ArticleJMIR medical informatics2020
Impact of a Commercial Artificial Intelligence-Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study.
Article in JMIR medical informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial.International journal of environmental research and public health · 2023Trial
- Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.International journal of environmental research and public health · 2021Trial
- Artificial Intelligence in Physical Therapy: A Narrative Review of Artificial Intelligence Applications Across the Patient and Client Management Model.Health science reports · 2026Article
- Impact of expected waiting time on pediatric outpatient satisfaction: a behavioral experiment study.BMC health services research · 2025Article
- Artificial Intelligence in Clinics: Enhancing Cardiology Practice.JMA journal · 2025Review
- Artificial intelligence in outpatient management: a simulation-based study in a Chinese tertiary hospital.Frontiers in artificial intelligence · 2025Article
- Facilitating Trust Calibration in Artificial Intelligence-Driven Diagnostic Decision Support Systems for Determining Physicians' Diagnostic Accuracy: Quasi-Experimental Study.JMIR formative research · 2024Article
- Review
- Preserving Patient Stories: Bioethical and Legal Implications Related to the Shift from Traditional to Digital Anamnesis.Clinics and practice · 2024Review
- Longitudinal Changes in Diagnostic Accuracy of a Differential Diagnosis List Developed by an AI-Based Symptom Checker: Retrospective Observational Study.JMIR formative research · 2024Article
- Effects of Combinational Use of Additional Differential Diagnostic Generators on the Diagnostic Accuracy of the Differential Diagnosis List Developed by an Artificial Intelligence-Driven Automated History-Taking System: Pilot Cross-Sectional Study.JMIR formative research · 2023Article
- Artificial intelligence in medicine: A comprehensive survey of medical doctor's perspectives in Portugal.PloS one · 2023Article
- Article
- Incidence of Diagnostic Errors Among Unexpectedly Hospitalized Patients Using an Automated Medical History-Taking System With a Differential Diagnosis Generator: Retrospective Observational Study.JMIR medical informatics · 2022Article
- Computational healthcare: Present and future perspectives (Review).Experimental and therapeutic medicine · 2021Review
- Article
- Evaluation of machine learning solutions in medicine.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2021Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPatient waiting time at outpatient departments is directly related to patient satisfaction and quality of care, particularly in patients visiting the general internal medicine outpatient departments for the first time. Moreover, reducing wait time from arrival in the clinic to the initiation of an examination is key to reducing patients' anxiety. The use of automated medical history-taking systems in general internal medicine outpatient departments is a promising strategy to reduce waiting times. Recently, Ubie Inc in Japan developed AI Monshin, an artificial intelligence-based, automated medical history-taking system for general internal medicine outpatient departments.
objectiveWe hypothesized that replacing the use of handwritten self-administered questionnaires with the use of AI Monshin would reduce waiting times in general internal medicine outpatient departments. Therefore, we conducted this study to examine whether the use of AI Monshin reduced patient waiting times.
methodsWe retrospectively analyzed the waiting times of patients visiting the general internal medicine outpatient department at a Japanese community hospital without an appointment from April 2017 to April 2020. AI Monshin was implemented in April 2019. We compared the median waiting time before and after implementation by conducting an interrupted time-series analysis of the median waiting time per month. We also conducted supplementary analyses to explain the main results.
resultsWe analyzed 21,615 visits. The median waiting time after AI Monshin implementation (74.4 minutes, IQR 57.1) was not significantly different from that before AI Monshin implementation (74.3 minutes, IQR 63.7) (P=.12). In the interrupted time-series analysis, the underlying linear time trend (-0.4 minutes per month; P=.06; 95% CI -0.9 to 0.02), level change (40.6 minutes; P=.09; 95% CI -5.8 to 87.0), and slope change (-1.1 minutes per month; P=.16; 95% CI -2.7 to 0.4) were not statistically significant. In a supplemental analysis of data from 9054 of 21,615 visits (41.9%), the median examination time after AI Monshin implementation (6.0 minutes, IQR 5.2) was slightly but significantly longer than that before AI Monshin implementation (5.7 minutes, IQR 5.0) (P=.003).
conclusionsThe implementation of an artificial intelligence-based, automated medical history-taking system did not reduce waiting time for patients visiting the general internal medicine outpatient department without an appointment, and there was a slight increase in the examination time after implementation; however, the system may have enhanced the quality of care by supporting the optimization of staff assignments.
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