How Do We Solve The Med Device Industry's Data Dysfunction Conundrum?

Barbara Argibay Gonzalez, Vice President of Development-Data Division,Anju Software

The COVID-19 pandemic was certainly an unforeseen event in 2020 that significantly impacted the pace of medical device research and development and new device 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 R&D and healthcare stakeholder and patient group education strategies. Ironically, that same factor(COVID or no COVID)– a relative weakness of available data and particularly in data intelligence capabilities –may be equally responsible for steep losses to the device industry for every new device targeted for launch in 2020 that never made it due to everything from ill-informed 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 device to market had been a real challenge for the industry. However, in recent years, the explosive volume of public-sourced medical, clinical and scientific data and content available online has never put the medtech industry in a better position than today to expand their product portfolios while also optimizing every aspect of new device-to-market costs.

The chief barriers today to making this a reality are still incomplete, not updated or untrustworthy or unstructured public data types and sources combined with old manual-driven processes to aggregate, analyze, and rank this data to obtain the best insights for making the best product development and go-to-market decisions. Imprecision in decision making is something the device industry can no longer afford nor should it even be an issue given the technologies available to understand and translate complex data sets that today can reasonably predict R&D outcomes as well as healthcare marketplace adoption of new medical technologies.

Why is much of the medical device industry still stuck in this state of data dysfunction conundrum and still at a decidedly “data disadvantage”, and what steps can it take to remove itself from this morass?

Why Big Data Didn’t Bring Nirvana for MedTech

With the evolution of technology and the internet in recent years, the volumes of data at the medtech’s fingertips should have been giving the industry a huge advantage in accelerating critical new product to market for patients. Unfortunately, it has never been about the amount of data, which continues to grow at exponential rates every day, but has always been about the quality of that data and how and how quickly it is used and interpreted for device companies to be able to reap the full value of data that can impact every aspect of its growth.

To be sure, medtech has been doing its best to get its arms around the explosive growth of big data to improve processes ranging from every level of research and development to building relationships with top medical influencers who can make or break a product’s success or failure. However, the processes they need to focus on first are how to optimize the data available. While some device organizations are far more advanced in ways to make “big” data “smaller”, much of the industry is still either utilizing time-consuming data analysis techniques and/or underutilizing technology that can speed extraction of insights of largely unstructured data sets. This means a significant number of the time-pressured R&D and market strategy outcomes seen today -- good and not so good –are based on incomplete, less than optimal information vetting.

Dated Data& Deficient Data Gathering Further Challenge The Device Industry

Again, the rich panoply of large amounts of data accessible to device companies is not the issue. The medtech sector would rather have the opportunity to make decisions based on more than less data but the mere existence of big data and access to it does not automatically translate to knowing which new devices and technologies are most likely to succeed or to optimally planning R&D or which healthcare providers will champion a certain product or not.

Furthermore, while there is an endless sea of new data growing each day, many of the key data sets medtech companies need related to R&D such as public research registries, abstract databases and publication libraries, are often inaccurate and not up to date. This data is purposely kept incomplete or not regularly updated by device companies to keep information secret from competitors, so 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. Today, it is frequently challenging for medtech companies to get a good picture of what competitors are doing, which can impact everything from knowing there is a large enough or right patient profile pool for trials in a particular geography to whether the right investigators are available to influence device adoption rates.

Another big blind spot for medtech companies is knowledge about the data they already have, 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 for one-time projects and then erasing it from their systems or never using it again. Also, a majority of data device companies collect is often segregated into silos by business units sitting in separate databases or even only recorded on excel spread sheets, 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 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 medtech organizations 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 medical device delivery to the patients that need them most.

Automating Data Analysis for Faster, Smarter Decisions

Medical and clinical data that can be utilized for the industry’s benefit will only continue to grow, including from incomplete or unvalidated data sources. The mission for medtech is to identify the right technology tools that can quickly and efficiently sift through both the good data and as well as the good nuggets in the bad data to make product development and delivery as precise and free of uncertainty as possible. There are AI and algorithim-based technologies that can centralize, integrate and cross-compare in advanced visual formats private, public and third-party aggregated data across a medical device organization and analyze, rank and score that data to optimize clinical trial decision-making and outcomes, to understand changing HCP and influencer dynamics that can impact device adoption, and ultimately maximize the work of every function within a medtech company. Social media and health insurance claims data, which can provide the freshest insights into a fast-changing healthcare environment, are two examples of categories that the industry is not necessarily utilizing advanced data intelligence analysis platforms to extract full value from.
With today’s available technologies, device companies can achieve a new level of performance by linking internal and external data sets to build a predictive machine-learning model with a higher degree of precision in predicting drivers of clinical trial site performance (for example, site congestion, protocol parameters and excitement around the target). The use of predictive models can sometimes help achieve a three to six month faster enrollment of patients for clinical trials and a reduced default rate for trials. New machine-learning technologies can also create new standards of data trustworthiness and accountability for the device industry where they can use internal tools to run trial feasibility simulations and share that data with CROs to identify the best PIs for a clinical trial, saving time and money.

The end result of the medtech industry increasing its utilization of predictive AI-based data intelligence and analytics tools is faster, smarter data-driven decisions. This is critical when every day a new drug or device is delayed coming to market where depending on market share and indication, sponsors stand to lose $600,000-$8,000,000 in revenues *. ( * https://drug-dev.com/cost-of-disrupted-clinical-research-due-to-covid-19-equates-to-10-billion-potential-study-delays/)Reducing these delays is a win for device companies and the patients they serve.

Ultimately, the goal of combining new approaches to data handling with advances in clinical and medical data intelligence technologies is for the medtech sector to achieve the highest levels possible for data optimization, which if reached, can greatly accelerate products to market. To transform device development, this acceleration can be combined with improved quality and compliance, enhanced patient and healthcare professional experience, better insights and decision-making, and a reduction in development costs.

Clearly, the medical device industry has a mandate for finding solutions to a data dysfunction conundrum that has existed for far too long that 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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