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From a financial perspective, clinical inefficiency costs the U.S. healthcare system $202 billion annually.1 Relying on cumbersome processes for workflow management in healthcare also carries a heavy personal cost for healthcare professionals, including radiologists, OB/GYNs, Cardiologists and ED Physicians.
Ill-designed workflows can place undue pressure on individuals working in healthcare, increasing their stress levels and limiting their productivity. This is particularly true with diagnostic imaging services. As imaging utilization continues to rise—particularly with ultrasound—strong systems are vital to avoid burnout and delays in clinical care.
When implemented well, workflow management in healthcare can benefit both providers and patients.
Provider burnout is an issue that predates the COVID-19 pandemic. However, the tsunami of patient care needs over the past two years has driven this level of fatigue higher. Today, 76 percent of doctors and nurses report being burned out,2 with one in five doctors and two in five nurses indicating they may leave the healthcare industry altogether.3 Within imaging, only 25 percent of sonographers report still being happy with their jobs after a year, and roughly the same —24 percent—become dissatisfied within two years.4 Many point to an increasing number of patients, short appointment times, and the stress of consistently being exposed to adverse outcomes as the reasons behind sonographer turnover.5
Burnout is a problem specifically for radiologists, as well. Roughly 30 percent of practicing radiologists reveal they experience burnout largely because they have "too many bureaucratic tasks."6 In fact, many report they complete documentation, respond to emails, and answer phone called after work hours at home.7 The sheer volume of data they must process daily—between 20 to 100 scans with thousands of images each—can also be overwhelming.8 Many also indicate they would like more time for patient interaction.
To sidestep a mass exodus and alleviate provider burnout, facilities should consider re-designing workflows with artificial intelligence (AI). AI may help relieve time demands by leveraging algorithms to facilitate image capture, automate segmentation, reduce set up time, standardize protocols, and even help with patient triage and clinical decision support.8 By assuming each of these tasks, AI could return precious minutes to the radiologist and sonographer, alleviating their stress and augmenting the level of patient care they deliver.
Outdated ultrasound systems, such as handwritten sonographer worksheets, are time-consuming, cumbersome and error prone. But newer, complicated software platforms also plague many ultrasound workflow systems. These platforms are rarely designed with healthcare professionals in mind. For example, a complex login process that requires multiple password entries can eat up valuable patient care time or hinder timely treatments and therapies.
Other examples of existing challenges which could be addressed by digital solutions include:
More than 20 percent of ultrasound results contain errors that may lead to duplicative imaging, diagnosis delay, and increased healthcare costs.9 Addressing the hurdles above could minimize the opportunities for these errors to occur.
One way to revamp efficiency and productivity is to leverage the power of new technology and digital solutions. Secure, web-based portals that enable real-time radiology consultations and smart devices that support image viewing away from workstations are examples of tools and software solutions that support greater efficiency and productivity.
Additional benefits of effective workflow management in ultrasound may include:
Ultimately, a new approach to ultrasound workflow management changes the day-to-day functioning of healthcare professionals. Providers can concentrate on their patients and reviewing images while sonographers can benefit from automation that streamlines the scanning process and reduces the likelihood of errors. The result of better workflow management in healthcare can be reduced burnout, fewer mistakes, and augmented attention to patient care.
1. Zuger A, MD. Tallying the waste in American healthcare. NEJM Journal Watch. January 2021; https://www.jwatch.org/na52991/2021/01/05/tallying-waste-american-healthcare
2. Office of the Surgeon General. New Surgeon General advisory sounds alarm on health worker burnout and resignation. U.S. Department of Health and Human Services. May 2022; https://www.hhs.gov/about/news/2022/05/23/new-surgeon-general-advisory-sounds-alarm-on-health-worker-burnout-and-resignation.html
3. Sinsky C, MD; Brown R, PhD; Stillman M, JD; Linzer M, MD. COVID-related stress and work intentions in a sample of US health care workers." Mayo Clinic Proceedings. December 2021; DOI: https://doi.org/10.1016/j.mayocpiqo.2021.08.007
4. Zippia. Sonographer demographics and statistics in the US." https://www.zippia.com/sonographer-jobs/demographics/
5. Younan K; Walkley D; Quinton AE; Alphonse J. Burnout in the sonographer environment: The identification and exploration of the causes of sonographer burnout and strategies for prevention and control. Sonography. September 2022; https://doi.org/10.1002/sono.12333
6. Hicks L. Disrespect from colleagues is a major cause of bnurnout, Radiologists Say." Medscape. February 2022; https://www.medscape.com/viewarticle/968776
7. Bailey C, MD; Bailey AM, MD; McKenney AS, MD; Weiss CR, MD. Understanding and appreciating burnout in radiologists. Radiographics. July 2022; https://pubs.rsna.org/doi/10.1148/rg.220037
8. GE Healthcare. Addressing radiology staff burnout with AI solutions. July 2022. https://www.gehealthcare.com/insights/article/addressing-radiology-staff-burnout-with-ai-solutions
9. Physician Documentation Deficiencies in Abdominal Ultrasound Reports: Frequency, Characteristics, and Financial Impact Richard Duszak Jr, MDa,b, Michael Nossal, MAc, Lyle Schofield, BSc, Daniel Picus, MDd.
10. Should We Ignore, Follow, or Biopsy? Impact of Artificial Intelligence Decision Support on Breast Ultrasound Lesion Assessment. Victoria L. Mango, Mary Sun, Ralph T. Wynn, and Richard Ha. American Journal of Roentgenology 2020 214:6, 1445-1452