Establishing medical intelligence: leveraging fast healthcare interoperability resources to improve clinical management : retrospective cohort and clinical implementation study
Background: FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. Objective: This study aimed to design and implement a conceptual medical intellige...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
31.10.2024
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| In: |
Journal of medical internet research
Year: 2024, Jahrgang: 26, Heft: 1, Pages: 1-12 |
| ISSN: | 1438-8871 |
| DOI: | 10.2196/55148 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.2196/55148 Verlag, lizenzpflichtig, Volltext: https://www.jmir.org/2024/1/e55148 
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| Verfasserangaben: | Alexander Brehmer, MSc; Christopher Martin Sauer, MD, MPH, PhD; Jayson Salazar Rodríguez, MSc; Kelsey Herrmann, MD; Moon Kim, MD; Julius Keyl, MD; Fin Hendrik Bahnsen, MSc; Benedikt Frank, MD; Martin Köhrmann, Prof Dr Med; Tienush Rassaf, Prof Dr Med; Amir-Abbas Mahabadi, MD; Boris Hadaschik, MD; Christopher Darr, MD; Ken Herrmann, Prof Dr Med; Susanne Tan, Prof Dr Med; Jan Buer, Prof Dr Med; Thorsten Brenner, Prof Dr Med; Hans Christian Reinhardt, Prof Dr Med; Felix Nensa, PhD, Prof Dr Med; Michael Gertz, Prof Dr; Jan Egger, PhD; Jens Kleesiek, PhD, Prof Dr Med |
| Zusammenfassung: | Background: FHIR (Fast Healthcare Interoperability Resources) has been proposed to enable health data interoperability. So far, its applicability has been demonstrated for selected research projects with limited data. Objective: This study aimed to design and implement a conceptual medical intelligence framework to leverage real-world care data for clinical decision-making. Methods: A Python package for the use of multimodal FHIR data (FHIRPACK [FHIR Python Analysis Conversion Kit]) was developed and pioneered in 5 real-world clinical use cases, that is, myocardial infarction, stroke, diabetes, sepsis, and prostate cancer. Patients were identified based on the ICD-10 (International Classification of Diseases, Tenth Revision) codes, and outcomes were derived from laboratory tests, prescriptions, procedures, and diagnostic reports. Results were provided as browser-based dashboards. Results: For 2022, a total of 1,302,988 patient encounters were analyzed. (1) Myocardial infarction: in 72.7% (261/359) of cases, medication regimens fulfilled guideline recommendations. (2) Stroke: out of 1277 patients, 165 received thrombolysis and 108 thrombectomy. (3) Diabetes: in 443,866 serum glucose and 16,180 glycated hemoglobin A1c measurements from 35,494 unique patients, the prevalence of dysglycemic findings was 39% (13,887/35,494). Among those with dysglycemia, diagnosis was coded in 44.2% (6138/13,887) of the patients. (4) Sepsis: In 1803 patients, Staphylococcus epidermidis was the primarily isolated pathogen (773/2672, 28.9%) and piperacillin and tazobactam was the primarily prescribed antibiotic (593/1593, 37.2%). (5) PC: out of 54, three patients who received radical prostatectomy were identified as cases with prostate-specific antigen persistence or biochemical recurrence. Conclusions: Leveraging FHIR data through large-scale analytics can enhance health care quality and improve patient outcomes across 5 clinical specialties. We identified (1) patients with sepsis requiring less broad antibiotic therapy, (2) patients with myocardial infarction who could benefit from statin and antiplatelet therapy, (3) patients who had a stroke with longer than recommended times to intervention, (4) patients with hyperglycemia who could benefit from specialist referral, and (5) patients with PC with early increases in cancer markers. |
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| Beschreibung: | Gesehen am 02.07.2025 |
| Beschreibung: | Online Resource |
| ISSN: | 1438-8871 |
| DOI: | 10.2196/55148 |