Eva Kriegova1, Milos Kudelka2, Martin Radvansky2, Jiri Gallo3,4. 1. Department of Immunology, Faculty of Medicine and Dentistry, Palacky University Olomouc & University Hospital Olomouc, Hnevotinska 3, 775 15, Olomouc, Czech Republic. 2. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, 17. listopadu 2175/15, Poruba, 708 00, Ostrava, Czech Republic. 3. Department of Orthopedics, Faculty of Medicine and Dentistry, Palacky University Olomouc, Hnevotinska 3, 775 15, Olomouc, Czech Republic. jiri.gallo@volny.cz. 4. Department of Orthopedics, University Hospital Olomouc, I. P. Pavlova 6, 779 00, Olomouc, Czech Republic. jiri.gallo@volny.cz.
Abstract
BACKGROUND: The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. METHODS: We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. RESULTS: The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients' and physicians' decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. CONCLUSION: This theoretical model introduces future HT management as an understandable way of conceiving patients' futures with a view to positively (or negatively) changing their behaviour. The model's ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.
BACKGROUND: The burden of chronic and societal diseases is affected by many risk factors that can change over time. The minimalisation of disease-associated risk factors may contribute to long-term health. Therefore, new data-driven health management should be used in clinical decision-making in order to minimise future individual risks of disease and adverse health effects. METHODS: We aimed to develop a health trajectories (HT) management methodology based on electronic health records (EHR) and analysing overlapping groups of patients who share a similar risk of developing a particular disease or experiencing specific adverse health effects. Formal concept analysis (FCA) was applied to identify and visualise overlapping patient groups, as well as for decision-making. To demonstrate its capabilities, the theoretical model presented uses genuine data from a local total knee arthroplasty (TKA) register (a total of 1885 patients) and shows the influence of step by step changes in five lifestyle factors (BMI, smoking, activity, sports and long-distance walking) on the risk of early reoperation after TKA. RESULTS: The theoretical model of HT management demonstrates the potential of using EHR data to make data-driven recommendations to support both patients' and physicians' decision-making. The model example developed from the TKA register acts as a clinical decision-making tool, built to show surgeons and patients the likelihood of early reoperation after TKA and how the likelihood changes when factors are modified. The presented data-driven tool suits an individualised approach to health management because it quantifies the impact of various combinations of factors on the early reoperation rate after TKA and shows alternative combinations of factors that may change the reoperation risk. CONCLUSION: This theoretical model introduces future HT management as an understandable way of conceiving patients' futures with a view to positively (or negatively) changing their behaviour. The model's ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.
Entities:
Keywords:
Clinical decision-making tool; Early reoperation; Electronic health record; Formal concept analysis; Health trajectory; Lifestyle factors; Precision health; Precision medicine; Revision rate; Total knee arthroplasty
Authors: Anh Thy H Nguyen; Anum Saeed; Claudia E Bambs; Justin Swanson; Nnadozie Emechebe; Fahad Mansuri; Karan Talreja; Steven E Reis; Kevin E Kip Journal: Am J Cardiol Date: 2020-10-13 Impact factor: 2.778
Authors: Salim Yusuf; Philip Joseph; Sumathy Rangarajan; Shofiqul Islam; Andrew Mente; Perry Hystad; Michael Brauer; Vellappillil Raman Kutty; Rajeev Gupta; Andreas Wielgosz; Khalid F AlHabib; Antonio Dans; Patricio Lopez-Jaramillo; Alvaro Avezum; Fernando Lanas; Aytekin Oguz; Iolanthe M Kruger; Rafael Diaz; Khalid Yusoff; Prem Mony; Jephat Chifamba; Karen Yeates; Roya Kelishadi; Afzalhussein Yusufali; Rasha Khatib; Omar Rahman; Katarzyna Zatonska; Romaina Iqbal; Li Wei; Hu Bo; Annika Rosengren; Manmeet Kaur; Viswanathan Mohan; Scott A Lear; Koon K Teo; Darryl Leong; Martin O'Donnell; Martin McKee; Gilles Dagenais Journal: Lancet Date: 2019-09-03 Impact factor: 79.321
Authors: Thomas Bahls; Johannes Pung; Stephanie Heinemann; Johannes Hauswaldt; Iris Demmer; Arne Blumentritt; Henriette Rau; Johannes Drepper; Philipp Wieder; Roland Groh; Eva Hummers; Falk Schlegelmilch Journal: J Transl Med Date: 2020-10-19 Impact factor: 5.531