Abstract
Readmission prediction in hospitals is a highly complex task involving multiple risk factors that can vary among different disease groups. We address this issue by implementing multiple cross-validated classification models within an intelligent CDSS to enhance patient discharge management. Depending on the diagnosis, the system selects and applies the appropriate model and visualises the prediction results. In addition, the cost and reimbursement development for each episode are determined. The architecture of the CDSS and the integration of the prediction models are presented in this paper.
About the authors
Isabella Eigner is a research assistant and PhD student at the Chair of Information Systems (Services – Processes – Intelligence) at the FAU Erlangen-Nuremberg in Nuremberg, Germany. Her research focuses on machine learning and risk prediction in healthcare.
Freimut Bodendorf is a Professor of Information Systems and Head of the Institute of Information Systems at the FAU Erlangen-Nuremberg in Nuremberg, Germany.
Literature
1. M. W. M. Jaspers, M. Smeulers, H. Vermeulen, and L. W. Peute, “Effects of clinical decision-support systems on practitioner performance and patient outcomes: A synthesis of high-quality systematic review findings,” Journal of the American Medical Informatics Association: JAMIA, vol. 18, no. 3, pp. 327–334, 2011.10.1136/amiajnl-2011-000094Search in Google Scholar
2. E. Steeman, P. Moons, K. Milisen, N. de Bal, S. de Geest, C. de Froidmont, V. Tellier, C. Gosset, and I. Abraham, “Implementation of discharge management for geriatric patients at risk of readmission or institutionalization,” International Journal for Quality in Health Care, vol. 18, no. 5, pp. 352–358, 2006.10.1093/intqhc/mzl026Search in Google Scholar
3. M. Naylor, “Comprehensive discharge planning for the hospitalized elderly,” Annals of Internal Medicine, vol. 120, no. 12, p. 999, 1994.10.7326/0003-4819-120-12-199406150-00005Search in Google Scholar
4. K. H. Bowles, A. Hanlon, D. Holland, S. L. Potashnik, and M. Topaz, “Impact of discharge planning decision support on time to readmission among older adult medical patients,” Professional case management, vol. 19, no. 1, pp. 29–38, 2014.10.1097/01.PCAMA.0000438971.79801.7aSearch in Google Scholar
5. G. Phillips-Wren, M. Mora, G. A. Forgionne, and J. Gupta, “An integrative evaluation framework for intelligent decision support systems,” European Journal of Operational Research, vol. 195, no. 3, pp. 642–652, 2009.10.1016/j.ejor.2007.11.001Search in Google Scholar
6. H. Zhou, P. R. Della, P. Roberts, L. Goh, and S. S. Dhaliwal, “Utility of models to predict 28-day or 30-day unplanned hospital readmissions: An updated systematic review,” BMJ open, vol. 6, no. 6, p. e011060, 2016.10.1136/bmjopen-2016-011060Search in Google Scholar
7. D. Kansagara, H. Englander, A. Salanitro, D. Kagen, C. Theobald, M. Freeman, and S. Kripalani, “Risk prediction models for hospital readmission: A systematic review,” JAMA, vol. 306, no. 15, pp. 1688–1698, 2011.10.1001/jama.2011.1515Search in Google Scholar
8. AIHW, “National healthcare agreement: Pi 23–unplanned hospital readmission rates, 2017,” 2017.Search in Google Scholar
9. M. Mora, G. Forgionne, F. Cervantes, L. Garrido, J. N. Gupta, and O. Gelman, “Toward a comprehensive framework for the design and evaluation of intelligent decision-making support systems (i-dmss),” Journal of Decision Systems, vol. 14, no. 3, pp. 321–344, 2012.10.3166/jds.14.321-344Search in Google Scholar
10. R. H. Sprague, “A framework for the development of decision support systems,” MIS Quarterly, vol. 4, no. 4, p. 1, 1980.10.2307/248957Search in Google Scholar
11. R. Grol and J. Grimshaw, “From best evidence to best practice: Effective implementation of change in patients’ care,” The Lancet, vol. 362, no. 9391, pp. 1225–1230, 2003.10.1016/S0140-6736(03)14546-1Search in Google Scholar
12. J. A. Osheroff, Improving outcomes with clinical decision support: An implementer’s guide. Chicago IL: HIMSS, 2nd ed. ed., 2012.10.4324/9781498757461Search in Google Scholar
13. P. E. Beeler, D. W. Bates, and B. L. Hug, “Clinical decision support systems,” Swiss medical weekly, vol. 144, p. w14073, 2014.10.4414/smw.2014.14073Search in Google Scholar PubMed
14. E. S. Berner, “Clinical decision support systems: State of the art,” AHRQ publication, vol. 90069, 2009.Search in Google Scholar
15. N. Pombo, P. Araujo, and J. Viana, “Applied computer technologies in clinical decision support systems for pain management: A systematic review,” Journal of Intelligent & Fuzzy Systems, vol. 26, no. 5, pp. 2411–2425, 2014.10.3233/IFS-912Search in Google Scholar
16. T. A. Pryor, “Development of decision support systems,” International journal of clinical monitoring and computing, vol. 7, no. 3, pp. 137–146, 1990.10.1007/978-1-4612-2698-7_4Search in Google Scholar
17. A. Wright, D. F. Sittig, J. S. Ash, J. Feblowitz, S. Meltzer, C. McMullen, K. Guappone, J. Carpenter, J. Richardson, L. Simonaitis, R. S. Evans, W. P. Nichol, and B. Middleton, “Development and evaluation of a comprehensive clinical decision support taxonomy: Comparison of front-end tools in commercial and internally developed electronic health record systems,” Journal of the American Medical Informatics Association: JAMIA, vol. 18, no. 3, pp. 232–242, 2011.10.1136/amiajnl-2011-000113Search in Google Scholar PubMed PubMed Central
18. K. Kawamoto, C. A. Houlihan, E. A. Balas, and D. F. Lobach, “Improving clinical practice using clinical decision support systems: A systematic review of trials to identify features critical to success,” BMJ, vol. 330, no. 7494, p. 765, 2005.10.1136/bmj.38398.500764.8FSearch in Google Scholar PubMed PubMed Central
19. R. N. Shiffman, Y. Liaw, C. A. Brandt, and G. J. Corb, “Computer-based guideline implementation systems: A systematic review of functionality and effectiveness,” Journal of the American Medical Informatics Association: JAMIA, vol. 6, no. 2, pp. 104–114, 1999.10.1136/jamia.1999.0060104Search in Google Scholar PubMed PubMed Central
20. J. L. Y. Liu, J. C. Wyatt, J. J. Deeks, S. Clamp, J. Keen, P. Verde, C. Ohmann, J. Wellwood, M. Dawes, and D. G. Altman, “Systematic reviews of clinical decision tools for acute abdominal pain,” Health technology assessment (Winchester, England), vol. 10, no. 47, pp. 1–167, iii–iv, 2006.10.3310/hta10470Search in Google Scholar PubMed
21. A. X. Garg, N. K. J. Adhikari, H. McDonald, M. P. Rosas-Arellano, P. J. Devereaux, J. Beyene, J. Sam, and R. B. Haynes, “Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: A systematic review,” JAMA, vol. 293, no. 10, pp. 1223–1238, 2005.10.1001/jama.293.10.1223Search in Google Scholar PubMed
22. D. W. Bates and A. A. Gawande, “Improving safety with information technology,” The New England journal of medicine, vol. 348, no. 25, pp. 2526–2534, 2003.10.1056/NEJMsa020847Search in Google Scholar PubMed
23. P. E. Beeler, E. Eschmann, C. Rosen, and J. Blaser, “Use of an on-demand drug-drug interaction checker by prescribers and consultants: A retrospective analysis in a swiss teaching hospital,” Drug safety, vol. 36, no. 6, pp. 427–434, 2013.10.1007/s40264-013-0022-1Search in Google Scholar PubMed
24. I. Eigner, A. Hamper, N. Wickramasinghe, and F. Bodendorf, “Decision makers and criteria for patient discharge-a qualitative study,” 2017.10.18690/978-961-286-043-1.10Search in Google Scholar
25. IHPA, “National hospital cost data collection, public hospitals cost report,” 2017.Search in Google Scholar
26. R. B. Fetter, Y. Shin, J. L. Freeman, R. F. Averill, and J. D. Thompson, “Case mix definition by diagnosis-related groups,” Medical care, vol. 18, no. 2 Suppl, pp. iii, 1–53, 1980.Search in Google Scholar
27. AIHW, “Admitted patient care 2015–16: Australian hospital statistics,” 2017.Search in Google Scholar
28. Department of Health and Human Services, Department of Health and Human Services policy and funding guidelines 2016. 2016.Search in Google Scholar
29. J. Donzé, D. Aujesky, D. Williams, and J. L. Schnipper, “Potentially avoidable 30-day hospital readmissions in medical patients: Derivation and validation of a prediction model,” JAMA internal medicine, vol. 173, no. 8, pp. 632–638, 2013.10.1001/jamainternmed.2013.3023Search in Google Scholar PubMed
30. S. Jablonski, I. Petrov, C. Meiler, and U. Mayer, Guide to Web Application and Platform Architectures. Berlin and Heidelberg: Springer, 2004.10.1007/978-3-662-07631-6Search in Google Scholar
31. R. Steiner, Grundkurs Relationale Datenbanken: Eine grundlegende Einführung in die Praxis der Datenbankentwicklung für Ausbildung, Studium und Beruf. IT erfolgreich lernen, Wiesbaden: Friedr.Vieweg & Sohn Verlag | GWV Fachverlage GmbH Wiesbaden, 6., überarbeitete und erweiterte auflage ed., 2006.Search in Google Scholar
32. Microsoft, “Implementing the mvvm pattern using the prism library 5.0 for wpf,” 2017.Search in Google Scholar
33. M. Hofmann and R. Klinkenberg, Rapidminer: Data mining use cases and business analytics applications. [Place of publication not identified]: Chapman & Hall Crc, 2017.10.1201/b16023Search in Google Scholar
34. E. S. Berner, Clinical decision support systems: Theory and practice. Health informatics, New York: Springer, 2nd ed. ed., 2007.10.1007/978-0-387-38319-4Search in Google Scholar
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