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It’s no longer sufficient for a pharma to develop better products or even build simple apps. The industry increasingly needs to create sophisticated predictive algorithms to maximize the ability of its products, write Tom Agan and Fred Geyer.
Pharma companies are quickly approaching a crossroads. It’s no longer sufficient for a pharmaceutical company to develop better products or even to build some simple apps. The industry increasingly needs to do more by creating sophisticated predictive algorithms to maximize the ability of its products to improve the health and wellness of individuals and populations. But not ones using traditional statics. Instead, these will be powered by the latest generation of artificial intelligence – deep learning.
Predictive algorithms that use data to create relevant offers have been around for decades – think of how airfare pricing varies based on demand. But the advent of big data has allowed for far larger, more diverse and more individualized data sets to be mined as digital has permeated our lives. Combined with greater computer processing power and increasingly sophisticated modeling, new approaches based on deep learning are producing algorithms of unprecedented accuracy.
For example, IBM Watson Health working with Medtronics has developed a cognitive model that predicts with high degree of accuracy blood glucose levels two to three hours in advance – paving the way for determining optimal insulin usage.
Agrochemical and agricultural biotechnology companies are also at the forefront of this revolution, creating ever more advanced predictive algorithms to help farmers increase yields. Capturing and analyzing the resulting data allows for rapid improvement of the genetic quality of seeds and effectiveness of chemicals. The result: a win-win for farmer and company.
How about pharma?
In some cases, companies like Teva have announced partnerships with IBM Watson Health to leverage their cognitive technology to drive more powerful insights in areas such as drug discovery. But that’s too often not the case, or is limited to a small pockets of people within a company.
As an R&D leader at one major pharma company recently said, “We know digital is out there but we have no meaningful R&D dedicated to it. We’re still a science company, not a software one. I don’t spend much time on this.” This could be a dangerous position to take given that the digital health transformation – although already well underway – is nothing compared to what’s coming next.
Three Stages of the health transformation
Today we are in the midst of targeted digital health. Pharma companies are already releasing a horde of apps for many conditions. However, roughly 50 percent of people downloading health apps discontinue using them. More seriously, scant evidence exists that the ones being used actually work, with few having undergone rigorous clinical trials proving efficacy.
MoovCare is one exception. It’s a medical software device used to detect cancer relapse or complications during the follow-up of lung cancer patients at high risk of relapse. And it’s gone through same clinical trials that drugs and implantable devices must pass proving it works.
But pharma is not alone in this first wave. A wide range of other companies more savvy about digital, from FitBit to Apple, are diving into the big business of health and wellness.
However, the second stage of the transformation – patient digital health – is an order of magnitude even more powerful than what has been accomplished so far and is surprisingly close to making impact. Here, for example, the EU is leading the way with its Virtual Physiological Human (VPH), a government-academic initiative involving about 2,000 researchers. The project will create not an avatar, but rather a digital duplicate specific to the individual person, a true complete patient picture, based on detailed simulations of metabolism (down to unique genotype protein expression), organs and systems combined with data on medical history, lifestyle and potentially social determinants. The result will be a virtual twin that can be experimented on, testing health and wellness outcomes for different drugs, surgical interventions and lifestyle choices.
The third and final stage of the health transformation will be population digital health. In this stage the real-time condition of individuals and how they engage with the physical and social world will be aggregated to understand health and wellness, make coordinated interventions affecting groups of people and monitor outcomes. And although privacy issues and resistance among the public loom large, a glimmer of this new world is already starting to emerge. Google for one has revealed interest in leveraging big data and predictive analytics in health across large populations. Earlier this year New Scientist reported that Google’s DeepMind Artificial Intelligence initiative has established a data-sharing relationship with Britain’s National Health Service far more extensive than previously thought.
A Shift in value creation
The implications are profound as the digital transformation unfolds. Pharma’s ability to create economic value has traditionally been based on developing innovative products, proving efficacy through proprietary research, pricing to value, promoting adherence and, especially in the U.S., massive sales and marketing efforts. However, going forward, digital undermines the traditional approach – in some cases profoundly, such as with sales and marketing. Algorithms, both traditional and more advanced ones that leverage artificial intelligence, will increasingly make recommendations that can’t be as readily altered by a sales rep call, an ad or a pharma company-sponsored seminar as they might be with a care provider or end consumer.
The first steps for pharma
For pharma to successfully respond to the digital disruption at their gates, the industry must consider a wide range of factors from rethinking the business model to building new internal competencies. But where to start?
1. Conduct mandatory digital boot camps. The CEO leads sessions across the organization to learn about and discuss different aspects of digital. These discussions should explore a range of digital topics from those directly applicable, such as the potential use of chatbots to answer patient and physician questions, to broader ones such as Facebook’s 10-year strategy.
2. Allocate similar levels of funding and rigor to creating the digital tools underlying algorithms needed to improve the complete patient experience as would be applied to developing new drugs. For example, put all apps through clinical trials and take off the market those that don’t deliver.
3. Focus on the patient experience. By patient type, map out in detail their step-by-step experience today. To identify needs, especially the unmet ones most likely to lead to breakthrough digital solutions, view a variety of perspectives beyond the patient: their families, providers, healthcare payers, etc.
The new value equation
Digital disruptors like Monsanto in agricultural biotech, Oscar in health insurance and Republic Wireless have three things in common. They are customer obsessed, offer better value and seen as more trustworthy. It’s precisely these three values that will define the winners and losers in the new era of BioDigital Pharma.