Disease surveillance, asymptomatic, long-term outcomes, health disparities and many more terms have stepped outside of the public health and life sciences research realms and into our everyday kitchen table and grocery store line conversations. Over the past year while we waited for results from randomized control trials for vaccines to SARS-CoV-2 and novel treatments for COVID-19; many of our day to day discoveries and learnings - especially those that guide behavior changes - came from real-world data, ad hoc integration of multiple data sources, and real-time data analytics.
What are Real-World Data and Evidence?
The US FDA defines real-world data (RWD) as “data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources” and real-world evidence (RWE) as “the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from analysis of RWD”. RWD play an increasingly important role in clinical research and healthcare decision making, defining the natural history of diseases, and providing information on the efficacy and safety of treatments in the real-world, especially in populations typically excluded from randomized control trials - diverse racial and socioeconomic populations, the elderly, and patients with comorbidities.
As we think back to how COVID and RWD have intruded on our everyday lives, so grows the discussions of data quality and the need for fit-for-purpose data. Have you asked yourself, “why aren’t they reporting race and gender with infection numbers” only to discover your local government didn’t request these variables to be collected? “Why doesn’t my doctor know that I was vaccinated?” only to discover that the mass vaccination site run by your health system was not integrated with your doctor’s office’s records system. To say the least, the COVID pandemic has laid bare that many single sources of data are lacking and data obtained for clinical care is often not fit for research as initially collected.
“Evidence will inevitably be limited by the quality of the underlying data”
RWD has the potential to inform novel drug discoveries, new regulatory approvals and labeling expansions, payers conversations, clinical decision support, and much more, but the RWE generated will inevitably be limited by the quality of the underlying data.
Much like in traditional clinical research studies, in RWD studies intention is important and understanding the research questions drive the variables of interest - in reality the data model and the data that’s collected have to fit the purpose of the research study. But in addition to specifying the right variables, it is critically important to access or source complete information.
We believe that the best way to capture a patient's complete medical journey is to go straight to the patient; so we’re centering on the patient and empowering them to own their own medical records. Rather than running away from dirty data, we work directly with patients to build higher-quality data by finding ways to improve data capture, organize medical records and enhance that data as needed with additional data types like patient-reported outcomes and claims data.
Building Blocks
PicnicHealth is on a mission to make medical data live up to its potential so that everyone within the healthcare ecosystem benefits - we are building ‘Good Data’.
At PicnicHealth, good data is:
- Complete for an individual patient
- Harmonized across sites of care and data sources
- Structured to be well-organized and formatted
- Patient-level for easy human review and analysis
- Transparent and documented with provenance and well-defined protocols
- Accurate or error-free and reliable
- Representative of the population of interest
- Accessible to patients, researchers, and other healthcare stakeholders
Each of these concepts contribute to the creation of high-quality, fit-for-purpose data. But how exactly do we achieve good data? What metrics are we tracking and how do we know when we’re done?
We’re beginning a series: “Good Data Is”, where we’ll take a look at how to build good data and explore how best to optimize data capture, transformation and patient engagement by expanding on each of the key elements of good data.
Follow along and join our discussion on LinkedIn and Twitter.
