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| Name | Class |
|---|---|
| The Research Council of Norway | OTHER |
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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.
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:
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Easy-ICD interface | Active Comparator | This arm uses our AI-based computer-assisted clinical coding (CAC) system, Easy-ICD |
|
| Control interface | No Intervention | This control arm uses an interface similar to Easy-ICD, but without the AI functionality |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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. |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Hercules Dalianis, PhD | Norwegian Centre for E-health Research | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Norwegian Centre for E-health Research | Tromsø | Troms | 9019 | Norway |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 38470476 | Derived | 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. |
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The data we collect in the study are all anonymous and will be shared publicly after the user study is published.
At the publishing of the user study.
The anonymous data will be publicly available.
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| ID | Term |
|---|---|
| D005767 | Gastrointestinal Diseases |
| ID | Term |
|---|---|
| D004066 | Digestive System Diseases |
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