The Diagnostic Efficacy of an App-based Diagnostic Health Care Application in the Emergency Room: eRadaR-Trial. A prospective, Double-blinded, Observational Study

OBJECTIVE

To evaluate the diagnostic accuracy of the app-based diagnostic tool Ada and the impact on patient outcome in the emergency room (ER).

BACKGROUND

Artificial intelligence (AI)-based diagnostic tools can improve targeted processes in healthcare delivery by integrating patient information with a medical knowledge base and a machine learning system, providing clinicians with differential diagnoses and recommendations.

METHODS

Patients presenting to the ER with abdominal pain self-assessed their symptoms using the Ada-App under supervision and were subsequently assessed by the ER physician. Diagnostic accuracy was evaluated by comparing the App-diagnoses with the final discharge diagnoses. Timing of diagnosis and time to treatment were correlated with complications, overall survival, and length of hospital stay.

RESULTS

In this prospective, double-blinded study, 450 patients were enrolled and followed up until day 90. Ada suggested the final discharge diagnosis in 52.0% (95% CI [0.47, 0.57]) of patients compared to the classical doctor-patient interaction, which was significantly superior with 80.9% (95% CI [0.77, 0.84], P<0.001). However, when diagnostic accuracy of both were assessed together, Ada significantly increased the accuracy rate (87.3%, P<0.001), when compared to the ER physician alone. Patients with an early time point of diagnosis and rapid treatment allocation exhibited significantly reduced complications (P<0.001) and length of hospital stay (P<0.001).

CONCLUSION

Currently, the classical patient-physician interaction is superior to an AI-based diagnostic tool applied by patients. However, AI tools have the potential to additionally benefit the diagnostic efficacy of clinicians and improve quality of care.

Study protocol for a prospective, double-blinded, observational study investigating the diagnostic accuracy of an app-based diagnostic health care application in an emergency room setting: the eRadaR trial

INTRODUCTION

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.

METHODS AND ANALYSIS

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.

ETHICS AND DISSEMINATION

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.

TRIAL REGISTRATION NUMBER

DRKS00019098.