Frontiers in public health · 2025
Artificial intelligence-enhanced mapping of the international classification of functioning, disability and health via a mobile app: a randomized controlled trial.
СтатьяРандомизированное исследованиеНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Kurban Zhandos, Khassenov Didar, Burkitbaev Zhandos, Bulekbayeva Sholpan, Chinaliyev Azat, Bakhtiyar Serik, Saparbayev Samat, Sultanaliyev Tokan, Zhunissova Ulzhalgas, Slivkina Natalia, Titskaya Elena, Arias Luis, Aldakuatova Dana, Yessenbayeva Gulfairus, Ermakhan Zhanerke
Аннотация
BACKGROUND: Mobile health applications and artificial intelligence (AI) are increasingly utilized to streamline clinical workflows and support functional assessment. The International Classification of Functioning, Disability and Health (ICF) provides a standardized framework for evaluating patient functioning, yet AI-driven ICF mapping tools remain underexplored in routine clinical settings.
OBJECTIVE: This study aimed to evaluate the efficiency and accuracy of the MedQuest mobile application-featuring integrated AI-based ICF mapping-compared to traditional paper-based assessment in hospitalized patients.
METHODS: A parallel-group randomized controlled trial was conducted in two medical centers in Astana, Kazakhstan. A total of 185 adult inpatients (≥18 years) were randomized to either a control group using paper questionnaires or an experimental group using the MedQuest app. Both groups completed identical standardized assessments (SF-12, IPAQ, VAS, Barthel Index, MRC scale). The co-primary outcomes were (1) total questionnaire completion time and (2) agreement between AI-generated and clinician-generated ICF mappings, assessed using quadratic weighted kappa. Secondary outcomes included AI sensitivity/specificity, confusion matrix analysis, and physician usability ratings via the System Usability Scale (SUS).
RESULTS: The experimental group completed questionnaires significantly faster than the control group (median 18 vs. 28 min, p < 0.001). Agreement between AI- and clinician-generated ICF mappings was substantial (κ = 0.842), with 80.6% of qualifiers matching exactly. The AI demonstrated high sensitivity and specificity for common functional domains (e.g., codes 1-2), though performance decreased for rare qualifiers. The micro-averaged sensitivity and specificity were 0.806 and 0.952, respectively. Mean SUS score among physicians was 86.8, indicating excellent usability and acceptability.
CONCLUSION: The MedQuest mobile application significantly improved workflow efficiency and demonstrated strong concordance between AI- and clinician-assigned ICF mappings. These findings support the feasibility of integrating AI-assisted tools into routine clinical documentation. A hybrid model, combining AI automation with clinician oversight, may enhance accuracy and reduce documentation burden in time-constrained healthcare environments.
TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT07021781.
OBJECTIVE: This study aimed to evaluate the efficiency and accuracy of the MedQuest mobile application-featuring integrated AI-based ICF mapping-compared to traditional paper-based assessment in hospitalized patients.
METHODS: A parallel-group randomized controlled trial was conducted in two medical centers in Astana, Kazakhstan. A total of 185 adult inpatients (≥18 years) were randomized to either a control group using paper questionnaires or an experimental group using the MedQuest app. Both groups completed identical standardized assessments (SF-12, IPAQ, VAS, Barthel Index, MRC scale). The co-primary outcomes were (1) total questionnaire completion time and (2) agreement between AI-generated and clinician-generated ICF mappings, assessed using quadratic weighted kappa. Secondary outcomes included AI sensitivity/specificity, confusion matrix analysis, and physician usability ratings via the System Usability Scale (SUS).
RESULTS: The experimental group completed questionnaires significantly faster than the control group (median 18 vs. 28 min, p < 0.001). Agreement between AI- and clinician-generated ICF mappings was substantial (κ = 0.842), with 80.6% of qualifiers matching exactly. The AI demonstrated high sensitivity and specificity for common functional domains (e.g., codes 1-2), though performance decreased for rare qualifiers. The micro-averaged sensitivity and specificity were 0.806 and 0.952, respectively. Mean SUS score among physicians was 86.8, indicating excellent usability and acceptability.
CONCLUSION: The MedQuest mobile application significantly improved workflow efficiency and demonstrated strong concordance between AI- and clinician-assigned ICF mappings. These findings support the feasibility of integrating AI-assisted tools into routine clinical documentation. A hybrid model, combining AI automation with clinician oversight, may enhance accuracy and reduce documentation burden in time-constrained healthcare environments.
TRIAL REGISTRATION: ClinicalTrials.gov, identifier NCT07021781.