Improving Remote Patient Monitoring with AI
Razmi sees the potential for digital health AI to improve RPM and enable diagnostics remotely. He cites an example in which patients can now test themselves for urinary tract infections by using a deep learning app that can analyze a scan from a strip that was dipped in a urine sample.
Meanwhile, amid a clinician shortage, AI can help providers diagnose conditions like atrial fibrillation or an abnormal heart rhythm, he says.
“[RPM apps] use deep-learning AI and unstructured data. They have been FDA-approved, so they can be used today,” Razmi says. “They’ve been deemed to be safe and accurate enough to be used in the daily practice of medicine.”
RPM allows patients to get better treatments earlier. In addition, some services offer physical therapy for musculoskeletal diseases at home; AI can monitor a patient’s exercises and provide feedback, he says.
READ MORE: Remote patient monitoring enhances nurse workflows.
In another example, Razmi notes that an AI app can analyze an individual’s voice to detect if depression is getting worse. It can then offer instructions and advise patients to follow up with a provider.
AI tools allow clinicians to set threshold parameters for remote monitoring. AI can change a clinician’s workflow by providing critical alerts similar to those for critical lab values, Elliott explains. The tools also allow changes from baselines to occur over time; for example, if a patient’s blood pressure has been rising over weeks or months, she adds.
“It’s all about setting the right alerting thresholds and getting enough diverse data sets so that we know which thresholds matter and which do not — signal versus noise,” Elliott says.
The Potential of AI and Virtual Care
Once the FDA has approved healthcare AI tools, they can assist providers in performing patient triage and chronic disease management, Razmi says.
In the future, look for AI to handle all administrative tasks and fix incorrect data — names, addresses, insurance information, duplicate accounts and pharmacy — in real time, Elliott predicts.
Decision support applications incorporating AI will be a part of connected care. Although AI aids in clinical decision support today, in the future these tools will be part of the standard of care, Elliott says.
“AI will be used for triage and intake — think virtual AI medical assistant,” Elliott says. “This is done in a rudimentary way today, but the future could encompass many medical assistant activities that are currently inefficient and not the highest and best use of their time.”
