Creating Patient Profiles: How “Patient Insights” Can Be Deceptive

Written by
Mads Krogh Petersen
Creating Patient Profiles: How “Patient Insights” Can Be Deceptive

Measured by the number of Pharma reminder apps and patient education materials related to adherence, prevalent underlying patient insights on adherence would appear to be:

  • “Patients did not have the opportunity to walk through the relevant educational materials related to negative consequences of non-adherence”
  • “Adherence is also largely non-intentional and the busyness of life means that patient often forget to take the medication.”

In contrast, the main reasons for non-adherence derived from a recent Vertic DigitalIQ study are:

  • I don’t like feeling like I’m tied down to taking a medicine
  • I don’t feel whole if I have to rely on a medicine
  • I know that I can remember it if I try… I will get better in time
  • I feel better sometimes so I don’t feel I need it… the doctor tells me different, but I trust my feelings more than her
  • I don’t know why I don’t adhere

These insights combined with the Trans Theoretical Model of Change tell us that neither a reminder app nor education are likely to make much difference to adherence.

Building a detailed and addressable profile of the patient

Before any defining any patient oriented activity, the most important task is to build a patient profile by uncovering, cataloging and prioritizing the needs and barriers of the patients at an extremely granular level.

The above example shows key psychological barriers for a patient with a long term degenerative disease. We can apply this understanding to your behavior model of choice to define behavior change activities in this specific area. Below is the example of the “Sense of Doom” suffered by the patient driven by Fixation on the Disease end-stages.

On the basis of analyzing each of the barriers, you can map the profile of the patient combining the strength of the available models, e.g. as below the Trans Theoretical Model of Change and Fogg’s Behavior Model.

The underlying model for deriving the patient insights is the DigitalIQ. DigitalIQ is a Big Data, Observational Study anchored in Social Media behavior, Search behavior, Influencer Analysis and other New Classes of data from the Digital Ecosystem such as via APIs from tracking and monitoring mobile devices.

Written by

Mads Krogh Petersen

President and Co-Founder

Connect on LinkedIn

Want to learn more about Gartner’s 2022 Market Guide?

Learn more
Our Thinking

Our View on modern brands in a digital world.

View all articles