Integrating Artificial Intelligence Into International Classification of Functioning, Disability, and Health Coding: Effectiveness of a Mobile Application for Patient Questionnaires

NCT07021781 | Completed

• 1 other identifier: NROC 2
• Study Type: interventional
• Target: 185
• Locations: 1 country
• Sites: 2

Brief Summary

Mobile applications and artificial intelligence are increasingly integrated into medical practice, yet their impact on workflow optimization and diagnostic accuracy remains understudied. This study evaluates the effectiveness of the MedQuest mobile application in optimizing patient questionnaire processes and assesses the accuracy of AI-driven International Classification of Functioning, Disability and Health (ICF) coding in comparison to traditional clinician-based coding.

Conditions

ICF; Rehabilitation; Artifical Intelligence; Application

Keywords

rehabilitation; surveys and questionnaires; artificial intelligence; international classification of functioning, disability and health; mobile applications

Outcome Measures

Primary Outcomes (2)

• Time required to complete and process questionnaire

  Comparative analysis of the total time spent on completing standardized questionnaires between the control group (paper-based) and the experimental group (mobile app). In the control group, this includes both the time it takes for patients to complete the questionnaire and the time it takes for the medical staff to digitize the data. In the experimental group, only the time it takes to complete the questionnaire is measured, as the digital format eliminates the need for additional data processing.

  Baseline

• Accuracy of AI-generated ICF coding compared to clinician coding

  Agreement between International Classification of Functioning, Disability, and Health (ICF) codes generated by an AI system and codes from trained medical professionals. Agreement was assessed using the quadratic weighted kappa coefficient across 22 ICF domains covering body functions, body structures, activities and participation, and environmental factors.

  At study completion, approximately 3 months

Secondary Outcomes (2)

• Clinician Satisfaction with the MedQuest Mobile App

  At study completion, approximately 3 months

• AI Performance Metrics for ICF Code Classification

  At study completion, approximately 3 months

Study Arms (2)

Control

ACTIVE COMPARATOR

Paper-based group

Other: Traditional Paper-Based Questionnaires

Experimental

EXPERIMENTAL

MedQuest group

Other: MedQuest mobile application

Interventions

MedQuest mobile application OTHER

Participants in this group will complete standardized questionnaires and scales using the MedQuest mobile application. Physicians will provide patients with a QR code, allowing them to access assigned digital questionnaires on their personal mobile devices or provided tablets. The application facilitates the digital input of patient responses. Following completion, the MedQuest application automatically processes the questionnaire data and utilizes an integrated artificial intelligence system (Claude 3.5 Sonnet, version from 22.10.2024) to generate recommended International Classification of Functioning, Disability and Health (ICF) codes. The application also provides an integrated calculator for questionnaire scores. This intervention aims to streamline the assessment process, reduce completion time, and enhance the efficiency and accuracy of ICF coding through AI integration.

Experimental

Traditional Paper-Based Questionnaires OTHER

Participants in this group will complete all standardized questionnaires and scales (e.g. SF-36, Barthel Index, etc.) manually on printed paper forms. Physicians will complete the forms and patients will complete their responses with a pen or pencil. Subsequent evaluation and processing of these questionnaires will be performed by physicians via the Medquest application, followed by manual identification and recording of International Classification of Functioning, Disability, and Health (ICF) codes based on clinical judgment and established guidelines. This method represents the traditional approach to patient assessment and data collection.

Control

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Aged 18 and older.
• Owned a smartphone (iOS or Android operating system) with internet access.
• Deemed by the investigator to be able to understand and comply with the study requirements.
• Provided a signed and dated written informed consent form, along with any necessary personal data processing permissions, before any examination procedures.

You may not qualify if:

• Patients with severe cognitive impairments that would prevent them from understanding and completing the questionnaires independently
• Patients with severe visual impairments that would interfere with their ability to use mobile applications
• Patients who declined to participate after being informed about the study protocol

Sponsors & Collaborators

• Tulip Medicine: lead

Study Sites (2)

"Green Clinic" LLC

Astana, 010000, Kazakhstan

Location

"National Scientific Oncological Center" LLC

Astana, 010000, Kazakhstan

Location

Related Publications (1)

• Kurban Z, Khassenov D, Burkitbaev Z, Bulekbayeva S, Chinaliyev A, Bakhtiyar S, Saparbayev S, Sultanaliyev T, Zhunissova U, Slivkina N, Titskaya E, Arias L, Aldakuatova D, Yessenbayeva G, Ermakhan Z. Artificial intelligence-enhanced mapping of the international classification of functioning, disability and health via a mobile app: a randomized controlled trial. Front Public Health. 2025 Aug 5;13:1590401. doi: 10.3389/fpubh.2025.1590401. eCollection 2025.

  PMID: 40837960 DERIVED

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of department

Study Record Dates

• First Submitted: May 29, 2025
• First Posted: June 15, 2025
• Study Start: November 25, 2024
• Primary Completion: February 26, 2025
• Study Completion: February 26, 2025
• Last Updated: June 15, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will not share

Locations

KA (2)