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

Abstract

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.

Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study

“…The study used 16 written clinical vignettes that only included patient history and vital signs. All vignettes were generated by an AI-driven AMHT system from real patients [ 15 ]. Clinical vignettes were selected by the authors (YH, SK, and TS) as follows.…”

“…The quality of clinical documentation generated by an AI-driven AMHT system was reported to be as high as those of expert physicians [ 14 ]. AI-driven AMHT systems that also generate differential-diagnosis lists (so-called next-generation diagnosis-support systems [ 13 ]), were recently implemented in clinical practice [ 15 , 16 ]. A previous study reported that AI-driven AMHT systems with AI-driven differential-diagnosis lists could improve less-experienced physicians’ diagnostic accuracy in an ambulatory setting [ 16 ].…”

Evaluation of machine learning solutions in medicine

“…30,31 Because conducting RCTs is challenging, other approaches are often used for generating evidence of clinical benefit of machine-learned systems, such as matched cohorts, quasiexperimental interrupted time series analyses, and prospective before-and-afer studies. [32][33][34] In a related article, we described how we planned to use an observational matched cohort study design to evaluate a machine-learned early warning system in a General Internal Medicine unit, given that an RCT was estimated to require about 25 000 patients. 1 Although findings from observational studies are ofen considered to be a lower level of evidence than RCT findings, they provide a compromise between the needs of stakeholders and clinicians seeking timely evidence of clinical impact with machine-learned interventions and the resources required to conduct RCTs.…”

Computational healthcare: Present and future perspectives (Review)

“…By contrast, for the identification of a disease from various symptoms in a patient and the results of tests, a wide range of knowledge, not specific knowledge, and advanced information processing is necessary. To meet this demand, AI assistants such as Watson are also being developed that can learn the literature on a subject by enabling the processing of natural language, and can make complex decisions using expert systems (28,36).…”