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Remote patient monitoring adoption expands use-cases in acute care

UCLA, Penn and and UPMC systems have built system integrations for remote patient monitoring for urgent and acute conditions.
By admin
Apr 7, 2022, 7:30 AM

The trendline for remote patient monitoring (RPM) technology looks like an ascending staircase, with an estimated 45 million U.S. users in 2022 and at least 14 percent year-over-year growth projected through 2025. Hospitals and health systems see RPM technology as a cost-effective means of monitoring patient health outside of traditional clinical settings, extending the reach of caregivers and potentially improving outcomes by assessing continuous streams of real-time health data.

In basic terms, RPM systems collect physiological data, such as glucose levels and blood pressure, without the need for interactive audio-video hookups. The technology has its roots in chronic care, enabling ongoing tracking for patients with diabetes, heart conditions, asthma, and hypertension. However, more recently, healthcare organizations have demonstrated success using RPM for urgent and acute conditions as well.

Related story: NIST unveils final telehealth, remote patient monitoring guidance

Device manufacturers are fueling RPM’s popularity by making units smaller and less invasive in the form of implantable sensors that can transmit health data to monitors or smartphones via Bluetooth. 

RPM programs across the country show the breadth of current applications:

University of California Los Angeles (UCLA) has a post-surgery RPM program for heart procedure patients. The devices provide ongoing biometric data to caregivers to lower the risk of readmissions with post-surgical complications. According to UCLA, the technology immediately detects shortness of breath, dehydration, abnormal heart rhythm, fluid retention, early signs of pneumonia, and negative reactions to medications.

University of Pittsburgh Medical Center (UPMC) uses RPM for postpartum blood pressure monitoring in cases of hypertensive disorders during pregnancy. The program tracks patients from the time of hospital discharge through the first six weeks after delivery. UPMC reports improved control of hypertension and reduced hospital readmissions among program enrollees. Patient participants also exhibited higher rates of engagement in postpartum care.

University of Pennsylvania (Penn) is using a $20 million grant from the National Institute on Aging to study the use of artificial intelligence and RPM to improve in-home care for older adults and individuals with Alzheimer’s disease. Penn’s effort aims to use RPM to collect and process data from older adults in their homes. The data will be integrated with clinical data from electronic health records, setting the stage for analysis via AI and eventual deployment of validated decision-support models at the point of care.

Putting RPM into practice

As with any category of health IT, progress with RPM will build on a base of well-vetted, stepwise adoption. Key considerations apply in the following areas:

  • Security. Data transmitted over an RPM platform must meet healthcare security standards to prevent patient data from falling into the hands of hackers. Security concerns extend to any third parties responsible for data management processes.
  • Data accuracy. Patient acceptance will depend on RPM devices being easy to use, intuitive and accurate when generating data. Meanwhile, doctors and nurses must be able to quickly determine which data points are most relevant to the patient’s situation. Successful assessment depends, in large part, on data presentation in an easily digestible format.
  • Real-time data access. The path from patient device to mobile network to service provider likely travels through multiple data centers. Network outages along the way could cause delay in timely delivery of data
  • Systems integration. How will the RPM platform mesh with existing patient record systems? Upgrades and migrations to core systems must preserve RPM functionality and usability.

As RPM technology continues to evolve, expect refinements that improve data quality, information flow, and analysis, enabling patient treatment to be less reactive. Early disease intervention and will not only help hospitals avoid costly admissions, but also identify at-risk patients before they get sick.


Frank Irving is a Philadelphia-based content writer and communications consultant with specialties in healthcare, technology and sports. When not following those beats, he writes creative fiction.

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