Clinical trial NCT06626399: ChatGPT-4 for Surgical Site Infection Detection From Electronic Health Records After Colorectal Surgery.
Official title: Evaluation of ChatGPT-4 for the Detection of Surgical Site Infections From Electronic Health Records After Colorectal Surgery: A Diagnostic Accuracy Study.
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Surgical Site Infection
Keywords: Surgical site infection
Surveillance
ChatGPT
Natural language processing
Artificial inteligence
Lead sponsor: Hospital de Granollers
Sponsor class: OTHER
First posted: 2024-10-03
Enrollment: 1100
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients undergoing elective colorectal surgery in Hospital General de Granollers in the period 2010-2023

Brief summary: Epidemiological surveillance is one of the eight core components of the World Health Organization Infection Prevention and Control Programmes. These include surveillance programmes for surgical site infection (SSI).

At present, for SSI surveillance, infection control teams perform a manual time-consuming work, which could make a transition to automated surveillance leveraging the new information technology.

This study aimed to evaluate the ability of ChatGPT-4o to detect surgical site infection at the three anatomical levels.

Detailed description: Healthcare-associated infections (HAIs) have a negative impact on patient health, represent a significant healthcare and economic burden on healthcare systems and are considered the most preventable cause of serious adverse events in hospitalised patients.

Epidemiological surveillance is one of the eight core components of the World Health Organization (WHO) Infection Prevention and Control Programmes. These include surveillance programmes for surgical site infection (SSI), which have proven to be effective in all types of surgery and in a variety of settings.

For a programme to be effective, surveillance for HCAIs must be active, prospective and continuous, comprising a surveillance period up to 30-90 days post-intervention, to cover the high rate of SSIs detected after discharge.

At present, infection control teams perform a manual, prospective, time-consuming and almost artisanal work, which should make a transition to automated or semi-automated surveillance that leverages the possibilities offered by today\&#39;s information technology.

The evolution of surveillance systems should benefit from this new possibilities offered by artificial intelligence, allowing automated detection of suspected SSI adverse events from clinical course text, microbiology reports or coding of diagnoses, procedures, complications and readmissions.

This study aims to evaluate the ability of ChatGPT to detect surgical site infections (SSI) at the three anatomical levels described by the CDC.

The study will retrospectively compare the results of the AI chatbot in diagnosing SSI, trained using the US CDC definition criteria, with a large cohort of elective colorectal surgery patients already evaluated through a nationwide nosocomial infection surveillance system, which will be the comparative gold standard.

Eligibility criteria: Inclusion Criteria:

* Elective colorectal resection

Exclusion Criteria:

* Emergency surgery
* Infection present at operation
* Previous intestinal stoma

Primary outcomes:
measure: Rate of surgical site infection
timeFrame: 30 days
Secondary outcomes:
Other outcomes:
Locations:
city: Granollers
country: Spain
facility: Hospital General de Granollers
state: Barcelona
Study officials:
References: