Abstract
Shared decision making (SDM) is a process in which clinicians and patients collaboratively work together to reach a shared medical decision, where the best medical evidence is presented, and the preferences and priorities of the patient are respected. In this paper, we developed an evidence-based platform that aims to facilitate communication between patients and clinicians. We mined the Healthcare Cost and Utilization Project dataset (H-CUP) and extracted medical knowledge personalized to the patient’s medical characteristics. Our platform is tailored toward the hospital readmission problem. First, we employed machine learning techniques to build models that can predict the readmission and mortality for newly admitted patients at the hospital. Second, we built a graph named Procedure Graph to visualize the primary procedures during the course of treatment and show the number of potential readmissions. Third, we personalized the procedure graph to the patient’s medical characteristics by applying our developed Rough Clustering technique. The developed platform is highly significant and novel within the context of hospital readmission. It can enhance the patient-clinician communication by providing a visualized evidence-based knowledge extracted from the electronic health records and personalized to the patient’s level.
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References
Grad, R., Légaré, F., Bell, N.R., Dickinson, J.A., Singh, H., Moore, A.E., Kasperavicius, D., Kretschmer, K.L.: Shared decision making in preventive health care: What it is; what it is not. Can. Fam. Phys. 63(9), 682 (2017)
Washington State Legislature, RCW 7.70.060. Consent form-Contents-Prima facie evidence-Shared decision making-Patient decision aid-Failure to use. title 7, chapter 770, section 770060.
National Quality Forum: National standards for the certification of patient decision aids (2016)
Spatz, E.S., Krumholz, H.M., Moulton, B.W.: Prime time for shared decision making. JAMA 317(13), 1309–1310 (2017)
McCormack, J., Elwyn, G.: Shared decision is the only outcome that matters when it comes to evaluating evidence-based practice. BMJ Evid. Based Med. 23(4), 137–139 (2018)
Hill, B., Proulx, J., Zeng-Treitler, Q.: Exploring the use of large clinical data to inform patients for shared decision making. In: Studies in Health Technology and Informatics, pp. 851–855 (2013)
Shaoibi, A., Neelon, B., Lenert, L.A.: Shared decision making: from decision science to data science. Medical Decision Making (2020) 0272989X20903267
Puppala, M., He, T., Chen, S., Ogunti, R., Yu, X., Li, F., Jackson, R., Wong, S.T.: Meteor: an enterprise health informatics environment to support evidence-based medicine. IEEE Trans. Biomed. Eng. 62(12), 2776–2786 (2015)
Jin, W., Gromala, D., Neustaedter, C., Tong, X.: A collaborative visualization tool to support doctors’ shared decision-making on antibiotic prescription. In: Companion of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, pp. 211–214 (2017)
Lee, Y.J., Bryce, C., Jain, S., Kraschnewski, J., McTigue, K.: Patient-centered visualization on supporting patient’ decision-making. Eur. J. Pers. Cent. Healthc. 7(4), 630–639 (2020)
Hines, A.L., Barrett, M.L., Jiang, H.J., Steiner, C.A.: Conditions with the largest number of adult hospital readmissions by payer, HCUP Statistical Brief 172 (2011)
Silow-Carroll, S., Edwards, J.N., Lashbrook, A.: Reducing Hospital Readmissions: Lessons from Top-Performing Hospitals. CareManagement 17(5), 14 (2011)
Almardini, M., Raś, Z.W.: A supervised model for predicting the risk of mortality and hospital readmissions for newly admitted patients. Foundations of Intelligent Systems, Proceedings of International Symposium on Methodologies for Intelligent Systems in Warsaw, Poland, LNAI, vol. 10352, pp. 29–36 (2017)
Almardini, M., Hajja, A., Raś, Z.W., Clover, L., Olaleye, D., Park, Y., Paulson, J., Xiao, Y.: Reduction of readmissions to hospitals based on actionable knowledge discovery and personalization, beyond databases, architectures and structures-BDAS 2016, conference proceedings. Commun. Comput. Inf. Sci. 613, 39–55 (2016)
Mardini, M.T., Raś, Z.W.: Extraction of actionable knowledge to reduce hospital readmissions through patients personalization. Inf. Sci. 485, 1–17 (2019)
Almardini, M., Hajja, A., Raś, Z.W., Clover, L., Olaleye, D.: Predicting the primary medical procedure through clustering of patients’ diagnoses, in New Frontiers in Mining Complex Patterns, Post-proceedings of NFMCP, : ECML/PKDD Workshop in Riva del Garda. Italy, LNAI 10312(2016), 117–131 (2016)
Pawlak, Z.: Rough Sets. Int. J. Comput. Inf. Sci 11(5), 341–356 (1982). https://doi.org/10.1007/BF01001956
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Mardini, M.T., Hashky, A., Raś, Z.W. (2021). Personalizing Patients to Enable Shared Decision Making. In: Ras, Z.W., Wieczorkowska, A., Tsumoto, S. (eds) Recommender Systems for Medicine and Music. Studies in Computational Intelligence, vol 946. Springer, Cham. https://doi.org/10.1007/978-3-030-66450-3_5
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DOI: https://doi.org/10.1007/978-3-030-66450-3_5
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