Abstract
The increasing focus on evidence-based healthcare services as well as rising health expenditures for inpatient treatment forces hospitals to introduce new approaches to allow for a more efficient delivery of said services. As a new measure of healthcare quality, readmission rates are increasingly used to determine the quality of care, benchmark hospital performance and determine funding rates or even issue penalties. It is therefore key to determine patients at high risk of readmission. This can be done by using predictive risk models that are able to predict the risk of readmission to the hospital for individual patients using various data mining techniques and algorithms. Based on these models and with the increasing amount of data collected in hospitals, clinicians and hospital management can be supported in their daily decision-making to reduce readmission rates. Ultimately, the implementation of such prediction models can help avoid unnecessary costs as well as improve the quality of healthcare services. This work aims at identifying and analysing state-of-the-art risk prediction models in healthcare with regard to their specific application areas, applied algorithms and resulting accuracy to determine the suitability of different methods in different healthcare contexts.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Albers, S. (2007). Methodik der empirischen forschung. Wiesbaden: Springer Fachmedien.
Altman, D. G., & Royston, P. (2000). What do we mean by validating a prognostic model? Statistics in Medicine, 19(4), 453–473.
Aral, K. D., Güvenir, H. A., Sabuncuoğlu, I., & Akar, A. R. (2012). A prescription fraud detection model. Computer Methods and Programs in Biomedicine, 106(1), 37–46.
Au, A. G., McAlister, F. A., Bakal, J. A., Ezekowitz, J., Kaul, P., & van Walraven, C. (2012). Predicting the risk of unplanned readmission or death within 30 days of discharge after a heart failure hospitalization. American Heart Journal, 164(3), 365–372. https://doi.org/10.1016/j.ahj.2012.06.010.
Backhaus, K., Erichson, B., & Weiber, R. (2015). Fortgeschrittene multivariate analysemethoden. Berlin: Springer. https://doi.org/10.1007/978-3-662-46087-0.
Backhaus, K., Erichson, B., Plinke, W., & Weiber, R. (2016). Multivariate analysemethoden. Berlin: Springer. https://doi.org/10.1007/978-3-662-46076-4.
Bellazzi, R., & Zupan, B. (2008). Predictive data mining in clinical medicine: Current issues and guidelines. International Journal of Medical Informatics, 77(2), 81–97.
Bohsem, G. (2015). Millionen überflüssige Klinikaufenthalte, Sueddeutsche Zeitung.
Breiman, L. (2001). Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324.
Brown, A. (SAS Institut, Hrsg.). (2016). Top five reasons for using penalized regression for modeling your high-dimensional data. Zugriff am December 13, 2016, Verfügbar unter https://communities.sas.com/t5/SAS-Communities-Library/Tip-Top-five-reasons-for-using-penalized-regression-for-modeling/ta-p/223734
Carey, K., & Stefos, T. (2016). The cost of hospital readmissions: Evidence from the VA. Health Care Management Science, 19(3), 241–248. https://doi.org/10.1007/s10729-014-9316-9.
Chae, Y. M., Kim, H. S., Tark, K. C., Park, H. J., & Ho, S. H. (2003). Analysis of healthcare quality indicator using data mining and decision support system. Expert Systems with Applications, 24(2), 167–172.
Chan, C. L., & Lan, C. H. (2001). A data mining technique combining fuzzy sets theory and Bayesian classifier – An application of auditing the health insurance fee. In Proceedings of the International Conference on Artificial Intelligence.
Chechulin, Y., Nazerian, A., Rais, S., & Malikov, K. (2014). Predicting patients with high risk of becoming high-cost healthcare users in Ontario (Canada). Healthcare Policy, 9(3), 68–79.
Christy, T. (1997). Analytical tools help health firms fight fraud. Insurance & Technology, 22(3), 22–26.
Delen, D., Walker, G., & Kadam, A. (2005). Predicting breast cancer survivability: A comparison of three data mining methods. Artificial Intelligence in Medicine, 34(2), 113–127.
Delen, D., Fuller, C., McCann, C., & Ray, D. (2009). Analysis of healthcare coverage: A data mining approach. Expert Systems with Applications, 36(2), 995–1003.
Desai, M. M., Stauffer, B. D., Feringa, H. H. H., & Schreiner, G. C. (2009). Statistical models and patient predictors of readmission for acute myocardial infarction: a systematic review. Circulation. Cardiovascular Quality and Outcomes, 2(5), 500–507. https://doi.org/10.1161/CIRCOUTCOMES.108.832949.
Futoma, J., Morris, J., & Lucas, J. (2015). A comparison of models for predicting early hospital readmissions. Journal of Biomedical Informatics, 56, 229–238. https://doi.org/10.1016/j.jbi.2015.05.016.
Golmohammadi, D., & Radnia, N. (2016). Prediction modeling and pattern recognition for patient readmission. International Journal of Production Economics, 171, 151–161. https://doi.org/10.1016/j.ijpe.2015.09.027.
Görz, G. (2014). Handbuch der künstlichen Intelligenz (5th, revised. and updated edition). München: Oldenbourg.
Guerra, L., McGarry, L. M., Robles, V., Bielza, C., Larranaga, P., & Yuste, R. (2011). Comparison between supervised and unsupervised classifications of neuronal cell types: A case study. Developmental Neurobiology, 71(1), 71–82. https://doi.org/10.1002/dneu.20809.
Hair, J. F. (2007). Knowledge creation in marketing. The role of predictive analytics. European Business Review, 19(4), 303–315. https://doi.org/10.1108/09555340710760134.
Harrach, S. (2014). Neugierige strukturvorschläge im maschinellen lernen. Eine technikphilosophische verortung (Edition panta rei). Bielefeld: transcript.
Haykin, S. (1999). Neural networks. A comprehensive foundation (2nd ed.). Delhi: Pearson Education.
Hess, D. R. (2004). Retrospective studies and chart reviews. Respiratory Care, 49(10), 1171–1174.
Hilbert, J. P., Zasadil, S., Keyser, D. J., & Peele, P. B. (2014). Using decision trees to manage hospital readmission risk for acute myocardial infarction, heart failure, and pneumonia. Applied Health Economics and Health Policy, 12(6), 573–585. https://doi.org/10.1007/s40258-014-0124-7.
Howell, S., Coory, M., Martin, J., & Duckett, S. (2009). Using routine inpatient data to identify patients at risk of hospital readmission. BMC Health Services Research, 9, 96. https://doi.org/10.1186/1472-6963-9-96.
Huang, J. S., Chen, Y. F., & Hsu, J. C. (2014, January). Design of a clinical decision support model for predicting pneumonia readmission. In W.-Y. Chen (Ed.), International symposium on computer, consumer and control (IS3C), 2014. 10–12 June 2014, Taichung, Taiwan; proceedings (S. 1179–1182). Piscataway, NJ: IEEE.
Kaiser, C. (2009). Opinion mining im web 2.0—Konzept und fallbeispiel. HMD Praxis der Wirtschaftsinformatik, 46(4), 90–99. https://doi.org/10.1007/BF03340384.
Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., et al. (2011). Risk prediction models for hospital readmission: a systematic review. JAMA, 306(15), 1688–1698. https://doi.org/10.1001/jama.2011.1515.
Koh, H. C., & Tan, G. (2005). Data mining applications in healthcare. Journal of Healthcare Information Management, 19, 64–72.
Krogh, A. (2008). What are artificial neural networks? Nature Biotechnology, 26(2), 195–197. https://doi.org/10.1038/nbt1386.
Krumholz, H. M., Merrill, A. R., Schone, E. M., Schreiner, G. C., Chen, J., Bradley, E. H., et al. (2009). Patterns of hospital performance in acute myocardial infarction and heart failure 30-day mortality and readmission. Circulation. Cardiovascular Quality and Outcomes, 2(5), 407–413. https://doi.org/10.1161/CIRCOUTCOMES.109.883256.
Kudyba, S., & Gregorio, T. (2010). Identifying factors that impact patient length of stay metrics for healthcare providers with advanced analytics. Health Informatics Journal, 16(4), 235–245.
Lee, E. W. (2012). Selecting the best prediction model for readmission. Journal of Preventive Medicine and Public Health = Yebang Uihakhoe Chi, 45(4), 259–266. https://doi.org/10.3961/jpmph.2012.45.4.259.
Liang, C., & Gong, Y. (2015). Enhancing patient safety event reporting by K-nearest neighbor classifier. Studies in Health Technology and Informatics, 218, 93–99.
Liaw, A., & Wiener, M. (2002). Classification and regression by randomForest. R News, 2(3), 18–22. Verfügbar unter http://CRAN.R-project.org/doc/Rnews/
Loh, W.-Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14–23. https://doi.org/10.1002/widm.8.
Medicare (Medicare Payment Advisory Comission, Hrsg.). (2007). Report to the congress. Promoting greater efficiency in medicare. Zugriff am November 28, 2016, Verfügbar unter http://medpac.gov/docs/default-source/reports/Jun07_EntireReport.pdf?sfvrsn=0
Mishra, N., & Silakari, S. (2012). Predictive analytics: A survey, trends, applications, oppurtunities & challenges. International Journal of Computer Science and Information Technologies, 3, 4434–4438.
Müller, R. M., & Lenz, H.-J. (2013). Business intelligence. Berlin: Springer. https://doi.org/10.1007/978-3-642-35560-8.
Nyce, C. (American Institute for Chartered Property Casualty Underwriters/Insurance Institute of America, Hrsg.). (2007). Predictive analytics. White Paper. Zugriff am November 13, 2016, Verfügbar unter http://www.hedgechatter.com/wp-content/uploads/2014/09/predictivemodelingwhitepaper.pdf
Orem, T. (IBM, Hrsg.). (2015). 4 ways predictive analytics in finance can help companies see the future. Zugriff am December 02, 2016, Verfügbar unter http://www.ibmbigdatahub.com/blog/4-ways-predictive-analytics-finance-can-help-companies-see-future
Osheroff, J. A., Teich, J. M., Middleton, B., Steen, E. B., Wright, A., & Detmer, D. E. (2007). A roadmap for national action on clinical decision support. Journal of the American Medical Informatics Association: JAMIA, 14(2), 141–145.
Pearsall, B. (2010). Predictive policing: The future of law enforcement. National Institute of Justice Journal, 266, 16–19.
Philbin, E. F., & DiSalvo, T. G. (1999). Prediction of hospital readmission for heart failure: Development of a simple risk score based on administrative data. Journal of the American College of Cardiology, 33(6), 1560–1566.
Phillips-Wren, G., Sharkey, P., & Dy, S. M. (2008). Mining lung cancer patient data to assess healthcare resource utilization. Expert Systems with Applications, 35(4), 1611–1619.
Podgorelec, V., Kokol, P., Stiglic, B., & Rozman, I. (2002). Journal of Medical Systems, 26(5), 445–463. https://doi.org/10.1023/A:1016409317640.
Prasad, A. M., Iverson, L. R., & Liaw, A. (2006). Newer classification and regression tree techniques. Bagging and random forests for ecological prediction. Ecosystems, 9(2), 181–199. https://doi.org/10.1007/s10021-005-0054-1.
Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science and Systems, 2(1), 3.
Ross, J. S. (2008). Statistical models and patient predictors of readmission for heart failure: A systematic review. Archives of Internal Medicine, 168(13), 1371. https://doi.org/10.1001/archinte.168.13.1371.
Schneider, A., Hommel, G., & Blettner, M. (2010). Linear regression analysis: Part 14 of a series on evaluation of scientific publications. Deutsches Arzteblatt International, 107(44), 776–782. https://doi.org/10.3238/arztebl.2010.0776.
Segal, M. R. (2004). Machine learning benchmarks and random forest regression. Verfügbar unter http://escholarship.org/uc/item/35x3v9t4.pdf
Shams, I., Ajorlou, S., & Yang, K. (2015). A predictive analytics approach to reducing 30-day avoidable readmissions among patients with heart failure, acute myocardial infarction, pneumonia, or COPD. Health Care Management Science, 18(1), 19–34. https://doi.org/10.1007/s10729-014-9278-y.
Shmueli, G., & Koppius, O. (2011). Predictive analytics in information systems research. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1606674
Shulan, M., Gao, K., & Moore, C. D. (2013). Predicting 30-day all-cause hospital readmissions. Health Care Management Science, 16(2), 167–175. https://doi.org/10.1007/s10729-013-9220-8.
Singh, K., & Xie, M. (2008). Bootstrap: A statistical method. Unpublished manuscript, Rutgers University, USA. Retrieved from http://www.stat.rutgers.edu/home/mxie/RCPapers/bootstrap.pdf
Son, Y.-J., Kim, H.-G., Kim, E.-H., Choi, S., & Lee, S.-K. (2010). Application of support vector machine for prediction of medication adherence in heart failure patients. Healthcare Informatics Research, 16(4), 253–259.
Song, J. W., & Chung, K. C. (2010). Observational studies: Cohort and case-control studies. Plastic and Reconstructive Surgery, 126(6), 2234–2242. https://doi.org/10.1097/PRS.0b013e3181f44abc.
Speybroeck, N. (2012). Classification and regression trees. International Journal of Public Health, 57(1), 243–246. https://doi.org/10.1007/s00038-011-0315-z.
Strome, T. L. (2015). Healthcare analytics: From data to knowledge to healthcare improvement. Hoboken: Wiley.
Sushmita, S., Khulbe, G., Hasan, A., Newman, S., Ravindra, P., Roy, S. B., et al. (2016). Predicting 30-day risk and cost of “all-cause” hospital readmissions. Verfügbar unter http://www.aaai.org/ocs/index.php/WS/AAAIW16/paper/view/12669
Uno, H., Cai, T., Pencina, M. J., D’Agostino, R. B., & Wei, L. J. (2011). On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data. Statistics in Medicine, 30(10), 1105–1117.
Van Walraven, C., Bennett, C., Jennings, A., Austin, P. C., & Forster, A. J. (2011). Proportion of hospital readmissions deemed avoidable: A systematic review. CMAJ: Canadian Medical Association Journal = journal de l'Association medicale canadienne, 183(7), E391–E402. https://doi.org/10.1503/cmaj.101860.
von der Lippe, P. (1993). Deskriptive statistik (UTB für Wissenschaft Uni-Taschenbücher Wirtschaftswissenschaften, Bd. 1632). Stuttgart: Fischer.
Wang, T., Rudin, C., Wagner, D., & Sevieri, R. (2013, January). Learning to detect patterns of crime. In Joint European conference on machine learning and knowledge discovery in databases (S. 515–530).
Westreich, D., Cole, S. R., Funk, M. J., Brookhart, M. A., & Sturmer, T. (2011). The role of the c-statistic in variable selection for propensity score models. Pharmacoepidemiology and Drug Safety, 20(3), 317–320. https://doi.org/10.1002/pds.2074.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this chapter
Cite this chapter
Eigner, I., Hamper, A. (2018). Predictive Analytics in Health Care: Methods and Approaches to Identify the Risk of Readmission. In: Wickramasinghe, N., Schaffer, J. (eds) Theories to Inform Superior Health Informatics Research and Practice. Healthcare Delivery in the Information Age. Springer, Cham. https://doi.org/10.1007/978-3-319-72287-0_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-72287-0_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72286-3
Online ISBN: 978-3-319-72287-0
eBook Packages: MedicineMedicine (R0)