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

Gastrointestinal Diseases Clinical Trial

— ClinCode  

Official title:

Improving Quality of ICD-10 (International Statistical Classification of Diseases, Tenth Revision) Coding Using AI: Protocol for a Crossover Randomized Controlled Trial

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT06286865
• Other study ID #: 260972(REK)
• Status: Recruiting
• Phase: N/A
• Start date: October 20, 2023
• Est. completion date: April 2024

Study information

• Verified date: February 2024
• Source: University Hospital of North Norway
• Contact: Taridzo F Chomutare, PhD
• Phone: +4747680032
• Email: taridzo.chomutare[@]ehealthresearch.no
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

Description:

Once participants are recruited, they are randomly allocated to 2 groups without allocation concealment. Allocation concealment will not be relevant for clinical coders since it is known whether a participant is assisted or not, and we will not develop a placebo coding assistant. We will, however, conceal the allocation of subjects for the analyses.

In total, participants will code 20 clinical notes, where each note belongs to a single patient. The participants are asked to complete the experiment in 1 sitting without interruptions, and they cannot revisit or go back to previous notes. In the event that participants are interrupted, they are asked to exit the experiment, and any incomplete records are discarded as invalid.

The user study process can be summarized in the following steps:

1. Study participants are randomly allocated to group 1 and group 2.

2. To prepare participants for the experiment, a short video tutorial is played after the consent form is signed and right before the clinical coding task commences.

3. In period 1 with 10 clinical notes, group 1 uses the control interface, while group 2 uses the intervention interface.

4. Data are logged in the background using button presses (eg. time, assigned codes, and comments).

5. Then, there is an immediate crossover to period 2 for the last 10 clinical notes.

6. Data continue to be logged in the background using button presses.

7. At the end, participants in both groups will complete the system usability scale.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 30
• Est. completion date: April 2024
• Est. primary completion date: April 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

Inclusion Criteria:

• participant has coded clinical texts before, preferably ICD-10 coding

• is a healthcare professional, eg. clinician, nurse, professional coders

• can understand Swedish

Exclusion Criteria:

• participants outside Norway and Sweden

Related Conditions & MeSH terms

• Digestive System Diseases
• Gastrointestinal Diseases

Intervention

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.

Locations

Country	Name	City	State
Norway	Norwegian Centre for E-health Research	Tromso	Troms

Sponsors (2)

Lead Sponsor	Collaborator
University Hospital of North Norway	The Research Council of Norway

Country where clinical trial is conducted

Norway, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Time	Time in seconds taken to assign ICD-10 codes to each of the 20 clinical notes.	1 hour
Primary	Accuracy	Accuracy is calculated by dividing the number of correct ICD-10 codes by the total number of codes assigned.	1 hour