Redefine your digital health strategy with behavioral engagement patterns

There’s no question that digital and connected health solutions have tremendous potential to improve patient lives, provide clinical efficiencies and drive outcomes. The common challenge faced by digital pharma leaders is how to create solutions that provide value on a frequent basis to maximize patient, provider and payer engagement. The struggle to reach scale and realize value from digital health investment is real.

Pharma has invested in digital and connected health in an effort to gain competitive advantage, supplement molecule-based products, and improve patient compliance, loyalty and support. There’s no question that digital and connected health solutions have tremendous potential to improve patient lives, provide clinical efficiencies and drive outcomes. The common challenge faced by digital pharma leaders is how to create solutions that provide value on a frequent basis to maximize the engagement of patients, providers and payers. The struggle to reach scale and realize value from digital health investment is real.

Digital health solutions vary widely—so do the behavioral interventions that support different users and therapy areas. For example, designing a service to meaningfully engage audiences and improve outcomes for a sleep disorder requires a different set of mechanisms than solving for the complexities associated with medication adherence. Design accelerators can jumpstart strategy with scientific precision—tailored to different types of digital health solutions and the people who use them. Thanks to engagement patterns, digital health solution teams can guide planning and design activities that are rooted in a deep understanding of users’ needs as well as applying demonstrated best practices to optimize engagement.

Best practice engagement patterns incorporate proven mechanisms that can be applied to specific health scenarios. This is similar to how treatment protocols are followed in the care setting. For the most part, care providers and teams apply treatment protocols to standardize health practices and improve outcomes with consistency. Only when extreme cases emerge do care teams need to “invent” and create a specialized intervention. Why should a technology that extends and facilitates healthcare be any different? Risks associated with creating successful digital health solutions can be mitigated by applying best practice engagement patterns in a similar way that treatment protocols guide doctors.

Designing digital health solutions is complex. No formula or prefab components can solve that. Still, there’s substantial value in having defined principles and a library of patterns to enhance a disciplined human-centered design process. Impactful digital health solutions are not designed with directions and a big box of user interface Lego pieces that can be endlessly assembled. Successful teams include patients, providers and payers early and often, applying behavioral insights and a design systems approach to create meaningful experiences. A strong design system links engagement patterns to goals and keeps teams focused on customer value. Better outcomes depend on both innovation and reuse.

It’s most effective to initiate engagement patterns during the early planning stages to inform strategy efforts and start teams on the right foot. These patterns give stakeholders a clear understanding of their audience and what matters most for each solution type. Great value can be gained from industry-demonstrated patterns to guide digital health strategy and design an experience that leads to measurable results. Engagement patterns draw from behavioral science, human-computer interaction and digital product management. Together, these practices help teams know when—and how—to use engagement patterns for maximum impact.

Here are three key principles that can be applied right now:

1. Align your design system with purpose. Engagement patterns support the specific goals of a solution and should be rooted in user centricity. Whether a solution aims to help patients adhere to their treatment or enables health providers to make clinical decisions with greater confidence, it’s essential that the needs of primary users be clearly understood and aligned to the right pattern. This overview shows how common solution types align to specific users and goals.

2.  Verify patterns through evidence. Patterns must be backed by measurable results. While there are various forms of evidence that can be examined, success is best defined through:

• Commercial evidence indicating market success such as acquisitions/venture funding, deal size, investor round, number of users/clients and partnerships
• Clinical evidence showing a digital health solution has helped patients self-manage their disease or condition and achieve outcomes such as adherence, reduced weight or the adoption or avoidance of any specific behavior
• Experience design evidence showing that target users value the solution and can complete key tasks with effectiveness, efficiency and satisfaction

3. Apply patterns with accuracy. Engagement patterns can dramatically affect the way teams create solutions since they have the power to enforce prioritization and accelerate design decisions. A consistent framework helps teams choose and apply the most relevant patterns to create effective digital health solutions. Incorporating patterns during the early stages of opportunity design can have an especially powerful effect by de-risking decisions and neutralizing guesswork that’s much more costly to address later.

Pharma teams can improve outcomes by aligning solutions to audience needs, using evidence, and applying engagement patterns early in the process. This knowledge supports every phase, from early planning to redesigning underperforming solutions already in the market. Engagement patterns give teams a science-backed foundation to build on—boosting adoption, deepening engagement and improving digital health outcomes. Digital health only works when people see value and choose to stay engaged. Design systems improve solution impact, reduce technical debt and lower investment risk.

*Source: This classification is based on several available frameworks including the Classification of Digital Health Interventions v1.0 by the World Health Organization and the Digital Health Venture Database published by Rock Health.