Current Use and Barriers to Point-of-Care Ultrasound in Primary Care: A National Survey of VA Medical Centers

Nathanson, Robert; Williams, Jason P.; Gupta, Neil; Rezigh, Austin; Mader, Michael; Haro, Elizabeth K.; Drum, Brandy; O'Brien, Edward; Khosla, Rahul; Boyd, Jeremy S.; Bales, Brian; Sauthoff, Harald; Schott, Christopher K.; Basrai, Zahir; Resop, Dana; Lucas, Brian; Soni, Nilam J.

doi:10.1016/j.amjmed.2023.01.038

Cited by 15 publications

(12 citation statements)

References 11 publications

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“…As POCUS usage continues to expand, 7,19 AI-enabled devices represent a possible means to enhance competency among novice users and aid in interpretation. 13,14,20 In this randomized, controlled trial, we observed that internal medicine residents randomized to carry an AI-POCUS device for two weeks without feedback were able to obtain A4C views more quickly, had higher A4C image quality scores, and were more likely to identify reduced systolic function compared to residents who carried non-AI devices.…”

Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes

Baum

Megha

Ren

et al. 2023

Preprint

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Background Point-of-care ultrasound (POCUS) machines may utilize artificial intelligence (AI) to enhance image interpretation and acquisition. This study investigates whether AI-enabled devices improve competency among POCUS novices. Methods We conducted a randomized controlled trial at a single academic institution from 2021-2022. Internal medicine trainees (N=43) with limited POCUS experience were randomized to receive a POCUS device with (Echonous, N=22) or without (Butterfly, N=21) AI-functionality for two weeks while on an inpatient rotation. The AI-device provided automatic labeling of cardiac structures, guidance for optimal probe placement to acquire cardiac views, and ejection fraction estimations. Participants were allowed to use the devices at their discretion for patient-related care. The primary outcome was the time to acquire an apical 4-chamber (A4C) image. Secondary outcomes included A4C image quality using the modified Rapid Assessment for Competency in Echocardiography (RACE) scale, correct identification of pathology, and participant attitudes. Measurements were performed at the time of randomization and at two-week follow-up. All scanning assessments were performed on the same standardized patient. Results Both AI and non-AI groups had similar scan times and image quality scores at baseline. At follow-up, the AI group had faster scan times (72 seconds [IQR 38-85] vs. 85 seconds [IQR 54-166]; p=0.01), higher image quality scores (4.5 [IQR 2-5.5] vs. 2 [IQR 1-3]; p<0.01) and correctly identified reduced systolic function more often (85% vs 50%; p=0.02) compared to the non-AI group. Trust in the AI features did not differ between the groups pre- or post-intervention. The AI group did not report increased confidence in their abilities to obtain or interpret cardiac images. Conclusions POCUS devices with AI features may improve image acquisition and interpretation by novices. Future studies are needed to determine the extent that AI impacts POCUS learning.

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Section: Discussionmentioning

confidence: 99%

Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes

Baum

Megha

Ren

et al. 2023

Preprint

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“…At the end of the course, we evaluated the students’ POCUS handling skills with the previously established six-minute exam (see exam scoring criteria, S2 Appendix ) [ 11 ]. This exam evaluates skills in obtaining key transthoracic cardiac views.…”

Section: Methodsmentioning

confidence: 99%

“…One American study conducted in 2020 surveyed POCUS use in all Veterans Affairs medical centers. It showed that the number of physicians using POCUS has not changed significantly between 2015 and 2020 despite the availability of equipment [ 11 ]. The low utilization rates among experienced physicians and the limited experience of novice clinicians underscore the importance of integrating high-quality POCUS training early in medical education as well as implementing a feedback system independent of the user’s personal experience.…”

Section: Introductionmentioning

confidence: 99%

“…In our study, we aimed to address the shortage of experienced POCUS operators among medical school graduates. Considering previous research indicating that students often struggle with apical cardiac views [ 11 ], we hypothesized that an AI-based quality indicator tool, specifically designed for apical 4- and 5-chamber views, could serve as an effective teaching aid, to improve the cardiac ultrasound skills of novice operators. Our study was designed to compare the success rates and quality of apical views, as well as other cardiac views, between students using the AI-based tool and those using the standard POCUS device, in order to test the added value of integrating such tools in medical education and clinical work.…”

Section: Introductionmentioning

confidence: 99%

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The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students

Aronovitz,

Hazan,

Jedwab

et al. 2024

PLoS ONE

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Purpose Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance. Methods The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups. Results The results showed an advantage in quality indictor users’ median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views. Conclusions The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.

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“…Despite the growing body of evidence supporting POCUS use and increasing availability of POCUS devices, lack of training continues to be the primary barrier to POCUS use among physicians in-practice ( Nathanson et al , 2023 ; Wong et al , 2020 ). Societies including the Society of Hospital Medicine, American Collage of Chest Physicians, and American College of Physicians have published guidelines and developed POCUS training programs for physicians in-practice ( American College of Physicians, 2023 ; Greenstein et al , 2017 ; Society of Hospital Medicine, 2023 ; Soni et al , 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Development of a national point-of-care ultrasound training course for physicians in Japan: A 3-year evaluation

Yamada,

Minami,

Kitano

et al. 2023

MedEdPublish

Self Cite

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Purpose: Point-of-care ultrasound (POCUS) allows bedside clinicians to acquire, interpret, and integrate ultrasound images into patient care. Although the availability of POCUS training courses has increased, the educational effectiveness of these courses is unclear. Methods: From 2017 to 2019, we investigated the educational effectiveness of a standardized 2-day hands-on POCUS training course and changes in pre- and post-course exam scores in relationship to participants’ (n = 571) clinical rank, years of POCUS experience, and frequency of POCUS use in clinical practice. Results: The mean pre- and post-course examination scores were 67.2 (standard deviation [SD] 12.3) and 79.7 (SD 9.7), respectively. Higher pre-course examination scores were associated with higher clinical rank, more years of POCUS experience, and more frequent POCUS use (p < 0.05). All participants showed significant changes in pre- to post-course exam scores. Though pre-course scores differed by clinical rank, POCUS experience, and frequency of POCUS use, differences in post-course scores according to participant baseline differences were non-significant. Conclusion: A standardized hands-on POCUS training course is effective for improving POCUS knowledge regardless of baseline differences in clinical rank, POCUS experience, or frequency of POCUS use. Future studies shall evaluate changes in POCUS use in clinical practice after POCUS training.

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Current Use and Barriers to Point-of-Care Ultrasound in Primary Care: A National Survey of VA Medical Centers

Cited by 15 publications

References 11 publications

Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes

Use of Artificial Intelligence for Acquisition of Limited Echocardiograms: A Randomized Controlled Trial for Educational Outcomes

The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students

Development of a national point-of-care ultrasound training course for physicians in Japan: A 3-year evaluation

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