Not only are healthcare organizations gaining more and more health data, patients are also actively adding to it by generating their own data. The general public is becoming increasingly more interested in their own wellness, a McKinsey report found, with 84% of US consumers, 79% of UK consumers, and 94% of Chinese consumers reporting wellness as a top or important priority. As such, health tracking devices or wearables are also on the rise in both invention and ownership, with Care Evolution finding 20-30% of adult Americans own one, and a large percentage of that group uses it daily. So, how are healthcare organizations dealing with this influx of patient-generated data?
We reached out to our talented Healthcare IT Today Community to ask — how is patient-generated data, such as data from wearables or remote monitoring devices, being integrated into clinical workflows? The following is what they had to share.
DJ Tucker, Managing Director, Healthcare Informatics at Healthcare IT Leaders
RPM and wearable integration is a clinical workflow problem first and a technical integration problem second. The data existing is no longer the challenge. The challenge is whether it arrives in the right place, in the right format, at the right moment to influence a clinical decision. A nurse toggling between five systems to assemble a complete patient picture is being failed by the informatics infrastructure.
The integration architecture the industry is converging on (FHIR-native, AI-summarized, embedded directly in the clinical view) is the right direction. Being able to see a clinically relevant synthesis of what changed, what matters, and what warrants attention is key. Getting there requires a clinical use case definition before device selection, alert threshold governance to prevent fatigue, and dashboards that connect monitoring data to actionable workflows.
Joey Kennedy, SVP, Sales at Tendo
Patient-generated data has the potential to expand the clinical picture beyond what we see during appointments. Wearables and remote monitoring tools can provide valuable signals about a patient’s health between visits. But the real challenge for health systems is integrating that information into workflows in a way that supports clinical decision-making. Organizations are focusing on identifying which signals are most meaningful and connecting that data with existing clinical and operational systems. When that integration happens effectively, clinicians gain earlier visibility into changes in a patient’s condition, and patients become more engaged partners in managing their health.
Adam Hesse, CEO at Full Spectrum
I believe we are only scratching the surface of what is possible with patient-generated data. Non-consumer products, such as continuous glucose monitors or cardiac monitors, are well integrated, but consumer products (e.g., fitness monitors) are far less utilized in a clinical setting. Cardiac monitors result in detailed reporting and alerts that shorten the time to diagnosis, whereas glucose monitors drive rich discussions with a patient’s care team to identify habits or behaviors that are negatively impacting that patient’s condition.
The most obvious and immediate impact is the ability for a care team to have a long-term view of a patient’s condition at the beginning of a clinic visit, rather than relying heavily on a patient’s self-assessment or tests that may occur after a visit. This results in more precise care with less time in the clinic, which is both lower cost and more convenient. But, more importantly than the in-clinic workflow is the ability to inform patient decisions in real time in the real world. Empowering a patient with data is a powerful method to either drive behavior changes and/or a decision to engage your care team.
Niki Panich, MD, Chief Medical Officer at Penguin Ai
Patient-generated data is only valuable if it reaches the clinician at the right moment, in a usable form. Right now, most of it is sitting in apps that do not talk to the EHR. The organizations making progress are building structured ingestion pipelines and defining clear clinical thresholds for when that data should trigger clinical action. The goal is NOT more data. It’s the right signal, surfaced at the right time.
Lucy Bichakhchyan, Marketing Manager at NeckCare
Patient-generated data from remote monitoring devices is increasingly being integrated into clinical workflows through web-based platforms that sit alongside existing practice management systems. In musculoskeletal rehabilitation — particularly cervical spine care — this typically means a clinician prescribes a home exercise program through the platform, the patient completes the exercises using a connected device, and completion data is automatically reported back to the clinician without requiring a visit or phone call.
Wearable sensors also play a role at the assessment end of the workflow. Clinicians use them to establish a baseline at intake and reassess at intervals throughout the care plan, generating objective measurements that replace or supplement manual testing.
That objectivity matters for multiple reasons. Patients who can see their own data — actual numbers showing where they started and how they’re tracking — are more likely to engage with and continue their treatment plan. The same documentation serves different purposes for insurers, referring providers, and, in personal injury cases, legal teams: it provides a defensible, timestamped record of functional status and clinical progress that subjective notes cannot replicate.
The workflow value is in consolidating that data — remote adherence, in-clinic assessment, reassessment over time — into a single record that is accessible, reportable, and billable. Remote Therapeutic Monitoring (RTM) codes provide a reimbursement pathway for the remote monitoring component, making the business case more concrete for practices that were previously skeptical of the overhead.
The remaining integration challenge is largely one of adoption: the data and billing pathways exist, but getting clinicians to build remote monitoring into standard intake rather than treating it as an add-on is where most of the friction lies.
Antoine Pivron, Vice President at Withings Health Solutions
The majority of clinicians are flying blind between clinic visits, but wearables can surface the micro-patterns in subtle sleep disruption, activity drops, or stress spikes that can predict a patient’s risk for a health issue before it becomes a clinical event. This isn’t about overwhelming clinicians with raw data; it’s about giving them distilled, evidence-based insights that help them intervene earlier and keep patients on track to improve outcomes. Although patients generate data daily through connected devices, clinicians receive only relevant alerts directly within the dashboards they already use, allowing them to focus on what truly requires attention.
The next frontier in cardiometabolic care is continuous risk detection, with risk scores embedded directly into everyday devices. For example, we have established strong partnerships in the congestive heart failure space. In this field, rehospitalization represents a significant cost, and the ability to anticipate and detect decompensation as early as possible is both life-saving for patients and a key decision factor for clinicians.
Looking ahead, wearables and connected health tools will quietly synthesize signals from heart rate variability to sleep patterns to metabolic biomarkers, generating real-time probabilities and calculating risk scores for conditions like heart failure, hypertension, or diabetes long before symptoms appear. We’re moving toward a world where cardiometabolic risk isn’t assessed annually in a clinic, but continuously in the background of daily life.
Dr. Scott Schell, Chief Medical Officer at Cognizant
The challenge is not collecting more data but making it actionable. Health systems are integrating remote monitoring platforms and AI-powered triage tools that filter patient-generated data and surface clinically meaningful signals. When done well, this allows care teams to intervene earlier in chronic disease management and shift care from episodic visits toward continuous monitoring.
What great insights here! Huge thank you to everyone who took the time out of their day to submit a quote to us! And thank you to all of you for taking the time out of your day to read this article! We could not do this without all of your support.
How do you think patient-generated data (such as data from wearables or remote monitoring devices) is being integrated into clinical workflows? Let us know over on social media, we’d love to hear from all of you!
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