Litmus Health, the research-ready infrastructure platform for real world data, announced the release of the next generation of their platform, focused on reliable data engineering, data quality, and flexible deployment. The platform now supports Actigraph, the medical-grade wearables provider for monitoring patient physical and sleep activity. The end-to-end platform supports the entire trial process: from data collection at the point of experience to laboratory analytics that can inform decision making.
The pharmaceutical industry has been increasingly looking to real world data to validate patient reporting, as well as to develop novel endpoints for discovery during research and development and all phases of clinical trials. However, the industry has faced challenges in data integration due to the absence of industry-wide data standards and complexity surrounding data integrity. Of the pharma companies either establishing or currently investing in real world data tools and platforms, only 45% currently have capabilities mature enough to do so. The same survey found that 65% of pharma respondents cited a lack of access to the external data necessary to make real world analyses valuable.
The Litmus platform is built from the ground-up to meet the scalability and security needs of the industry. The platform leverages distributed storage technologies and containerization to allow for flexible partitioning of data based on team security needs. The platform has been built to meet enterprise infrastructure best practices, creating a fault tolerant and scalable backbone for clinical data.
“We’re proud to have built a platform that meets the rigorous demands of the pharma industry, and we’re excited to support dedicated companies in their journey to achieving real world data integration,” said Dr. Samuel Volchenboum, Chief Medical Officer of Litmus Health. “Our new release will allow research teams to effectively harness insights collected at the point of experience, streamline the data collection process, and ultimately enable new insights and predictions.”
Efforts to learn from data and make predictions based on them often fail due to poor data quality, lack of standardization and interoperability, and non-actionable insights derived from those data. This is especially true with clinical data collected from wearables, sensors, and other devices, due to the complexities of synchronizing multiple devices, sensors, and data streams. By providing a research-ready platform for high velocity, high volume standardized data collection and storage, Litmus frees researchers and clinicians from the costly and time-consuming data engineering projects that have stymied their efforts to integrate novel endpoints.
Key features of the Litmus platform now include:
Secure, reliable data engineering
– The entire platform is architected along scalable microservices principles and implemented using modern production-grade technologies.
Sustainable data quality
– Data are always stored in the most raw format possible, unbiased by secondary transformations.
– Immutable records and audit trails ensure that any new normalizations, configurations, or changes can be traced back to the source and its original form.
– The platform has a fully-portable infrastructure, offering managed private clouds for single or multiple tenants as well as hybrid and multi-datacenter deployments on-premises.
– Leveraging supervised and unsupervised machine learning techniques, Litmus facilitates continuous mining of data for patterns and signals.
– This process includes configurable standardization and normalization of the data, optimized for search, aggregation, and time series analyses.
Secure by design
– Data are collected and stored according to industry-standard security and encryption best practices.
– Monitoring, backup systems, audit trails and rigorous automated processes ensure a reliable data infrastructure that’s built for HIPAA and 21 CFR Part 11 compliance.
As with previous versions, the entire platform is manageable through user-friendly, customizable dashboarding that can be used for patient adherence, communication between subjects and providers, and both patient-level and study-level summaries of trial progress.
“We’re now at a point where everyone is hungry to put real world data to use, and they’re all looking for a solution that supports the entire trial process,” said Daphne Kis, CEO of Litmus Health. “Since the beginning, we’ve built Litmus Health to steward these data responsibly from the point of collection to actionable insights. Our mission has always been to turn the whole world into a clinical trial, and we’re so excited to debut the next-generation of our platform that will scale that mission to researchers and clinicians everywhere.”
In addition to upgrades to allow for greater auditability and on-prem capabilities, the upgraded platform now supports Actigraph in addition to FitBit and Garmin. As the leading provider of wearable physical activity and sleep monitoring solutions for the global scientific community, Actigraph enables leading pharmaceutica to improve study efficiency, data quality, and patient outcomes. By supporting Actigraph integrations, the Litmus platform fulfills industry demand for medical-grade sensors built into clinical trial protocols.
“Pharma is at a critical point where it’s possible to innovate quicker and at a larger scale than before,” said Jeremy Wyatt, President of ActiGraph. “There is a huge opportunity for wearables and other devices to find their place in clinical trials. We are excited to be integrated with a platform that understands the value of real-world data in pharma, and helps translate that data into meaningful decisions.”
As the industry moves to integrate wearables into trials, the early results are promising. Litmus Health is in the midst of a 500 patient, 3-year study funded by Takeda Pharmaceuticals and run by Dr. David Rubin’s Digestive Diseases Center at the University of Chicago. The partnership has already yielded promising initial results, including the discovery that passive biosensor monitoring can be used to predict elevated biomarkers indicative of inflammation in IBD, effectively tracking flares as they relate to everyday activities like patient activity and heart rate.