Real-world Effectiveness Evaluation of Clinical Decision Support System Based on Artificial Intelligence (AI-CDSS)

NCT05065931 | Completed

• 1 other identifier: 324-01
• Study Type: observational
• Target: 34,113
• Locations: 1 country
• Sites: 1

Brief Summary

This study intends to explore the accuracy of clinical diagnosis of AI based CDSS system and promotion of clinical work by comparing CDSS before and after the online.

Conditions

Medical Informatics Applications

Outcome Measures

Primary Outcomes (3)

• Accuracy Rate of Recommended Diagnosis by CDSS, up to 12 weeks

  Based on the patient's discharge diagnosis as a standard, it is explored whether the diagnosis given by the CDSS is consistent with the discharge diagnosis in the patient's medical record.

  When the subject was discharged from the hospital

• Patients' hospitalization time (days), up to 24 weeks

  The length of a patient's stay is the number of days he or she experiences from the time of admission to the time of discharge.

  When the subject was discharged from the hospital

• consistency between admission diagnosis and discharge diagnosis up to 12 weeks

  When the patient comes to the hospital, the clinician will write an inpatient record and give a preliminary diagnosis, which we call admission diagnosis.After the patient is hospitalized, all kinds of examinations will be improved. After all the examination results come out, the patient's diagnosis on admission may be modified. Because there are no auxiliary examination results on admission, the diagnosis on admission may not be completely correct.This modified diagnosis is called discharge diagnosis.This study compared the consistent rate of admission diagnosis and discharge diagnosis before and after CDSS on-line.

  When the subject was discharged from the hospital

Secondary Outcomes (1)

• length of confirmed time, up to 6 weeks

  When the subject was discharged from the hospital

Study Arms (2)

Before

before CDSS on-line

After

after CDSS on-line

Other: Auxiliary diagnostic system by AI-CDSS

Interventions

Auxiliary diagnostic system by AI-CDSS OTHER

Helping clinicians to make diagnoses by using CDSS based-on AI

After

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Study population was the hospitalized patient from December 2016 to February 2019 in 6 clinical departments. The six clinical departments were Otolaryngology, Orthopaedic, Respiratory Medicine, General Surgery, Cardiology and Hematology.

You may qualify if:

• all hospitalized patients in 6 clinical departments, Otolaryngology, Orthopaedic, Respiratory Medicine, General Surgery, Cardiology and Hematology from December 2016 to February 2019.

You may not qualify if:

• Missing data for key variables

Sponsors & Collaborators

• Peking University Third Hospital: lead

Study Sites (1)

Peking University Third Hospital

Beijing, China

Location

Study Officials

• Hong Qi, Ph.D

  Peking University Third Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: July 12, 2019
• First Posted: October 4, 2021
• Study Start: March 1, 2019
• Primary Completion: May 1, 2019
• Study Completion: June 1, 2019
• Last Updated: October 4, 2021

Record last verified: 2019-07

Locations

CN (1)