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Improving Quality of ICD-10 Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

NCT06286865 · Status: RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 30

Last updated 2024-02-29

No results posted yet for this study

Summary

The goal of this randomised trial is to learn about the role of AI in clinical coding practice. The main question it aims to answer is:

Can the AI-based CAC system reduce the burden of clinical coding and also improve the quality of such coding? Participants will be asked to code clinical texts both while they use our CAC system and while they do not.

Conditions

  • Gastrointestinal Diseases

Interventions

OTHER

Easy-ICD

Easy-ICD is an AI-based computer-assisted clinical coding (CAC) system that helps clinical coder assign ICD-10 codes to clinical notes such as discharge summaries.

Sponsors & Collaborators

  • The Research Council of Norway

    collaborator OTHER

  • University Hospital of North Norway

    lead OTHER

Principal Investigators

  • Hercules Dalianis, PhD · Norwegian Centre for E-health Research

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
SINGLE
Model
CROSSOVER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2023-10-20
Primary Completion
2024-04-30
Completion
2024-04-30

Countries

  • Norway

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06286865 on ClinicalTrials.gov