Gabriel Stolze, Vinodh Kakkassery, Danny Kowerko, Martin Bartos, Katja Hoffmann, Martin Sedlmayr, Katrin Engelmann
{"title":"[MiHUBx: a digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema].","authors":"Gabriel Stolze, Vinodh Kakkassery, Danny Kowerko, Martin Bartos, Katja Hoffmann, Martin Sedlmayr, Katrin Engelmann","doi":"10.1007/s00347-024-02146-x","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence.</p><p><strong>Goal: </strong>The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure.</p><p><strong>Material and methods: </strong>Step by step, the necessary partners in hospitals and practices will be brought together technically or through research questions within Use Case 1 \"Ophthalmology meets Diabetology\", a regional digital progress hub in health, the medical informatics hub in Saxony (MiHUBx ) of the nationwide medical informatics initiative (MII).</p><p><strong>Results: </strong>Based on joint studies with diabetologists, robust serological and imaging biomarkers were selected that provide evidence of the development of diabetic macular edema (DME). In the future, these and other scientifically proven prognostic markers will be incorporated into a treatment algorithm that is supported by artificial intelligence (AI). For this purpose, model procedures are being developed together with medical informatics specialists. At the same time, a data integration center (DIZ) was established.</p><p><strong>Conclusion: </strong>In addition to the structured and technical combination of the previously disseminated and partially heterogeneous treatment data, the Use Case 1 defines the chances and hurdles for using such real-world data to develop artificial intelligence.</p>","PeriodicalId":72808,"journal":{"name":"Die Ophthalmologie","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Die Ophthalmologie","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s00347-024-02146-x","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence.
Goal: The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure.
Material and methods: Step by step, the necessary partners in hospitals and practices will be brought together technically or through research questions within Use Case 1 "Ophthalmology meets Diabetology", a regional digital progress hub in health, the medical informatics hub in Saxony (MiHUBx ) of the nationwide medical informatics initiative (MII).
Results: Based on joint studies with diabetologists, robust serological and imaging biomarkers were selected that provide evidence of the development of diabetic macular edema (DME). In the future, these and other scientifically proven prognostic markers will be incorporated into a treatment algorithm that is supported by artificial intelligence (AI). For this purpose, model procedures are being developed together with medical informatics specialists. At the same time, a data integration center (DIZ) was established.
Conclusion: In addition to the structured and technical combination of the previously disseminated and partially heterogeneous treatment data, the Use Case 1 defines the chances and hurdles for using such real-world data to develop artificial intelligence.