As policymakers continue to look for ways to keep patients out of the hospital, there has been a growing interest around the effects of home telemonitoring on patients. Certainly, wearable medical devices posses the potential to create a trove of medical data, and help doctors and other healthcare professionals make real-time decisions on patients’ health even when they're not in the hospital. But as Spyros Kitsiou, Ph.D., Assistant Professor at the Department of Biomedical and Health Information Sciences (BHIS) at the College of Health Sciences, University of Illinois at Chicago, points out, we have only hit the tip of that mobile health (mHealth) iceberg.
As such, Kitsiou and colleagues recently wrote a paper, “Effects of Home Telemonitoring Interventions on Patients With Chronic Heart Failure,” that was published in the Journal of Medical Internet Research. They wanted to know, what about studies that empirically show the value of telehealth and mHealth? Do they exist? In the paper, the authors aimed to collect, appraise, and synthesize existing evidence from multiple systematic reviews on the effectiveness of home telemonitoring interventions for patients with chronic heart failure to inform policymakers, practitioners, and researchers. A total of 15 reviews published between 2003 and 2013 were selected for meta-level synthesis. Kitsiou recently spoke with HCI Senior Editor Rajiv Leventhal about the findings of the study, its implications, and the effectiveness of home telemonitoring as it relates to the future of healthcare and healthcare management.
What was the motivation behind this research?
We wanted to look at the technologies and interventions for patients with chronic conditions, and more specifically, heart failure. Systematic reviews have tried to aggregate multiple studies in order to arrive to the conclusion on if home telemonitoring is effective or not so they can inform practitioners and policymakers. One of the problems we have noticed, and this was part of the motivation for this study, was that several systematic reviews with studies in this area have reached different results with various findings and conclusions. This creates fuzziness and confusion, and makes it difficult for practitioners and policymakers to act, and for researchers to know where gaps exist. Not all reviews have been conducted in the same way. Another thing we wanted to address is that we don’t know which telemonitoring technologies are most effective for which types of patients.
Spyros Kitsiou, Ph.D.
What were your main findings?
The main conclusion from this is that we have evidence that devices that use automated monitoring—meaning they don’t require patients to actually type in results such as values of blood pressure or heart rate—are more effective than other non-sufficient modes of monitoring such as videoconferencing or websites that require patients to insert their vital signs manually. We also saw that mobile devices, despite seeing a limited number of studies (we reviewed about four studies that use devices such as cell phones or personal data assistants) seem to be effective in reducing mortality in heart failure hospitalizations.
So these findings certainly have implications. On one hand we need to start shifting away from randomized controlled trials (RCTs). We need more evaluations of large-scale implementations. Technology as evolved at a very fast pace, so it is difficult for research to keep up with the evolutions. What policymakers need to take into consideration is that our field is different than other clinical domains. These are complex interventions, so traditional RCTs might not be applicable. We need real-time data and evaluation of large-scale projects. These are the studies that will inform practitioners about the effectiveness of these types of interventions.
How is mobile specifically working to reduce mortality?
Mobile-based telemonitoring interventions allow clinicians to gather data from patients remotely, be it at home or elsewhere. We can gather data from them and provide clinical feedback after analyzing it, and then take action. By using the vital signs, we can anticipate exacerbations and react to them. For instance we can invite patients to come to the ED before anything that may jeopardize their lives occurs. In this way, you can avoid mortalities and hospital admissions/readmissions.
More broadly speaking, we are seeing a trend towards mobile apps helping with chronic care management. What effect do you see these apps having?
There are a lot of apps that assist patients with chronic diseases that allow patients to keep track of blood pressure, lifestyle, and nutrition habits. The question is with so much out there, which of these applications are most effective? We don’t have real answers to that right now. These are behavioral change applications that allow management of lifestyles, so it’s hard to say to what extent they help with right now. We think it can provide a starting point for help, but any more than that we don’t know yet.
There have been applications that are accessible through iTunes or Google Play that allow patients to learn about what they need to eat and manage their chronic conditions. These are very helpful, as in an effort to manage their condition, patients will turn to these apps to track data, manage blood pressure, and learn about nutrition and so on. In the next few years, apps through [Apple’s] ResearchKit platform that collects data from large populations will become increasingly important and relevant. There is a shift of interest; we already are seeing a lot of action in research institutions and universities, as they are conducting research studies with these mHealth apps, collecting data from 70,000-80,000 people so we can learn more about them—their habits, if they’re eating right, exercising, and family history. We can collect that and start making associations to try to see what can predict behaviors and who these people are.
But with these apps, there are issues with data quality and EMR integration, no?
We are at the very beginning of this mHealth wave. The reservations and concerns about data validity are definitely relevant. How do we know patients exercised as they say, for instance? We rely on their responses. But in the next few years, these questions will be addressed through development of new tools. We will improve.
With that being said, in terms of integration with EMRs, these types of lifestyle/behavioral change mobile apps are not able to send data to the EMR right now in most cases. However, home telemonitoring interventions that use mobile technologies and include clinical feedback and identifiable data, so we know who the patient is, can [integrate]. We know that we are receiving data from patient X. That type of data from remote patient monitoring can be integrated into the EMR, and we have already seen examples of that. That has become really important, as clinicians need data to make decisions, and the better tools they have will help that happen in a timely manner, hopefully when the patient is at home and not in the hospital. We can reduce the rate of readmissions, particularly in the 30-day penalized period for hospitals. Data from mHealth apps will most likely not be able to integrate through EMRs just yet. But right now, these aren’t the main objectives of these apps. It’s more about how to educate these patients and help them self-manage.
This is all a new world for healthcare professionals and for patients. How will this play out in the future?
We are shifting from hospital care to home care, since hospital care is very expensive. Policymakers are very interested in ideas that support keeping the patients at home so we can monitor them from distance where they can be part of the decision-making process with more self management. The ability we have with mobile devices to monitor and support patients while they self-manage is huge. We’re only at the tip of the iceberg right now. I think there are still lots of things to be done and issues to be resolved, but even more opportunities for groundbreaking research in this area.