Evidence map›Paper›PMID 32865504›Full record

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.

Yukinori Harada, Taro Shimizu

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed.

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  15. Computational healthcare: Present and future perspectives (Review).Experimental and therapeutic medicine · 2021
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  16. CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2021
    Article
  17. Evaluation of machine learning solutions in medicine.CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Yukinori HaradaDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.ORCID https://orcid.org/0000-0001-6042-7397
Taro ShimizuDepartment of Diagnostic and Generalist Medicine, Dokkyo Medical University, Mibu, Japan.ORCID https://orcid.org/0000-0002-3788-487X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

artificial intelligenceautomated medical history taking systemeHealthinterrupted time-series analysiswaiting time

Identifiers

PMID32865504
PMCPMC7490680

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.