A theoretical model of health management using data-driven decision-making: the future of precision medicine and health

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.

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.

Fig. 1

Scheme of general health trajectory (HT) management. HT management consists of three steps: (1) context leading to the definition of a medical problem and risk event, acquisition and evaluation of patient data; (2) an analytical data model based on data analysis, analysis of factors associated with risk events, identification of risk factors associated with risk events and a data model for a CDMT; and (3) CDMT for patient management based on a patient’s personal characteristics. Newly generated patient data can enter the data modelling step, refining the assessment of the likelihood of a medical event

Fig. 2

Sequence of concepts associated with reducing the likelihood of reoperation in a particular woman (shown in colour: 78 years old, non-smoker, not active, no long-distance walking, BMI of 36, no sports activity). A representative example of a CDMT based on real-world data. The edge (arrow) strength and its label correspond to the reduction of the risk of reoperation after adding a factor (percentage of how much the risk of reoperation would be reduced). The same holds for the vertex labels with factors and the numbers of patients. Methods of reducing the likelihood of reoperation in this specific case are coloured light green, and the most effective method is shown in dark green. Positive factors were activity (Activity), long-distance walking (LongDistWalk), no smoking (NoSmoking), a BMI < 30 (lowBMI) and no positive factors present (NO COMMON FACTORS). The colour of the presented case changes (from red to orange then green) as the probability of reoperation decreases

Fig. 3

The output of the clinical decision-making tool (CDMT) for the older woman (78 years old, a BMI of 36, no activity, no sport, non-smoking)–a representative example. The screens show a the revision rate in the whole group of older women; b the likelihood of revision rate in a particular older woman, based on her lifestyle parameters; c the likelihood of revision rate and improvements after adding physical activity for this particular woman (reduction of the likelihood of reoperation by 29%); d the likelihood of revision rate and improvements after adding physical activity + BMI < 30 for this particular woman (likelihood of reoperation reduced by 45%)

Fig. 4

Concepts associated with reducing the likelihood of reoperation in a particular man (shown in colour: 75 years old, smoker, not active, no long-distance walking, BMI of 33). A representative example of a CDMT based on real-world data. Men and women are expected to undertake different physical activities. The edge (arrow) strength and its label correspond to the reduction of the risk of reoperation after adding a factor (percentage of how much the risk of reoperation would be reduced). The same holds for vertex labels with factors and the numbers of patients. Methods of reducing the likelihood of reoperation in this specific case are coloured light green, and the most effective method is shown in dark green. Positive factors were activity (Activity), long-distance walking (LongDistWalk), no smoking (NoSmoking), a BMI < 30 (lowBMI) and no positive factors present (NO COMMON FACTORS). The colour of the presented case changes (from red to orange then green) as the probability of reoperation decreases