Remote Patient Monitoring – an Enhancement to the Model of Care

Remote patient monitoring, commonly referred to as RPM, has received a lot of attention lately. At the beginning of 2019, CMS released reimbursement codesspecific to the components of RPM that are required to provide a solid program: the hardware and data needed, the virtual clinical management of patients, and the education, onboarding, and consenting of patients. Recently, MobileHelp addressed the topic of RPM reimbursement in a webinarUnderstanding CMS Reimbursement Opportunities for Remote Patient Monitoring.

According to a Deloitte paper, over the next 25 years, RPM is expected to save $200 billion in healthcare costs globally.

For the many stakeholders who stand to benefit, it is a good time to consider an RPM program. Patients benefit, as do clinicians, and caregivers. Organizations with population health initiatives and clinical teams benefit as they have historically absorbed the “between clinical visit” time to manage patients with chronic conditions. RPM is now a welcome addition to the traditional model of care. It is now an enhancement of the model of care.

In addition to CMS, the American Heart Association has also acknowledged the value that RPM brings to care. In an article, “Using Remote Patient Monitoring Technologies for Better Cardiovascular Outcomes Guidance,” the AHA highlights the benefits in the following point: RPM empowers patients, informs clinicians, and expedites intervention when necessary.

From the same article:

  • Federal healthcare spending is approaching 20% of the GDP

  • The cost to treat chronic diseases is 3.5 times higher than that of other conditions

  • Chronic conditions account for 80% of all hospital admissions

On average, a hospitalization for congestive heart failure as a primary diagnosis is about a week in duration and the cost is more than $20,000. This is an example of one of the associated hard costs. Others include the penalty costs hospitals face when patients are readmitted. Soft costs are less documented. However, as we consider the impact of managing an illness and a hospital occurrence to the individual patient and their families, we must consider how RPM might better relieve costs such as travel, lost wages, and caregiver stress. In fact, according to According to a 2016 New England Health Care Institute study, RPM for a patient with congestive heart failure might result in a net savings of $5,034 per patient per year when compared to standard care. We expect even greater savings to be reported at the close of 2019.

RPM provides an affordable option for clinical teams to understand a patient’s status outside of clinical visits. It provides data that prioritizes patients when their health declines, speeding the time to intervene and manage the situation before it becomes unmanageable. It also supports population health initiatives as it informs clinical teams of which patients to prioritize within a group by symptom control, adherence, and survey response. The communication paths become proactive and care plans are reinforced at the time an issue arises.

While highlighting the cardiovascular outcomes to date, such as improved systolic and diastolic blood pressure with RPM, improved heart failure related outcomes, and the potential benefits with atrial fibrillation, the AHA suggests a need for more significant evidence yielded through long-term, large population clinical trials.

As attention is drawn to RPM programs, the healthcare community will benefit from our shared knowledge about ongoing programs. Overall, building programs can be a lot of work. It is important to note that qualitative and quantitative measures for the purpose of tracking outcomes must be included in the planning process.

One approach, when in the process of planning or redesigning a solid RPM program, is to include analytics to identify the right patient populations and meaningful metrics to ensure the program is successful. It is to everyone’s benefit that solid evidence is shared.

A health analytics company that provides such support is Versatile MED Analytics, a partner of MobileHelp, which offers additional insight into healthcare organization data. The company recommends:

Define the target audience based on clinical criteria, where the opportunities exist, and budget. Programs require data to substantiate the potential impact and prioritize where to start. From there, patient identification should begin with analysis to identify the total universe of people who could benefit from an intervention. However, it is likely that your program budget will not support engaging all of them through outreach and care delivery.

Therefore, it will be necessary to match the initial budget to a specific sector of patients where the data indicates they can realistically be reached and clinically impacted by your proposed program.

Start early during the planning stages to define desired outcomes and assign resources for tracking. Why? Because this will be your opportunity to:

1) Confirm what you want to track (your outcome or “measure of success”) is supported by existing data

2) Build data capture mechanisms, if necessary; and

3) Document baseline values which are key for demonstrating affected change after the program has been implemented

In short, choose your program goals and include the process to measure the outcomes before you implement. Some areas that can be measured include:

  • Adherence, compliance

  • Symptom improvements/decline

  • Patient satisfaction

  • Cost of care

  • Travel costs

  • Behavioral change

  • Patient engagement/activation (PAM Scores)

The change in reimbursement has highlighted the value of RPM to the model of care. To support continued positive outcomes, the AHA suggests, “Future research should focus on understanding the process by which RPM works in terms of improving HF (heart failure) related outcomes, identify optimal strategies and the duration of follow-up for which it confers benefits, and further investigate whether there is differential effectiveness between chronic HF patient groups and types of RPM technologies.”

With solid partnerships in place, accomplishing such tasks is manageable as partners offer shared resources, intelligence and skills. Remember that innovation isn’t about invention – it’s collaboration and the leveraging of partnerships that can be the innovative solution. Building teams of healthcare organizations, vendors, consultants, and providers to design trials that include insight into all the areas can help make for a successful RPM program with measurable outcomes.

More about the partnership:

To deliver successful remote patient monitoring programs, MobileHelp provides options to our client partners. These options are both valuable and flexible, fitting our client partners’ specific program and population needs. In addition to offering exceptional hardware, clinical monitoring software, and a patient portal – MobileHelp also provides logistics, contact center services, integration and now – data analytics.

With a continued focus to improve patient-centric care and balancing organizational goals to reduce care costs, MobileHelp continues to expand services.


Published by: Lisa Levitt

Lisa Levitt writes about healthcare analytics, innovation and digital health. She is passionate about improving customer experience, measuring results and collaborative problem solving. Lisa is a thought leader at the intersection of business, analytics and technology. She is the Senior Director, Strategic Solutions and West Coast leader at Versatile MED Analytics, a healthcare analytics and data solutions company headquartered in Albuquerque, NM. To learn more, visit Also as Professor at the University of California Irvine, Lisa develops and teaches courses on Digital Health, Business Data Management, and comparative health care systems.


Published by: Jerriene Cordova

Jerriene Cordova is a healthcare executive with more than two decades of experience in developing new markets and building strategic alliances with patient-oriented organizations. Her areas of expertise include telehealth programs, remote patient monitoring, case management solutions and clinical research. Currently, Jerriene is the Director of Telehealth Programs at MobileHelp Healthcare Division. Her extensive prior experience includes companies such as GlaxoSmithKline (GSK), Aflac, and Ideal Life and she is a member of the American College of Healthcare Executives. She holds a BS in Biology from the University of New Mexico and is currently pursuing her MBA from LSUS.

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Five ways to start listening to your data

I’m talking to you…
I have something to say and I think you’ll want to hear it…
Stop ignoring me…
Why are you still ignoring me?

This is what your data would say to you if it could talk.

Data has been itching to tell us all a story, but it’s only been within the last five years that it has been able to through better data capture, integration, and meaningful analysis. If you work with even the tiniest sliver of the 2.5 million terabytes of data that is generated daily, you can glean valuable information about your organization.

Here are five simple ways you can start listening to your data now.

1.       Pay attention — but not too much attention — to those pesky outliers.

Outliers are those rogue data points that make us raise an eyebrow when we see them. Outliers may crop up when something has changed, but it has to be significant enough to cause your little outlier friend to be a noticeable distance from the other data points. They weren’t invited to the party, so why are they here?

Well, they could have something critical to point out or they could just be normal noise (or “variation”), so think hard before acting. The spotting of an outlier doesn’t automatically mean something is wrong, so don’t go chasing every single one. You should take those outliers with a grain of salt until you’ve looked at them through a thorough analysis lens and meaningful process to classify their significance. However, if you see multiple outliers in a row, it may be a good indication that your something is different from past behavior. This could be good, bad or indifferent, but it’s your job to understand the “why.”

2.       Compare current to historical trends.

Looking for trends in the data implies you’ve already done your due diligence by validating your data. Data validation is extensive enough to require its own blog post and is not for the faint of heart. If you haven’t done this yet, you should stop reading this post and validate first.

But if you have, let’s discuss data patterns (or rhythms) that exhibit a consistent behavior — which includes consistently inconsistent. Sometimes your data can demonstrate a steady rhythm with occasional outliers. Upon further examination, you might find these “outliers” actually reflect cyclical, seasonal, or other common cause changes. Mark Graban offers key guidelines for identifying outliers and when to be concerned about them in his book, Measures of Success. And remember, two datapoints do not constitute a trend.

A good example of an outlier that we’ve been asked to look into is a health plan having a dramatic spike in Inpatient admits/k and spend one month. Upon examining the last 3 years of Inpatient data, we may find that this month’s “outlier” value is within normal variation limits, but appears in this month to be anomalous because the initial run chart that showed the outlier only spanned one year’s time and it happened to be an early flu season — and a particularly bad one!

3.       Consult the humans.

In our world of expanding data technologies, talks of machine learning and artificial intelligence (AI) seem to permeate every conversation. Though these topics are legitimate game changers, some folks may be at risk of losing sight on the business intelligence that is crucial to making data meaningful and actionable. In short: we still need humans! Even the most sophisticated algorithm will never be as powerful as it is when coupled with a human element that understands the domain. The human element is what drives the questions being asked of the data and what leads to other sources that will shed more understanding.

Our favorite example is when a data scientist unfamiliar with healthcare baked oxygen use and chronic obstructive pulmonary disease (COPD) into their model, then later exclaimed that the greater the occurrence of COPD among patients, the greater the utilization of oxygen. Understanding your domain data and what influences it is critical to knowing what you should include, exclude, or combine in your report out.

4.       Use data to design your strategy.

Data should play a critical role in your strategy. The most harmful thing you can do with data is not use it. Data tells us a story, but simply hearing that story isn’t enough. As consumers of data, we own the responsibility of listening to it and putting the insights into action. Whether an inefficiency or opportunity has presented itself, it’s up to us to create policies, procedures, processes, and targeted initiatives. Reliable, actionable data must be placed in the hands of leadership and teams alike so these insights aren’t left to fade into the abyss.

5.       Monitor, monitor, monitor.

The laws of nature cause many new initiatives to revert back to where they started if they are not routinely monitored, shared, discussed, and improved upon. This is one of the barriers to long-term adoption and often leads to your data story ending right where it began. Organizations get stuck in a vicious cycle of hearing their data’s story, taking some action based on that story, but then failing to invest time and resources on continued commitment. This often leads to a recurrence of the issue that your data pointed out in the first place. Identify, track, trend, monitor, repeat.

Data gives us meaningful insights and invaluable information, but only when we’re willing to take the time to listen to the story and commit to its long-term use. This is much easier said than done, and organizations of all sizes continue to struggle with it for many reasons.

Data technologies and capabilities continue to grow at breakneck speed.

If you want to reap the rewards that your data has to offer, you’ll first need to learn how to listen to what it has to say.




Angelica Bruhnke writes about healthcare analytics, , and entrepreneurial pursuits. She is passionate about actionable data, helping the underserved, and community impact. She is Executive Officer at Versatile MED Analytics, a healthcare analytics and data solutions company headquartered in Albuquerque, NM. To learn more, visit