Patient-Level Data: New Insights, New Opportunities
Over the past decade, consumer-driven, patient-centered health care has emerged and continues to gain importance within the life sciences industry. In conjunction with the proliferation of technology at the point of care, the availability and quality of patient-level data sources has significantly improved. We now have a wealth of longitudinal data accessible, which tracks patient medical history (prescriptions, diagnoses, procedures, physician visits, hospitalizations, lab tests, etc.) over time.
Similar to historical financial data, patient information not only provides us a retrospective view into health care, but also gives us foresight into future treatment strategies. This view provides an understanding of the full lifecycle of a patient; tracking first, second, and third lines of therapy. Similarly, the data can be leveraged to gain predictive insights, such as determining the profile of patients who are at high risk for chronic disorders and outlining their likely treatment algorithm.
Just a few years ago, patient-centric data was gathered only for research and development and was rarely utilized for other purposes within the industry. In pharmaceutical marketing, for example, we focused solely on physician prescribing behavior to evaluate marketing campaign effectiveness. With the heightened importance of direct-to-consumer marketing and the increase of patient-level data, we are now able to accurately measure the true impact of a campaign on the target population.
As the life sciences industry continues to move toward a more patient-focused approach to care, the application of longitudinal data will become increasingly critical to an organization’s success. Looking forward, the widespread popularity of social media adds an entirely new dimension of patient-level data. The insights gleaned from online social and community channels have the potential to fill the qualitative gap - the ‘why’ behind treatment decisions - that currently exists in data. It will be interesting to see how social media will allow us to enhance patient data to ultimately better predict sentiment and behavior.