gms | German Medical Science

67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 13. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

21.08. - 25.08.2022, online

The European medical device regulation – lessons learned from SMITH-HELP

Meeting Abstract

  • Ariadna Pérez Garriga - Institut of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
  • Jonas Fortmann - Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
  • Cord Spreckelsen - Institut für Medizinische Statistik, Informatik und Datenwissenschaften, Universitätsklinikum Jena, Jena, Jena, Germany
  • André Scherag - Institut für Medizinische Statistik, Informatik und Datenwissenschaften, Universitätsklinikum Jena, Jena, Jena, Germany
  • Raphael W. Majeed - Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
  • Rainer Röhrig - Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany
  • Myriam Lipprandt - Institute of Medical Informatics, Medical Faculty, RWTH Aachen University, Aachen, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 67. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 13. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 21.-25.08.2022. Düsseldorf: German Medical Science GMS Publishing House; 2022. DocAbstr. 62

doi: 10.3205/22gmds041, urn:nbn:de:0183-22gmds0412

Published: August 19, 2022

© 2022 Pérez Garriga et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Introduction: As part of the German Medical Informatics Initiative (MI-I), the SMITH consortium [1] planned the clinical Use Case HELP: to use data from data integration centers (DIC) to support physicians using a guideline-based antibiotic therapy clinical decision support system (CDSS) for Staphylococcus bloodstream infections [2]. Since 2010 legislation has classified Medical Device Software (MDSW) as a medical device (MD), but with the European Medical Device Regulation (MDR) that came into effect in 2021, people are (increasingly) aware of it [3], [4], [5]. During the planning phase of HELP in 2017, the team thought that the regulation would not apply to research and that it would not be classified as Class IIb or III (MDR-Rule 11) [3], [6], [7], [4], [5], [8], [9]. We want to expose our lessons learned while implementing the HELP-App under MDR.

Concept: The goal of the HELP study is to evaluate the application of the created CDSS for the treatment of Staphylococcus bloodstream infections using the data from the DIC. Since this scenario would lead to an MDSW, we decided to divide the project into two parts. On one hand, we have the HELP-Manual that supports the physicians once a Staphylococcus bloodstream infection is detected in a patient: It informs the treating physician about the next steps in the (best practice) clinical workflow without using data for the particular patient in front of her/him. On the other hand, we have the HELP-App which is further subdivided into two tasks: i) Acquisition and validation of the data items from the DIC needed for the CDSS and ii) Implementation of the CDSS which returns diagnostic and treatment-relevant information based on the provided DIC data.

Implementation: The HELP-Manual has been developed as an electronic progressive Web-App (static linked PDF pages) and therefore, is not considered MDSW. It can be used as quick access to the guidelines or use the implemented CDSS. The HELP cluster-randomized trial [2] evaluates this solution in five participating university hospitals with 133 wards. The HELP-App is being developed as an MDSW. This software will directly use the imported patient data from the DIC (relying on interoperable HL7 FHIR) and then apply the CDSS to it. Since the actual part of the software that is being regulated under the MDR is the CDSS, we decided to detach the independent user interface that allows visualizing the DIC data. Regarding the implementation of the MDSW, it turned out that some of the necessary data (e.g., data from Medical Microbiology which are not part of the MI-I core data set) were not available or that the required data quality for a medical device was not met in all the DIC.

Lessons learned: There are different paths to reach the objectives of a project. One is to reduce the regulatory requirements by reducing functionalities and simplifying the concept and architecture. Before starting a CDSS project, it is important to verify the availability and quality of the data to be used and even more to check the different regulatory requirements behind the use of the data and the CDSS.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


References

1.
Winter A, Stäubert S, Ammon D, Aiche S, Beyan O, Bischoff V, et al. Smart Medical Information Technology for Healthcare (SMITH). Methods Inf Med. 2018;57(S 01):e92–105. DOI: 10.3414/ME18-02-0004 External link
2.
Hagel S, Gantner J, Spreckelsen C, et al. Hospital-wide ELectronic medical record evaluated computerised decision support system to improve outcomes of Patients with staphylococcal bloodstream infection (HELP): study protocol for a multicentre stepped-wedge cluster randomised trial. BMJ Open. 2020;10:e033391. DOI: 10.1136/bmjopen-2019-033391 External link
3.
Regulation (EU) 2017/745 of the European Parliament and of the Council of 5 April 2017 on medical devices, amending Directive 2001/83/EC, Regulation (EC) No 178/2002 and Regulation (EC) No 1223/2009 and repealing Council Directives 90/385/EEC and 93/42/EEC (Text with EEA relevance). vol. 117. 2017 [cited 2022 Mar 22]. Available from: http://data.europa.eu/eli/reg/2017/745/oj/eng External link
4.
Becker K, Lipprandt M, Röhrig R, Neumuth T. Digital health – Software as a medical device in focus of the medical device regulation (MDR). it - Information Technology. 2019;61(5-6):211-218. DOI: 10.1515/itit-2019-0026 External link
5.
Terhechte A. Medizinische Software/Medical Apps. Bundesgesundheitsbl. 2018 Mar 1;61(3):321–7. DOI: 10.1007/s00103-017-2683-x External link
6.
MDCG 2021-24 - Guidance on classification of medical devices. [cited 2022 Mar 22]. Available from: https://ec.europa.eu/health/latest-updates/mdcg-2021-24-guidance-classification-medical-devices-2021-10-04_en External link
7.
Exchange of information between medical device competent authorities on borderline and classification cases - Helsinki Procedure 2021. [cited 2022 Mar 22]. Available from: https://ec.europa.eu/health/system/files/2021-09/md_border-class_helsinki-proc-mdr-ivdr_en_0.pdf External link
8.
Rämsch-Günther N, Stern S, Lauer W. Abgrenzung und Klassifizierung von Medical Apps. Bundesgesundheitsbl. 2018 Mar 1;61(3):304–13. DOI: 10.1007/s00103-017-2687-6 External link
9.
Beckers R, Kwade Z, Zanca F. The EU medical device regulation: Implications for artificial intelligence-based medical device software in medical physics. Physica Medica. 2021 Mar 1;83:1–8. DOI: 10.1016/j.ejmp.2021.02.011 External link