A research team in Australia encountered a surprising problem: their successful tracking of osteoporosis patients through medical records actually hindered their ability to recruit participants for a clinical trial. The team, led by Christopher White, PhD, had developed a comprehensive database of over 30,000 patients with osteoporosis and their fracture risks. However, they underestimated the severity of vertebral fractures in some patients, leading to underrecruitment. This experience prompted White to collaborate with other centers facing similar challenges, leveraging AI and AES coding to identify patients at risk for osteoporotic fractures more effectively.
be so successful that it becomes a potential liability? Yes, according to Christopher White, PhD, executive director of Maridulu Budyari Gumal, the Sydney Partnership for Health , Education, Research, and Enterprise, a research translation center in Liverpool, Australia., White described the results after his fracture liaison team at Prince of Wales Hospital in Randwick, Australia, tried to plug the “osteoporosis treatment gap” by mining medical records to identify patients with the disorder.
Those patients were not being identified sooner for multiple reasons, White explained. Sometimes, the radiologist would report osteoporosis, but it wouldn’t get coded. Or, in the emergency department, a patient with a fracture would be treated and then sent home, and the possibility of osteoporosis wasn’t reported.
What’s happening now, he said, is that the technology is identifying a high number of vertebral crush fractures, and there’s no education or evaluation. “The radiologist just refers the patient to a primary care physician and hopes for the best. AI isn’t contributing to solving the treatment gap problem; it’s amplifying it. It’s ahead of the ability of organizations to accommodate the findings.”
Furthermore, he advised, organizations need to be “very, very careful with safety and security — that is, managing the digital risks.”
Osteoporosis Fracture Risk Medical Records Clinical Trials AI
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