A wave of emerging health technologies is reshaping the healthcare landscape. Some are designed for clinical use by healthcare professionals, while others are consumer-facing tools intended for direct use by individuals.
These innovations can enhance healthcare delivery by improving assessments, diagnostics, monitoring, information access, and data management. For insurance providers, they offer countless potential crossover benefits. However, selection and implementation of these technologies present complex challenges.
Digital health technology includes an increasing vast range of tools, applications, software, and sensors that can be interconnected, typically developed to improve healthcare and health outcomes. While some modernize familiar medical tools, recent years have seen a sharp rise in consumer-centric solutions designed for everyday use – think wearable fitness watches.
This shift is influencing attitudes around health tools and solutions. Many support a wide range of medical functions, including assessment, screening, monitoring, detection, diagnosis, and access to health information. They also offer secure data storage and privacy protection, enabling legitimate data-sharing. The scope of health tech now extends to tools that support not just physical wellbeing, but also mental health.
Many newer health technologies prioritize user convenience with features that encourage frequent use and continuous data generation.
Most modern health technologies include built-in data collection and storage capabilities. This allows for the capture of health data not only at the individual level, but also across different geographic regions and populations. When anonymized, such data can support meaningful analysis and advance scientific understanding.
These growing datasets have also expanded the opportunities for applying artificial intelligence (AI). When used with data generated from either general populations or specialized clinical groups, AI has enabled breakthrough developments in diagnosis, medical risk evaluation, and predictive modeling.
For insurers, these advances present promising but largely untapped opportunities. Yet integrating them into insurance processes remains uncharted territory and will require rethinking traditional evaluation frameworks.
This article explores some of those considerations by highlighting recent developments in health technology and sharing insights from a real-world business case.
Remote photoplethysmography (rPPG) is a non-contact method for measuring physiological signals by analyzing subtle color changes in the skin captured through standard video cameras.1 Like traditional photoplethysmography (PPG), which detects blood volume changes in the microvascular bed of tissue, rPPG does so remotely without physical sensors, registering color changes that occur with each heartbeat. Sophisticated algorithms can then compute vital signs such as heart rate (HR), respiratory rate, and blood oxygen saturation – often by capturing a short video of the face.1
rPPG has attracted growing interest in healthcare2 due to its potential for:
One promising metric measurable via rPPG is heart rate variability (HRV) – the variation in time intervals between heartbeats.3 HRV is a powerful indicator of:
rPPG provides a non-invasive, continuous method for measuring HRV and other biomarkers, making it a compelling area of research. Combined with biomarker analysis, it enables health assessments without physical contact or wearable devices.
Accurate measurement using rPPG requires controlled conditions, including consistent lighting, stable camera frame rates, and minimal subject movement. Despite these challenges, advances in computer vision and deep learning are steadily improving the reliability of rPPG-based analysis.
The retinal vasculature mirrors the body’s general circulatory system, while retinal nerve fibers extend directly from the central nervous system. As such, examining the eye, fundus, and retina have long been part of systemic health evaluation.4,5
Advances in digital optics and imaging – including fundus photography, retinal CT, and MR scanning – have enabled wide adoption of non-invasive retinal imaging. Hand-held fundus cameras offer added portability, and high-resolution imaging now reveals retinal biomarkers that were previously invisible to the human eye.
Oculomics – the use of ophthalmic biomarkers to detect, predict, and understand disease – has been made possible by these optical and digital innovations.4,5 Large volumes of retinal images have become fertile ground for AI. Machine learning and deep learning techniques have produced algorithms that accurately detect conditions like diabetic retinopathy, glaucoma, and macular degeneration.6 Some mature models can now make diagnoses without a human specialist, using only high-quality optical images. Achieving sensitivity and specificity of 93% and 91% respectively for diabetic retinopathy detection, FDA approval has been granted to specific AI models.6
Beyond eye disease, researchers are developing retinal biomarkers to detect and predict systemic conditions, including cardiovascular, neurodegenerative, chronic kidney, and hepatobiliary diseases. Researchers are also examining whether specific retinal features correlate with metrics like BMI, blood pressure, cholesterol, and hemoglobin levels – potentially offering a noninvasive alternative to traditional blood tests.4,5
Despite strong momentum, AI models still require validation in large, diverse, real-world populations. Much of the current research remains siloed. Best practice guidelines could help standardize protocols and improve consistency across imaging techniques. Still, even with user acceptance, cost-effectiveness must be demonstrated.
As clinical adoption progresses, the overlap between clinical and insurance objectives may open a nascent path of multi-system risk segmentation through a single non-invasive assessment.
Developers worldwide have created online cognitive assessment tools, ranging from short-gamified tasks to comprehensive tests incorporating fine-motor response, reaction time, and voice biomarkers.7,8 Most evaluate key domains like memory, problem-solving, and executive function, with language adaptations for local markets.
Performance is typically calibrated against clinically validated tools such as the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE). Systemic reviews report sensitivities above 80% with varying specificities.8,9 Many tools have received FDA safety exemptions but are intended only for screening, not diagnosis of cognitive impairment or dementia.
Most platforms do not require a trained professional, allowing for convenient single or repeated use.8 Depending on product strategy, sales channels, and risk evaluation needs, online cognitive screening tools may offer insurers meaningful value.
Novel health technologies can significantly impact various stages of the insurance value chain. The following outlines the key areas of influence:
(Click the arrows to expand each section)
While the case to adopt health technologies is strong, insurers must carefully consider the following:
(Click the arrows to expand each section)
Successful implementation of new medical technologies requires a clear, collaborative process across all relevant stakeholders. Project teams should include representatives from product development, medical, underwriting, claims, technology, pricing, marketing, and business development. Compliance and regulatory experts should also be involved to help shape the proposition. Early stakeholder engagement enables thorough and informed strategic decisions.
A robust, verifiable and replicable assessment is essential. It should define, review, communicate, and document key adoption considerations, with clearly established criteria for what constitutes a “pass” in each critical area outlined in the preceding Insurance and Adoption Consideration sections. These benchmarks should be agreed upon before deeper research, development, or resource commitment.
While detailed planning is important, testing the solution with end users such as customers and agents is invaluable during the pre-launch phase. Early feedback and acceptability can shape development and integration. Later-stage feedback, gathered through pilot testing or refined journey walkthroughs with target users, helps ensure real-world fit and business success. Depending on the research goals, this feedback may be collected through surveys, simulated scenarios, or formal proof-of-concept testing.
New health technologies may follow development and validation pathways that differ from traditional medical solutions. Evaluating their value, suitability, and feasibility requires a paradigm shift for insurers.
Use cases vary by market and product and must be assessed through coordinated input from relevant insurance functions, aligned to clear business objectives.
By addressing the full range of considerations, insurers can responsibly leverage novel health technologies to improve operations, enhance customer experience, and reduce risk. At the same time, it is essential to balance these opportunities with ethical obligations, regulatory compliance, and operational efficiency to ensure sustainable and responsible implementation.
RGA experts are eager to engage with clients to better understand and tackle the industry’s most pressing challenges together. Contact us to learn more about RGA's capabilities, resources, and solutions.
