Improving Quality of ICD-10 Coding Using AI: Protocol for a Crossover Randomized Controlled Trial
NCT ID: NCT06286865
Last Updated: 2024-02-29
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
30 participants
INTERVENTIONAL
2023-10-20
2024-04-30
Brief Summary
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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.
Detailed Description
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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.
Conditions
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Study Design
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RANDOMIZED
CROSSOVER
DIAGNOSTIC
SINGLE
Study Groups
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Easy-ICD interface
This arm uses our AI-based computer-assisted clinical coding (CAC) system, Easy-ICD
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.
Control interface
This control arm uses an interface similar to Easy-ICD, but without the AI functionality
No interventions assigned to this group
Interventions
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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.
Eligibility Criteria
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Inclusion Criteria
* is a healthcare professional, eg. clinician, nurse, professional coders
* can understand Swedish
Exclusion Criteria
ALL
Yes
Sponsors
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The Research Council of Norway
OTHER
University Hospital of North Norway
OTHER
Responsible Party
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Taridzo Chomutare
Senior Researcher
Principal Investigators
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Hercules Dalianis, PhD
Role: PRINCIPAL_INVESTIGATOR
Norwegian Centre for E-health Research
Locations
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Norwegian Centre for E-health Research
Tromsø, Troms, Norway
Countries
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Central Contacts
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Facility Contacts
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Taridzo F Chomutare, PhD
Role: primary
Hercules Dalianis, PhD
Role: backup
References
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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.
Other Identifiers
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260972(REK)
Identifier Type: -
Identifier Source: org_study_id