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.