3 Reasons Healthcare Needs Artificial Intelligence
The growing complexity of the healthcare stakeholder ecosystem is driving the production of increasingly diverse, unstructured, and difficult-to-integrate data sources, making it harder for Life Sciences companies to get access to insights that are essential to their brand’s success. This is particularly true for the commercial functions of these companies, including marketing and sales who frequently utilize different and outdated insights on the customer and market. Here are three ways commercial teams in Life Sciences companies can benefit from artificial intelligence:
1. Save money
Life Sciences companies spend upwards of $10-50M+ on annual consulting engagements, in addition to purchasing data that can be even more expensive, ranging upwards of $100M+. These engagements tend to remain point-solution consulting, as opposed to holistic and “living” systems that can support the entire enterprise. Implementing an automated technology platform will create greater transparency in data assets (removing duplicative purchases), enhance the value of purchased data through enrichment and analytics, and develop continuity across the entire product lifecycle, from medical to commercial. This also allows companies to maximize the use of cross-company budgets.
2. Integrate efficiently
Just ten years ago, companies were able to manage their customers without CRM, and now, having a CRM system is table-stakes for account management. Today, the quantity and complexity of data available to Life Sciences companies is overwhelming, hence the need for advanced analytics platforms that can integrate this data and extract timely, relevant, and predictive insights. But, we no longer have the ability to process and interpret this data manually. Solutions that incorporate machine-learning (a type of artificial intelligence) increases our capability to process this data – and to do so more precisely.
Our platform collects a high variety of data and information and uses advanced methods to determine the importance and weight of each attribute. For example, our machine learning can detect two physicians with similar but different names, practicing in different locations, yet with several other common characteristics that would determine that these two physicians are in fact the same person. These systems constantly learn how important the different metrics, similarities, and attributes incorporated into our models are to indicate and predict relevant information.
3. Reach the right customers
Simply being able to integrate more, diverse data sets is a huge advantage. These then give you a deeper view of the customer which, with advanced segmentation capabilities, is helping commercial teams get the right message, to the right customer, at the right time – the nirvana of sales and marketing. This translates to everything from improved response rates on email campaigns from high-value customers to improved market share with high-potential customers.
One of our customers was recently able to significantly increase referrals and nurture existing referring physician relationships, leading to a jump in sales. By segmenting referring and potential referring healthcare providers (HCPs), as well as those with a high historical referral rate, they quickly found a cohort of HCPs the marketing team could target with personalized messaging. The marketing team then analyzed these HCPs’ affiliated accounts and uncovered additional HCPs who had a high propensity to refer patients for this product, and extended their campaign to them. As a result, they were able to increase their referral sales to 33% above the national average.
David Azaria is the Vice President of Engineering for Zephyr Health. With over 10 years of experience in software development, David is a leader with a unique perspective and passion for new technologies. He leads a diverse team of software engineers and data scientists connecting large and varied data sets to create actionable insights and visualization solutions for Life Sciences business users.