Establishing Medical Intelligence-Leveraging Fast Healthcare Interoperability Resources to Improve Clinical Management: Retrospective Cohort and Clinical Implementation Study.

IF 5.8 2区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Alexander Brehmer, Christopher Martin Sauer, Jayson Salazar Rodríguez, Kelsey Herrmann, Moon Kim, Julius Keyl, Fin Hendrik Bahnsen, Benedikt Frank, Martin Köhrmann, Tienush Rassaf, Amir-Abbas Mahabadi, Boris Hadaschik, Christopher Darr, Ken Herrmann, Susanne Tan, Jan Buer, Thorsten Brenner, Hans Christian Reinhardt, Felix Nensa, Michael Gertz, Jan Egger, Jens Kleesiek
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引用次数: 0

Abstract

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.

建立医疗智能--利用 FHIR 改善临床管理:一项回顾性队列和临床实施研究。
背景:FHIR(快速医疗互操作性资源)的提出是为了实现医疗数据的互操作性。迄今为止,其适用性只在数据有限的选定研究项目中得到了证明。目标:在此,我们设计并实施了一个概念性医疗智能框架,以利用真实世界的医疗数据进行临床决策:方法:我们开发了一个用于利用多模态 FHIR 数据的 Python 软件包(FHIRPACK),并在心肌梗死(MI)、中风、糖尿病、败血症和前列腺癌(PC)等五个真实世界的临床用例中率先使用。根据 ICD-10 编码识别患者,并从实验室检测、处方、程序和诊断报告中得出结果。结果以基于浏览器的仪表板形式提供:2022 年,共分析了 1,302,988 次患者就诊。心肌梗死:72.7%的病例(N=261)的用药方案符合指南建议。中风:在1277名患者中,165名患者接受了溶栓治疗,108名患者接受了血栓切除术。糖尿病:在对 35,494 名患者的 443,866 次血清葡萄糖和 16,180 次 HbA1c 测量中,发现血糖异常的比例为 39%(N=13,887)。在出现血糖异常的患者中,有 44.2% 的患者(N=6138)得到了诊断编码。败血症:在 1,803 名患者中,表皮葡萄球菌是主要分离病原体(N=773,28.9%),哌拉西林/他唑巴坦是主要处方抗生素(N=593,37.2%)。PC:54名接受根治性前列腺切除术的患者中有3人被确定为PSA持续或生化复发病例:结论:通过大规模分析利用 FHIR 数据可以提高医疗质量,改善五个临床专科的患者预后。我们发现了 i) 需要较少抗生素治疗的败血症患者;ii) 可从他汀类药物和抗血小板治疗中获益的心肌梗死患者;iii) 干预时间长于推荐时间的中风患者;iv) 可从专科转诊中获益的高血糖患者;v) 癌症标志物早期升高的 PC 患者:
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来源期刊
CiteScore
14.40
自引率
5.40%
发文量
654
审稿时长
1 months
期刊介绍: The Journal of Medical Internet Research (JMIR) is a highly respected publication in the field of health informatics and health services. With a founding date in 1999, JMIR has been a pioneer in the field for over two decades. As a leader in the industry, the journal focuses on digital health, data science, health informatics, and emerging technologies for health, medicine, and biomedical research. It is recognized as a top publication in these disciplines, ranking in the first quartile (Q1) by Impact Factor. Notably, JMIR holds the prestigious position of being ranked #1 on Google Scholar within the "Medical Informatics" discipline.
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