Artificial intelligence tools are dramatically cutting the time needed to diagnose, stage and treat complex wounds in the elderly.
Ultimately, the promise of AI could lead to improved wound classification and assessments while providing long-term care clinicians with powerful tools to improve care and make documentation more accurate.
“The role of AI in the post-acute care setting is still in its infancy stages but developing in the domains of diagnostic assistance, treatment optimization, predictive analytics, remote monitoring, and clinical decision support,” Vittoria Pontieri-Lewis, president of the Wound, Ostomy, and Continence Nurses Society, told McKnight’s Long-Term Care News.
Pressure injury rates are rising, and more than 40% of the nursing home population is diabetic, pre-diabetic or obese — making them susceptible to debilitating wounds.
“Chronic wounds are stuck in inflammatory mode,” said Amin Setoodeh, vice president of Skin Health Solutions for Medline.
While the wound care market has been slower than many in responding to new technology, that may be changing.
“We’re now seeing a shift in the need and desire to utilize AI,” said Setoodeh, acknowledging evidence that shows how it can improve clinical processes in order to promote patient safety, optimize staffing and improve outcomes and healing rates.
Among the promising AI connections is driving exponential improvements in accuracy and tissue classification.
Medline recently invested significantly in a FDA-approved, HIPAA-compliant, “intelligent” digital platform named the “NE1 Wound Management System” (WMS) that allows clinicians to photograph wounds to accurately and consistently document the wound assessment, wound classification and measurement.
“NE1 WMS incorporates intelligence by guiding clinicians to choose the appropriate tissue types within workflows, leading to improved accuracy of tissue classification,” he added.
“It actually increases accuracy of the wound classification by 100%, regardless of whether it was captured by a PT, OT or a licensed nurse,” Setoodeh added.
