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

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%).

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

2018 ◽  
Author(s):  
Mehmet Gunver ◽  
Mustafa Sukru Senocak ◽  
Eray Yurtseven
Keyword(s):  
Internal Medicine ◽  
Sample Size ◽  
General Surgery ◽  
Representative Sample ◽  
Online Survey ◽  
Free Text ◽  
Medical Interview ◽  
University Hospitals

UNSTRUCTURED The medical interview is one of the most challenging topics in medicine. The ways in which the interview is implemented varies greatly depending on the clinician. The ways in which a.) the interview is evaluated and, b.) the outcomes are determined, are also dependent on the bias of the clinician. For those reasons, the process of the medical interview is thought of as an art, rather than a science. In this study, clinicians were asked "how do you categorize the outcomes of the medical interview?” in relation to: a patient’s own disease history, a patients maternal and paternal disease history, and a patient’s current occupation. To obtain a sample representative of all clinicians in Turkey, we invited participants from 18 university hospitals dispersed through 14 providences. 1,270 clinicians representing specializations of otology, general surgery, internal medicine, cardiology, pulmonology and psychiatry were invited to participate in the study. Of those 1,270 clinicians, 77 of them responded to the survey. We obtained participation from clinicians in six of the 18 clinics. Our representative sample size was approximately 6% of the intended population.

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

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.

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

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) (<i>P</i>=.12). In the interrupted time-series analysis, the underlying linear time trend (–0.4 minutes per month; <i>P</i>=.06; 95% CI –0.9 to 0.02), level change (40.6 minutes; <i>P</i>=.09; 95% CI –5.8 to 87.0), and slope change (–1.1 minutes per month; <i>P</i>=.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) (<i>P</i>=.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.

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

10.2196/21056 ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. e21056
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.

Exploring early interprofessional socialization: a pilot study of student’s experiences in medical history taking

2020 ◽  
pp. 1-8 ◽  
Author(s):  
Christine Arnold ◽  
Sarah Berger ◽  
Nadine Gronewold ◽  
Denise Schwabe ◽  
Burkhard Götsch ◽  
...  
Keyword(s):  
Pilot Study ◽  
Medical History ◽  
Medical History Taking ◽  
History Taking

scholarly journals Validity of medical history taken by pharmacists using a medical history taking tool

2017 ◽  
Vol 18 (6) ◽  
pp. 403-408 ◽  
Author(s):  
Aya Matsushita ◽  
Junji Haruta ◽  
Madoka Tsutumi ◽  
Takuya Sato ◽  
Tetsuhiro Maeno
Keyword(s):  
Medical History ◽  
Medical History Taking ◽  
History Taking
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