TY  - JOUR
AU  - Brehmer, Alexander
AU  - Sauer, Christopher Martin
AU  - Salazar Rodríguez, Jayson
AU  - Herrmann, Kelsey
AU  - Kim, Moon
AU  - Keyl, Julius
AU  - Bahnsen, Fin Hendrik
AU  - Frank, Benedikt
AU  - Köhrmann, Martin
AU  - Rassaf, Tienush
AU  - Mahabadi, Amir-Abbas
AU  - Hadaschik, Boris
AU  - Darr, Christopher
AU  - Herrmann, Ken
AU  - Tan, Susanne
AU  - Buer, Jan
AU  - Brenner, Thorsten
AU  - Reinhardt, Hans Christian
AU  - Nensa, Felix
AU  - Gertz, Michael
AU  - Egger, Jan
AU  - Kleesiek, Jens
PY  - 2024
DA  - 2024/10/31
TI  - Establishing Medical Intelligence—Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study
JO  - J Med Internet Res
SP  - e55148
VL  - 26
KW  - clinical informatics
KW  - FHIR
KW  - real-world evidence
KW  - medical intelligence
KW  - interoperability
KW  - data exchange
KW  - clinical management
KW  - clinical decision-making
KW  - electronic health records
KW  - quality of care
KW  - quality improvement
AB  - 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. 
SN  - 1438-8871
UR  - https://www.jmir.org/2024/1/e55148
UR  - https://doi.org/10.2196/55148
UR  - http://www.ncbi.nlm.nih.gov/pubmed/39240144
DO  - 10.2196/55148
ID  - info:doi/10.2196/55148
ER  -