Artikel
Monitoring individual clinical practice guideline adherence by integrating FHIR-based guideline recommendations with patient data in the OMOP common data model
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Autoren
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Gregor Lichtner
- University Medicine Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany
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Fridtjof Schiefenhövel
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Anaesthesiology and Intensive Care (AINS), Munich, Germany; Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Chair of Medical Informatics, Institute for Artificial Intelligence and Informatics in Medicine (AIIM), Munich, Germany
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Bora Gashi
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Chair of Medical Informatics, Institute for Artificial Intelligence and Informatics in Medicine (AIIM), Munich, Germany
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Ingrid Martin
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Chair of Medical Informatics, Institute for Artificial Intelligence and Informatics in Medicine (AIIM), Munich, Germany
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Carlo Jurth
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
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Lisa Vasiljewa
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
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Anika Müller
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
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Dana Kleimeier
- University Medicine Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany
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Sebastian Gibb
- University Medicine Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany
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Markus Heim
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Anaesthesiology and Intensive Care (AINS), Munich, Germany
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Jürgen Brugger
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Würzburg, Germany
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Peter Kranke
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Würzburg, Germany
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Patrick Meybohm
- University Hospital Würzburg, Department of Anaesthesiology, Intensive Care, Emergency and Pain Medicine, Würzburg, Germany
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Felix Balzer
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Institute of Medical Informatics, Berlin, Germany
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Claudia Spies
- Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Anesthesiology and Operative Intensive Care Medicine, Berlin, Germany
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Gerhard Schneider
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Department of Anaesthesiology and Intensive Care (AINS), Munich, Germany
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Klaus Hahnenkamp
- University Medicine Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany
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Dagmar Waltemath
- University Medicine Greifswald, Core Unit Data Integration Center, Greifswald, Germany
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Martin Boeker
- Technical University of Munich, School of Medicine, Klinikum rechts der Isar, Chair of Medical Informatics, Institute for Artificial Intelligence and Informatics in Medicine (AIIM), Munich, Germany
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Falk von Dincklage
- University Medicine Greifswald, Department of Anesthesia, Critical Care, Emergency and Pain Medicine, Greifswald, Germany
| Veröffentlicht: | 15. September 2023 |
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Gliederung
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Introduction: Guideline-based decision support systems have the potential to improve patient care by alerting medical personnel to situations in which certain clinical guideline recommendations may be applied to patients [1]. However, the development of such systems that integrate guideline recommendations with patient data remains a challenge largely due to the prior lack of a format for representing machine-readable clinical guideline recommendations that is based on modern standards for medical interoperability [2]. Ongoing international efforts to standardize patient data and clinical knowledge have substantially mitigated this issue. This study presents the development of a software system that integrates machine-readable guideline recommendations with patient data to monitor individual guideline applicability and adherence.
Methods: Based on an extensive screening of clinical practice guidelines, we selected 7 recommendations that cover multiple aspects of clinical care and implemented them in a Fast Healthcare Interoperability Resources (FHIR)-based representation format, which we had previously developed (CPG-on-EBMonFHIR) [3]. We built an execution engine that interprets recommendations specified according to that guideline representation format and executes them on patient data stored in the Observational Medical Outcomes Partnership (OMOP) common data model [4]. Following a work process analysis, we propose a user interface designed for quality management that effectively displays individual guideline recommendation adherence [5]. We implemented the software system and retrospectively validated its functionality in several German university hospitals.
Results: The execution engine effectively integrates the recommendations with patient data, providing individualized monitoring of guideline adherence. The participating university hospitals were able to implement varying subsets of the specified 7 guideline recommendations, depending on the availability of data needed to evaluate each recommendation's applicability. The example user interface underwent iterative improvements with input from a focus group of clinicians until consensus was reached. It provides a concise overview of patients' adherence to the implemented recommendations. The system was successfully adapted to different underlying patient database systems and validated in multiple German university hospitals participating in the Network University Medicine and Medical Informatics Initiative, demonstrating its feasibility, reliability, and potential to improve patient care.
Discussion: We have demonstrated the successful development of a system that integrates machine-readable guideline recommendations with patient data stored in the OMOP common data model to monitor individual guideline adherence. The development and application of the CPG-on-EBMonFHIR representation format for machine-readable guidelines was instrumental in achieving this integration. By implementing recommendations in this format, they can be automatically integrated with patient data if the required data items are provided in the OMOP common data model. The example user interface highlights the potential for using this system as a tool for quality management and clinical performance improvement.
Conclusion: The development of a software system integrating machine readable guideline recommendations with patient data in the OMOP common data model provides a novel approach to monitoring individual guideline adherence. This system holds promise for improving patient care, adherence to guidelines, overall quality management and individualized clinical decision support. Future work will involve expanding the range of guideline recommendations and exploring the integration of the system into clinical workflows.
The authors declare that they have no competing interests.
The authors declare that a positive ethics committee vote has been obtained.
References
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