Patient Adherence & Drop Offs: The Untold Story
The day has finally arrived!
It has taken 10 years, thousands of people, and over $1.2 B to bring this drug therapy to market. The FDA approval was a big celebration at the company and patient advocacy groups alike. Another $312 million was spent in post-approval development. The scientists are especially proud of conquering yet another disease frontier. Doctors are excited that they now have a new treatment for their patients, who have been suffering without hope thus far. The manufacturing plant is churning out aseptic, neatly labeled bottles filled with the new drug. The launch team is ready with educational materials and conference presentations. The nurse educators, reimbursement specialists and patient assistance program are ready to help patients cross the last mile to treatment.
Somewhere inside an elated bio-pharma company is a conference room whiteboard with a tally. A new tally mark commemorates every new prescription of the coveted new drug therapy. Everyone is excited about the impact they’ve helped create on lives of patients. The market projections look like a hockey stick. The management and investors are all eager to see the valuation uptick.
But wait…what’s happening? Only a few weeks down the line, the launch isn’t going as expected and the revenue misses projections. Then someone realizes that even though there are a high number of new prescriptions, existing patients are rapidly dropping off treatment!
Prepared for this, the biopharma commercial team runs adherence calculations on their specialty pharmacy data to identify patients who are late on refills and provides them reminders through personalized calls. But even this is not as effective as anticipated. Everyone has the same question on their minds: “Why are patients dropping off our drug?”
Our industry often (incorrectly) assumes that patients don’t adhere to therapy because they forget to take medications. In fact, there are a multitude of complex reasons that result in non-adherence to therapy. Perhaps the patient lives too far from their doctor’s office or the regimen is too complex to follow. Maybe the side effects aren’t being managed properly. They may also have changed their insurance mid-year and lost coverage for the drug. In fact, one of the best predictors of a patients’ adherence to their medications has been their drug adherence habits/history measured from other medications they have taken in the past.
The good news is that the reasons for non-adherence are usually addressable once identified. The current practice of calling patients to remind them to continue taking medications is inadequate. The complexity of patients’ individual reasons for non-adherence requires that the solutions be customized. With comprehensive, near real-time, 360 degree patient level data and clinically informed analytics, the biopharma team can gain insights into the non-adherence drivers for franchise patients and take actions accordingly.
Such a patient-centric approach requires an understanding of several factors that include patients’ demographics, concomitant medications, treatment history, daily behaviors, prescribers, health plans, and pharmacies. Linking patient specific data from a variety of data sources into a common view can help glean a three dimensional understanding of the patient. However, one must be confident that the linked data belongs to the same patient. Very often, probabilistic approaches link de-identified data from different sources creating imperfect matching (60-85% probability based on algorithm used). Such linkages can lead to misleading insights, consequently leading to costly mistakes instead of cost-effective problem solving.
Once the 360 degree patient level data is curated and collated, visualization techniques, interactive data exploration tools, and predictive modeling can help segment patients, thereby giving specific addressable insights. For example, for one particular franchise we worked with, patients were dropping off the franchise drug because the prescribing physician, a tertiary provider, was not adequately managing or preparing them for the side effects. Understanding this issue led to a physician education program for the physicians to co-prescribe certain support medications to mitigate side effects. For another franchise we assisted, the drug was an in-office injectable drug. 360 degree analyses revealed that one of the key drivers of patient drop off was their distance from their doctor’s office. The solution was a transportation voucher to patients. Yet another franchise facing drop offs had a drug which had to be given to patients as a combination therapy with other drugs. Our analyses revealed that the patients were receiving shipments of the different drugs in the regimen at different times. This caused the treatment to go off synch and affected outcomes, eventually leading to drop off. The solution was to co-package or co-ship the regimen drugs from the same pharmacy.
Each of these situations required a different intervention, tailored by franchise, and even by patient. At a time when most biopharma franchises have extensive touch points with patients through patient support programs, it is feasible to deploy such interventions systemically.
Clinically meaningful analysis of 360 degree patient level data can reduce patient drop offs, ensure achievement of revenue projections and result in the best outcomes for patients. The key is understanding the realities of patients’ lives and their journey through treatment, not just making assumptions.
Dr. Deepti Sodhi Jaggi
Pharma/Digital Health Executive/Board Director