Department for General, Visceral and Transplant Surgery, Hospital of the Goethe University Frankfurt Surgery Centre, Frankfurt am Main, Germany
Institute of Biostatistics and Mathematical Modeling, Goethe-University, Frankfurt/Main, Frankfurt, Germany.
BMJ Open. 2021 Jan 8;11(1):e041396. doi: 10.1136/bmjopen-2020-041396.
Occurrence of inaccurate or delayed diagnoses is a significant concern in patient care, particularly in emergency medicine, where decision making is often constrained by high throughput and inaccurate admission diagnoses. Artificial intelligence-based diagnostic decision support system have been developed to enhance clinical performance by suggesting differential diagnoses to a given case, based on an integrated medical knowledge base and machine learning techniques. The purpose of the study is to evaluate the diagnostic accuracy of Ada, an app-based diagnostic tool and the impact on patient outcome.
The eRadaR trial is a prospective, double-blinded study with patients presenting to the emergency room (ER) with abdominal pain. At initial contact in the ER, a structured interview will be performed using the Ada-App and both, patients and attending physicians, will be blinded to the proposed diagnosis lists until trial completion. Throughout the study, clinical data relating to diagnostic findings and types of therapy will be obtained and the follow-up until day 90 will comprise occurrence of complications and overall survival of patients. The primary efficacy of the trial is defined by the percentage of correct diagnoses suggested by Ada compared with the final discharge diagnosis. Further, accuracy and timing of diagnosis will be compared with decision making of classical doctor-patient interaction. Secondary objectives are complications, length of hospital stay and overall survival.
Ethical approval was received by the independent ethics committee (IEC) of the Goethe-University Frankfurt on 9 April 2020 including the patient information material and informed consent form. All protocol amendments must be reported to and adapted by the IEC. The results from this study will be submitted to peer-reviewed journals and reported at suitable national and international meetings.
DRKS00019098.
不准确或延迟诊断的发生是患者护理中的一个重大问题,特别是在急诊医学中,由于高通量和不准确的入院诊断,决策往往受到限制。已经开发了基于人工智能的诊断决策支持系统,通过基于集成医学知识库和机器学习技术为给定病例建议鉴别诊断,从而提高临床性能。本研究的目的是评估基于人工智能的诊断工具 Ada 的诊断准确性及其对患者结局的影响。
eRadaR 试验是一项前瞻性、双盲研究,纳入因腹痛就诊于急诊室(ER)的患者。在 ER 初始接触时,将使用 Ada-App 进行结构化访谈,直到试验完成,患者和主治医生都对建议的诊断列表保持盲态。在整个研究过程中,将获得与诊断发现和治疗类型相关的临床数据,随访至第 90 天,包括并发症的发生和患者的总生存率。试验的主要疗效定义为 Ada 建议的正确诊断百分比与最终出院诊断相比。此外,将与经典医患互动的诊断准确性和时间进行比较。次要目标是并发症、住院时间和总生存率。
歌德大学法兰克福分校的独立伦理委员会(IEC)于 2020 年 4 月 9 日批准了该伦理,包括患者信息材料和知情同意书。所有方案修正案都必须报告给 IEC,并由其进行调整。这项研究的结果将提交给同行评议的期刊,并在合适的国家和国际会议上报告。
DRKS00019098。