Peking University Third Hospital
Beijing, China
Completed
NCT Number: NCT05065931
Real-world Effectiveness Evaluation of Clinical Decision Support System Based on Artificial Intelligence (AI-CDSS)
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.
Completed
Looking for future studies?
Notify MeKey information
Conditions
Sex eligibility
All sexes
Study type
Observational
Primary location
About this study
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. The difference of diagnostic accuracy before and after AI-CDSS application will be compared by the before and after design, and the role of AI-CDSS will be explored.
Who can participate
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
- all hospitalized patients in 6 clinical departments, Otolaryngology, Orthopaedic, Respiratory Medicine, General Surgery, Cardiology and Hematology from December 2016 to February 2019.
Exclusion criteria
- Missing data for key variables
Treatment and study plan
Auxiliary diagnostic system by AI-CDSS
OtherHelping clinicians to make diagnoses by using CDSS based-on AI
Primary outcomes
-
Accuracy Rate of Recommended Diagnosis by CDSS, up to 12 weeks
Time frame: When the subject was discharged from the hospital
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.
-
Patients' hospitalization time (days), up to 24 weeks
Time frame: When the subject was discharged from the hospital
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.
-
consistency between admission diagnosis and discharge diagnosis up to 12 weeks
Time frame: When the subject was discharged from the hospital
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.
Secondary outcomes
-
length of confirmed time, up to 6 weeks
Time frame: When the subject was discharged from the hospital
he length of confirmed time (days) was the duration between the preliminary admission diagnosis and the definite diagnosis.
Sponsors and collaborators
Lead sponsor
Peking University Third Hospital
Other
Registry information
Official study title
Real-world Effectiveness Evaluation of Clinical Decision Support System Based on Artificial Intelligence (AI-CDSS) on Diagnosis
Important dates
- Study start
- 2019
- Primary completion
- 2019
- Study completion
- 2019
- First posted
- Oct 4, 2021
- Registry last updated
- Oct 4, 2021
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
Related clinical trials
Published trials that share one or more normalized conditions with this study.
Completed
Aligning Patients and Their Primary Care Providers
NCT02707146
Behavior, Communication
Oakland, California, United States
View Trial Details