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

NCT06286865 | Recruiting DMC

• 1 other identifier: 260972(REK)
• Study Type: interventional
• Target: 30
• Locations: 1 country
• Sites: 1

Brief 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

Outcome Measures

Primary Outcomes (2)

• Time

  Time in seconds taken to assign ICD-10 codes to each of the 20 clinical notes.

  1 hour

• Accuracy

  Accuracy is calculated by dividing the number of correct ICD-10 codes by the total number of codes assigned.

  1 hour

Study Arms (2)

Easy-ICD interface

ACTIVE COMPARATOR

This arm uses our AI-based computer-assisted clinical coding (CAC) system, Easy-ICD

Other: Easy-ICD

Control interface

NO INTERVENTION

This control arm uses an interface similar to Easy-ICD, but without the AI functionality

Interventions

Easy-ICD OTHER

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.

Easy-ICD interface

Eligibility Criteria

• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• participant has coded clinical texts before, preferably ICD-10 coding
• is a healthcare professional, eg. clinician, nurse, professional coders
• can understand Swedish

You may not qualify if:

• participants outside Norway and Sweden

Sponsors & Collaborators

• University Hospital of North Norway: lead
• The Research Council of Norway: collaborator

Study Sites (1)

Norwegian Centre for E-health Research

Tromsø, Troms, 9019, Norway

RECRUITING

Related Publications (1)

• Chomutare T, Lamproudis A, Budrionis A, Svenning TO, Hind LI, Ngo PD, Mikalsen KO, Dalianis H. Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial. JMIR Res Protoc. 2024 Mar 12;13:e54593. doi: 10.2196/54593.

  PMID: 38470476 DERIVED

MeSH Terms

Conditions

Gastrointestinal Diseases

Condition Hierarchy (Ancestors)

Digestive System Diseases

Study Officials

• Hercules Dalianis, PhD

  Norwegian Centre for E-health Research

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Taridzo F Chomutare, PhD

CONTACT

+4747680032; taridzo.chomutare@ehealthresearch.no

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: PARTICIPANT
• Purpose: DIAGNOSTIC
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Senior Researcher

Study Record Dates

• First Submitted: February 16, 2024
• First Posted: February 29, 2024
• Study Start: October 20, 2023
• Primary Completion: April 1, 2024
• Study Completion: April 1, 2024
• Last Updated: February 29, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF
• Time Frame: At the publishing of the user study.
• Access Criteria: The anonymous data will be publicly available.

Locations

NO (1)