Automation of the patient history - evaluation of ergonomic aspects

Quaak, Mirjam; Voort, P. J. G. van der; Westerman, R. F.; Hasman, Arie; Bemmel, Jan H. van

doi:10.1016/0020-7101(87)90094-8

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Automated medical history taking itself has a long history since it was introduced in the late 1960s [ 7 , 8 ]. Until recently, automated medical history taking was used outside of clinics or hospitals and took a long time to complete [ 9 , 10 ], but it has now been implemented in hospital and clinic waiting rooms through computing systems [ 11 , 12 ] and takes only 5 to 10 minutes to complete [ 11 - 14 ]. Its usability and acceptance by patients have been on the rise, and most patients (including older adults) can use automated medical history–taking devices without assistance [ 11 - 15 ].…”

Section: Introductionmentioning

confidence: 99%

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

Harada¹,

Shimizu²

2020

JMIR Med Inform

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Background Patient 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. Objective We 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. Methods We 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. Results We 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). Conclusions The 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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Section: Introductionmentioning

confidence: 99%

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

Harada¹,

Shimizu²

2020

JMIR Med Inform

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“…Inadequate attention to the problem of conveying complex information probably underlies, in part, the negative results cited by Quaak et al [52]. It is difficult to grasp the meaning of data in the output from their program [56].…”

Section: Physician Perception Of Clinical Value From Self-reported Comentioning

confidence: 99%

Development and significance of automated history‐taking software for clinical medicine, clinical research and basic medical science

Zakim

2016

J Intern Med

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“…Automated medical history taking itself has a long history since it was introduced in the late 1960s [7,8]. Until recently, automated medical history taking was used outside of clinics or hospitals and took a long time to complete [9,10], but it has now been implemented in hospital and clinic waiting rooms through computing systems [11,12] and takes only 5 to 10 minutes to complete [11][12][13][14]. Its usability and acceptance by patients have been on the rise, and most patients (including older adults) can use automated medical history-taking devices without assistance [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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

Harada¹,

Shimizu²

2020

Preprint

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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.

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Automation of the patient history - evaluation of ergonomic aspects

Cited by 9 publications

References 9 publications

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

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

Development and significance of automated history‐taking software for clinical medicine, clinical research and basic medical science

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

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