The Analytics Backbone of Next-Gen Patient Support

Emerging next-generation patient support tools offer pharmaceutical companies the ability to deliver more tailored communications that effectively address patients’ concerns about their therapies. The end goal here is to improve adherence to prescribed therapies. There’s no need to rehash the adherence problem in the health care industry. It is a long-standing challenge, and all parties (from patients to providers to pharma companies) stand to benefit from even a slight uptick in adherence.

Patient support tools aren’t new, but the wide availability of anonymized patient-level data has enabled innovation in the space. Today, these platforms offer pharma companies the ability to take patient engagement efforts to new levels of sophistication and effectiveness by better customizing messages and delivering them at the most pivotal points in the treatment journey.

But, to unlock the full potential of these patient support tools, pharmaceutical companies must recognize the crucial role advanced analytics plays in facilitating enhanced patient engagement that yields improved adherence. These tools can deliver a meaningful return on investment for pharma companies if they marry adoption of these platforms with a sophisticated data analytics operation that includes three key components:

A multitude of data sources (from anonymized patient-level diagnosis, prescription, and medical claims data to payer data to HCP affiliation data) that companies can layer on top of each other and mine to uncover insights.

Machine learning models that help them analyze these layers of data, account for the various levers that impact adherence, and generate insights into adherence motivators for different patients (e.g., safety, efficacy, administration, cost, etc.).

Messages informed by analytics-derived insights that drive action from patients.

For pharmaceutical companies, the prescription is clear: adopt these next-gen platforms that offer more intelligent and real-time communications capabilities. But, to make the most of these platforms and ensure their communications with patients are timely, appropriate and impactful, they must construct a sturdy analytics backbone for these platforms.

A move toward more intelligent communication with patients

Pharma companies should acquire layers of data they can plug into machine learning models to uncover insights about treatment journeys and roadblocks to adherence for different categories of patients (e.g., patients in different geographies, with different comorbidities, with different payer cost structures, etc.). To acquire patient-level data, pharma companies must secure opt-ins from patients. To do so, it’s important to develop a strong patient recruitment methodology that makes clear for patients the connection between data-sharing and improved care.

A pharma company’s communications with patients must take into account the fact that each patient has a different set of concerns that affects his or her willingness to stay on therapy. For some, drug efficacy is a top concern. For others, side effects are the biggest issue. For still others, price and payer red-tape issues stand in the way of therapy starts or refills.Adherence impediments also will vary by therapy area. After all, the worries a patient may have about an oncology treatment will be vastly different from the concerns a patient has when prescribed a primary care drug.

Companies must aim to understand at a granular level the factors that impact adherence. From there, companies can develop more effective messaging campaigns and better assess the impact of different elements of those campaigns. The goal is to gain a 360-degree view of the patient treatment journey to enable communication that is helpful to the patient and encourages them to start and remain on important therapies, in line with a physician’s guidance. The better a pharma company understands the patients taking its drug, the more effectively it will be able to tailor messages to those patients to drive adherence. To take effective action, companies first need the comprehensive intelligence that comes from advanced analytics.

Creating a data analytics infrastructure for patient support

A pharma company’s data ecosystem should mirror the path of a drug from the health care professional to the pharmacy to the payer to the patient. A company can weave patient-level data in with position-level data, affiliation data, payer data, and campaign data from its patient support platform. Then, apply sophisticated analytics methods to gain a detailed understanding of the interplay between the many overlapping factors that influence adherence. The specific analytics methods a company uses will vary depending on what insights the company hopes to tease out of the data, but these methods can include supervised modeling, unsupervised modeling, correlation analysis, and much more.

From there, the company can start to home in on the proper timing, context and content for its patient messaging campaigns. Then, with this analytics infrastructure in place, it can more agilely adjust course as circumstances change.

The end result is more impactful content, a messaging cadence that aligns with key points in a patient’s treatment journey, and more proactive interventions to address patient concerns. For example, the introduction of a copay card at the right time can ease cost concerns. And clear and pointed messaging about the importance of taking a medicine exactly as its prescribed in order to realize promised benefits can help reinforce drug efficacy claims.

As pharma companies continue to work to become more patient centric in their commercial efforts, they must recognize the essential role a sophisticated data analytics operation plays in enabling next-generation patient support and engagement. By building a robust analytics infrastructure alongside adoption of advanced patient support platforms, pharma companies will start to communicate in more intelligent ways with patients and therefore develop more trusting relationships with patients, presenting themselves less as impersonal conglomerates and instead as key enablers of improved health.

David Laros is Vice President of Digital Strategy, Analytics and Insights at Beghou Consulting.