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

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.

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The outpatient assessment of patients with anemia by a general internal medicine service

Journal of Hospital Administration ◽

10.5430/jha.v6n3p41 ◽

2017 ◽

Vol 6 (3) ◽

pp. 41

Author(s):

H. McFadgen ◽

S. Couban ◽

S. Doucette ◽

A. Kreuger-Naug ◽

S. Shivakumar

Keyword(s):

Internal Medicine ◽

Chronic Disease ◽

Iron Deficiency ◽

General Internal Medicine ◽

Iron Deficiency Anemia ◽

Deficiency Anemia ◽

General Internist ◽

Anemia Of Chronic Disease ◽

General Internal ◽

Number Of Patients

At the Queen Elizabeth II Health Sciences Centre in Halifax, Nova Scotia, 2,400-2,800 new outpatient referrals for hematology consultation are received annually and approximately 10% of these referrals are specifically for isolated anemia. In recent years, such referrals have been sent from hematology to general internal medicine (GIM) for assessment and management. A retrospective chart review was conducted of a cohort of 99 patients from 2013 to describe the demographics, assessment, management and outcome of these patients, as well as to inform whether this practice should continue. The median age of patients was 60.3 years (min 19.4, max 97.6) and 62% were female. The median hemoglobin level was 109.0 g/L (min 66, max 137) at the time of referral and the median wait time was 53 days (min 8 days, max 171 days). Pearson’s correlation analysis revealed that those with lower hemoglobin levels were seen more quickly. The patients had an additional 2.8 comorbidities on average, and were significantly more likely to receive non-anemia related adjustment to care with increasing number of comorbidities. A small proportion of patients (n = 5, 5.1%) were referred from GIM back to hematology, whereas 21% were referred to gastroenterology. A small number of patients (n = 5, 5.1%) underwent a bone marrow aspirate and biopsy. The most common diagnoses identified in the initial clinic letters were iron deficiency anemia (n = 59, 59.6%) and anemia of chronic disease (n = 8, 8.1%). 26.3% did not have a diagnosis identified. These findings support our practice to have patients with an isolated anemia evaluated by a general internist rather than a hematologist. Most of these patients had iron deficiency anemia or the anemia of chronic disease and received additional care for their comorbid conditions in the GIM clinic. Further work will help to define how such patients can be most effectively assessed and treated.

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Polypharmacy and potential inappropriate drug use in the elderly admitted to the general internal medicine outpatient clinic

Turkish Journal of Internal Medicine ◽

10.46310/tjim.879724 ◽

2021 ◽

Author(s):

Celaleddin DEMİRCAN ◽

Ulviyya HASANZADE

Keyword(s):

Internal Medicine ◽

Drug Use ◽

General Internal Medicine ◽

Outpatient Clinic ◽

The Elderly ◽

Inappropriate Drug ◽

General Internal ◽

Inappropriate Drug Use

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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.

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Abstract 233: Quality of Collaboration Between Cardiologists and Internists During Inpatient Consultations

Circulation Cardiovascular Quality and Outcomes ◽

10.1161/hcq.13.suppl_1.233 ◽

2020 ◽

Vol 13 (Suppl_1) ◽

Author(s):

Jordan L Gavin ◽

Elizabeth Walters ◽

Kevin J O'Leary

Keyword(s):

Internal Medicine ◽

General Internal Medicine ◽

Care Hospital ◽

High Quality ◽

General Internal ◽

Resident Physicians ◽

Significant Difference ◽

Internal Medicine Faculty ◽

Very High

Background: Collaboration between cardiologists and internists is essential to providing safe, effective, and patient-centered cardiovascular care. The objective of this study was to determine the quality of collaboration between these clinicians during inpatient consultations and identify areas for improvement. Methods: We surveyed hospitalists, general internal medicine faculty, and resident physicians from inpatient general medicine services and consulting cardiology attendings and fellows at a large tertiary care hospital over a two month period. Respondents were asked to rate each other’s level of collaboration on a 5-point ordinal response scale and answer multiple choice and free-response questions on consultation practices and personal preferences. Results: Overall, 92 of 155 (59%) eligible hospitalists, general internal medicine faculty, and resident physicians completed the primary survey. Collaboration with cardiology consultants was rated as high or very high quality by 72% of respondents. There was no significant difference between ratings of collaboration quality based on respondents’ level of training. Internists identified resistance or disagreement with indication for consultation, difficulty receiving a timely response, and poor follow-up communication as the largest barriers to high quality collaboration. Most internists preferred receiving recommendations by page, phone, or written in a consult note, rather than in person. Internists appreciated close communication throughout the consultation process. For longitudinal consultations, internists preferred when an intention to sign-off was communicated by page or wrote in that day’s consult note. In total, 9 cardiology attendings and fellows completed the specialty survey. Cardiologists reported providing recommendations the same day for routine consultations, or within 2-3 hours when urgent. Most consultants communicated their recommendations thru page, phone conversations, or written consult note. Providing recommendations in person was rare. Half of cardiology attendings and fellows rated collaboration with internists as high or very high quality. There was no significant difference between their ratings of collaboration with hospitalists and residents. Cardiologists appreciated when outside hospital records had already been obtained. They identified receiving an unclear reason for consult, consulting prior to initial work-up, late in the day, or when an outpatient appointment was more appropriate as the largest barriers to high quality collaboration. Conclusions: While cardiologists and internists appear to agree on modes of communication, they have different perceptions of timeliness and disagreements on appropriateness of consultations. Further research is needed to design and study interventions that address these barriers to high quality collaboration.

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Prevalence and clinical features of cough variant asthma in a general internal medicine outpatient clinic in Japan

Respirology ◽

10.1111/j.1440-1843.2005.00709.x ◽

2005 ◽

Vol 10 (3) ◽

pp. 354-358 ◽

Cited By ~ 21

Author(s):

Kumiko SHIRAHATA ◽

Keisaku FUJIMOTO ◽

Hiroko ARIOKA ◽

Ryousuke SHOUDA ◽

Kouichirou KUDO ◽

...

Keyword(s):

Internal Medicine ◽

General Internal Medicine ◽

Clinical Features ◽

Outpatient Clinic ◽

Cough Variant Asthma ◽

General Internal

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

JMIR Medical Informatics ◽

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.

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Morning Discharges and Patient Length of Stay in Inpatient General Internal Medicine

Journal of Hospital Medicine ◽

10.12788/jhm.3605 ◽

2021 ◽

Vol 16 (6) ◽

Author(s):

Abirami Kirubarajan ◽

Saeha Shin ◽

Michael Fralick ◽

Janice Kwan ◽

Lauren Lapointe-Shaw ◽

...

Keyword(s):

Internal Medicine ◽

Length Of Stay ◽

Hospital Mortality ◽

General Internal Medicine ◽

Patient Flow ◽

Situational Factors ◽

General Internal ◽

Number Of Patients ◽

Improve Patient ◽

Multivariable Adjustment

BACKGROUND: Many initiatives seek to increase the number of morning hospital discharges to improve patient flow, but little evidence supports this practice. OBJECTIVE: To determine the association between the number of morning discharges and emergency department (ED) length of stay (LOS) and hospital LOS in general internal medicine (GIM). DESIGN, SETTING, AND PARTICIPANTS: Multicenter retrospective cohort study involving all GIM patients discharged between April 1, 2010, and October 31, 2017, at seven hospitals in Ontario, Canada. MAIN MEASURES: The primary outcomes were ED LOS and hospital LOS, and secondary outcomes were 30-day readmission and in-hospital mortality. The number of morning GIM discharges (defined as the number of patients discharged alive between 8:00 am and 12:00 pm ) on the day of each hospital admission was the primary exposure. Multivariable regression models were fit to control for patient characteristics and situational factors, including GIM census. RESULTS: The sample included 189,781 patient admissions. In total, 36,043 (19.0%) discharges occurred between 8:00 am and 12:00 pm . The average daily number of morning discharges and total discharges per hospital was 1.7 (SD, 1.4) and 8.4 (SD, 4.6), respectively. The median ED LOS was 14.5 hours (interquartile range [IQR], 10.0- 23.1), and the median hospital LOS was 4.6 days (IQR, 2.4-9.0). After multivariable adjustment, there was not a significant association between morning discharge and hospital LOS (adjusted rate ratio [aRR], 1.000; 95% CI, 0.996-1.000; P = .997), ED LOS (aRR, 0.999; 95% CI, 0.997-1.000; P = .307), 30-day readmission (aRR, 1.010; 95% CI, 0.991-1.020; P = .471), or in-hospital mortality (aRR, 0.967; 95% CI, 0.920-1.020; P = .183). The lack of association between morning discharge and LOS was generally consistent across all seven hospitals. At one hospital, morning discharge was associated with a 1.9% shorter ED LOS after multivariable adjustment (aRR, 0.981; 95% CI, 0.966-0.996; P = .013). CONCLUSIONS: The number of morning discharges was not significantly associated with shorter ED LOS or hospital LOS in GIM. Our findings suggest that increasing the number of morning discharges alone is unlikely to substantially improve patient throughput in GIM, but further research is needed to determine the effectiveness of specific interventions.

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