Improving Patient Services and Outcomes with Data-Driven Design to Hub Services

Data drawn from collaborating sources can benefit the patient journey


Twenty years ago, the notion of data collection and intelligence on the scale common today was nearly unheard of. Predictive and collaborative analytics are used across almost all industries to target marketing efforts that increase customer satisfaction and build brand loyalty. An estimated 150 million Americans turn to their phones for health information, using websites like Google that are able to store and analyze their search data. [1] The output is data-driven applications that recommend products or actions, customized to each individual consumer to enhance their user experience. At the same time, these practices encourage a continuous exchange of information—data that are more relevant and analytics that are more impactful.

The healthcare industry is driven by innovation. According to a Forbes report, healthcare companies dominate among the 25 most innovative companies. [2] However, its expansive size and stakeholders, as well as regulatory challenges, have often slowed the implementation of new technologies. Recently, we have seen changes in the industry as patient-support service providers are putting the same data-driven and personalized tactics leveraged by more consumer-facing industries into place. These enhanced services have the ability to improve quality of life through better patient outcomes.

These innovations are driving new solutions that promise a seamless and tailored omni-channel experience for patients; an automated, streamlined workflow for providers; and promotion of information exchange that keeps manufacturers informed about their product and its patient population’s needs. While data-driven design is transforming patient engagement, there is still an absence of accurate, comprehensive insights from across all touch points of a patient’s unique journey.

This is further complicated by the complexities associated with each disease state and each patient within a particular disease state. These unique aspects underscore the need for data driven programs that get to the heart of what a patient needs. Through a smart program design and the right digital solutions, manufacturers can use data-driven hub services to boost confidence in their product and its patient services.

Improving Patient Services and Outcomes with Data-Driven Design to Hub ServicesFrom tribal knowledge to factual evidence
Historically, patient support services program-design decisions relied largely on industry tribal knowledge and best practices learned from partners. Today, data analytics give manufacturers a more calculated approach that, as part of a multi-pronged plan, can inform patient support programs. Big data in healthcare is, in many cases, still chaotic. Unstructured and structured data sources must be combined in order to create a comprehensive view of the patient’s journey. The right patient services partner uses data analytics from multiple sources to identify unspoken patient and provider needs and pinpoint a targeted approach that enhances outcomes and optimizes program spend.

In a particular example, a program that had been in place for more than three years had an unusual and sudden spike in patient drop-off after the benefit verification process was completed. In analyzing the program data and reasons for drop-off, the patient-support services company noticed that physician withdrawals also spiked significantly during the same period. From there, a deeper segmentation and targeted analysis led to a recommended change in field strategy and provider and patient communications to reverse the emerging pattern. As a result, the manufacturer achieved a 10% reduction in physician withdrawals, many of whom had a high number of patients on that specific treatment. This recommendation, born from a collaborative and comprehensive approach, ultimately helped more patients start and stay on therapy.

Differentiating with collaborative analytics
The differentiating factor in this patient service design is collaborative analytics. Collaborative analytics brings together people, processes, data, and tools into one cohesive ecosystem that is enabled by technology and by an open, adaptive and knowledgeable culture.

Over the past decade, the healthcare industry has shifted away from broad, general programs that serve massive, large-volume product launches. Patient populations today are smaller, targeted and personalized. Increased nuance in the types of data manufacturers are able to obtain and leverage is critical to combatting the increased scrutiny that has come from how manufacturers engage with patients and providers throughout the product life cycle. For example, patient interactions with a nurse via a telehealth call can be proactively and collaboratively shared with the manufacturer team. Collaborative analytics affords manufacturers the ability to leverage the combined knowledge of their analysts and our patient support services analysts to generate deeper insights about smaller, more niche populations.

Recently, Lash Group executed a project to better understand the impact of patient-support program utilization on prescriber behavior, conversations with patients and adherence rates. We combined master data from the manufacturer with collaborative data available through a physician service organization, ION Solutions (a sister company to Lash Group), to analyze prescribing behavior and payer data, and to identify provider channel preferences. Ultimately, this collaboration allowed us to uncover a list of providers who were using the services and those who dropped off. These insights became the basis of an updated service program design, which shifted the mix of services to include the manufacturer sales representatives, ultimately improving the physician experience. After implementation, physician attrition slowed by 10%—a significant improvement.

We must not forget that the basis of these services is to work as a support system that ultimately optimizes patient outcomes and helps treat disease. Improved data connectivity and aggregation will benefit all stakeholders in the healthcare system, and patient support services are only one piece of the puzzle. The industry must work together to aggregate data across manufacturers, providers, patients and third-party partners. As the depth of predictive and collaborative analytics grow, strategic and targeted recommendations on how to optimize patient services will come to fruition.

About the Author
Shishir Desai is Sr. Director, Client Analytics at Lash Group, an AmerisourceBergen company.