There’s data, data everywhere in the life sciences and clinical care ecosystem, but not all of it is ready for primetime – especially for complex use cases like oncology research and drug development. Before raw, real-world data (RWD) can be transformed into actionable insights, it has to go through a number of different processes, from aggregation and abstraction to standardization and careful curation.
All this work should culminate in a fit-for-purpose dataset that supports high-value use cases for life science companies and clinical trial sponsors. However, not all companies offering RWD have the same processes, and not all produce the same results.
Finding a partner that can consistently and reliably provide meaningful oncology RWD can be a challenge, especially without knowing what to look for. When choosing someone to work with, it’s important to look for a deep understanding of quality, compliance, and collaborative problem-solving.
Here are the top 3 traits to look for in an oncology real-world data partner.
1. Comprehensive understanding of the quality management process
Quality management is a science all its own, requiring a firm grasp on how to identify and address an avalanche of issues around the validity, integrity, precision, reliability, and timeliness of data. In the health and life sciences industry, it also demands detailed knowledge of a rapidly evolving regulatory environment, as well as familiarity with emerging privacy and ethical concerns.
RWD companies shouldn’t be winging it when it comes to these issues. They should be able to demonstrate their understanding of the full RWD lifecycle and have a robust quality management system (QMS) to monitor and document all activities while enforcing applicable compliance standards.
A top-notch quality management process is vital for making certain that data moves through the curation process seamlessly and with the highest possible level of integrity so that the end result is primed and ready to be applied to projects.
2. Collaborative investment in compliance, privacy, and security
Health-related data is heavily regulated for a good reason: it is highly sensitive and must be safeguarded at all times. Unfortunately, breaches and cyber crimes are common in healthcare and life sciences, making it challenging to ensure the privacy and security of RWD throughout its lengthy, often fragmented lifecycle.
When incidents do occur at some point in this chain, all stewards of the data need to be ready to respond. Companies must have a complete understanding of their obligations and be prepared to take action immediately. That means they have to complete their due diligence and rehearse their action plan well in advance of an actual event.
Consider a partner who conducts regular reviews of compliance documents, leverages tokenization to keep data secure, and holds all-hands-on-deck roundtables and roleplaying sessions about privacy and security protocols to prepare for any eventuality.
3. Creative and agile approach to problem-solving
Real-world data comes from all types of places in all states of standardization and completeness. And it all needs to be integrated into a single dataset that is in a usable format for current – and future – research and analytics tasks.
Sometimes, bringing the data together is relatively easy: the data structure is familiar, there are few issues with the integrity of the information, and the process can proceed without any hiccups.
Other times, novel data sources require a bit of creative problem-solving before they can be added to the mix. Companies must have source-agnostic and statistically sound methods of controlling for issues such as missingness, duplication, timeliness, and other inconsistencies that could unintentionally bias research results down the line.
RWD curators need to have strategies in place to identify when issues might arise and how those bumps in the road might affect the end result – and that requires having exceptional quality management protocols locked down from the beginning.
Complete, accurate, trustworthy, high-quality data quality is a must-have when engaging in something as important as oncology research. To secure the best possible data for use in life sciences and clinical trials, look for a partner that plans for all eventualities, prioritizes data quality from start to finish, and produces results that support game-changing oncology breakthroughs.
