From Raw Wearable Data to Clinical Action: How AI Closes the Loop
As wearable health devices become more advanced, the challenge isn't just capturing data — it's transforming it into real-time, actionable insights that clinicians can trust. Discover how AI is bridging the gap between raw data and clinical decisions, turning streams of health information into meaningful action.
The digital revolution has swept across the world, and nowhere is this more visible than in healthcare. Back in 2019, the World Economic Forum estimated that the average hospital produced 50 petabytes of data per year. This included patient notes, with lab test results, medical imaging, readings from sensors and genetic and genomic information, as well as hospital operational and financial data.
The growth of healthcare data has not slowed – according to RBC Capital Markets, around 36% of the world’s data by volume is expected to be generated by the healthcare industry by 2025. One of the areas of growth is the data generated by wearables, digital MedTech devices that track vital signs from patient temperature and heart rate to movement and sleep. These allow healthcare professionals to monitor patients in hospitals and clinics, and at home. On top of this, there is the health data generated by people’s personal fitness trackers and smartwatches; based on a 2019 Pew Research Center survey, around one in five adults in the US regularly wear a smartwatch or wearable fitness tracker.
Wearable data streams can provide insight into disease trajectories over longer periods, for example in a longitudinal study in people with long COVID, wearables were able to detect the infection’s impact on resting heart rate compared with people without long COVID by analyzing smartwatch data prior to and for a year or more after symptom onset. They also have potential in improving diagnosis and predicting outcomes in conditions such as cardiovascular disease.
The Challenges of Wearable Data Streams
There are a number of challenges associated with generating clinical insights from wearables data, and perhaps the most significant is the amount of raw data that the devices create. While this information is valuable for healthcare professionals, a large percentage of it goes unused, at least in part because the sheer volume is too much for individuals to understand or use.
Another challenge is the format of the information – the wearables industry is siloed, meaning that data streams from different wearables may be in different formats, and have different levels of quality and consistency. This information will need to be integrated into a single data stream for analysis. Clinical insights generated from wearables data will also need to be adapted in real time or as close as possible as injuries heal or diseases progress.
Medical information is highly sensitive, and its use is likely to become more regulated to protect patients.
From Wearable Data Streams to Actionable Clinical Insights: The Role of AI
Over the past few years, AI has had a powerful impact on the MedTech industry, from driving forward the development of medical devices, in vitro diagnostics and digital health solutions, through supporting commercialization, to aiding physician decision-making and improving patient care.
AI, machine learning and advanced data analytics have the power to sort and analyze data from wearables, filter out noise and enhance reliability and accuracy, understand the relationship between sensor outputs and health status, and create personalized and actionable clinical insights. These prognostic and predictive insights will allow healthcare professionals and researchers to access the value in the data streams more easily.
Transforming Wearable Health Data into Meaningful, Compliant Insights
difficult to translate into clinical or patient-ready insights. Agilisium bridges this gap with a suite of AI-powered agents designed specifically for life sciences.
Our Data Observability Agent ensures seamless integration of structured and unstructured data from diverse sources, while maintaining high fidelity even at scale, from terabytes to petabytes. It actively detects anomalies and ensures that data streams remain accurate and usable.
Once the data is ingested, our Insights Generation Agent applies advanced AI analytics to forecast outcomes, uncover trends, and distill complex patterns into clear, actionable summaries using natural language processing.
To ensure that insights are communicated effectively and compliantly, the Content Generation Agent creates tailored, audience-specific content aligned with FDA, EMA, and ICH guidelines. With built-in quality checks and seamless integration into your existing workflows, it ensures precision, clarity, and consistency every time.
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