Unlock the Potential of Real World Data in Healthcare
Data Collection from Diverse Sources
The availability of data from various sources such as electronic medical records, medical images, and wearables has significantly expanded. This wealth of information allows for a more comprehensive understanding of patient conditions and health trends. For example, medical images can provide detailed visual information about a patient's internal organs, helping doctors make more accurate diagnoses. Wearables, on the other hand, can continuously monitor a patient's vital signs and activity levels, providing real-time data that can be used to assess overall health and detect potential issues early on.
In addition to these sources, the ability to collect data from devices and diagnostics has also become crucial. These tools can provide specific measurements and readings that add to the overall data pool. By combining data from all these sources, healthcare providers can gain a more holistic view of a patient's health and make more informed decisions.
Data Management and Transformation
Data management technologies, AI tools, and bioinformatics platforms are playing a vital role in transforming the way data is handled. These technologies enable more efficient data collection, processing, and analysis. AI algorithms can quickly sift through large amounts of data and identify patterns and trends that might otherwise be overlooked. Bioinformatics platforms, on the other hand, are specifically designed to handle biological data and provide tools for analyzing and interpreting it.
For instance, AI-powered diagnostic tools can analyze medical images and provide accurate diagnoses in a fraction of the time it would take a human doctor. This not only saves time but also improves the accuracy of diagnoses. Bioinformatics platforms can also be used to study genetic data and identify potential genetic markers for diseases, leading to more personalized treatment plans.
Sharing Data for Collaborative Research
The ability for institutions to share data has far-reaching implications for various aspects of healthcare, including drug development, clinical trial recruitment, and companion diagnostics. Aggregated RWD is particularly useful as it can address the shortcomings of randomized clinical trials, which often take a long time to recruit targeted patients. By using aggregated RWD, drug developers can model outcomes more accurately and make more informed decisions about drug development.
However, for data to be easily shared and facilitate collaboration, it needs to be aggregated, structured, and standardized. Custodians of RWD, such as health systems and labs, are exploring avenues for data monetization while also ensuring the privacy and security of the data. An effective federated data access model is essential in this regard, as it enables connections between healthcare data custodians and potential data consumers, providing greater flexibility, access, and control.
The Role of Federated Data Models
Datma's white paper, "A New Paradigm for Healthcare Data Monetization," highlights the evolution of data monetization models and revenue models aligned with custodian interests. A federated data model allows healthcare data custodians, such as health systems and labs, to connect with potential data consumers, such as pharmaceutical companies and research institutions. This model offers greater flexibility, as it allows data to be shared and accessed only when needed, while still maintaining control over the data.
For example, a pharmaceutical company can access relevant RWD from multiple health systems and labs through a federated data model. This enables them to conduct research and develop new drugs more efficiently. At the same time, health systems and labs can benefit from data monetization by licensing access to their data and receiving monetary benefits from data consumers.
To download the white paper, fill out the form below.Photo: metamorworks, Getty Images