The intersection of telehealth and AI: How can they reinforce each other?

The intersection of telehealth and AI: How can they reinforce each other?

Now that telemedicine is mainstream, artificial intelligence is helping healthcare providers with imperatives such as patient triage. Early results are promising.

Read on healthcareitnews.com

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The integration of artificial intelligence (AI) with telemedicine has advanced significantly, particularly during the pandemic, offering tools that remotely diagnose patients and guide them to appropriate care settings. AI-powered telemedicine aims to enhance access to healthcare services, improve decision-making for providers, and ensure timely and suitable care. Piotr Orzechowski, CEO of Infermedica, emphasizes the potential of AI in addressing key issues in telemedicine.

According to Orzechowski, AI plays a pivotal role in helping patients identify the correct level of care, as approximately 75% of patients are uncertain about their healthcare needs. AI assists in guiding patients to telemedicine when suitable and suggests alternative care options when necessary, optimizing patient care efficiency. Furthermore, AI facilitates healthcare providers in managing extensive documentation tasks, alleviating some of the workload and mitigating physician burnout, as highlighted by the "I Cry, but No One Cares": Physician Burnout & Depression Report 2023.

While AI tools are not authorized to diagnose patients, they contribute significantly to the telemedicine process. The AI-driven triage process involves collecting patient information, prompting diagnostic-like questions, and providing reliable triage suggestions. The output includes recommendations for self-care, consultations with healthcare professionals, or urgent/emergency care, with guidance on suitable consultation modes.

Orzechowski emphasizes the importance of positioning AI tools as supporters, not enforcers, of patient decision-making. Despite notable progress in AI capabilities, regulatory frameworks need to be established to guide and set standards for AI-assisted diagnosis. The outcomes of incorporating AI into telemedicine include improved care efficiency, enhanced patient and provider satisfaction, and positive results in various global collaborations with telemedicine organizations. For example, Solv's symptom checker saw increased in-network bookings and high satisfaction ratings, while Diagnostikare in Mexico achieved improved patient care efficiency and increased telemedicine utilization.




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