Remote patient monitoring is not new, but technological advances in devices and data transfer have enabled many new uses and opportunities. Today, RPM and Internet of Medical Things (IoMT) devices can be used to continuously monitor patients’ vital signs, including weight, blood pressure, heart rate, glucose levels and blood oxygen levels, all from home or wherever they may be.
This real-time capture of a patient’s health status is much different from traditional care models, in which a patient comes to a healthcare facility and data is collected for that single point in time. For example, a visit to the doctor’s office will capture a single heart rate measurement, compared with hundreds or thousands captured with a remote heart rate sensor.
Limited data makes it difficult to pinpoint health trends. Having more data points allows care teams to create a clearer picture of a patient’s health and to understand what normal is for an individual rather than comparing it with a population average. For example, some people have a higher or lower average temperature, and a 99.5 F temperature might mean something different for that patient compared with the overall population.
This information enables personalized treatment plans and preventive care measures due to the greater potential for early detection. If providers can detect a condition at an early, treatable stage, they can reduce emergency visits, hospital admissions and readmissions, and unnecessary healthcare costs.
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RPM solutions are often used in traditional acute care following discharge to monitor recovery, and increasingly more in chronic care management, but adoption is also growing in independent living and skilled nursing spaces. In a long-term care center, having a personalized medicine approach means that the care team can make faster treatment decisions, such as titrating medications or changing rehabilitation routines.
As reimbursement continues to expand beyond episodic care, RPM will play an important role in overall patient wellness. Combined with IoMT devices and sensors, care teams will have never-before-seen insights into overall patient health.
To support RPM strategies, healthcare organizations must focus on data maturity and interoperability with the electronic health record. Here are some considerations to help healthcare IT teams as health systems begin to implement RPM solutions and collect patient-generated data on a larger scale.
EXPLORE: RPM plays an important role in advancing home healthcare.
AI and Interoperability Support RPM Initiatives and Preventive Care
The ability to collect continuous patient data is leading to an explosion of data in healthcare. However, current EHR systems are not designed to make useable sense of these larger amounts of data. A nurse or doctor can’t process thousands of heart rate data points to determine if something is wrong. What’s more, data is dispersed across many systems, requiring care teams to hunt for clues to help their patients. Bringing data together is key to achieving personalized care. To support care teams in processing this data, organizations need a new data paradigm.
All of that data can’t just be added to the EHR with the expectation that clinicians can consume the information. Organizations must provide an abstraction layer that surfaces actionable trends and outliers. Artificial intelligence solutions can help care teams identify important information.
Integrating data analytics and AI tools with RPM can alert clinicians when there’s a significant change in a patient’s health status and allows the care team to make decisions more quickly based on real-time data. Additionally, data analytics can detect subtle changes faster to allow for more predictive care before conditions escalate.
Another benefit of this integration is that it gives clinicians more time to focus on patient care and engagement. Clinicians can make decisions more quickly since they don’t have to spend time sorting through patient data that could be analyzed automatically.
The human mind is limited by the number of variables at a time that it can consider, whereas AI can evaluate hundreds or even thousands of variables. An AI algorithm can analyze vital signs, lab values and social determinants of health to find patterns quickly, saving time, resources and lives. Machine learning comes into play as it starts to make connections between these different variables and patient outcomes.
AI is a valuable addition to IoMT and RPM, but they won’t be used most effectively by care professionals if the data they collect isn’t useful and turned into meaningful insights through data analytics, resulting in lower costs and a higher quality of care.
How Technology Partners Can Support RPM Adoption
As healthcare organizations begin to implement or scale RPM and IoMT, it’s important that they have data governance and storage strategies in place to leverage the data for personalized and predictive care.
A technology partner with extensive experience in healthcare can work with IT and clinical leaders to design governance strategies to support your new initiatives.
CDW has the resources to help healthcare organizations on their data journeys, determining sources of truth, finding appropriate technology partners, and forming strategies for data collection and storage.
Our healthcare strategists have extensive experience working in healthcare. They come from large healthcare delivery organizations, regional health systems, post-acute and senior care organizations, and more. CDW strategists will ensure that the people, processes and technology fit the needs and goals of the organization. It’s important to partner with people who understand different types of healthcare organizations, the latest advancements in healthcare IT and how healthcare organizations can prepare for the future.
This article is part of HealthTech’s MonITor blog series.
