Remote patient monitoring could trigger a multi-channel data tsunami

by Felix Matthews, MD, MBA, managing director, and Ken Abrams, MD, MBA, managing director, Virtual Health Market Offering leader, Deloitte Consulting LLP

Even before the COVID-19 pandemic, 88 percent of health care providers had already invested in remote patient monitoring (RPM) or said they planned to do so in the future.¹ In addition, a recent study conducted by the Deloitte Center for Health Solutions and the American Telemedicine Association found that 88 percent of health care executives predicted wearable devices would soon be integrated with care delivery—a clear derivative of consumer expectations for a tailored, hyper-personalized virtual health experience.

The reduced use of health care during the pandemic could drive greater adoption of RPM. For example, chronically ill patients who put off routine care during the pandemic could see their conditions worsen. RPM can be used to monitor and manage many chronic conditions and help prevent acute care episodes associated with them. Additionally, as both patients and providers try to limit in-person care, RPM could be seen as a necessary enabler of the virtual care relationship.

Multi-feed remote patient monitoring

Traditionally, RPM has involved the transmission of a single form of data (generally biometrics from patients outside of clinical settings to their doctors and other clinicians). As technology evolves, new types of data (e.g., voice data, data from facial scans) have become increasingly prevalent, clinically valuable, and will likely add to today’s biometrics.

As the health care landscape evolves, the future of RPM will likely not be defined by an organization’s ability to measure and monitor one type of data at a time, but rather its ability to combine data from multiple channels and draw inferences across these channels. Startups and medical-device companies that offer only a narrowly focused point solution might have limited longevity in this ecosystem.

We expect cross-functional data and analytics companies will win the RPM race if they can build a platform that effectively assimilates feeds from multiple channels, uses sophisticated algorithms that combine biometric data with self-reported information, and derives inferences to establish a personalized treatment plan.

Source: Deloitte Consulting, 2020

The future is data pooling

We expect the future of RPM will be in simultaneous pooling and synthesis of data from continuous measurements and from interval-based assessments to generate even more meaningful, personalized insights for each patient.

1. Continuous measurements come from ongoing monitoring of physiological and environmental variables that physicians traditionally use to observe patients who are recovering from acute episodes. These include:

• Traditional biometrics (biological data, such as blood-glucose levels, heart rate, and blood pressure)
• Emerging physiological biometrics (new measures suitable for statistical analysis of functional integrity, such as electrophysiological tonus, sleep patterns, voice patterns in-home movement, daily activity levels, and exercise patterns)
• Environmental metrics (used to quantify external factors—such as weather conditions, pollutant levels, and the presence and potency of sunlight—that could affect the health of at-risk patients)

2. Interval-based assessments come from measurements taken during recurring evaluations. Physicians often use measures like these to manage chronic conditions. Data from interval-based assessments includes:

• Patient reported outcomes (PROs) come from questionnaires about the patient’s health status, quality of life, and mental well-being. They might be used to measure satisfaction/mood, pain, and optimism as well as whether the patient feels better after a procedure.
• Socio-biometrics come from personal and community data. This could include an individual’s social media activity, eating patterns, commute patterns, and community crime rates. Clinicians can use this information to understand the influence that certain drivers of health have on the patient.
• Personalized treatment plan information includes patient-specific details about key events along the patient’s care journey. This might include previous interventions, medication prescriptions, physical therapy recommendations, and outcomes.

Two use cases help illustrate the future of RPM

The RPM framework outlines the major channels we expect will be prevalent in the future as well as the data points we expect those channels to capture. Technology already exists to pull data from these disparate sources, however consolidation and analysis of these data to create a personalized treatment plan is not yet widespread.

The two use cases below illustrate how RPM might work in the future:

Use Case 1: Continuous measurements only (environmental metrics + traditional biometrics)

Sensors in the home or at work are beginning to be used to monitor pollutant levels. Clinicians are starting to harness this type of data in some areas, which could make it possible for them to reach out to asthmatics who might be at risk of an adverse reaction. Case in point: Deloitte’s 2019 paper on smart health communities profiles a private-public partnership that made GPS-enabled “smart” inhalers available to individuals who have asthma. Each time an individual took a puff, the inhaler logged the location, time, weather, and pollutants in the air.

Imagine supplementing environmental biometrics with traditional biometrics in real-time on high pollution days to create a personalized and effective intervention. Devices for asthmatics that combine a peak flow meter, which measures a patient’s ability to push air out of their lungs, with a smartphone (by plugging the device into the phone’s headphone jack) are already in development.² The devices would connect with a corresponding mobile application that monitors a patient’s peak expiratory flow rate (the key indicator of an asthmatic’s ability to breathe out air) and the level of obstruction in the asthmatic’s airway.

In the future, environmental biometrics and traditional biometrics, such as peak expiratory flow rate, could be combined to help physician monitor an asthmatic’s well-being any time pollution levels are high. Monitoring could be combined with highly proactive behavioral nudges or medication-management reminders to help keep the individual from having an asthma attack.

Use Case 2: Interval-based assessments and continuous measurements (socio-biometrics + patient-reported outcomes + emerging physiological biometrics)

Incorporating both interval-based assessment information and continuous measurement data can generate a 360-degree view of the patient, allowing clinicians to take a more holistic approach to treatment.

In the future, there could be a platform that harnesses patient data across multiple channels to help both the patient and the clinician more effectively manage the onset of depression. It is now feasible to monitor a host of lifestyle factors that affect a person’s mental health via big data found publicly on social media platforms. Patients can report self-report certain outcome measures (e.g., PROs) to providers via their smart phones, such as their current levels of satisfaction, level of optimism, and their mood. Add in data from wearable devices that monitor activity levels, exercise levels, and sleeping patterns for even more insights and personalization. Together, these data—supported by powerful machine-learning algorithms—could help clinicians identify depressed patients earlier on, differentiate between emergency and non-emergency situations, and prescribe more effective treatments.

Preparing for the future of RPM

With the future of RPM looming, we lay out five key questions that health care organizations should consider as they take their first steps toward the future:

1. Which RPM use cases can most effectively serve the needs of your population? 
2. Which capabilities does your organization need to pool data across these new channels?
3. How can you enhance your analytics engine to derive highly personalized insights based on data gathered from disparate sources?
4. How can you build a relationship with your patients in a way that ensures they are comfortable and willing to consent to around-the-clock monitoring?
5. Which of your strategic partners can help you trigger a multi-channel data tsunami?

Acknowledgements: Elizabeth Baca, MD, MPA, Andrew Wiesenthal, MD, MIS, Kulleni Gebreyes, MD, Neelam Patel, Zach Miller, Mamta Elias

Endnotes

^{1. 88 percent of Providers Investing in Remote Patient Monitoring Tech, mHealth Intelligence, November 4, 2019}

^{2. Smart Peak Flow detects asthma exacerbations before patients feel it, Pharmafield, January 29, 2019}

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