As clinical trials continue to evolve in terms of accessibility and scope, we are seeing massive volumes of patient data generated by wearables, sensors, electronic clinical outcome assessments (eCOAs) and electronic health records (EHRs). Software embedded in smartwatches now passively records cardiac rhythms. Glucometers transmit readings wirelessly as they are recorded. eCOA platforms can trigger precisely calibrated surveys that capture a patient’s feelings about their symptoms at the precise moment they’re experiencing them. By almost every measure, meeting the historic goal of farming enough diverse data is no longer a challenge.
To put it simply, clinical trials don’t have a data collection problem.
And yet, finding the insights to improve clinical outcomes buried in that data remains a struggle for many sponsors.
To put it simply, the challenge clinical trial sponsors now face is one of data integration.
Rich data sits across systems; actionable insight does not. Making clinical decisions on the back of mountains of collected data, without the systems to connect it all together, is like trying to understand the mood of a restaurant by looking at each patron individually. You might learn a lot about each patron, but you’ll never understand the atmosphere of the room.
Progressive clinicians and sponsors are looking to do more than collect data. Instead, they want to focus on building technology and research strategies that tie every data stream together. Only then will they start to see the clinical signals buried in terabytes of disparate data points.
Moving from Collection to Integration
The conversation around clinical data has long centered on collection, focusing on how to produce more patient data and capture it more reliably. Decentralized trials and hybrid models accelerated this trend exponentially by depositing smartphones, bring-your-own-device (BYOD) protocols and connected devices of all kinds into study protocols.
The results have been impressive and significant: eCOA software integrates with connected devices to send millions of bytes of patient-generated data into clinical systems around the clock. The only drawback is that data volume without context is just noise.
And clinical trials in 2026 need context, not just collection.
I see a real focus this year on integrating data streams, connecting sensors, EHR records, structured surveys and passive monitoring technologies to form a comprehensive view of the patient experience.
Think, for example, about a diabetic patient using a glucometer and keeping a symptom diary. If that glucometer triggers an out-of-range alert, modern eCOA platforms can automatically send the patient an extra set of questions about how they’re feeling that day. When glucose readings and patient-reported symptoms collected within minutes of each other feed into one clinical dataset, disconnected data points start to form connected insights. Clinical data integration is not a nice-to-have; it’s now a must-have.
The Tools are Already Here
The great news is that the tools for integration exist. A suite of AI-powered technologies has emerged in recent years to help sponsors surface clinical signals from disparate data sources. Real-time scoring, longitudinal analytics, adaptive trial management tools and automatic algorithmic scoring embedded directly into electronic outcome assessment (EOA) solutions are just a few examples. As clinical research evolves, these technologies have stopped being buzzwords and are starting to prove their value across live trials.
It’s now time to introduce clinical strategies that are powered by these technologies. We want technology to serve science, not the other way around. Technology should enable, inform and accelerate clinical strategy, not create obstacles as sponsors seek to derive valuable insights from them. That’s why it’s key that an integrated data collection strategy starts with the science. What outcomes matter most to patients and clinicians? What digital endpoints can be validated against established outcome measures? How can emerging technologies support the integration and accessibility of novel digital endpoints that surface insights to support your scientific objectives, not vice versa?
AI is the Infrastructure
Part of the reason AI technologies are moving from novelty to foundational infrastructure in 2026 is that clinicians are starting to use them for what they’re actually good at: keeping humans in the loop.
Consider automated scoring and real-time endpoint analytics as an example. Clinical trial technicians used to enter COA data into spreadsheets that were fed to biostatisticians, often months later. Someone then had the unenviable task of calculating endpoint scores by hand — usually the sponsor — using their own bespoke algorithms. But what if those algorithms were applied incorrectly because the analyst wasn’t deeply familiar with the instrument’s scoring rules? You end up with scoring errors that potentially aren’t discovered until weeks into a database lock.
Now, imagine calculating endpoint scores automatically at the point of capture using regulatory-grade algorithms built into COA software libraries. Beyond the ease of automatic capture, you avoid the cost of scoring errors discovered months into your study.
The broader use of AI across clinical research will be infrastructure-based, too. Closed-loop automation, longitudinal analytics and adaptive trial management tools will help sponsors optimize studies in real-time. Natural language processing paired with intelligent data review tools will make reviewer jobs easier while simultaneously speeding up decisions that affect the entire trial.
Clinical research isn’t abandoning AI tools because they’re novelty, but rather, it is embracing them because they’re absolutely necessary.
Patients Don’t Notice Good Technology
Sponsors should strive for patients to not notice technology when it’s working as it should. Successful software seamlessly connects devices, automates reimbursements and intelligently combines passive data streams with structured surveys without patients ever lifting a finger.
Building a truly patient-centric trial experience means reducing burden whenever possible. Practical examples include integrating software with patients’ existing smartphones, adopting BYOD models to reduce cognitive burden and tailoring study experiences to specific age groups and demographic populations. Gamification techniques can make participation and engagement fun for younger populations, while engagement portals show patients how their contributions to science help others like them. When patients experience positivity and understand how (and why) they fit into the bigger picture and that they are making a tangible difference, they’re more likely to stick with a study for the long haul.
The Final Hurdle: Regulatory Acceptance
Despite advances in technology and strategy, regulatory acceptance of digital endpoints remains the final hurdle for some sponsors. Current FDA guidelines on evidence don’t cover continuous physiological monitoring, passive behavioral data streams or advanced multimodal sensor technologies.
Until those guidelines are updated, early engagement with regulators will be critical to justify novel technology use in pivotal trials. Generating your own scientific validation data to support the use of new wearables or passive data streams will be key to acceptance and approval. It’s good practice, therefore, to gather that evidence sooner rather than later, so it’s ready when regulators come knocking with questions.
Clinical trial sponsors are building that evidence today through organizations like Critical Path Institute, which convenes sponsors, tech vendors and regulatory scientists each month to build guidance based on real scientific data.
Standards Interoperability
On the technical side, improved standards interoperability will be a make-or-break issue for data convergence. Data standards fragmented among EHRs, wearables vendors, eCOA software platforms and others create needless integration challenges at every step. Until vendors adopt common data standards like HL7 FHIR and CDISC where possible — and until sponsors demand that of vendors — we won’t begin to approach true convergence.
Making Data Matter
At its core, clinical research in 2026 isn’t about collecting data from patients. It’s about integrating all those data streams to learn more about the patient than ever before. Science leaders and technology innovators can already connect every piece of clinical data they need into a single integrated dashboard for visualization. The problem is, doing so doesn’t magically improve trial efficiency or clinical outcomes. That takes a fundamentally different strategy.
The data is already there. The question is whether the strategy is. 2026 will be the year that strategy arrives. Sponsors will look at their legacy clinical data and ask not how to collect more of it, but how to understand it.
About the Author
Melissa Mooney has more than 20 years of experience in the development of eCOA solutions for use in clinical trials. Melissa’s area of expertise is eCOA solution design, where she has helped clients and eCOA vendors to develop robust and usable eCOA software solutions that meet eCOA protocol requirements. She also brings a plethora of experience in eCOA requirement gathering, leading eCOA user acceptance testing, eCOA data management and business development support.