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By John Coritz, Senior Director, Data Science Division, Anju Software
The COVID-19 pandemic was certainly an unforeseen event in 2020 that significantly impacted the pace of drug research and development, and new drug market launches and uptake. One of the key factors, however, that may have been responsible for mitigating that impact was data – both access to it and the ability to leverage it to inform both clinical development and healthcare stakeholder and patient group education strategies. Ironically, that same factor – a relative weakness of available data and particularly in data intelligence capabilities – may be equally responsible for millions of dollars in losses to pharma. These losses resulted from every drug targeted for launch in 2020 that never made it due to everything from ill-informed clinical studies and trials to not understanding the real key influencers that can make market adoption successful or not.
In previous years, the paucity of data, and access to it, to support bringing a promising drug to market had been a real challenge for pharma. However, in recent years, the explosive volume of public-sourced medical, clinical and scientific data, and content available online has never put the pharmaceutical industry in a better position than today to expand their medicine portfolios, while also optimizing every aspect of new drug-to-market costs.
A key barrier today to making this a reality is still incomplete, unrevised, untrustworthy or unstructured public data types and sources. This, combined with old manual-driven processes to aggregate, analyze, and rank this data to obtain optimal insights to make the best clinical development and go-to-market decisions, is an obstacle to drug development. Imprecision in decision-making is something pharma can no longer afford. Actually, it should not even be an issue given the technologies available to understand and translate complex data sets that can reasonably predict clinical study outcomes as well as healthcare marketplace adoption of new therapies.
Why is much of pharma still stuck in this data dysfunction conundrum and still at a decidedly “data disadvantage,” and what steps can it take to remove itself from this morass?
Again, the rich panoply of data accessible to pharma is not the issue. Pharma would rather have the opportunity to make decisions based on more versus less data. But the mere existence of big data and access to it does not automatically translate into knowing new drug candidates that are most likely to succeed, planning clinical studies and trial results optimally, knowing which healthcare providers will champion a certain therapy or not.
Furthermore, while there is an endless sea of new data growing each day, many of the key data sets pharma needs related to clinical drug development, such as public trial registries, abstract databases and publication libraries, are often inaccurate and dated. This data is purposely kept incomplete or not regularly updated by pharma clinical trial sponsors to keep information secret from competitors. In that way, organizations that primarily rely on this publicly sourced “old” data, without analyzing it alongside other data sets, are making high-risk and potentially high-cost decisions if anticipated outcomes fall short.
Additionally, today, it is frequently challenging for pharma companies to get a good picture of what competitors are doing. This can impact everything from knowing if there is a large enough or right patient profile pool for trials in a particular geography, to knowing whether the right investigators are available to influence drug adoption rates.
Another big blind spot for pharma is knowledge about the data they already have internally, where it is located, and whether it is being repurposed efficiently. Today, it is conceivable that companies may be utilizing more than 50% of data researched only for one-time projects, and then they are erasing it from their systems or never using it again. Also, a majority of data that pharma collects is often segregated into silos by business units using separate databases or only recorded on Excel spreadsheets. This data is never shared across the organization to determine if it may be relevant for other projects and initiatives.
An additional challenge comes with merging data and similar data types from different sources. This is already on top of managing an array of data fields, naming conventions, terminologies, update frequencies, data accuracy, data rules, and constantly changing public domain data. It becomes unbelievably challenging for pharma to keep track of all these data elements and still provide a high-quality end product. All of these data process inefficiencies are holding back medical advances and timely medicine delivery to the patients that need them most.
Clearly, pharma has a mandate to find solutions to the data dysfunction conundrum that has existed for far too long. This can be reached by changing mindsets about how to truly make data an asset and adopting technologies that can turn data assets into robust data insights.
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