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Problem Tech Solves
Tech Brief
Clinical variability can critically undermine care quality, often contributing to improper treatment decisions, adverse events, and poor patient outcomes. With a shortage of wound specialists (1 specialist for every 500 patients), minimal formal wound care education for most providers (less than 9 hours of formal education), an abundance of wound types (over 80 wounds types), and predominantly manual, error-prone methods of assessing wounds (i.e. paper-rulers), it is no surprise that clinical variability is a massive challenge in wound care - especially at scale. Critical parameters like wound size and margins, tissue types, and 3-Dimensional topography have previously been impossible to collect objectively, or required large, expensive, single-purpose devices to capture. These and the hundreds of other parameters governing wound healing, make the scale of the problem of making data-driven decisions optimized for the patient intractable for individual providers. Further, the process of caring for wounds and making these clinical decisions has forever been a lengthy, painful, and isolating experience for both patient and clinician.
Tech Differentiators
Swift, unlike some competitors, by virtue of its breadth and partnerships, provides tools across the care continuum, allowing our technology to ‘walk with the patient’ throughout their entire care journey, from hospital, to wound care clinic, to skilled nursing facility, to ‘hospital-in-the-home’ home care. This allows our AI models to see much more dense data, connecting the dots into a true healing picture, and giving caregivers data-driven insights and superpowers they need to operate at the top of their license. Fundamentally, we differ in our focus on “understanding” healing. Not just correlating in-situ data, but using it to obtain a deeper understanding of the underlying physics of healing. For example, instead of inferring tissue composition from color, we are directly inferring tissue types and wound severity from the images with our unique deep learning architectures that was trained using vast amounts of chronic wound imaging data . Our FDA registered HealX calibration marker enables our system to calibrate every image, regardless of the differences visit to visit in device, environmental lighting, and user, into a common colorspace reference. This edge gives our dataset, now numbering millions of wound images and tens of millions of patient and wound assessments, longitudinal predictive powers unmatched by any other wound dataset in the world. Our push to create custom, mobile capable, deep learning and vision pipelines, rather than using off-the-shelf tools, allows us to get deeper into the clinical process and actually assist with empathy at the bedside.
Validation
Swift has conclusively demonstrated the accuracy, consistency, and objectivity improvements afforded by our solution in a paper published in PLOS One (https://pubmed.ncbi.nlm.nih.gov/28817649/), and in award winning publications at industry conferences like SAWC: -https://www.eventscribe.net/2021/SAWCS_Posters/fsPopup.asp?efp=WVBQRE9ISlExMzg4OA%20&PosterID=372911%20&rnd=0.4438625&mode=posterinfo -https://swiftmedical.com/poster-clinical-validation-of-digital-wound-management/, -https://swiftmedical.com/poster-fluorescence-based-quantification-of-bacterial-presence/ -https://www.hmpgloballearningnetwork.com/site/woundcare/poster/can-big-data-provide-predictive-analytics-wound-trajectories Our tools have improved accuracy in the industry, eliminating 88% of measurement inaccuracy vs. paper methods known to overestimate wound size by more than 40%. Measurements taken using Swift provide greater accuracy and improved inter-rater reliability, even when measurements were taken by non-wound experts. Swift helped reduce the prevalence of pressure ulcers by 10% at a skilled nursing facility as detailed in this peer-reviewed paper published in the International Wound Journal (https://pubmed.ncbi.nlm.nih.gov/30864302/). Pressure Ulcers in skilled nursing facilities can range in cost from $8,000-$20,000+ depending on the severity. In a benefits evaluation with Swift home health client, Reach Healthcare, we demonstrated a 20% reduction in wound visits and a savings of 85$ per patient per month post adoption. (https://www.reachhealthcareservices.com/uploads/userfiles/files/documents/Swift%20Medical%20Case%20Study%20-%20Reach%20Healthcare%20-%20April%202020.pdf) And during COVID pandemic, Swift leaned in, creating a Telewound Coalition(https://www.prnewswire.com/news-releases/groundbreaking-coalition-of-industry-leaders-bring-telehealth-to-wound-care-at-no-cost-during-coronavirus-pandemic-301039773.html) and winning a $2+ million grant project to deploy our solutions toward reducing in-person clinical wound care visits, reducing risk to patients and caregivers, while ensuring the standards of care could be maintained. In one example of the success of that program, a homeless patient who was refusing in-patient care due to COVID risk, was willing to be treated and assessed in the field by emergency care personnel, with wound data and measurements returned to a remote specialist.
