ratio in 2015(First)," Nov. 2016.
14 Health Insurance Review & Assessment Service, "Results of Appropriateness for Risk-Standardized Readmission Ratio in 2017(Second)," Dec. 2018,
15 Canadian Institute for Health Information, "All-Cause Readmission to Acute Care and Return to the Emergency Department," Canadian Institute for Health Information, 2012. ISBN: 978-1-77109-040-7
16 Eun Youmg Choi, Minsu Osk, SangOil Lee, " Is the Risk-Standardized Readmission Rate Appropriate for a Generic Quality Indicator of Hospital Care?," Health Policy and Management, vol.26, N0.2, 148-152, 2016.   DOI
17 D. Kansagara, H. Englander, A. Salanitro, D. Kagen, C. Theobald, M. Freeman, S. Kripalani, "Risk prediction models for hospital readmission: a systematic review," J. Am. Med. Assoc., vol. 306, pp. 1688-1698, 2011 DOI: https://doi.org/10.1001/jama.2011.1515   DOI
18 Amber K. Sabbatini, M.D., M.P.H., and Brad Wright, Ph.D. Excluding Observation Stays from Readmission Rates - What Quality Measures Are Missing, N Engl J Med 2018; 378:2062-2065 DOI: https://doi.org/10.1056/NEJMp1800732   DOI
19 Harlan M. Krumholz, M.D., Kun Wang, Ph.D., Hospital-Readmission Risk - Isolating Hospital Effects from Patient Effects, N Engl J Med 2017; 377:1055-1064 DOI: https://doi.org/10.1056/NEJMsa1702321   DOI
20 E.Y. Choi, M.S. Ock, S.I. Lee, "Is the Risk-Standardized Readmission Rate Appropriate for a Generic Quality Indicator of Hospital Care?" Health Policy and Management, vol. 26, no.2, pp. 148-152, 2016. DOI: https://doi.org/10.4332/KJHPA.2016.26.2.148   DOI
21 J. Futoma, J. Morris, J. Lucas, "A comparison of models for predicting early hospital readmissions," Journal of Biomedical Informatics, vol. 56, pp. 229-238, 2015. DOI: https://doi.org/10.1016/j.jbi.2015.05.016   DOI
22 K. Shameer, K.W. Johnson, A. Yahi, R. Miotto, L.I. Li, D. Ricks, et al., "Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: A case-study using mount sinai heart failure cohort," Pac Symp Biocomput., vol. 22, pp. 276-287, 2017. DOI: https://doi.org/10.1142/9789813207813_0027