The hype around the benefits of AI in healthcare shows no signs of slowing down. An exciting development generating optimism and driving change is the implementation of AI in remote patient monitoring systems.
During a recent CareTalk Podcast segment, special guest Zeke Emanuel weighed in on the topic, expressing his skepticism towards the much buzzed-about use of AI in remote patient monitoring. Emanuel suggested that instead of focusing on remote patient monitoring, AI should be used to innovate more proactive areas of care, namely remote patient management.
In the CareTalk episode, “Zeke Emanuel on AI in Healthcare” hosts John Driscoll and David Williams are joined by oncologist and world leader in health policy and bioethics, Dr. Zeke Emanuel to share his thoughts on the artificial intelligence craze and how it could impact healthcare.
What is Remote Patient Monitoring?
Remote patient monitoring is a cutting-edge technological solution that promises to revolutionize how healthcare is delivered to patients. It is the use of advanced digital tools and software to remotely monitor and manage patients' health from their homes or any location outside a hospital. Like telehealth, remote patient monitoring's popularity grew significantly during the COVID-19 pandemic, where it became an essential tool for health professionals to monitor and care for their patients while minimizing contact.
With the help of AI algorithms, healthcare providers can now monitor patients' vital signs and symptoms in real time, allowing for faster detection of potential health issues. Using sensors and wearables, data is collected and transmitted to healthcare providers, who can analyze and identify any abnormalities. AI algorithms can then be used to analyze the data, recognize patterns and predict future readings. This allows healthcare providers to make informed decisions about patient care, potentially preventing serious health issues from developing or worsening.
Limitations of Remote Patient Monitoring
Remote patient monitoring has brought tremendous opportunities for medical professionals to keep track of patient's health conditions without face-to-face consultations. However, like any other technology, it has its limitations. One of the most critical challenges is the inability to provide immediate physical intervention in certain medical emergencies. Issues such as equipment malfunction and patient privacy concerns have highlighted the need for continued development and refinement of this technology.
Lastly, while the systems are efficient in gathering data, they cannot make personal observations compared to healthcare professionals conducting an actual examination or laboratory testing that demands personal interaction.
Despite these limitations, remote patient monitoring remains extremely valuable in various healthcare settings, and it has increased the accessibility of healthcare to millions of patients worldwide. As such, healthcare providers have a significant role to play in ensuring that remote patient monitoring is effectively used, so as to maximize their benefits while minimizing the potential risks.
Building on The Foundation: Remote Patient Monitoring vs. Remote Patient Management
While the use of AI in remote patient monitoring has proven to be a powerful tool, it's important to remember that we can always build on a great idea. In order to continue advancing the way healthcare is delivered to patients we need to be flexible and think outside the box. Can we offer more services and bring treatments and testing to the patient’s doorstep?
Innovative thinking urges us to explore more than just the "reactive" data-collecting aspect of AI in remote patient monitoring. While its use in this area is groundbreaking, we should not be content to stop there. Instead, let's consider the limitless possibilities AI could offer in remote patient management. By thinking proactively, we can bring about improved patient care and empower them and their caregivers to take better control of their health.
“What we really need is remote patient management and that is a lot more complicated than just putting in a bunch of sensors and getting information back. You need the AI to soup up the signal to noise. You need the AI to say this signal needs to be attended to. That one doesn't, and then need a mechanism, a way to get to the patient in the right way so you're not wasting a lot of resources every time something goes wrong” – Zeke (CareTalk)
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