From Hypothesis to Healing: How Sickbay Accelerates Research-to-Bedside
How validated, high-fidelity physiologic data enables rapid research translation, clinical validation, and real-world patient impact.
Author: Raajen Patel, EVP of Innovation and Client Engagement
Our industry operates in an environment where translating research into practice poses a major challenge for innovation.
Despite massive amounts of data and accelerating algorithm development, bridging the “valley of death” between clinical trials and clinical practice remains slow.
Often, “bench” science uses incomplete or low-resolution patient monitoring data, or data that doesn’t reflect real-world patient populations. Limited access to high-fidelity physiological data restricts translational impact.
As well, electronic health record (EHR) systems typically operate in silos, creating gaps in data visibility and workflow continuity even when attempting to use real-world physiologic data for clinical research.
If our industry wants to drive meaningful innovation that truly improves patient outcomes, data management sits at the forefront. The rapid development of algorithms that have the ability to accelerate research brings this need to light even more.
Advanced research algorithms that drive real-world clinical impacts need granular, validated physiologic datasets that reflect diverse patient populations, built through continuous monitoring and scalable data analytics frameworks.
Why High-Fidelity Physiologic Data Is the Foundation of Clinical Innovation
Clinical research requires lots of data. Without quality, standardized data, researchers can’t execute the type of in-depth analysis that provides the foundation for outcomes-based research practices.
But simply having data available isn’t enough. A critical part of ensuring research effectively reflects the challenges faced by health systems and clinicians on a daily basis is the type of data researchers use.
That’s where continuous, waveform-level patient monitoring comes into play.
Continuous patient monitoring allows researchers to pull and utilize data that reflects real-world patient populations. Data is gathered in milliseconds then redisplayed and stored at a second-by-second rate meaning that the data researchers use to develop intensive analysis remains constantly up-to-date as conditions change.
In this sense, researchers, just like clinicians, are kept informed about the moment-by-moment conditions of patients through the comprehensive datasets they use to conduct their research.
Intermittent data collection doesn’t provide the same level of continuous granularity. That means the data researchers rely on can be riddled with critical gaps in patient condition and don’t provide the same level of nuanced insight as continuous monitoring of patients in near-real time.
High-fidelity patient data is the only way for researchers to unlock the clinical impacts they strive for.
From Raw Signals to Research-Ready Data
High-fidelity physiologic data from patients must be cleaned, standardized, and validated before being implemented into research practices.
While cleaning, standardizing, and validating any large datasets can be a tedious process, the way data is initially gathered can help ease the burden of data standardization.
The process begins with applying consistent data standards across devices and vendors. This reduces data variability, meaning that the data that’s initially inputted into research datasets comes prepared and easy-to-manage.
Health systems can, today, implement a variety of practices that help ensure high-fidelity physiologic patient data is collected in a consistent manner that streamlines research initiatives.
Accelerating Clinical Validation with Streaming Insights & Analytics
Often, small-scale digital health pilots, while well-informed and guided by clear needs and challenges, struggle to scale.
Moving innovation from clinical validation into established care delivery can represent the toughest step in the bridge between research and practice.
However, continuous patient monitoring can shorten the path from hypothesis to insight, in several ways:
- Starting from strength, not scratch: Instead of guessing at challenges, accessing complete patient information means that the hypotheses researchers use to inform their research are based in real-world conditions and rooted in a high level of clarity.
- Reduced data loss: With continuous patient monitoring and high-fidelity data gathering, researchers don’t have to worry about the loss of data potentially invaluable to the outcome of their experiments.
- Faster interim analysis and validation: Entering research with “all of the lights on”, that is, by having a total view of historic and current physiological data, allows researchers to more quickly and easily move from bedside data into high-level analysis and validation, and back again.
Real-World Evidence at Scale
The promise of research insights is realized once these insights are put into clinical practice. This starts with validation and incorporation into a real workflow.
High-fidelity physiologic data, collected continuously from numerous bedside devices, builds trust between researchers and clinical leaders, because the clinicians implementing research-based algorithms and care techniques know that those research outcomes were directly informed by the real-world patient data they interact with every day.
This extends directly to clinical trials. Instead of entering trials with a “wait and see” mindset, clinical leaders unlock a clearer understanding of the purpose and expected outcome of trials, meaning that trial outcomes can be more directly translated to clinical impact.
Enabling AI-Driven Clinical Decision Support, Responsibly
The elephant in the room is AI – how advanced algorithms accelerate research, and how data factors into developing those algorithms in the first place.
Simply put, for AI, data is everything. This extends across disciplines, including research.
With continuous high-fidelity data monitoring and collection and direct integration with EHR systems, Sickbay provides the foundation researchers require for effectively implementing AI into their research pipelines.
Sickbay empowers:
- Predictive analytics, where AI is at the forefront of anticipating and responding to patient conditions far in advance of when they appear.
- Risk stratification across patient populations when AI is used to effectively gauge physiologic markers in patient conditions.
- Clinical decision support, which is in turn bolstered by advanced, translatable research.
Closing the Loop From Research Insights Back to the Bedside
Impactful research is driven by everyday challenges faced by clinicians.
For example, insights derived from high-fidelity, continuous physiologic data drive research that can:
- Reshape care protocols: For years, clinical protocols might have restricted how parents interact with critically ill infants due to safety concerns. Research using high-fidelity data tested the hypothesis that parental holding is safe, proving it actually stabilizes the patient. By analyzing continuous physiologic data during holding events, researchers proved that “parental holding” (a care protocol) resulted in measurable hemodynamic improvements, validating family-centered care with hard data.
- Provide clear monitoring thresholds: Clinicians often rely on a single, static number for all patients. New research shows that a patient’s true “safe zone” is unique to their own physiology, and staying within that specific threshold is critical for survival. A 2025 study demonstrated that when clinicians understand a patient’s individualized autoregulation limit (derived from high-frequency trends), they can see that time spent below this specific threshold correlates with worse outcomes.
- Streamline patient risk stratification: Retrospective analysis of high-resolution data allows hospitals to cluster patients into specific risk groups based on how their blood pressure behaves over time, rather than waiting for a lab test to show failure. For instance, by retrospectively analyzing continuous blood pressure trajectories, researchers identified specific “hemodynamic phenotypes” associated with higher hypotension burden, allowing clinicians to stratify risk early in the dialysis process and intervene before severe injury occurs.
The benefits to hospitals and health systems are numerous, from improved patient outcomes to reduced length of stay to less unnecessary ICU transfers.
The Growth of Data-Driven Research: A Case Study
Utilizing high-fidelity data in research is growing in real time.
In September 2025, a group of researchers published a paper that included a systematic review of pediatric literature to assess the use of complete, high-fidelity patient data pulled from a pair of patient monitoring platforms.
The literature review found that the number of publications using either data platform has been “significantly increasing” over the past 9 years.
“Although the majority of these are single-centre and pertain to cardiac patients,” the authors write, “growth in publication volume suggests growing utilisation of high-fidelity physiologic data beyond clinical application.”
Using Sickbay’s Analytics tools, researchers have earned more than $10 million in grant funding and published over 100 publications, posters, and presentations.
Turning Clinical Data into Real-World Impact
Healthcare innovation has never moved faster, as the paper cited above indicates, and advanced research remains the essential driver to innovation. Yet researchers are often left conducting research with data that doesn’t easily translate to clinical practice.
High-fidelity physiologic data gathered directly from patient populations on a continuous basis provides the data foundation needed to ensure advanced research can meet the impacts it hopes to achieve.
Sickbay, the clinical data monitoring platform, enables collection of high-fidelity data in near-real time, while seamlessly integrating with EHRs to inform both researchers and clinicians.
In this way, Sickbay accelerates clinical research by more easily connecting researchers with sources of data, providing a clear, holistic picture of patient trends. As well, researchers are better able to validate data because of Sickbay’s seamless integration and device interoperability.
Overall, Sickbay helps researchers and clinicians alike bridge the gap from hypothesis to healing.
Trusted data is the foundation of modern clinical care. It should be the foundation of modern research innovation, as well.
