January 22, 2020 – Machine learning algorithms have the potential to accelerate and refine the drug development process, but the industry should expand data access and create consistent data standards to ensure drug companies can fully leverage these tools, according to a report from the Government Accountability Office (GAO).

Drug companies spend ten to 15 years bringing a drug to market, often at high costs. Only about one in every 10,000 chemical compounds initially tested for drug potential makes it through the research and development pipeline, GAO noted, and is then approved by the FDA for marketing in the US. Machine learning tools could accelerate and improve the drug development process.

“Machine learning can make drug development more efficient and effective, decreasing the time and cost required to bring potentially more effective drugs to market,” GAO said.

“Both of these improvements could save lives and reduce suffering by getting drugs to patients in need more quickly. Lower research and development costs could also allow researchers to invest more resources in disease areas that are currently not considered profitable to pursue, such as rare or orphan diseases.”

Although drug companies already use machine learning throughout the drug development process, there are several challenges that hinder its advancement in this area, including barriers to data access and sharing.

“According to one industry representative, collecting data from the early drug discovery phase can be cost prohibitive. This representative said that certain health-related data may cost tens of thousands of dollars, as compared to just cents for other consumer related data that many technology companies use,” GAO stated.

“Data sharing also presents unique legal issues. According to stakeholders, privacy laws such as HIPAA can make it difficult for drug companies, especially those that are not regulated by HIPAA, to share or access data.”

To increase data sharing and access, GAO recommended that policymakers create mechanisms or incentives to share data held by private or public sectors, while also ensuring patient information is protected.

“To promote greater availability of data, policymakers could consider forming or facilitating research consortia that allow for secure data sharing,” the organization wrote.

“Policymakers could also consider creating a data repository through encouraging an industry-driven solution, establishing a public-private partnership, or creating a repository of all data under their control.”

In creating new ways to share and access data, stakeholders should ensure they adhere to laws around information exchange.

“Improper data sharing or use could have legal consequences. Increased data sharing could therefore require a careful review of the legal ramifications, because data are often gathered through a wide variety of mechanisms and governed by multiple legal frameworks,” GAO advised.

In addition to data sharing and access, policymakers will need to address the lack of quality data in the drug development process.

“Machine learning requires a large amount of accurate and representative data. This poses a unique challenge in drug development, as much of the data were not originally collected with machine learning in mind and may not be machine-readable or model-ready,” GAO wrote.

“Furthermore, according to an industry representative, data collected across different organizations and environments come in different formats, and this lack of standardization in data quality is a barrier.”

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Overcoming data quality issues will require policymakers to collaborate with appropriate stakeholders to establish data standards, GAO said.

“For example, a standard that defines synthetic data and how they can be used can help reduce bias by allowing researchers to generate data that could be used to better represent currently underrepresented communities,” the agency stated.

“Similarly, a standard data format for uploading and sharing data across platforms could reduce the need for data scientists to spend time converting data sets to machine-readable formats.”

GAO also named drug development research gaps as an obstacle to machine learning use.

“Research gaps present a significant challenge to advancing the use of machine learning in drug development. These gaps fall into two broad categories: gaps in understanding of fundamental biology and chemistry, and gaps in domain-specific machine learning research,” GAO said.

“Experts in the field have noted that addressing these issues may be transformational for future applications of machine learning in drug development.”

GAO suggested that policymakers promote basic research to generate new and better data to improve understanding of machine learning in drug development.

“Policymakers could promote the field in multiple ways, including approaches such as support for intramural research, grants, or other subsidies. Policymakers could choose to use one of these approaches or combine them,” GAO said.

“Policymakers could also support collaboration across sectors. The Machine Learning for Pharmaceutical Discovery and Synthesis Consortium (MLPDS) is a collaboration between large drug companies such as Pfizer, Merck, and Novartis with the Chemical Engineering, Chemistry, and Computer Science departments at the Massachusetts Institute of Technology, and has published a variety of papers at the intersection of machine learning and drug development.”

With these recommendations, policymakers and other stakeholders can advance the use of machine learning in drug development, refining and speeding the process to benefit patients.

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November 26, 2019 – As the foundation of nearly every healthcare trend, process, and solution, data has a vitally important role to play in care delivery and success.

From risk stratification to chronic disease management, precision medicine and medical research, data is at the center of everyday healthcare tasks and broader industry improvements, making it an incredibly valuable resource for organizations.

“If you talk to any data scientist, they’ll tell you that the more quality, scientifically validated data they have, the more likely they’re going to be able to generate useful trends and insights,” said Todd Frech, CIO at Press Ganey.

“The core of everything we do is taking the vast amounts of data that we collect and creating value for hospitals that are trying to improve their operations.”

With healthcare quickly becoming a digital industry, more and more entities are gathering meaning from this big data using artificial intelligence and other advanced analytics technologies.

“The challenge that we’re trying to overcome is that we have more data than a human can process, and we’re trying to develop insights based on those volumes of data. This issue is a natural fit for AI, so the use of this technology is going to continue to accelerate,” Frech said.

“AI can augment humans’ understanding of data, not only from the perspective of generating new insights, but also in generating those insights faster than a typical human analyst processes.”

There are countless examples of AI outperforming humans in analyzing and extracting insights from clinical data. The technology’s potential to transform the industry has led to concerns about robots encroaching on healthcare jobs, creating an environment run entirely by machines and devoid of human interaction.

While AI may disrupt standard care delivery, it’s unlikely that advanced analytics tools will completely take over the role of clinicians. In a field where high-stakes situations and sensitive data are routine, technology can’t simply be left to operate on its own, Frech stressed.

“AI is going to play a bigger part in healthcare, and humans will also continue to play a big part,” he said.

“We can’t just assume that AI is making the right decisions without human validation. There’s a trend that you’re going to see more – what’s called AI augmentation, or human augmentation with AI, more than what you would call complete robotic AI, meaning that you’re letting the AI make decisions without human intervention.”

Recent research has demonstrated that when implementing AI tools, human intervention can lead to optimal results. A study conducted by a team at NYU School of Medicine and the NYU Center for Data Science showed that combining AI with analysis from human radiologists significantly improved breast cancer detection.

Using an AI augmentation approach could also help organizations analyze and measure unstructured data.

“We collect hundreds of thousands of survey data points in the forms of responses to questions, as well as unstructured data in the form of comments. We use AI to look at the comments that come in our surveys. Those comments are obviously in the form of unstructured text, and they convey information on perception of the providers and of the service,” explained Frech.

“Those aren’t yes or no questions. Those are questions that require some soft skills to interpret. We can use AI to do an initial sentiment analysis, and that provides a way for us to really measure this type of data, which is not as binary as some of the data we typically evaluate.”

However, data-driven technologies can’t improve care if they’re fed inaccurate or incomplete information – in fact, this could have the opposite effect.

“Never underestimate the importance of data quality,” said Frech.  “No AI tool is going to work well without high-quality data. People talk about data lakes and unstructured data, and these things are great tools. But without quality data, you’re going to have more of a data swamp than a data lake.”

“If you’re trying to use AI to gather insights without high-quality data, obviously the results aren’t going to pan out. Or even worse, the results could potentially offer dangerous recommendations that could negatively impact people,” he added.

Having a solid data ecosystem ensures that any innovative tools will contribute positively to a health system’s operations, Frech said, as well as communicating openly with other organizations.

“When implementing artificial intelligence or any new technology, make sure that the foundation is strong. Make sure that there’s testing and validation. If that doesn’t happen, there is potential for organizations to take steps backward rather than forward,” he said.

“Find opportunities with your peers, find case studies, talk to people who are using the technology. The more that your organizations can collaborate and learn from each other, the more ideas and successes will increase.”

AI has massive potential to revolutionize the way providers deliver care and make treatment decisions. The road to industry-wide adoption won’t be without its challenges, but the technology will likely make its way into regular clinical care.

“There are a lot of different ways to use AI, and there has been a lot of experimentation. Over time, there will be more and more successes, and those successes will come in fits and starts, depending on how the data in the market mature. There’s too much investment in AI right now to not have some of those successes come into play,” Frech concluded.

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