Using RWD and clinical narratives to generate commercial insights on discontinuation, treatment and prescribing patterns

Using RWD and clinical narratives to generate commercial insights on discontinuation, treatment and prescribing patterns

To improve outcomes, reduce healthcare costs, and improve commercial success of medications, a better understanding of why patients stop taking and providers stop prescribing a particular medication is needed.

Medication discontinuation and non-adherence are big problems for both pharmaceutical manufacturers and patients. Annually, pharmaceutical companies lose an estimated $250 billion due to medication non-adherence and premature discontinuation. Approximately 50% of patients stop taking their medication for their chronic condition within the first year — with an estimated 125,000 U.S. deaths each year attributed to medication non-adherence.

To improve outcomes, reduce healthcare costs, and improve commercial success of medications, a better understanding of why patients stop taking and providers stop prescribing a particular medication is needed. Currently, manufacturers primarily use claims data to analyze who is or isn’t taking a medication and focus groups with either patients or prescribing physicians to understand the drivers as to why.

While focus groups can provide valuable direct feedback, they also present challenges and limitations. A reliance on small sample sizes with significant recall bias limits their value. For example, a provider who recently had an immunologic patient discontinue a TNF-alpha inhibitor is more likely to talk about the recency of that stoppage in a focus group because it is most top of mind. While offering some insight into discontinuation, that example may also be telling a different story than other similar patients and certainly doesn’t get at why patients with the same disease but different characteristics or disease severity may be continuing or discontinuing the same treatment.