Cancer, Disease, Heart Diseases, Infections, Pneumonia, Respiratory Failure
Conditions
Brief summary
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.
Interventions
DIAGNOSTIC_TESTGPT-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_TESTGPT-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_TESTMedicalGPT
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_TESTClaude-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_TESTClaude 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.
DIAGNOSTIC_TESTReal 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.
Sponsors
Affiliated Hospital of North Sichuan Medical College
University of Glasgow
Peking University
Peking University First Hospital
Beijing Institute of Petrochemical Technology
Case Western Reserve University
Monash University
North Sichuan Medical College
Study design
Observational model
OTHER
Time perspective
RETROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| 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. |
| 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. |
Secondary
| 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. |
Countries
China
Contacts
Primary ContactZining Luo, Doctor
Outcome results
None listed