Artificial Intelligence, Preoperative Evaluation
Conditions
Brief summary
Patients applied to the anesthesia clinics of Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospital and Basaksehir Cam and Sakura City Hospital were included in the study. Evaluation forms which will be filled in every preoperative examinations will be saved in the hospitals systems. Patients datas without indentification informations will be asked to ChatGpt to give anesthesiological risc scores. This scores will be compared with the scores already given by anesthesiologists.
Detailed description
Patients of all ages and sexes with ASA scores ranging from I to IV who applied to the anesthesia clinics of Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospital and Basaksehir Cam and Sakura City Hospital were included in the study. Patients with ASA scores of V and VI were not included in the study as their statistical distribution would be disrupted. Data Collection Data were collected daily from preoperative examination evaluation forms in this study, which included Patient's Age Patient's Gender Patient's Weight Patient's Height Additional Illnesses Medications Used Abnormal Laboratory Findings Abnormal Imaging Findings Operation to be Performed Consultation Notes Given ASA Score ChatGPT's ASA Score. At Health Science University Istanbul Kanuni Sultan Suleyman Training and Research Hospita, the patient will be under the care of Specialist Doctor Engin İhsan Turan, and at Basaksehir Cam and Sakura City Hospital, under the care of Specialist Doctor Abdurrahman Engin Baydemir. The natural language processing module ChatGPT 4, developed by OpenAI, was consulted for providing ASA scores based on the collected data. In addition to the collected data, ASA scores given by anesthesia experts were also entered into the system. The investigators chose ChatGPT-4 among artificial intelligence models for our study due to its extensive use in the literature. Primary Objective: To evaluate the success of ChatGPT-4 in predicting postoperative intensive care needs and mortality in adult patients with ASA scores of III and IV. Secondary Objectives: To examine the effectiveness of ChatGPT-4's recommendations on anesthesia methods and additional suggestions in the clinical decision-making process. Benefits: Understanding the contributions of artificial intelligence-based systems to clinical decision-making processes. Risks: The potential for ChatGPT-4's recommendations to be misleading, but the risk will be mitigated by doctors being the final decision-makers. Use of ChatGPT 4 After the data mentioned above regarding the patients were transmitted to ChatGPT 4, ChatGPT 4 was asked to predict the ASA scores of the patients. To do so the investigators will create a special GPT which can provide ASA scores according to the newest guidelines. This GPT will assign patients an ASA score based on abnormal data while recognizing that the patients' other results and physical examination findings are normal.
Interventions
OTHERproviding ASA score
ASA scoring, or the American Society of Anesthesiologists (ASA) Physical Status Classification System, is a framework used by anesthesiologists to assess the preoperative physical fitness of a patient before surgery. It categorizes patients into six classes based on their overall health status, ranging from ASA I (a healthy patient) to ASA VI (a declared brain-dead patient whose organs are being removed for donor purposes). The ASA score helps in predicting perioperative risks and assists healthcare professionals in making informed decisions about anesthesia management and the need for special precautions during and after surgery.
Sponsors
Kanuni Sultan Suleyman Training and Research Hospital
Study design
Observational model
CASE_ONLY
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
Yes
Inclusion criteria
* Patient who admitted to anesthesia clinics for both hospital * Patient with ASA scores between I-IV
Exclusion criteria
* ASA scores higher than IV * Emergency cases patients have symptoms like upper and lower airway infections. * patients haven't got enough informations to provide ASA scores. * Patients with symptoms of an active upper or lower respiratory tract infection
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| The Success of ChatGPT in Providing American Society of Anesthesiologist (ASA) Scores | 2 weeks | Comparison of ASA scores provided from ChatGpt and provided from anesthesiologists. ASA 1: A normal healthy patient. ASA 2: A patient with mild systemic disease. ASA 3: A patient with severe systemic disease that is not incapacitating. ASA 4: A patient with severe systemic disease that is a constant threat to life. ASA 5: A moribund patient who is not expected to survive without the operation. ASA 6: A declared brain-dead patient whose organs are being removed for donor purposes. Additional classifications include: E: This designation can be added to any of the above classifications (ASA 1E to ASA 6E) to indicate that the surgery is an emergency, which increases the risk of the procedure. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| The Success of ChatGPT in clinical decisions | 2 weeks | Comparison of ASA scores provided from ChatGpt and provided from anesthesiologists. ASA 1: A normal healthy patient. ASA 2: A patient with mild systemic disease. ASA 3: A patient with severe systemic disease that is not incapacitating. ASA 4: A patient with severe systemic disease that is a constant threat to life. ASA 5: A moribund patient who is not expected to survive without the operation. ASA 6: A declared brain-dead patient whose organs are being removed for donor purposes. Additional classifications include: E: This designation can be added to any of the above classifications (ASA 1E to ASA 6E) to indicate that the surgery is an emergency, which increases the risk of the procedure. |
Countries
Turkey (Türkiye)
Outcome results
None listed