BACKGROUND AND OBJECTIVES: The use of ultrasound technology for vascular access and regional anesthesia is gaining widespread acceptance among anesthesia providers. As a result, many group practices and medical institutions are considering purchasing ultrasound equipment. Currently, comparative information regarding the ergonomic design, physical and adjustable features, data management, ease of use, cost, and image quality of various ultrasound machines is not available. The primary goal of this investigation was to develop an objective process of evaluating ultrasound equipment before purchase. The process of evaluation used in the current investigation may be used when comparing a variety of medical technologies. METHODS: A randomized, side-by-side comparative evaluation of 7 different ultrasound machine models was performed. Sixteen resident physicians without prior ultrasound experience (inexperienced providers) completed a formal evaluation of each machine model after performing a standardized machine configuration and performance checklist. Inexperienced providers and 10 faculty members experienced in ultrasound-guided regional anesthesia evaluated the image quality of 2 standardized images acquired from each machine model. RESULTS: Overall, evaluators rated questions on the machine evaluation form as "very good" or "outstanding" 70% or more of the time for all machine models. The largest, most complex ultrasound machine was rated as having the best image quality by both inexperienced and experienced providers. Ultrasound machine models with the simplest ergonomic design and user interface were rated highest by inexperienced study participants. CONCLUSIONS: Anesthesia providers considering an ultrasound equipment purchase should objectively evaluate machine models that have features most important to their own clinical practice. Ergonomic design, physical and adjustable features, data management, ease of use, image quality, and cost are important features to consider when evaluating an ultrasound machine.
RCT Entities:
BACKGROUND AND OBJECTIVES: The use of ultrasound technology for vascular access and regional anesthesia is gaining widespread acceptance among anesthesia providers. As a result, many group practices and medical institutions are considering purchasing ultrasound equipment. Currently, comparative information regarding the ergonomic design, physical and adjustable features, data management, ease of use, cost, and image quality of various ultrasound machines is not available. The primary goal of this investigation was to develop an objective process of evaluating ultrasound equipment before purchase. The process of evaluation used in the current investigation may be used when comparing a variety of medical technologies. METHODS: A randomized, side-by-side comparative evaluation of 7 different ultrasound machine models was performed. Sixteen resident physicians without prior ultrasound experience (inexperienced providers) completed a formal evaluation of each machine model after performing a standardized machine configuration and performance checklist. Inexperienced providers and 10 faculty members experienced in ultrasound-guided regional anesthesia evaluated the image quality of 2 standardized images acquired from each machine model. RESULTS: Overall, evaluators rated questions on the machine evaluation form as "very good" or "outstanding" 70% or more of the time for all machine models. The largest, most complex ultrasound machine was rated as having the best image quality by both inexperienced and experienced providers. Ultrasound machine models with the simplest ergonomic design and user interface were rated highest by inexperienced study participants. CONCLUSIONS: Anesthesia providers considering an ultrasound equipment purchase should objectively evaluate machine models that have features most important to their own clinical practice. Ergonomic design, physical and adjustable features, data management, ease of use, image quality, and cost are important features to consider when evaluating an ultrasound machine.
Authors: Robert Strony; Jennifer R Marin; John Bailitz; Anthony J Dean; Mike Blaivas; Vivek Tayal; Chris Raio; Rachel Liu; Aimee Woods; Michael Zwank; Matthew Fields; Alyssa Abo; Stan Wu; Tarina Kang; Teresa Liu; Megan Leo; Courtney Smalley; Jerry Chiricolo; Mikaela Chilstrom; Resa E Lewiss Journal: West J Emerg Med Date: 2018-06-05