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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT06286865
Other study ID # 260972(REK)
Secondary ID
Status Recruiting
Phase N/A
First received
Last updated
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
Health authority
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

Study Design


Related Conditions & MeSH terms


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
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