Using AI to Solve Continuous Care’s Greatest Challenges


Reading Time: 3 minutes

The challenge of treating patients with chronic long-term conditions is one of anticipation: being able to recognize early that a patient’s condition is about to deteriorate.

Enabling that type of hands-on monitoring has historically been costly and time-consuming. It requires hospitalizing patients and attaching them to invasive monitoring machines, complete with cables and monitors and network attachments.

But new advances in healthcare IT are making it possible to allow that type of continuous care monitoring to take place at home, through wearable medical-grade devices.

Collecting the data is just the first step. Unless you have someone watching the numbers continuously, you might miss something. The maturing of artificial intelligence/machine-learning capabilities, however, offers a different approach. Patient-wearable devices are using artificial intelligence (AI) to solve continuous care challenges by providing automated constant-monitoring capabilities that sound the alarm when something is wrong. These new capabilities can help guide early intervention and save lives.

Challenges of Remote Patient Monitoring

Most continuous care today takes place in hospital settings. But even then, the system is subject to error. Warning signs are missed for various reasons.

For example, many continuous care systems are monitored through a “spot check” approach. If a nurse or doctor is not there to see the data when it comes across, response times are delayed.

Another challenge is the chain-of-command. Even when troublesome data is detected, it is often by a nurse, who then must locate and alert the proper doctor for a treatment decision.

No technology is perfect. Patient monitors can generate bad data, leading to false alarms. If that happens enough times, the response teams become desensitized.

Continuous remote patient monitoring systems integrated with AI and machine learning are situated to help resolve some of these issues, in part by providing an environment where patients can take greater ownership of their care.

Using AI to Solve Continuous Care Challenges

AI offers solutions for many problems by automating data analysis to identify the moment when patients are heading for trouble. These systems help supplement the current “spot check” system.

AI-powered remote patient monitoring is truly continuous. Systems can be set to sound warning alarms for action, either for the patient or the provider, but only when algorithms predict life-threatening events. This helps optimize the clinical response to those who need it most.

Consider one example: the ability for a diabetic patient to monitor their blood sugar. Currently, it depends on the ability of patients to remember to capture measurements using glucometers (and to do so properly).

AI-driven alternatives allow for blood-sugar monitoring on an ongoing basis. These tools are in the experimental stage but demonstrate a few of the opportunities available for improved care and reduced costs.

Continuous remote monitoring of COPD patients is also an area that shows promise in using AI to solve continuous care challenges, one that Oxitone is focusing on with its solution set.

How Oxitone Is Advancing Intelligent Healthcare

Oxitone is playing a leading role in combining the data collected by medical-grade wearable patient devices into actionable clinical insights. AI provides the key to that transformation.

The company’s Oxitone 1000M wearable device has continuous remote patient monitoring of vital signs and other data points and delivers them reliably to healthcare teams for evaluation and action.

The Oxitone 1000M continuously measures several variables, including:

  • SpO2
  • Pulse rate
  • Heart rate variability (physiological stress)
  • Activity
  • Skin temperature
  • Sleep apnea and disorders

But numbers alone may not necessarily point to a course of action. They lack context. At the core of the Oxitone 1000 M is the VitalsTone™ AI-powered personalized data analytics engine.

VitalsTone compares vital signs against baseline data and makes other calculations that can highlight the potential for problems in real time. In effect, it is always keeping a set of virtual eyes on the patient. It is part of Oxitone’s solution to using AI to solve continuous care challenges.

The Oxitone 1000M is comfortable and easy to wear for patients. It provides peace of mind because patients know that their condition is being evaluated. It also provides them with a feedback loop that encourages greater participation in their own care.

The system works by detecting signs of worsening conditions. Armed with that information, doctors can advise patients to act ahead of time, leading to better outcomes, enabling patients to stay healthier, and reducing the number of costly healthcare readmissions. The types of use cases for continuous patient remote monitoring are expanding.

Use AI to Solve Continuous Care Challenges: Embrace the Opportunities with Oxitone

Oxitone is dedicated to addressing one of the most challenging realities in health care: that 20% of severe chronic patients consume 80% of resources, including a clinician’s time. Our solutions provide augmented intelligence for real-time decision-making that can help high-risk patients live healthier and safer lives.

Here at Oxitone, we boost value-based healthcare by delivering extraordinary patient, clinical, and economical outcomes at reduced medical utilization and cost. Patients need a prompt response to emergencies. Physicians need an easy and timely follow-up with patients. Our mission is to transform chronic disease management and help save lives worldwide.

Let’s save lives together! To see how we help remote patient monitoring companies and physicians improve the management and care of high-risk patients, contact us today!

Share this insight