Many hospitalists incorporate point‐of‐care ultrasound (POCUS) into their daily practice to answer specific diagnostic questions or to guide performance of invasive bedside procedures. However, standards for hospitalists in POCUS training and assessment are not yet established. Most internal medicine residency training programs, the major pipeline for incoming hospitalists, have only recently begun to incorporate POCUS in their curricula. The purpose of this document is to inform a broad audience on what POCUS is and how hospitalists are using it. This document is intended to provide guidance for the hospitalists who use POCUS and administrators who oversee its use. We discuss POCUS 1) applications, 2) training, 3) assessments, and 4) program management. Practicing hospitalists must continue to collaborate with their local credentialing bodies to outline requirements for POCUS use. Hospitalists should be integrally involved in decision‐making processes surrounding POCUS program management.

Background Point-of-care ultrasound (POCUS) is rapidly becoming ubiquitous across healthcare specialties. This is due to several factors including its portability, immediacy of results to guide clinical decision-making, and lack of radiation exposure to patients. The recent growth of handheld ultrasound devices has improved access to ultrasound for many clinicians. Few studies have directly compared different handheld ultrasound devices among themselves or to cart-based ultrasound machines. We conducted a prospective observational study comparing four common handheld ultrasound devices for ease of use, image quality, and overall satisfaction. Twenty-four POCUS experts utilized four handheld devices (Butterfly iQ+™ by Butterfly Network Inc., Kosmos™ by EchoNous, Vscan Air™ by General Electric, and Lumify™ by Philips Healthcare) to obtain three ultrasound views on the same standardized patients using high- and low-frequency probes. Results Data were collected from 24 POCUS experts using all 4 handheld devices. No single ultrasound device was superior in all categories. For overall ease of use, the Vscan Air™ was rated highest, followed by the Lumify™. For overall image quality, Lumify™ was rated highest, followed by Kosmos™. The Lumify™ device was rated highest for overall satisfaction, while the Vscan Air™ was rated as the most likely to be purchased personally and carried in one’s coat pocket. The top 5 characteristics of handheld ultrasound devices rated as being “very important” were image quality, ease of use, portability, total costs, and availability of different probes. Conclusions In a comparison of four common handheld ultrasound devices in the United States, no single handheld ultrasound device was perceived to have all desired characteristics. POCUS experts rated the Lumify™ highest for image quality and Vscan Air™ highest for ease of use. Overall satisfaction was highest with the Lumify™ device, while the most likely to be purchased as a pocket device was the Vscan Air™. Image quality was felt to be the most important characteristic in evaluating handheld ultrasound devices.

Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced (<30%). Additionally, LV function was visually estimated for each view by 10 experienced physicians. Accuracy of the detection of reduced LV function (EF<53%) by the automated classification and physicians’ interpretation was assessed against the reference classification. In protocol 2, we tested the new machine-learning algorithm in the POC setting on images acquired by nurses using a portable imaging system. Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases <2%. Machine-learning classification of LV function showed similar accuracy to that by physicians in most views, with only 10% to 15% cases where it was less accurate. Protocol 2: the agreement with the reference values was excellent (intraclass correlation=0.84) with a minimal bias of 2.5±6.4%. Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.

Callender and Cohen (2006) argue that there is no need for a special account of the constitution of scientific representation. I argue that scientific representation is communal and therefore deeply tied to the practice in which it is embedded. The communal nature is accounted for by licensing, the activities of scientific practice by which scientists establish a representation. A case study of the Lotka-Volterra model reveals how the licensure is a constitutive element of the representational relationship. Thus, any account of the constitution of scientific representation must account for licensing, meaning that there is a special problem of scientific representation.