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

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.

Download Full-text

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

10.2196/preprints.21056 ◽

2020 ◽

Author(s):

Yukinori Harada ◽

Taro Shimizu

Keyword(s):

Internal Medicine ◽

Artificial Intelligence ◽

Medical History ◽

General Internal Medicine ◽

Waiting Time ◽

Waiting Times ◽

Medical History Taking ◽

History Taking ◽

General Internal ◽

Outpatient Departments

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.

Download Full-text

Incidence of diagnostic errors in unplanned hospitalized patients using an automated medical history-taking system with differential diagnosis generator: retrospective observational study (Preprint)

10.2196/preprints.35225 ◽

2021 ◽

Author(s):

Ren Kawamura ◽

Yukinori Harada ◽

Shu Sugimoto ◽

Yuichiro Nagase ◽

Shinichi Katsukura ◽

...

Keyword(s):

Internal Medicine ◽

Differential Diagnosis ◽

Clinical Practice ◽

Medical History ◽

Diagnostic Errors ◽

Clinical History ◽

Final Diagnosis ◽

Outpatient Department ◽

Medical History Taking ◽

History Taking

BACKGROUND Automated medical history-taking systems that generate differential diagnosis lists have been suggested to contribute to improved diagnostic accuracy. However, the effect of this system on diagnostic errors in clinical practice remains unknown. OBJECTIVE This study aimed to assess the incidence of diagnostic errors in an outpatient department, where an artificial intelligence (AI)-driven automated medical history-taking system that generates differential diagnosis lists was implemented in clinical practice. METHODS We conducted a retrospective observational study using data from a community hospital in Japan. We included patients aged 20 and older who used an AI-driven automated medical history-taking system that generates differential diagnosis lists in the outpatient department of internal medicine for whom the index visit was between July 1, 2019, and June 30, 2020, followed by unplanned hospitalization within 14 days. The primary endpoint was the incidence of diagnostic errors, which were detected using the Revised Safer Dx instrument by at least two independent reviewers. To evaluate the differential diagnosis list of AI on the incidence of diagnostic errors, we compared the incidence of diagnostic errors between the groups in which AI generated the final diagnosis in the differential diagnosis list and in which AI did not generate the final diagnosis in the differential diagnosis list; Fisher’s exact test was used for comparison between these groups. For cases with confirmed diagnostic errors, further review was conducted to identify the contributing factors of diagnostic errors via discussion among the three reviewers, using the Safer Dx Process Breakdown Supplement as a reference. RESULTS A total of 146 patients were analyzed. The final diagnosis was confirmed in 138 patients and the final diagnosis was observed in the differential diagnosis list of the AI in 69 patients. Diagnostic errors occurred in 16 of 146 patients (11.0%; 95% confidence interval, 6.4-17.2%). Although statistically insignificant, the incidence of diagnostic errors was lower in cases in which the final diagnosis was included in the differential diagnosis list of AI than in cases in which the final diagnosis was not included (7.2% vs. 15.9%, P=.18). Regarding the quality of clinical history taken by AI, the final diagnosis was easily assumed by reading only the clinical history taken by the system in 11 of 16 cases (68.8%). CONCLUSIONS The incidence of diagnostic errors in the internal medicine outpatients used an automated medical history-taking system that generates differential diagnosis lists seemed to be lower than the previously reported incidence of diagnostic errors. This result suggests that the implementation of an automated medical history-taking system that generates differential diagnosis lists could be beneficial for diagnostic safety in the outpatient department of internal medicine.

Download Full-text

How To Categorize Occupation And Disease History in Medicine? A Free-Text Online Survey On Six Clinics and 18 University Hospitals in Turkey (Preprint)

10.2196/preprints.13976 ◽

2019 ◽

Author(s):

Mehmet Guven Gunver ◽

Eray Yurtseven

Keyword(s):

Internal Medicine ◽

Medical History ◽

General Surgery ◽

Online Survey ◽

Medical History Taking ◽

Free Text ◽

History Taking ◽

University Hospitals

UNSTRUCTURED Medical history taking is one of the most difficult topics in medicine. The ways in which patient medical history is taken and interpreted varies greatly and may be impacted by the bias of the clinician. For this reason, the process is thought of as an art, rather than a science. In this study, we sought to determine how clinicians categorize the outcome of medical history taking in relations to patient maternal and paternal disease history, the patient own disease history and their current occupation [1]. Clinicians were invited to participate in the survey from eighteen (18) university hospitals dispersed throughout fourteen (14) provinces in Turkey. This sample therefore represented 1270 clinicians representing the specializations of otology, general surgery, internal medicine, cardiology, pulmonology and psychiatry. The researchers obtained responses from seventy seven (77) clinicians or approximately six percent (6%).

Download Full-text

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 ◽

10.3390/ijerph18042086 ◽

2021 ◽

Vol 18 (4) ◽

pp. 2086

Author(s):

Yukinori Harada ◽

Shinichi Katsukura ◽

Ren Kawamura ◽

Taro Shimizu

Keyword(s):

Artificial Intelligence ◽

Differential Diagnosis ◽

Diagnostic Accuracy ◽

Medical History ◽

Correct Diagnosis ◽

Medical History Taking ◽

Randomized Controlled Study ◽

Controlled Study ◽

History Taking ◽

Randomized Controlled

Background: The efficacy of artificial intelligence (AI)-driven automated medical-history-taking systems with AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy was shown. However, considering the negative effects of AI-driven differential-diagnosis lists such as omission (physicians reject a correct diagnosis suggested by AI) and commission (physicians accept an incorrect diagnosis suggested by AI) errors, the efficacy of AI-driven automated medical-history-taking systems without AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy should be evaluated. Objective: The present study was conducted to evaluate the efficacy of AI-driven automated medical-history-taking systems with or without AI-driven differential-diagnosis lists on physicians’ diagnostic accuracy. Methods: This randomized controlled study was conducted in January 2021 and included 22 physicians working at a university hospital. Participants were required to read 16 clinical vignettes in which the AI-driven medical history of real patients generated up to three differential diagnoses per case. Participants were divided into two groups: with and without an AI-driven differential-diagnosis list. Results: There was no significant difference in diagnostic accuracy between the two groups (57.4% vs. 56.3%, respectively; p = 0.91). Vignettes that included a correct diagnosis in the AI-generated list showed the greatest positive effect on physicians’ diagnostic accuracy (adjusted odds ratio 7.68; 95% CI 4.68–12.58; p < 0.001). In the group with AI-driven differential-diagnosis lists, 15.9% of diagnoses were omission errors and 14.8% were commission errors. Conclusions: Physicians’ diagnostic accuracy using AI-driven automated medical history did not differ between the groups with and without AI-driven differential-diagnosis lists.

Download Full-text

Effect of A Brief Education On The Occupational History Taking In Hypertension Patients

Acta Medica ◽

10.32552/2018.actamedica.324 ◽

2018 ◽

Vol 49 (4) ◽

pp. 1-5

Author(s):

Seval Müzeyyen Ecin ◽

Adem Koyuncu ◽

Abdulsamet Sandal ◽

Sultan Pınar Çetintepe ◽

Nursel Çalık Başaran ◽

...

Keyword(s):

Internal Medicine ◽

General Internal Medicine ◽

Outpatient Clinic ◽

Occupational Diseases ◽

History Taking ◽

University Hospitals ◽

Occupational History ◽

General Internal ◽

Number Of Patients ◽

Significant Difference

Objective: This study is designed to measure the effect of 10-minutes training about occupational diseases, history taking and relation of occupation and hypertension on occupational history taking rates of physicians. Materials and Methods: This research is conducted between 01 April 2018 to 31 May 2018 at Hacettepe University Faculty of Medicine, Department of Internal Medicine, Division of General Internal Medicine Outpatient Clinic. The training including the importance and methods of taking occupational history, and the relation between occupation and diseases is given to the new assistant doctor group as an extra 10 minutes’ education session. At the end of May, researchers screened electronic medical files of patients who diagnosed with hypertension (ICD10 code I10) of outpatients in General Internal Medicine Division in April and May 2018. Results: We reviewed the data of 3619 adult patients administered to General Internal Medicine Outpatient Clinic of Hacettepe University Hospitals in between 01 April to 31 May 2018. A total of 395 (10.9%) patients had hypertension diagnosis code. The total number of patients whose occupational history taken were 151 (38.2%). There were statistically significant difference between physician groups not trained in April and trained 10 minutes in May, 62 (32%), and 89 (44.3%), respectively (p:0.012). Among the hypertensive patients whose occupational history were recorded, 36 (23.8%) had an occupation. Conclusion: This result emphasizes the importance of education in raising awareness of taking an occupational history. As occupational diseases are 100% preventable diseases, taking occupational history will enhance the diagnosis and effective treatment of the occupational or work-related diseases. Beginning from the medical faculty lectures, seminars and post-graduate education have to be added and increased regarding this important issue. Keywords: Occupational history, occupational disease, hypertension.

Download Full-text