Skip to main content
OpenTrials
Completed

NCT Number: NCT06286865

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

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.

Completed

Looking for future studies?

Notify Me

Key information

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Norwegian Centre for E-health Research

Tromsø, Troms, 9019, Norway

About this study

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:

  • Study participants are randomly allocated to group 1 and group 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.
  • In period 1 with 10 clinical notes, group 1 uses the control interface, while group 2 uses the intervention interface.
  • Data are logged in the background using button presses (eg. time, assigned codes, and comments).
  • Then, there is an immediate crossover to period 2 for the last 10 clinical notes.
  • Data continue to be logged in the background using button presses.
  • At the end, participants in both groups will complete the system usability scale.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

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.

Primary outcomes

  1. Time

    Time frame: 1 hour

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

  2. Accuracy

    Time frame: 1 hour

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

Sponsors and collaborators

Lead sponsor

University Hospital of North Norway

Other

Collaborators

  • The Research Council of Norway

Registry information

Official study title

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

Acronym: ClinCode

Important dates

Study start
2023
Primary completion
2024
Study completion
2024
First posted
Feb 29, 2024
Registry last updated
Jun 25, 2026

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.

Published trials that share one or more normalized conditions with this study.