Advertisement

A Literature Review on Predicting Unplanned Patient Readmissions

  • Isabella EignerEmail author
  • Andrew Cooney
Chapter
Part of the Healthcare Delivery in the Information Age book series (Healthcare Delivery Inform. Age)

Abstract

Research on predicting unplanned readmissions in hospitals is becoming more popular with a larger amount of hospital data becoming available. To gain an in-depth observation of recent insights in the field, a literature review analysing contributions between the years of 2005 and 2017 is conducted. The aggregated results show the most important risk factors included in prediction models so far, evaluation metrics of both all-cause and diagnosis-specific prediction models as well as the most prominent classification methods used in this context. Furthermore, the development of research on predicting unplanned patient readmissions over time is shown, and current gaps are identified.

Keywords

Risk prediction Predictive analytics Healthcare Data mining Readmissions 

References

  1. Amalakuhan, B., Kiljanek, L., Parvathaneni, A., Hester, M., Cheriyath, P., & Fischman, D. (2012). A prediction model for COPD readmissions: catching up, catching our breath, and improving a national problem. Journal of Community Hospital Internal Medicine Perspectives, 2(1), 9915.CrossRefGoogle Scholar
  2. Amarasingham, R., Moore, B. J., Tabak, Y. P., Drazner, M. H., Clark, C. A., Zhang, S., Reed, W. G., Swanson, T. S., Ma, Y., & Halm, E. A. (2010). An automated model to identify heart failure patients at risk for 30-day readmission or death using electronic medical record data. Medical Care, 48(11), 981–988.PubMedCrossRefGoogle Scholar
  3. Bardhan, I., Oh, J.-h., Zheng, Z., & Kirksey, K. (2015). Predictive analytics for readmission of patients with congestive heart failure. Information Systems Research, 26(1), 19–39.CrossRefGoogle Scholar
  4. Betihavas, V., Frost, S. A., Newton, P. J., Macdonald, P., Stewart, S., Carrington, M. J., Chan, Y. K., & Davidson, P. M. (2015). An absolute risk prediction model to determine unplanned cardiovascular readmissions for adults with chronic heart failure. Heart, Lung & Circulation, 24(11), 1068–1073.CrossRefGoogle Scholar
  5. Billings, J., Georghiou, T., Blunt, I., & Bardsley, M. (2013). Choosing a model to predict hospital admission: an observational study of new variants of predictive models for case finding. BMJ Open, 3(8), e003352.PubMedPubMedCentralCrossRefGoogle Scholar
  6. Brown, J. R., Conley, S. M., & Niles, N. W. (2013). Predicting readmission or death after acute ST-elevation myocardial infarction. Clinical Cardiology, 36(10), 570–575.PubMedPubMedCentralGoogle Scholar
  7. Brzan, P. P., Obradovic, Z., & Stiglic, G. (2017). Contribution of temporal data to predictive performance in 30-day readmission of morbidly obese patients. PeerJ, 5, e3230.CrossRefGoogle Scholar
  8. Choudhry, S. A., Li, J., Davis, D., Erdmann, C., Sikka, R., & Sutariya, B. (2013). A public-private partnership develops and externally validates a 30-day hospital readmission risk prediction model. Online Journal of Public Health Informatics, 5(2), 219.PubMedPubMedCentralCrossRefGoogle Scholar
  9. Demir, E. (2014). A decision support tool for predicting patients at risk of readmission. A comparison of classification trees, logistic regression, generalized additive models, and multivariate adaptive regression splines. Decision Sciences, 45(5), 849–880.CrossRefGoogle Scholar
  10. Demir, E., Chahed, S., Chaussalet, T., Toffa, S., & Fouladinajed, F. (2012). A decision support tool for health service re-design. Journal of Medical Systems, 36(2), 621–630.PubMedCrossRefPubMedCentralGoogle Scholar
  11. Demir, E., Chaussalet, T., Xie, H., & Millard, P. H. (2009). Modelling risk of readmission with phase-type distribution and transition models. IMA Journal of Management Mathematics, 20(4), 357–367.CrossRefGoogle Scholar
  12. Donzé, J., Aujesky, D., Williams, D., & Schnipper, J. L. (2013). Potentially avoidable 30-day hospital readmissions in medical patients: derivation and validation of a prediction model. JAMA Internal Medicine, 173(8), 632–638.PubMedCrossRefPubMedCentralGoogle Scholar
  13. Dorajoo, S. R., See, V., Chan, C. T., Tan, J. Z., Tan, D. S. Y., Abdul Razak, S. M. B., Ong, T. T., Koomanan, N., Yap, C. W., & Chan, A. (2017). Identifying potentially avoidable readmissions: a medication-based 15-day readmission risk stratification algorithm. Pharmacotherapy, 37(3), 268–277.PubMedCrossRefPubMedCentralGoogle Scholar
  14. Dugger, A., McBride, S., & Song, H. (2014). Can nurses tell the future? Creation of a model predictive of 30-day readmissions. ANS. Advances in Nursing Science, 37(4), 315–326.PubMedCrossRefPubMedCentralGoogle Scholar
  15. Fehnel, C. R., Lee, Y., Wendell, L. C., Thompson, B. B., Potter, N. S., & Mor, V. (2015). Post-acute care data for predicting readmission after ischemic stroke. A nationwide cohort analysis using the minimum data set. Journal of the American Heart Association, 4(9), e002145.PubMedPubMedCentralCrossRefGoogle Scholar
  16. Fleming, L. M., Gavin, M., Piatkowski, G., Chang, J. D., & Mukamal, K. J. (2014). Derivation and validation of a 30-day heart failure readmission model. The American Journal of Cardiology, 114(9), 1379–1382.PubMedCrossRefPubMedCentralGoogle Scholar
  17. Golmohammadi, D., & Radnia, N. (2016). Prediction modeling and pattern recognition for patient readmission. International Journal of Production Economics, 171, 151–161.CrossRefGoogle Scholar
  18. Hasan, O., Meltzer, D. O., Shaykevich, S. A., Bell, C. M., Kaboli, P. J., Auerbach, A. D., Wetterneck, T. B., Arora, V. M., Zhang, J., & Schnipper, J. L. (2010). Hospital readmission in general medicine patients: a prediction model. Journal of General Internal Medicine, 25(3), 211–219.PubMedCrossRefPubMedCentralGoogle Scholar
  19. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  20. Hummel, S. L., Katrapati, P., Gillespie, B. W., Defranco, A. C., & Koelling, T. M. (2014). Impact of prior admissions on 30-day readmissions in medicare heart failure inpatients. Mayo Clinic Proceedings, 89(5), 623–630. PubMedPubMedCentralCrossRefGoogle Scholar
  21. Huynh, Q. L., Saito, M., Blizzard, C. L., Eskandari, M., Johnson, B., Adabi, G., Hawson, J., Negishi, K., & Marwick, T. H. (2015). Roles of nonclinical and clinical data in prediction of 30-day rehospitalization or death among heart failure patients. Journal of Cardiac Failure, 21(5), 374–381.PubMedCrossRefPubMedCentralGoogle Scholar
  22. Jamei, M., Nisnevich, A., Wetchler, E., Sudat, S., & Liu, E. (2017). Predicting all-cause risk of 30-day hospital readmission using artificial neural networks. PLoS One, 12(7), e0181173.PubMedPubMedCentralCrossRefGoogle Scholar
  23. Kansagara, D., Chiovaro, J. C., Kagen, D., Jencks, S., Rhyne, K., O’Neil, M., Kondo, K., Relevo, R., Motu’apuaka, M., Freeman, M., & Englander, H. (2016). So many options, where do we start? An overview of the care transitions literature. Journal of Hospital Medicine, 11(3), 221–230.PubMedCrossRefPubMedCentralGoogle Scholar
  24. Kansagara, D., Englander, H., Salanitro, A., Kagen, D., Theobald, C., Freeman, M., & Kripalani, S. (2011). Risk prediction models for hospital readmission: a systematic review. JAMA, 306(15), 1688–1698.PubMedPubMedCentralCrossRefGoogle Scholar
  25. Kotu, V., & Deshpande, B. (2015). Predictive analytics and data mining: concepts and practice with RapidMiner. Amsterdam: Elsevier Ltd.Google Scholar
  26. 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.PubMedPubMedCentralCrossRefGoogle Scholar
  27. Leeds, I. L., Sadiraj, V., Cox, J. C., Gao, X. S., Pawlik, T. M., Schnier, K. E., & Sweeney, J. F. (2017). Discharge decision-making after complex surgery. Surgeon behaviors compared to predictive modeling to reduce surgical readmissions. American Journal of Surgery, 213(1), 112–119.PubMedCrossRefPubMedCentralGoogle Scholar
  28. Lin, K.-P., Chen, P.-C., Huang, L.-Y., Mao, H.-C., & Chan, D.-C. D. (2016). Predicting inpatient readmission and outpatient admission in elderly. A population-based cohort study. Medicine, 95(16), e3484.PubMedPubMedCentralCrossRefGoogle Scholar
  29. Lin, Y. K., Chen, H., Brown, R. A., Li, S. H., & Yang, H. J. (2017). Healthcare predictive analytics for risk profiling in chronic care: a Bayesian multitask learning approach. MIS Quarterly: Management Information Systems, 41(2), 473–495.CrossRefGoogle Scholar
  30. Lindenauer, P. K., Normand, S.-L. T., Drye, E. E., Lin, Z., Goodrich, K., Desai, M. M., Bratzler, D. W., O’Donnell, W. J., Metersky, M. L., & Krumholz, H. M. (2011). Development, validation, and results of a measure of 30-day readmission following hospitalization for pneumonia. Journal of Hospital Medicine, 6(3), 142–150.PubMedCrossRefGoogle Scholar
  31. McLaren, D. P., Jones, R., Plotnik, R., Zareba, W., McIntosh, S., Alexis, J., Chen, L., Block, R., Lowenstein, C. J., & Kutyifa, V. (2016). Prior hospital admission predicts thirty-day hospital readmission for heart failure patients. Cardiology Journal, 23(2), 155–162.PubMedCrossRefGoogle Scholar
  32. McManus, D. D., Saczynski, J. S., Lessard, D., Waring, M. E., Allison, J., Parish, D. C., Goldberg, R. J., Ash, A., & Kiefe, C. I. (2016). Reliability of predicting early hospital readmission after discharge for an acute coronary syndrome using claims-based data. The American Journal of Cardiology, 117(4), 501–507.PubMedCrossRefGoogle Scholar
  33. Mortazavi, B. J., Downing, N. S., Bucholz, E. M., Dharmarajan, K., Manhapra, A., Li, S.-X., Negahban, S. N., & Krumholz, H. M. (2016). Analysis of machine learning techniques for heart failure readmissions. Circulation. Cardiovascular Quality and Outcomes, 9(6), 629–640.PubMedPubMedCentralCrossRefGoogle Scholar
  34. Murray, C. J. L., & Lopez, A. D. (1996). The global burden of disease: A comprehensive assessment of mortality and disability from diseases, injuries, and risk factors in 1990 and projected to 2020; summary, Global burden of disease and injury series (Vol. 1., Published by the Harvard School of Public Health on behalf of the World Health Organization and the World Bank;). Cambridge, MA: Distributed by Harvard University Press.Google Scholar
  35. Nguyen, O. K., Makam, A. N., Clark, C., Zhang, S., Xie, B., Velasco, F., Amarasingham, R., & Halm, E. A. (2016). Predicting all-cause readmissions using electronic health record data from the entire hospitalization. Model development and comparison. Journal of Hospital Medicine, 11(7), 473–480.PubMedPubMedCentralCrossRefGoogle Scholar
  36. Pack, Q. R., Priya, A., Lagu, T., Pekow, P. S., Engelman, R., Kent, D. M., & Lindenauer, P. K. (2016). Development and validation of a predictive model for short- and medium-term hospital readmission following heart valve surgery. Journal of the American Heart Association, 5(9), e003544.PubMedPubMedCentralCrossRefGoogle Scholar
  37. Pencina, M. J., & D’Agostino, R. B. (2015). Evaluating discrimination of risk prediction models. The C statistic. JAMA, 314(10), 1063–1064.PubMedCrossRefGoogle Scholar
  38. Picker, D., Heard, K., Bailey, T. C., Martin, N. R., LaRossa, G. N., & Kollef, M. H. (2015). The number of discharge medications predicts thirty-day hospital readmission. A cohort study. BMC Health Services Research, 15, 282.PubMedPubMedCentralCrossRefGoogle Scholar
  39. Rana, S., Tran, T., Luo, W., Phung, D., Kennedy, R. L., & Venkatesh, S. (2014). Predicting unplanned readmission after myocardial infarction from routinely collected administrative hospital data. Australian Health Review, 38(4), 377–382.PubMedCrossRefPubMedCentralGoogle Scholar
  40. Sawhney, S., Marks, A., Fluck, N., McLernon, D. J., Prescott, G. J., & Black, C. (2017). Acute kidney injury as an independent risk factor for unplanned 90-day hospital readmissions. BMC Nephrology, 18(1), 9.PubMedPubMedCentralCrossRefGoogle Scholar
  41. Shadmi, E., Flaks-Manov, N., Hoshen, M., Goldman, O., Bitterman, H., & Balicer, R. D. (2015). Predicting 30-day readmissions with preadmission electronic health record data. Medical Care, 53(3), 283–289.PubMedCrossRefPubMedCentralGoogle Scholar
  42. 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.PubMedCrossRefPubMedCentralGoogle Scholar
  43. Shmueli, G., & Koppius, O. (2011). Predictive analytics in information systems research. MIS Quarterly, 35(3), 553–572.CrossRefGoogle Scholar
  44. Shulan, M., Gao, K., & Moore, C. D. (2013). Predicting 30-day all-cause hospital readmissions. Health Care Management Science, 16(2), 167–175.PubMedCrossRefGoogle Scholar
  45. Tabak, Y. P., Sun, X., Nunez, C. M., Gupta, V., & Johannes, R. S. (2017). Predicting readmission at early hospitalization using electronic clinical data: an early readmission risk score. Medical Care, 55(3), 267–275.PubMedCrossRefGoogle Scholar
  46. Taber, D. J., Palanisamy, A. P., Srinivas, T. R., Gebregziabher, M., Odeghe, J., Chavin, K. D., Egede, L. E., & Baliga, P. K. (2015). Inclusion of dynamic clinical data improves the predictive performance of a 30-day readmission risk model in kidney transplantation. Transplantation, 99(2), 324–330.PubMedPubMedCentralCrossRefGoogle Scholar
  47. Tong, L., Erdmann, C., Daldalian, M., Li, J., & Esposito, T. (2016). Comparison of predictive modeling approaches for 30-day all-cause non-elective readmission risk. BMC Medical Research Methodology, 16, 26.PubMedPubMedCentralCrossRefGoogle Scholar
  48. Walsh, C., & Hripcsak, G. (2014). The effects of data sources, cohort selection, and outcome definition on a predictive model of risk of thirty-day hospital readmissions. Journal of Biomedical Informatics, 52, 418–426.PubMedPubMedCentralCrossRefGoogle Scholar
  49. Webster, J., & Watson, R. T. (2002). Analyzing the past to prepare for the future: writing a literature review. MIS Quarterly, 26(2), xiii–xxiii.Google Scholar
  50. Whitlock, T. L., Tignor, A., Webster, E. M., Repas, K., Conwell, D., Banks, P. A., & Wu, B. U. (2011). A scoring system to predict readmission of patients with acute pancreatitis to the hospital within thirty days of discharge. Clinical Gastroenterology and Hepatology, 9(2), 175–180. quiz e18.PubMedCrossRefPubMedCentralGoogle Scholar
  51. Yeo, H., Mao, J., Abelson, J. S., Lachs, M., Finlayson, E., Milsom, J., & Sedrakyan, A. (2016). Development of a nonparametric predictive model for readmission risk in elderly adults after colon and rectal cancer surgery. Journal of the American Geriatrics Society, 64(11), e125–e130.PubMedCrossRefPubMedCentralGoogle Scholar
  52. Yu, S., Farooq, F., van Esbroeck, A., Fung, G., Anand, V., & Krishnapuram, B. (2015). Predicting readmission risk with institution-specific prediction models. Artificial Intelligence in Medicine, 65(2), 89–96.PubMedCrossRefPubMedCentralGoogle Scholar
  53. Zhu, K., Lou, Z., Zhou, J., Ballester, N., Kong, N., & Parikh, P. (2015). Predicting 30-day hospital readmission with publicly available administrative database. A conditional logistic regression modeling approach. Methods of Information in Medicine, 54(6), 560–567.PubMedCrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.University of Erlangen-Nuremberg, Institute of Information SystemsNurembergGermany

Personalised recommendations