The healthcare data landscape: Why variations in data type can generate marketing and sales insights

Healthcare data is growing exponentially, with information coming from multiple sources including EHRs, claims, registries, clinical trials, patient and provider surveys, wearables and more.

Amid this explosion of data, life sciences organizations have opportunities to better understand and use data to support their marketing and business development strategies.

During a Becker's roundtable sponsored by IBM Watson Health, data experts led a group of leaders from life sciences organizations in a discussion of these challenges and opportunities related to using data in life sciences for sales and marketing. Panelists included:

  • Brian Griffin, Director Market Analytics, Watson Health Life Sciences
  • Eleanor Gebauer, Real World Data Research & Analytics Leader, Watson Health Life Sciences
  • Luke Boulanger, Lead Offering Manager, MarketScan Research Databases, Watson Health Life Sciences

Five key takeaways:

1. The sheer volume, variety and velocity of data is overwhelming. According to Mr. Griffin, there has been an explosion in healthcare data. There are multiple types of data including structured and unstructured data, clinical and billing data, transactional and claims data and more. There is data that shows what people thought, what they did and what they should have done.

However, data alone doesn't provide value; it is what organizations do with data that produces value. "That doesn't necessarily translate into more insights," Mr. Griffin said. "It doesn't help us understand how to use these datasets or their relative strengths and weaknesses. It can be very difficult for users to even remember what they can get out of each of these datasets, and it's only going to become more difficult as we move forward."

2. A useful framework for thinking about data involves closed and open datasets. Closed datasets include all of the data about a patient over a period of time, which offers very rich data, but can take time to pull together. In contrast, an open dataset includes intermittent, incomplete data about patients, but is available quickly, in real time. This framework can help commercial analytics teams and cross-functional teams think about different types of datasets, their advantages and tradeoffs and what datasets they want to answer different questions.

3. Sales and marketing professionals use data for a variety purposes. Ms. Gebauer, who previously worked in the pharmaceutical industry, used real-world data to see how her company's medicines were being prescribed and whether there were off-label uses to be aware of and for possible label expansion. Other roundtable participants from commercial functions at pharma companies said their organizations frequently use data for market sizing and to understand their market share versus competition.

4. Triangulating between datasets can provide more usable insights. Mr. Griffin pointed out there are no perfect datasets. "The real issue is to understand where any biases may be," he explained. "You can compare results between datasets as a way of understanding what's going on. Triangulating data off of other datasets can help."

5. Using existing data for actionable change can prove valuable. A marketing professional at an international pharmaceutical company encouraged companies to better use data they already have. "I think sometimes there's a desire to get more data, but we also need to get insights out of what we already have," he said. "Focusing on how we can actually drive action from data is key; it's getting someone to act on what you learned."

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