Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models (MDTALLM)

Multi-Disciplinary Treatment on the Anthropomorphism of Large Language Models: A Parallel Controlled Study

This retrospective clinical trial aims to better explore the potential of large language models in medicine by comparing the effectiveness of MDT consultations conducted by human doctors with those conducted by large language models.

The main questions to be addressed are:

Does using large language models to conduct anthropomorphic MDT consultations yield better results than using non-anthropomorphic processes? Is there a significant performance gap between MDT consultations conducted by large language models and those conducted by humans? How much greater is the economic benefit of MDT consultations from large language models compared to those conducted by humans?

Retrospectively collect MDT consultation records from the past 20 years in northern Sichuan in China, as well as anonymized patient medical records. Group 1: Different large language models are assigned to act as doctors from different departments and as MDT secretaries to summarize consultations. Group 2: The large language model directly outputs diagnostic and treatment recommendations for patients. Compare the outputs of groups 1 and 2 with human performance retrospectively, score them, and select the best model from each department for a re-evaluation through anthropomorphic MDT consultations, once again comparing them to human results.

Study Overview

• Status: Not yet recruiting
• Conditions: Heart Diseases; Infections; Pneumonia; Disease; Cancer; Respiratory Failure
• Intervention / Treatment: Diagnostic test: GPT-4o; Diagnostic test: GPT-4o mini; Diagnostic test: MedicalGPT; Diagnostic test: Claude-3.5 Sonnet; Diagnostic test: Claude 3 Haiku; Diagnostic test: Real Doctors

• Study Type: Observational
• Enrollment (Estimated): 300

Contacts and Locations

• Study Contact: Name: Zining Luo, Doctor; Phone Number: 86 + 18161007029; Email: cblzn@nsmc.edu.cn

Study Locations

• China
  • Sichuan
    • Nanchong, Sichuan, China, 637000
      • The Affiliated Hospital of North Sichuan Medical College
      • Contact: Zining Luo; Phone Number: 86 + 18161007029; Email: cblzn@nsmc.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

From hospital

Description

Inclusion Criteria:

• 1. The medical records include interdisciplinary consultation notes, with recommendations from specialists of various departments and a well-documented final summary.
• 2. The medical records contain data from at least one year prior to and one year following the consultation (including intact reports and imaging records).
• 3. The patient's discharge conditions improved due to the multidisciplinary treatment plan after the consultation.

Exclusion Criteria:

• 1. The medical records do not include multidisciplinary consultation notes, or the recommendations from various departmental physicians and the final summary notes are incomplete or inadequate.
• 2. The medical records lack data from 1 year before and after the consultation, or miss necessary reports and imaging data, resulting in incomplete documentation.
• 3. The patient's condition at discharge has not improved following the multidisciplinary treatment plan, or the condition has worsened.

Study Plan

How is the study designed?

Number of groups / cohorts

4

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group; Using a locally deployed MedicalGPT, the commercially available online GPT-4o, Claude-3.5 Sonnet, GPT-4o mini, and Claude 3 Haiku, will each sequentially play the role of physicians from different departments involved in the Multi-Disciplinary Treatment Process. They will then sequentially take on the role of a summarizer to compile their recommendations into a final suggestion or treatment plan.	Diagnostic test: GPT-4o; Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: GPT-4o mini; Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o mini. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: MedicalGPT; Input all patient medical records, including text, examination reports, and imaging data, into MedicalGPT. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: Claude-3.5 Sonnet; Input all patient medical records, including text, examination reports, and imaging data, into Claude-3.5 Sonnet. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: Claude 3 Haiku; Input all patient medical records, including text, examination reports, and imaging data, into Claude 3 Haiku. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
Non-anthropomorphized Process Large Language Model Multidisciplinary Treatment Group; Using a locally deployed MedicalGPT, the commercial online GPT-4o, Claude-3.5 Sonnet, GPT-4o mini, and Claude 3 Haiku to output multidisciplinary consultation results in a single instance, without separately assuming roles for each department and then compiling the results.	Diagnostic test: GPT-4o; Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: GPT-4o mini; Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o mini. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: MedicalGPT; Input all patient medical records, including text, examination reports, and imaging data, into MedicalGPT. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: Claude-3.5 Sonnet; Input all patient medical records, including text, examination reports, and imaging data, into Claude-3.5 Sonnet. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: Claude 3 Haiku; Input all patient medical records, including text, examination reports, and imaging data, into Claude 3 Haiku. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.
Real Doctors Multi-Disciplinary Treatment Group; In traditional multidisciplinary treatments, the results are documented in the consultation records of the patients involved, including the recommendations from doctors of various departments who participated in the consultation and the final summary by the secretary.	Diagnostic test: Real Doctors; Retrospectively collect the diagnostic and treatment recommendations from the corresponding departments involved in the multidisciplinary treatment of past patients, as well as the overall recommendations.
Best Large Language Model Multidisciplinary Treatment Group; After scoring the results of the Anthropomorphized Process Large Language Model Multidisciplinary Treatment Group against the outcomes of the Real Doctors' Multi-Disciplinary Treatment Group on a department-by-department basis, the best substitute models and the best summary models for each department were selected. These top models are set to assume roles in a Multi-Disciplinary Treatment consultation.	Diagnostic test: GPT-4o; Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: GPT-4o mini; Input all patient medical records, including text, examination reports, and imaging data, into GPT-4o mini. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: MedicalGPT; Input all patient medical records, including text, examination reports, and imaging data, into MedicalGPT. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: Claude-3.5 Sonnet; Input all patient medical records, including text, examination reports, and imaging data, into Claude-3.5 Sonnet. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.; Diagnostic test: Claude 3 Haiku; Input all patient medical records, including text, examination reports, and imaging data, into Claude 3 Haiku. Use pre-tested prompts to establish department roles, enabling it to provide diagnostic and treatment recommendations pertinent to the respective department.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
Consultation Cost ($)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Consultation Time (min)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Comprehensiveness of the Multi-Disciplinary Treatment Results (Percentage Scale)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Clarity of Multi-Disciplinary Treatment Results (Percentage Scale)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Correctness of Multi-Disciplinary Treatment Results (Percentage Scale)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Cross-Professional Team Collaboration Practice Assessment (CPAT)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Rating Scale for Summarization	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.
Flesch-Kincaid Readability Test	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.

Secondary Outcome Measures

Outcome Measure	Time Frame
Ethical Compliance (Boolean)	From Multi-Disciplinary Treatment Process to Multi-Disciplinary Treatment Process until all json fields are output, the time taken by human doctors to record the time using His system generally does not exceed 12 hours.

Collaborators and Investigators

• Sponsor: North Sichuan Medical College
• Collaborators: Peking University; Peking University First Hospital; Monash University; Case Western Reserve University; University of Glasgow; Affiliated Hospital of North Sichuan Medical College; Beijing Institute of Petrochemical Technology

Publications and helpful links

General Publications

• Schroder C, Medves J, Paterson M, Byrnes V, Chapman C, O'Riordan A, Pichora D, Kelly C. Development and pilot testing of the collaborative practice assessment tool. J Interprof Care. 2011 May;25(3):189-95. doi: 10.3109/13561820.2010.532620. Epub 2010 Dec 23.

Study record dates

Study Major Dates

• Study Start (Estimated): October 1, 2024
• Primary Completion (Estimated): November 1, 2024
• Study Completion (Estimated): November 1, 2024

Study Registration Dates

• First Submitted: October 1, 2024
• First Submitted That Met QC Criteria: October 3, 2024
• First Posted (Actual): October 4, 2024

Study Record Updates

• Last Update Posted (Actual): October 4, 2024
• Last Update Submitted That Met QC Criteria: October 3, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Cardiovascular Diseases; Respiratory Tract Diseases; Respiration Disorders; Heart Diseases; Respiratory Insufficiency
• Other Study ID Numbers: 1426887-2024-3

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No