Understanding and staying engaged with patients throughout their treatment and wellness journey is a primary focus of the entire life sciences industry. The benefits are clear:
• Patients’ trust in the therapy and its ecosystem grows
• Longitudinal data on patient progress provides evidence for value-based contracts
• Pharma manufacturers gain “stickiness’ with patients and the healthcare system
• Companies increase competitive advantage through differentiation rather than price
But competition is no longer limited to the pharmaceutical company down the street. It may come from born-digital and consumer-focused companies such as Amazon, Apple, Google, or CVS Health that are experts at applying technology to the buyer journey. As acronyms for the technology enablers of competitive differentiation move from SMAC – social, mobile, analytics and cloud – to ABCD – AI, blockchain, cloud, and digital – it’s time for pharma companies to speak the language.
AI is the acronym being spoken most frequently, from the server room to the board room.
AI is poised to improve outcomes across the industry; yet today, each application of AI is narrow in nature and attempts to solve a very specific problem, such as recognizing cancer in images or performing routine activities in a patient contact center.
Machine learning (ML) is a branch of AI seeing increased adoption across the life sciences value chain.
In ML, computers ingest huge data sets and apply statistical learning techniques to identify patterns and help humans make accurate predictions. Algorithms, which are programs with a sequence of instructions, are created by government agencies and companies to address specific needs, ranging from ranking the risk of bird species carrying Asian H5N1 virus, to bladder treatment recommendations, to sentiment analysis during phone conversations and chat sessions.
AI adoption is catching up with its hype.
Successful companies must put the following dimensions in place to differentiate themselves and create sustainable competitive advantage:
1. A vision and roadmap for AI
2. Strong executive sponsorship
3. Access to bias-free data
4. Investments in computing power and algorithms
5. Tolerance to accept certain errors levels by machines
6. Early engagement of Security, Privacy, Ethics and HR teams
Every day, established high-tech consumer giants and new nimble born-digital companies are collecting vast amounts of real-world data, perfecting their algorithms, predicting the patient journey more precisely, and increasing their strategic advantage. Pharma, there’s no time to wait to learn your ABCDs. l
