Today, a small wearable device can track patient vital signs with the same accuracy as an ICU monitor, making it possible to provide acute care from the comfort of a patient’s home. For these high-risk patients, access to realtime, continuous patient vital signs can enable timely intervention and avoid hospital admissions—but only if it’s clear how to analyze and interpret the patient data.
Understandably, the idea of more data can be overwhelming, especially since legacy remote monitoring solutions struggled to create actionable insights with far fewer data points. That’s why we’ve taken a data-first approach to clinical monitoring and focused on creating actionable alarms that help prioritize patient care and catch early signs of health deterioration.
Here are a few ways our alarms are built with action in mind:
Configurability at population and individual levels.
With diseases taking different paths in different individuals, our alarms can be customized both at the population and patient level to be more targeted and precise. With a virus such as COVID-19 (see our example clinical pathway), where research and clinical guidance is frequently evolving, providers can tailor analytics to take into consideration the latest clinical recommendations and a patient’s individual health. No one knows a patient better than their provider, so this level of customization ensures that each patient’s monitoring captures the broadest picture of patient health possible and enables earlier identification of real risk without alarm fatigue.
Alarms based on trends across multiple vital signs.
Especially when looking at continuous data, it’s not uncommon for vital signs to momentarily go outside the normal threshold. However, this doesn’t always mean that there is a critical issue. For this reason, our algorithms look at sustained changes across multiple vital signs, reducing false alarms around momentary, non-critical changes. For example, a patient may be recovering from a surgery and have stood up for a short walk to the bathroom, which could result in a raised heart rate. A faster heart rate can be worrisome in some patients, especially if sustained over time, along with a drop in oxygen saturation. Being able to set thresholds for a specific clinical pathway, based on individual and population-level trends, can help identify when critical action is needed.
Automated, evidence-based workflows for patient check-ins.
Sometimes, data does not tell the whole story. By having automated workflows that prompt patient outreach based on certain vital signs, providers have access to additional information before making a treatment decision. For example, an alarm for low oxygen saturation can trigger outreach via chatbot to check on symptoms or escalate to a preliminary video call to see if further treatment is needed. Having this patient context integrated alongside real-time vital signs can help paint a holistic picture of patient health and direct a patient’s care plan.
Want to learn more? Download our RPM buyer’s guide if you haven’t already to learn the right questions to ask about data analysis and alarms.