Abstract
BackgroundThe 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.MethodsWe 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.ResultsThe 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.ConclusionThis 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.
Highlights
The burden of chronic and societal diseases is affected by many risk factors that can change over time
There is an urgent need to develop a process of automated analysis for this data, which could result in establishing a clinical decision-making tool (CDMT) as a component of a clinical decision support system (CDSS), which in turn, leads to the reduction of the individual risks associated with certain diseases or adverse health effects [7,8,9]
health trajectory (HT) management Working with a large amount of patient data in the form of electronic health records (EHR) and using automated processing and analysis methods based on machine learning or, generally, on artificial intelligence should result in CDMTs that can intelligently support clinicians’ and patients’ decision-making [11]
Summary
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. 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. The risks for a particular disease are influenced by many factors, which may change in specific situations over time [1, 2]. There is an urgent need to develop a process of automated analysis for this data, which could result in establishing a clinical decision-making tool (CDMT) as a component of a clinical decision support system (CDSS), which in turn, leads to the reduction of the individual risks associated with certain diseases or adverse health effects [7,8,9]
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