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Assessment of the Efficacy of ChatGPT in Detecting Surgical Site Infections Following Elective Colorectal Surgery

Assessing the Efficacy of ChatGPT in Detecting Surgical Site Infections Following Elective Colorectal Surgery

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06556017
Acronym
ChatCCR
Enrollment
122
Registered
2024-08-15
Start date
2024-08-15
Completion date
2024-09-01
Last updated
2024-10-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Surgical Site Infection

Keywords

Surgical Site Infection, Surveillance, Artificial intelligence, ChatGPT, natural language processing

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'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 pilot study aims to evaluate the ability of ChatGPT to detect surgical site infections (SSI) at the three anatomical levels described by the CDC. The aim is to retrospectively compare the results of the AI chatbot in diagnosing SSI, trained using the US CDC definition criteria, with a cohort of elective colorectal surgery patients evaluated through a nationwide nosocomial infection surveillance system.

Interventions

DIAGNOSTIC_TESTDiagnosis of SSI

Comparison of the manual system and ChatGPT for SSI diagnosis in colorectal surgery procedures enrolled in the SSI surveillance programme.

Sponsors

Hospital de Granollers
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum

Inclusion criteria

* Elective colorectal resection

Exclusion criteria

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

Design outcomes

Primary

MeasureTime frameDescription
Rate of surgical site infection30 daysRate of Surgical site infection according to the definitions of the CDC-NHSN (Centers for Disease Control and Prevention-National Healthcare Safety Network)

Countries

Spain

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 5, 2026