%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e55148
%T Establishing Medical Intelligence—Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study
%A Brehmer,Alexander
%A Sauer,Christopher Martin
%A Salazar Rodríguez,Jayson
%A Herrmann,Kelsey
%A Kim,Moon
%A Keyl,Julius
%A Bahnsen,Fin Hendrik
%A Frank,Benedikt
%A Köhrmann,Martin
%A Rassaf,Tienush
%A Mahabadi,Amir-Abbas
%A Hadaschik,Boris
%A Darr,Christopher
%A Herrmann,Ken
%A Tan,Susanne
%A Buer,Jan
%A Brenner,Thorsten
%A Reinhardt,Hans Christian
%A Nensa,Felix
%A Gertz,Michael
%A Egger,Jan
%A Kleesiek,Jens
%+ Institute for Artificial Intelligence in Medicine, University Hospital Essen, Hufelandstr. 55, Essen, 45147, Germany, 49 0201723 ext 77801, jens.kleesiek@uk-essen.de
%K clinical informatics
%K FHIR
%K real-world evidence
%K medical intelligence
%K interoperability
%K data exchange
%K clinical management
%K clinical decision-making
%K electronic health records
%K quality of care
%K quality improvement
%D 2024
%7 31.10.2024
%9 Original Paper
%J J Med Internet Res
%G English
%X 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. 
%M 39240144
%R 10.2196/55148
%U https://www.jmir.org/2024/1/e55148
%U https://doi.org/10.2196/55148
%U http://www.ncbi.nlm.nih.gov/pubmed/39240144