What Happens If My Heart Rate Spikes During a Video Visit?

The question of what happens during an unexpected medical event in a virtual setting is a frequent source of patient anxiety and a complex engineering challenge for telehealth platforms. A sudden heart rate spike during a video doctor visit, for instance, represents a critical data point that can be either a benign stress response or an indicator of a serious cardiac event. For the telehealth platform vendor, the challenge is to capture this data accurately, interpret it in real-time, and present it to the provider in a clinically meaningful way. The ability to manage these transient events is rapidly becoming a key differentiator in a crowded market.

"The phenomenon of 'white coat hypertension' - a temporary increase in blood pressure and heart rate - is observed in an estimated 15-30% of patients in traditional clinical settings. Studies suggest this effect is just as prevalent, if not more so, in telehealth encounters, where patient anxiety can be heightened." - Dr. Susan A. Alpert, former FDA official and medical device expert (2022).

The technical challenge of a heart rate spike during a video doctor visit

A sudden, transient increase in heart rate, clinically known as tachycardia, presents a significant signal processing challenge in a telehealth context. Unlike traditional monitoring that relies on physical contact (like an ECG or pulse oximeter), advanced telehealth platforms utilize remote photoplethysmography (rPPG). This technology analyzes subtle, sub-pixel changes in light reflected from a person's skin to measure blood flow dynamics and derive vital signs like heart rate from a standard video stream.

When a heart rate spike during a video doctor visit occurs, the underlying physiological signal changes rapidly. The accuracy of rPPG algorithms in tracking these sudden shifts is a primary focus of academic and commercial research. While rPPG has demonstrated high accuracy for resting heart rate, its performance can be affected by factors common in real-world video calls:

• Motion artifacts from a patient shifting in their seat.
• Fluctuations in ambient lighting.
• High levels of tachycardia, where the pulse signal can become less distinct.

Engineers must develop sophisticated filtering and machine learning models to isolate the true pulse signal from this noise. The goal is not just to report a single number, but to provide a continuous waveform and heart rate variability (HRV) data that allows a clinician to distinguish between an anxiety response and a potential arrhythmia.

| Feature | rPPG (Contactless Camera-Based) | ECG (Contact-Based) | | :--- | :--- | :--- | | Method | Optical analysis of skin color changes from video | Electrical signal measurement from the heart | | Patient Friction | None; uses existing device camera | Requires physical sensors and leads | | Scalability | High; integrates into any software with video | Low; requires shipping and managing hardware | | Data Output | Heart rate, HRV, respiration rate, blood pressure trends | Diagnostic 12-lead electrical waveform, heart rate | | Best For | Real-time screening and monitoring during video visits | Definitive diagnosis of cardiac conditions |

Industry applications and provider workflow

For telehealth platform CTOs and VPs of Engineering, the value of integrating camera-based vitals lies in the workflows it enables. When a patient experiences a heart rate spike, the platform can trigger a series of automated actions that support the provider without creating alert fatigue.

Real-time alerting and triage

An SDK-based vitals integration allows platforms to set customizable thresholds for individual patients. If a patient's heart rate exceeds a pre-defined level (e.g., >120 bpm) for a specific duration, the system can generate a discreet alert within the provider's user interface. This allows the clinician to immediately address the situation, calm the patient, and determine if further action is needed, transforming a moment of patient fear into a proactive clinical intervention.

Clinical decision support

Beyond a simple number, the rPPG data stream provides a rich source of information for clinical decision support (CDS) tools. An algorithm can analyze the morphology of the pulse wave during the spike to look for patterns indicative of specific arrhythmias. For example, studies using PPG have shown high accuracy (AUROC = 0.96) for detecting Atrial Fibrillation. This information, presented to the provider with appropriate context, elevates the clinical utility of the entire platform.

• Automated Documentation: The system can automatically log the event, its duration, and the peak heart rate in the visit notes, saving the provider administrative time.
• Contextual Prompts: The platform can suggest relevant questions for the provider to ask the patient, such as inquiring about symptoms like dizziness, chest pain, or shortness of breath.
• Historical Comparison: The current event can be instantly compared against vitals data from previous visits to identify trends or new patterns.

Current research and evidence

The core challenge for rPPG technology is maintaining accuracy during periods of physiological stress. A key 2021 study published in Scientific Reports by D. McDuff, et al., noted that the accuracy of some rPPG methods can decrease at elevated heart rates. This is because the underlying pulse signal can change shape and become harder to distinguish from noise.

However, ongoing advancements in signal processing are addressing these limitations. Researchers at institutions like the Massachusetts Institute of Technology (MIT) and Philips Research have pioneered methods using advanced AI models, such as convolutional neural networks (CNNs), to more effectively separate the pulse signal from motion and lighting artifacts. A 2022 study in the journal IEEE Transactions on Biomedical Engineering demonstrated a deep learning approach that maintained high accuracy even with significant subject motion. These studies validate the feasibility of using camera-based systems for reliable monitoring, provided the underlying algorithm is robust.

The future of real-time vitals in telehealth

The ability to detect a heart rate spike during a video doctor visit is just the beginning. The future of this technology lies in predictive analytics and longitudinal monitoring. By continuously collecting vital signs data over multiple telehealth visits, platforms can build a personalized physiological baseline for each patient. Machine learning models can then analyze this data to detect subtle deviations that may signal the early onset of a chronic condition or a deteriorating acute illness, long before the patient becomes symptomatic. This moves the paradigm from reactive care during a visit to proactive health management between visits.

For telehealth platform companies, building this capability is a strategic imperative. It enhances patient safety, improves clinical outcomes, and provides a powerful differentiator in a market where basic video calling has become a commodity.

Frequently asked questions

Q: Is camera-based rPPG accurate enough to replace a traditional ECG? A: No, and that is not its intended purpose. rPPG is a powerful screening and monitoring tool that provides real-time awareness and trend data within a video visit. An ECG is a diagnostic tool that measures the heart's electrical activity. If an rPPG system detects a significant anomaly, the standard of care would be to refer the patient for a diagnostic ECG.

Q: How do these systems handle common issues like poor lighting or low-bandwidth video calls? A: This is a critical engineering consideration. Robust rPPG SDKs incorporate algorithms specifically designed to function in non-ideal conditions. They use techniques like signal-to-noise ratio (SNR) analysis to assess data quality in real-time and apply advanced filtering to compensate for lighting shifts and video compression artifacts.

Q: What is the typical development effort to integrate contactless vitals into an existing telehealth platform? A: Integrating via a dedicated SDK is significantly faster and more cost-effective than building the capability in-house. A well-documented SDK can allow a development team to add core functionality within a few sprints. The process typically involves integrating the SDK into the video client (web or mobile), developing the UI components to display the data, and creating the backend logic for alerting and EHR integration.

Integrating advanced capabilities like contactless vital signs is essential for telehealth platforms looking to prove their clinical value and secure a competitive edge. By providing clinicians with real-time, actionable data, platforms can transform the standard video call into a sophisticated and safer care experience. Circadify offers a robust rPPG SDK designed for rapid integration into telehealth workflows. To learn more about adding these capabilities to your platform, explore our developer resources and request a demo at circadify.com/custom-builds.