Health care organizations today need to factor scalability attributes into their business model(s) to stay abreast of a rapidly changing competitive environment, or risk being left behind. For a business to be scalable, it must focus on improving the profitability and efficiency of services even when its workload increases. However, establishing this capability does not occur in a vacuum and involves a complex set of considerations and analysis.

COVID–19 pandemic has greatly exposed the critical importance of addressing the health–related social needs of vulnerable populations, especially older adults with complex medical conditions. Historically, single condition management programs have focused on one chronic disease, often missing opportunities to support other health care needs or interventions for primary prevention and health promotion. This represents a missed opportunity to engage Seniors in their own health and wellness activities by using additional benefits for services with greater impact on their overall health and conditions.

Healthcare providers and organizations looking to design and market services to supplement and enhance various aspects of health care delivery (from outreach and engagement, to add on features generating improvements in care and outcomes) are constantly challenged with creating the necessary proof points to forecast and validate impact.

The advent of value-based care delivery has opened new opportunities, where it is possible to demonstrate tangible improvements in both health cost and outcomes. However, this requires a more advanced approach to leveraging data and analytics to identify and quantify actionable cause and effect relationships.

Successful health care product innovations are more than high end engineering and design. They require a clear understanding of the best “fit” between a market (at risk population) need and the external (drivers of variability) and internal (capabilities, what is actionable) factors that, when taken together, reveal promising avenues for innovation. Identifying data and metrics to codify this understanding helps product teams move past solely relying on measurement of past and current performance (“what is”) toward creating the vision for what should come next (“what could be”). Predictive modeling plays a central role in unlocking the most promising opportunities to create something truly new and innovative.

Consumer Data refers to individual lifestyle, attitudinal and behavioral data generated by an individual’s engagement or participation in social activities and other life events (e.g., an individual’s online search history, social media activity, purchase transaction history). Living, learning, working, and playing conditions that can affect the health outcomes of populations are known as Social Determinants of Health (SDOH), which can be extracted from consumer data..