In her report,“The Age of AI: How Artificial Intelligence is Transforming Organizations,”Susan Etlinger calls attention to a seismic shift happening across multiple industries, saying “the combination of massive and available datasets, inexpensive parallel computing, and advances in algorithms has made it possible for machines to function in ways that were previously unthinkable.”
In healthcare, the age of AI will be especially disruptive. Improvements in healthcare IT systems and digitization combined with incentives for coordinated care are driving exploding volumes of healthcare data. And this data will fuel AI applications, which will provide new and powerful business advantages for those who possess it.
Data Haves and Data Have-Nots
But this data – and AI potential – is not equally distributed. There is a divide in healthcare, with data haves and data have nots. Those who pay for and provide healthcare treatment have far more data on outcomes, quality and value than those supplying treatment technology, e.g., life sciences (LS) companies. The data gap widens every day, and the implications are significant.
For example, real-world data will eventually supplant clinical trials as the gold standard evidence for a drug’s impact on disease. The data haves will be able to use their data to determine value and control pricing over pharmaceutical companies. This is happening already. And while risk sharing may eventually create equilibrium in some categories, in most, it will remain impossible or more complex and costly than justified.
In the age of AI, the data haves will gain another advantage. With AI-powered predictive interventions and tailored experiences, they will surpass traditional LS marketers in their ability to influence patient behavior and predict the role that medication plays in disease prevention and treatment. Gaining leverage in both pricing and impact, AI will allow the data haves to shift control in how life sciences innovation is commercialized.
To succeed, LS marketers must respond with data strategies of their own to create customer experiences that not only generate data to fuel AI, but also deliver patient value. The future relevance of life sciences marketing may depend on it.
Implications for Life Sciences Marketers– The Data Wealth Imperative
While life sciences companies are data rich during clinical trials, they are data poor when it comes to how patients interact with their products in the market. This is where they must increase their access to data.
But there are challenges life sciences companies face when collecting data, including:
- Patient data can bring risk
Life sciences companies are obligated to monitor their patient interactions to identify and report adverse events that are possibly related to the use of their product. Since this data contains false positives, and may result in negative market impacts, LS companies are conservative. For example, most eschew digital interactions (e.g., social media) with large numbers of patients unless there is a strong value proposition for doing so.
- Low interaction rates with patients over time
There are few reasons for patients to interact with most manufacturers over time, and privacy laws complicate using intermediaries to gain access to patients in the first place. It is also hard for LS companies to provide material value to patients, in part because of federal regulations that prohibit financial incentives for using their product if the government is paying for all or part of it. This prohibits offering most anything of real value in exchange for data or behavior. Therefore, it is challenging to establish patient relationships and maintain them over time in the way that companies do in other industries, like through loyalty programs that grant points or perks worth money and status.
Opportunities to engage the ecosystem:
- Near-term: Partner with the data haves. Life sciences companies can leverage one of their greatest strengths, the profitability of their products, to help finance initiatives that the data rich payers and providers aspire to pursue. These partnerships work because:
- Payers and providers are highly motivated to acquire and use data, even more so under value-based care structures.
- Both payers and providers have close and active patient relationships, the channels to communicate and influence, and incentive structures to motivate patient behavior.
- LS companies are able to share the cost and risk, help create value, and gain data in the process.
- Longer-term: Build patient relationships to acquire and use first-party data. To compete in the future, LS companies must have their own patient relationships that generate first-party data to fuel AI applications and provide the means of delivering value. These must include direct patient relationships, as well connections with the broader healthcare and consumer technology ecosystems.
How Life Science Companies Should be Preparing for the Age of AI
To be prepared, life sciences companies must transform the patient experience to build relationships that allow for intimate interactions, real impact, and the collection of valuable data on product use and outcomes. This requires two new capabilities for most LS companies:
- Data strategy that is linked to business strategy and incorporates a virtuous cycle to improve the volume, quality and impact of data over time.
- The ability to design powerful and relevant patient experiences and consistently deliver on them. This requires a fusion of insight, innovation, strategy and design.
- Insights: How to understand patient pain points and preferences to meet patient needs, both functional and emotional.
- Innovation: How to disrupt the next wave of experiences by allowing patients to engage on their terms.
- Strategy: How to create experiences by engaging with the ecosystem in ways that create the most value for patients, the company and other stakeholders.
- UX / Design: How to infuse emotion and reflect humanity through every patient interaction.
What’s next?
Bill Gates said, “We always overestimate the change that will occur in the next two years and underestimate change that will occur in the next ten.” The timing of commercial-scale AI is unknown, but it is certain to expand in ways we cannot predict and be disruptive when it arrives.
For those ready to act, Susan Etlinger offers three “thought starters” to catalyze the process:
- Approach data with a sound strategy — and a critical eye to business value.
- Initially, stick to use cases that are narrow and clear.
- Treat governance, privacy, ethics, and trust as critical to customer experience.
The healthcare ecosystem is rapidly changing and disrupting the status quo. Traditional life sciences marketers face waning influence in the age of AI. But those that transform now — to a more experience and data driven approach — will not only survive, but find powerful new ways to create value for patients and their business alike.
[Download the report: The Age of AI: How Artificial Intelligence is Transforming Organizations]
Note: This article addresses the potential impact of Artificial Intelligence (AI) on life sciences marketing and customer experience strategy. In a separate article, Pharma Brands Are Finally on Brink of Digital Transformation, my colleague Fred Geyer discusses the increasing availability of anonymized patient data and the opportunity for this to support more closed loop brand management. This starts before a consumer becomes a patient (market expansion), and throughout the patient journey (adherence). He proposes the notion of an experience operating system that can support a new age of agile brand management.]
