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Accepted for/Published in: JMIR Medical Informatics

Date Submitted: Jun 4, 2020
Open Peer Review Period: Jun 4, 2020 - Jul 7, 2020
Date Accepted: Aug 3, 2020
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

Impact of a Commercial Artificial Intelligence–Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study

Harada Y, Shimizu T

Impact of a Commercial Artificial Intelligence–Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study

JMIR Med Inform 2020;8(8):e21056

DOI: 10.2196/21056

PMID: 32865504

PMCID: 7490680

Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.

The effect of artificial intelligence-based automated medical history taking system on patient waiting time in the general internal medicine outpatient setting: interrupted time series analysis

  • Yukinori Harada; 
  • Taro Shimizu

ABSTRACT

Background:

Use of automated medical history taking (AMHT) systems in the general internal medicine outpatient department (GIM-OD) is a promising strategy to reduce waiting time.

Objective:

We evaluated the effect of AI Monshin, an AMHT system, on waiting time in the GIM-OD.

Methods:

We retrospectively analyzed waiting time length in a Japanese community hospital GIM-OD (April 2017–April 2020). AI Monshin was implemented in April 2019. We compared the mean waiting time before and after the AI Monshin implementation. An interrupted time series analysis of the mean waiting time/month was conducted.

Results:

We analyzed 21,933 cases. The mean waiting time after the AI Monshin implementation (87.0 min, SD 55.1) was significantly shorter than before the AI Monshin implementation (89.5 min, SD 56.6), with an absolute difference of −2.5 min (P = .003; 95% CI, −4.0 to −0.9). In the interrupted time series analysis, whereas the underlying linear time trend was statistically significant (−0.6 min/month; P = .005; 95% CI, −1.0 to −0.2), level change (26.2 min; P = .43; 95% CI, −16.1 to 68.5) and slope change (−0.6 min/month; P = .23; 95% CI, −2.0 to 0.8) did not differ significantly. In a sensitivity analysis of data between April 2018 and April 2020, the mean waiting time after the AI Monshin implementation (87.0 min, SD 55.1) was not significantly different compared with that before the AI Monshin implementation (86.8 min, SD 53.9) with the absolute difference of +0.2 min (P = .84; 95% CI −1.6 to 2.0).

Conclusions:

AI Monshin reduced waiting time only to a very limited extent.


 Citation

Please cite as:

Harada Y, Shimizu T

Impact of a Commercial Artificial Intelligence–Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study

JMIR Med Inform 2020;8(8):e21056

DOI: 10.2196/21056

PMID: 32865504

PMCID: 7490680

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