PHILADELPHIA — Unless they grew up in the Google era, most large corporations are trying to adapt the way they work for the new digital environment. The conventional wisdom on pharma’s own digital makeover is that it’s proceeding more slowly than that of other verticals.
Judging by drug makers’ comments at an industry event yesterday, though, the industry is picking up the pace.
“There is no doubt digital is changing marketing and changing how we think about interacting with customers,” said Adam Schechter, formerly president of global human health at Merck, which, he pointed out, has made data/analytics and digital one plank of its three-pronged company strategy.
“Our industry is behind and we’re regulated, so we have to think carefully about how to use digital capabilities. But make no mistake,” said Schechter, “we will be interacting with our customers very differently in the future than we do today, and we’ll use data in many better ways.”
Many large drug companies, including Merck, have appointed chief digital officers, a sign the industry is taking the shift seriously, but there have been challenges along the way. Only between 4% and 11% of pharma companies rate their digital makeover a success, which is far below the overall industry rate of 26%, according to a 2018 McKinsey study.
The study served to reinforce the long-held notion of “pharma as digital laggard,” but it’s not an established narrative in all corners of the sector.
“I disagree with McKinsey,” said a senior analyst on a panel at yesterday’s Veeva Summit, a meeting hosted by computer software firm Veeva Systems which drew commercial, medical and IT execs from more than 200 life science companies and which was characterized by one speaker as a gathering for “technology geeks.”
The senior analyst said that, “On the R&D side, we’re behind on digital, but on the commercial side, life science is on par with other industries and is doing better.”
Indeed, the industry’s setbacks on applying advanced analytic techniques like AI to areas such as clinical decision-making and drug discovery are well known. But according to market research firm IDC, which has conducted large surveys on industry’s digital strides, a majority have initiatives underway to support their strategies, and 70% said that this year 25-50% of their budgets will be focused on operationalizing data.
Other execs pointed out how industry has notched some digital successes on the commercial side, specifically in serving up information to reps that helps drug makers’ customers understand their products and helps get them into the hands of the right patients.
“We have been foundationally promoting face-to-face to physicians,” said Philippe Houben, head of data excellence and governance at Boehringer Ingelheim, on the same panel. “We have been laggards in terms of new channels, but now we are endorsing them, and this is why we’re generating more and more information.”
Beyond amassing its own data, companies are looking to external data sets. And a big part of digital transformation involves mashing up multiple data sets and analyzing them with AI to spawn helpful insights. The biggest challenge in doing so is one, not creating more “noise,” and two, orchestrating the rep’s informational needs.
“We just launched a product and that was our strategy,” said Sal Paolozza, director of sales operations for Antares Pharma, on another panel. “It’s a huge orchestration effort on the part of the rep, and they don’t always have the information and don’t always know where to go look for the information. To use some type of application or artificial intelligence to clean it up, prepare it and serve it up to the reps so that they can provide the best actionable information is really the vision we have.”
A lot can be accomplished with a small team. Biogen’s Steve Davenport, associate director, commercial data strategy and management, said the company has four or five people on its data science team who focus on such commercial work. “I’m a fan of small software teams,” he said.
And, given that transformation is a serious, large-scale undertaking, companies shouldn’t get discouraged. Leadership should be mindful that the above takes a fair bit of trial and error.
“You need good data science and infrastructure to embrace it,” said Davenport. “Start small and focused, and evaluate.”
This story has been changed to remove some identifying details.
