Who remembers that line from the movie Zoolander, “the files are IN the computer?!” Twenty years later and we’re now all painfully familiar with our computer’s file structure. But as we continue to digitise clinical trials, has access to new streams of valuable data become easier because “the data is IN the cloud”?
Connected sensor technologies and tools, from wearables, smart speakers, and bed mats to ingestible sensing capsules and implantables, are capturing unprecedented flows of data with the potential to transform our clinical decision making. Since DiMe’s launch in October 2019, there has been a 929% increase in the number of digital endpoints being used by the life sciences industry for the safety and efficacy of new drug development. Yet we remain unprepared to access reliable and trustworthy sensor data at scale.
Today it’s incredibly difficult to use this data efficiently and effectively, while maintaining privacy and security standards. To account for privacy and security, data from sensor technologies is collected, processed, stored, and transmitted through systems and infrastructure developed and owned by individual companies. Data users must integrate in a tailored way to meet the specifications of each data partner. This takes time – building 1:1 ‘point’ solutions is neither efficient, nor suitable for scale.
It takes specialised knowledge: Data scientists and software engineers are needed in real time to build, troubleshoot, and fix each individual connection. Not only is this cost prohibitive, but if we consider all the many flows of sensor data that could help us make better decisions, faster, then multiply them by the number of decisions, it is clear that this approach will not scale.
Data integration refers to the technical and business processes used to aggregate and combine data from multiple sources to provide a unified, single, usable view of the data. Effective sensor data integration has the power to realise the promise of these new flows of data to drive better decisions faster across the healthcare continuum and to advance new drugs, devices, and therapies that will improve patient outcomes.
But the current surge of data from sensor technologies is outpacing the industry’s ability to collect, store, analyse, protect, and use this data effectively for research and patient care. The promise of the data being collected will not be realised until we have systems and infrastructure in place to handle the wave of new data efficiently, effectively, and affordably.
DiMe is ready to help get data out of the cloud and into the hands of those who need it to improve patients’ lives. In our role as the society to convene experts from all disciplines comprising the diverse field of digital medicine, and in partnership with we have developed a set of free resources to support organisations with scoping, selecting, and implementing the integration of sensor data and algorithms to appropriate platforms so that they can manage research data and guide clinical care.
We’ve created four free comprehensive toolkits for data producers, processors, and consumers to effectively and efficiently use this new wave of data from wearables and digital sensing products at scale, all while keeping data private and secure.
Data architecture translates business needs into data and system requirements and seeks to manage data and its flow through the enterprise. Data architecture describes the structure of an organisation’s data assets. It includes the models, policies, rules, and standards that govern the collection, storage, arrangement, integration, and use of data within an ecosystem. Optimised data architecture is essential to realising the value of sensor-generated data for clinical decision-making. This toolkit includes:
- Logical Data Architecture – Providing a high-level blueprint for sensor data in the healthcare data ecosystem, irrespective of platform, operating system, file structure, or database technology. It defines the entire data landscape necessary to use sensor-generated data to drive clinical decision-making.
- Reference Data Architecture — A library of data architecture examples that 1) implement the logical data architecture, and 2) have been successfully implemented to support successful data integrations. Find what works for you and your team.
- Sensor Data Flow Design Tool – Using this tool you can map the flow of sensor data from any connected sensor technology you choose through to a final data set for analytics and querying, whether within a care delivery or research setting. After you answer a few questions to build out the right steps, you have the option to use a user-friendly design tool to build and annotate your bespoke sensor data flow for documentation and collaboration with your team and partners.
Every data ecosystem needs a common language and a shared approach to distributing, storing, and interpreting information. Following sensor data standards that protect patients and enable data exchange is essential. By embracing standards, we ensure that we’re using sensor data that is accessible, relevant, and trustworthy (ART).
Start differentiating your products and the data they generate by building to the appropriate standards. Discern between products and solutions to identify those that meet your needs. We’ve designed our interactive Landscape of Standards to help guide you as you think about how your data can be collected, stored, adapted, safeguarded, or retrieved for clinical decision-making in healthcare and research. This toolkit includes:
- Interactive Landscape of Standards – All of the standards relevant to sensor data integrations in an interactive landscape tool.
- Library of Standards – Database of standards. Note: DiMe is committed to keeping this up to date as new standards are developed.
Are you looking to begin or advance your sensor data integration journey? Six criteria are essential to your success: data collection, transmission, processing, security, privacy, and quality. Together, these key areas provide the building blocks for a successful sensor data integration strategy so you can make better decisions faster in healthcare and research. This toolkit includes:
- Accessible, Relevant, and Trustworthy (ART) Criteria – Six criteria critical to delivering ART sensor data to your downstream partners for clinical decision-making.
- Considerations and Best Practices – Short ‘cheat sheets’ of key considerations and best practices aligned with each criterion.
- ART Criteria Prioritization Tool – A tool to optimise approaches to sensor data integrations to meet the needs of the organisations and individuals using your products and the data they generate.
- Case Studies – Real world examples so you can see the ART criteria in action.
Are you looking to jumpstart your sensor data integration journey? The organisational readiness toolkit can help you identify areas of your team or business that could be built out to deliver success with a sensor data strategy. It can also help you evaluate your organisation’s level of progress towards the goal of making sensor-generated data accessible, relevant, and trustworthy to power better clinical decisions faster. This toolkit includes:
- Capabilities Maturity Model – A model to guide your understanding of your partners in the downstream market.
- Capabilities Maturity Calculator – An assessment tool to benchmark your partners’ preparedness and guide you to the right resources to meet them where they are for shared success.
As we increasingly rely on sensor-generated data to power decentralised clinical trials, we must prioritise building for scale. Our potential to drastically improve patient outcomes and advance the safe, effective, equitable, and ethical use of digital medicine to improve human health is within reach. And when we unite around standards and align on sensor data integration practices and infrastructure, our ability to make better decisions, and make them faster, will transform the healthcare system.
About the Author
Jennifer C. Goldsack founded and serves as the CEO of the Digital Medicine Society (DiMe), a 501(c)(3) non-profit organisation dedicated to advancing digital medicine to optimize human health. Previously, Jennifer spent several years at the Clinical Trials Transformation Initiative (CTTI), a public-private partnership co-founded by Duke University and the FDA, and . working in research at the Hospital of the University of Pennsylvania, first in Outcomes Research in the Department of Surgery and later in the Department of Medicine. More recently, she helped launch the Value Institute, a pragmatic research and innovation center embedded in a large academic medical center in Delaware. Jennifer earned her master’s degree in chemistry from the University of Oxford, England, her masters in the history and sociology of medicine from the University of Pennsylvania, and her MBA from the George Washington University.
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