Effects of Expert Arbitration on Clinical Outcomes When Disputes Over Diagnosis Arise Between Physicians and Their Artificial Intelligence Counterparts: a Randomized, Multicenter Trial in Pediatric Outpatients

Study Overview
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Brief Summary
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We have recently developed an artificial intelligence (AI) framework to diagnose common pediatric diseases. This randomized controlled clinical trial aims to investigate the effects of expert arbitration on clinical outcomes in the situation where the AI-based diagnosis differs from the diagnosis made by pediatricians.

Detailed Description
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Based on the historical clinical data of more than 1 million pediatric outpatients in the Guangzhou Women and Children's Medical Center, an AI diagnostic framework has recently been developed for common pediatric diseases [Liang H et al. evaluation and accurate diagnosis of pediatric disease using artificial intelligence. Nat Med. 2019;25(3):433-8]. This AI framework utilizes predefined schema to extract informative clinical data from free text and reaches clinical diagnoses by hypothetico-deductive reasoning. In internal validation, the AI system showed accuracy rates ranging from 0.85 for gastrointestinal disease to 0.98 for neuropsychiatric disorders, suggesting that it might be a promising assisting diagnostic tool in clinical practice. However, there is a lack of evidence-based strategy on how to handle the scenarios where the AI-based diagnosis and the diagnosis made by pediatricians are discordant. It is legitimate to assume that diseases with discordant diagnoses present more similar clinical features; in this case it is necessary to introduce an extra arbitrator for differential and decisive diagnosis. Therefore, we conduct this randomized controlled trial to: 1) compare the accuracy of the two diagnostic modes in a real-world clinical setting where the AI-based diagnosis and the diagnosis made by pediatricians are discordant by introducing an expert arbitrator; and 2) look further into the change of clinical outcomes (hospital revisit and hospitalization in the next 3 months after initial visit; average total outpatient cost) due to introduction of the expert arbitrator. Please note that although the aforementioned AI framework was designed for diagnosis of a wide range of diseases, this clinical trial is limited to outpatients encountered in three specialty clinics, i.e. respirology, gastroenterology, and genito-urology. The reason for this selection is that the discordant diagnoses are assumed to be more common for these two specialties according to the internal validation result.

Official Title
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Effects of Expert Arbitration on Clinical Outcomes When Disputes Over Diagnosis Arise Between Physicians and Their Artificial Intelligence Counterparts: a Randomized, Multicenter Trial in Pediatric Outpatients

Conditions
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Pediatric Outpatients Encountered in Three Specialty Clinics, i.e. Respirology, Gastroenterology, and Genito-urology

Intervention / Treatment
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* Other: expert arbitration over discordant diagnoses made by AI diagnostic system and human doctors, respectively


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Outpatients who visits the respirology clinics or the gastroenterology clinics during the recruitment period. Written informed consent is provided by parents/guardians Exclusion Criteria: 1. Patients with any conditions that require immediate diagnosis and treatment.

Ages Eligible for Study
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Maximum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: parallel assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Active Comparator: Experimental Arm<br>Each participant receives two diagnoses: one from the AI diagnostic system and the other from pediatricians, and the two diagnoses are discordant. Participants in the experimental arm will be referred to an expert arbitrator for differential and decisive diagnosis and will receive treatment prescribed by the expert arbitrator. | Other: expert arbitration over discordant diagnoses made by AI diagnostic system and human doctors, respectively<br>* Each participant receives two diagnoses: one from the AI diagnostic system and the other from pediatricians, and the two diagnoses are discordant. Participants in the experimental arm will be referred to an expert arbitrator for differential and decisive diagnosis and will receive treatment prescribed by the expert arbitrator.<br>|
| No Intervention: Control Arm<br>Each participant receives two diagnoses: one from the AI diagnostic system and the other from pediatricians, and the two diagnoses are discordant. Participants in the control arm will receive treatment prescribed by pediatricians. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Hospital revisit | Within the first 3 months after the initial visit, active follow-up via phone call will be performed each month to collect the information on hospital revisit. | The next 3 months after the initial visit | 
| Hospitalization in the next 3 months after the initial visit | be performed each month to collect the information on hospitalization. | The next 3 months after the initial visit | 
| Average total outpatient cost | be performed each month to collect the information on the amount of money spending on healthcare. | The next 3 months after the initial visit | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy rate of AI-based diagnosis and accuracy rate of the diagnoses made by pediatricians, using the diagnoses made by the expert arbitrator as the decisive diagnoses. |  | The next 3 months after the initial visit | 
| Counseling time spent with each patient |  | The next 3 months after the initial visit |