Development and Effectiveness of a Machine Learning-based Clinical Decision Support System for the Management of Post-embolization Syndrome after Transarterial Chemoembolization

Development and effectiveness of clinical decision support system for post-embolization syndrome in transartery chemoembolization patients based on machine learning

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

CRIS

Registry ID

KCT0008888

Enrollment

Registered

2023-10-20

Start date

2023-01-09

Completion date

Unknown

Last updated

2023-11-13

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

Conditions

None listed

Interventions

Others(clinical decision support system) : The intervention targeted 40 nurses (experimental group: 21

control group: 19) who were caring for TACE patients in four tertiary general hospitals and one general hospital. The POEM CDSS was provided to the experimental group for 6 weeks, while an educational

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: - A nurse who is currently working in a ward with TACE patients and has experience of nursing patients before and after the procedure for more than 3 months - Those who provide direct care to patients - A person who understands the purpose and method of this study and agrees to participate in the study

Exclusion criteria

Exclusion criteria: Those who do not provide direct nursing care for more than 2 weeks

Design outcomes

Primary

Measure	Time frame
system aspect (nurse change) ;nurse outcome aspect (comfort care);patient outcome aspect (patient comfort)	—

Secondary

Measure	Time frame
POEM CDSS system(accuracy, acceptance, utilization)	—

Countries

Korea, Republic of

Contacts

Public ContactMyoung Soo Kim

Pukyung National University

kanosa5782@gmail.com+82-51-629-5782

Outcome results