There was a time, not long ago, when conducting patient journey analyses was seen as a nice-to-have asset for those few drugs, devices, and procedures with resources to spare. Now, however, with all stakeholders in our healthcare system placing patients at the center, it’s an essential starting point for life sciences commercialization efforts, informing everything from product development priorities to patient support need, launch planning, commercial targeting, and value communication.
Traditional patient journey analyses relied chiefly on primary market research, and while surveys and focus groups continue to provide an invaluable window onto the patient experience, life sciences companies have increasingly been incorporating insights drawn from real-world data sources (claims and EHR data, for example). Pharmas and medtechs are also beginning to tap the massive stores of social conversation data to get a sense of the lived journey as described by patients in real-world environs.
By blending real-world and social patient data, life sciences companies can gain a much more granular view of the patient journey, gleaning powerful insights into patient unmet needs, behavioral drivers, triggers, and opportunities for intervention. These insights can allow companies to help their customers by:
1. Identifying Barriers To and Drivers of Diagnoses
One pharma company brand team we worked with was preparing to launch a specialty treatment against an entrenched competitor for a heavily underdiagnosed indication. They sought to better understand their patients’ journeys with the aim of optimizing engagement and positioning their product for first- or second-line use.
The brand team needed a clearer view of patient behaviors in the context of treatment progression over time. Together, we first identified patient cohorts and mapped them to relevant patient demographics. Then, using anonymized claims data to trace the longitudinal patient journey and integrating data from public social platforms, we were able to understand these patient cohorts’ varied attitudes, perceptions, and motivations—for example, identifying treatment drivers among a segment of working moms in their 40s and 50s. We could then map this information to treatment patterns, including diagnoses, treatment adoption, and switches, in order to inform messaging and content that spoke directly to patients in a familiar vernacular.
The data revealed key pathways to diagnosis, and helped the team model treatment progression and switching patterns, pinpointing moments of jeopardy, when patients might drop off treatment, and opportunity, when they might move on to a more effective treatment. These insights informed brand strategies to: Boost diagnoses; shift patients from ineffective home remedies and OTC treatments onto prescription medications; get patients started on newer therapeutics earlier in the treatment journey; and power more effective media planning and patient engagement.
2. Illuminating Factors Motivating Medication Switching
Another pharma team working on a specialty launch brand undertook a patient journey analysis to understand therapeutic dynamics and identify the factors motivating patients and physicians to opt for newer drugs over older treatments. Using anonymized claims and EHR data, we mapped out the treatment progression pathway for patients treated with the class of drugs concerned, flagging switches. We then used this real-world switch map to query anonymized patient conversation data related to medication changes within the condition. This data revealed a trove of invaluable insights, including: Factors driving switches; how comorbidities impact patient quality of life; patients’ emotional states as they progress from diagnosis to treatment; and patient attitudes and behaviors.
For example, we learned that nearly a third of the patients using treatments in this category switched out of it; we also found that those using Brand X experienced lessened symptoms and better management, while expressing concern about comorbidities and anxieties about costs and copays. These learnings enabled brand marketers to craft specific messaging for use in patient support materials helping to set expectations around efficacy and side effects, with the aim of improving adherence. It also helped them hone their value proposition for clinical and non-clinical stakeholders.
3. Understanding the Relationship Between Patient Emotions and Behaviors at Each Stage of the Treatment Journey
A biopharma brand team preparing to launch an innovative cancer procedure needed to better understand emotional factors driving patient behavior in order to craft targeted communications speaking to varied needs and motivations within a diverse patient population. They sought to identify: Where patients were struggling; what information and resources they were seeking; and at what points the brand might best address unmet needs.
We segmented anonymized patient social data by stage of the patient journey, then overlaid a 14-point Emotional Journey™ indexing solution to reveal the ebb and flow of emotions, with the aim of surfacing patient perceptions, motivations, and behavior and identifying unmet needs and pain points at each stage of the journey. One key insight: Tremendous anxiety among older patients around costs associated with care-related travel and lodging. This facilitated intervention by the brand to ease these travel-related anxieties, potentially promoting use, reducing cancellations, and realizing better outcomes. This effort also produced a patient lexicon, allowing the brand to speak to patients in real-world language and thereby strengthen patient support resources and improve quality of engagement.
Multiple Patient Journeys Become One
Of course, when we talk about “The Patient Journey,” we’re really talking about a number of journeys. There’s a clinical journey, comprising treatment and referral patterns, time to diagnosis, sites of care and line of therapy progression; a cost journey, reflecting impact of the cost burden and market access factors on treatment decisions and behaviors; an attitudinal journey, wherein patient perceptions and emotional states shift throughout treatment; and an informational journey, inclusive of all the channels, devices, and sites patients use to inform their treatment decisions. These are all lenses we can use to understand the patient experience. As with the different data sources available, they can be combined, as dictated by brand and patient needs, for a much more powerful view.
Many patient journey efforts are still being conducted in a fragmented, siloed manner, with different teams using different methodologies and data sources. This disjointed approach is not just inefficient—it robs companies of a richer view of the patient journey and with it, insights that could benefit patient engagement, care, and outcomes. A comprehensive approach combining traditional data sources with real world and social inputs can yield an understanding of how the disparate pieces of the patient experience connect. And that, in turn, can help companies develop better treatments, and help patients live longer, healthier lives.
