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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10254685" target="_blank" >RIV/61989100:27240/21:10254685 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00098892:_____/21:N0000156 RIV/61989592:15110/21:73607319

Výsledek na webu

<a href="https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-021-02714-8" target="_blank" >https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-021-02714-8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1186/s12967-021-02714-8" target="_blank" >10.1186/s12967-021-02714-8</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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&apos; and physicians&apos; 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&apos; futures with a view to positively (or negatively) changing their behaviour. The model&apos;s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

Název v anglickém jazyce

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

Popis výsledku anglicky

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&apos; and physicians&apos; 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&apos; futures with a view to positively (or negatively) changing their behaviour. The model&apos;s ability to influence beneficial health care decision-making to improve patient outcomes should be proved using various real-world data from EHR datasets.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

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OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/NU20-06-00269" target="_blank" >NU20-06-00269: Využití buněčných profilů a proteomiky synoviální tekutiny, případně tkání pro podporu klinického rozhodování u osteoartrózy kolena</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Journal of Translational Medicine

ISSN

1479-5876

e-ISSN

—

Svazek periodika

19

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

—

Kód UT WoS článku

000620257500002

EID výsledku v databázi Scopus

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