Humans, like the diseases and disorders they suffer from, are biologically complex. As a result, as scientists gain a better understanding of the body's systems, clinical trials get increasingly complicated as pharmaceutical and biotechnology companies develop novel therapies to target disease and treat the greatest number of people. During a study, investigators must collect massive volumes of data from each participant to guarantee a drug's safety and efficacy. Traditionally, such information is collected at a trial site, but when DCTs, wearable devices, telemedicine, and third-party labs are added to the mix, data management becomes even more complicated.
Managing, aggregating, and cleansing these data points might take so long that there isn't much time left to develop essential clinical analytics and visualizations that inform research choices. To enhance data management and eliminate human labor and delays, businesses should follow the FAIR principles that data are:
- Findable
- Accessible
- Interoperable
- Reusable
When optimized, FAIR allows businesses to spend less time maintaining data and more time understanding and using it. A centralized analytics platform can provide rapid, flexible, and scalable data preparation along with safety, efficiency, and operational insights, allowing businesses to make quick choices while protecting participants' well-being.
What Is FAIR?
Too often, the FAIR standard is underutilized in the clinical trial setting. The first principle, findable, is that data should be well-described, with permanent identifiers, and searchable. Accessibility implies that data, metadata, vocabularies, and so on can be understood by developers and machines. Analytic tools for searching, aggregating, and filtering data employ standard metadata to produce relevant findings inside and between clinical trials. Furthermore, data is best accessed in a safe, user-friendly shared environment rather than fragmented across several platforms.
Interoperable data may be merged and completely harmonized with other data rather than being compartmentalized, thanks to a formal, common, universally applicable language. Reusable means that the data has explicit usage licenses and is coupled with correct information. It also means that users may simply navigate between and within studies, allowing researchers to compare data from comparable experiments.
Following these standards promotes consistency and prevents mistakes such as vital information being in the title of a study but not in its clinical database. For example, an oncology trial may explore many types of tumors at the same time and specify which ones are being investigated in the title. However, if the individual patient data does not indicate their tumor type, HCPs or investigators searching for patients in the research with specific tumor kinds will be unable to locate them.
"Prioritizing FAIR implies that data managers search for circumstances where data may be missing from the database and ensure that it is added. When the data is integrated, the replies appear in the same columns from the same data sources, making it easier for researchers to review data from several studies".
- Krunal Bhatt, Technical Team Manager - Octalsoft
When Should Companies Implement FAIR?
To achieve regulatory clearance, industry best practices include FAIR principles in clinical trial planning from the start. For example, before research can begin, it must be approved by the FDA, which has strict guidelines for ethical trial design and participant demographics. FAIR data management proves to the FDA and other regulatory organizations that the study will satisfy their standards. Second, employing these techniques early on establishes consistency that may be extended to future trials.
For an initial trial, such as a first-in-human research, the ideal method is to begin by determining what data will be required after the trial. If a corporation knows it will require X, Y, and Z data for approval submission, arranging for that data from the start saves time and effort throughout the trial. As a result, businesses may reverse-engineer their data management approach, eliminating inefficiencies.
As a business creates subsequent trials, the reverse-engineering method will shorten its study planning durations by building on what it learned in earlier studies. Signals Clinical™ can map, load, access, harmonize, and standardize data for a Phase 1 experiment, producing techniques that can be used in the same company's Phase 2 trial with few adjustments. This considerably reduces the burden. As the old adage goes, if you do something once, it's OK; if you have to do it repeatedly, automate it.
Not all data is created equal, and enormous volumes of disorganized data result in chaos. Chaotic, unharmonized data does not provide organizations with the actionable insights that fully analyzable data offers. Siloed data necessitates substantial labor to integrate, increasing the likelihood of errors. Frequently, programmers discover problems in the data that need unlocking and cleaning of the database after a study concludes, resulting in delays.
In Summation
When information is centrally located and available for analysis, it satisfies compliance standards and speeds up the end-of-study process. Furthermore, visualization technologies that integrate with data management, like Octalsoft's eClinical suite, may produce actionable reports that businesses can utilize to better manage their research.
Octalsoft's eClinical suite starts with the fundamental notions of accessibility, interoperability, standardization, harmonization, and reusability. It generates harmonized and standardized data from the start of a clinical study, reducing work and increasing data value through automated processes.
Experience excellence with Octalsoft’s end-to-end, feature-loaded software solutions ranging from CTMS, to EDC to IWRS and on to ePRO and Portfolio & Project Management tools to name a few. Want to know more about the full extent of software solutions covered by our eClinical suite? Book a quick chat with one of our experts by following this Link. We look forward to hearing from you.
