For years, the twin goals of health plan leaders have been the same:
1.) Improve member care
2.) Reduce costs and utilization
Increasingly, digital health is recognized as the best means to reach these goals. But with more and more digital health solutions on the market, health plan leaders are left with one critical question: What’s the right digital health solution for us, and why?
We believe there are three critical criteria to consider: scalability, integration, and success metrics.
1.) ScalabilityWe all understand that managing population health requires scalable solutions, but this is especially true for digital health solutions. And while scalability begins with a partner who can adapt to the unique needs of your organization, population, and members – there are two distinct areas of opportunity in scalability: addressing multiple chronic conditions and employing AI to scale more efficiently.
First, multiple chronic conditions: Chronic conditions are the greatest challenge to value-based care. While the right solution will address the leading chronic conditions (diabetes, hypertension, heart failure, prediabetes, and behavioral health) it’s essential that they’re also addressed as comorbidities.
“A U.S. study of over a million people with diabetes found that 97.5% had at least one comorbid condition, and 88.5% had at least two—with the most common comorbid conditions being: hypertension (82.1%), overweight/obesity (78.2%), cardiovascular disease (21.6%), dyslipidemia (77.2%), and chronic kidney disease (24.1%).”1
The other major opportunity for scalability in a digital health solution is artificial intelligence (AI). AI can scale in ways human-only solutions cannot, ultimately reducing costs, while still delivering precise, actionable, in-the-moment feedback for members. Furthermore, AI powers more personalized experiences, scaling to your population’s unique needs.
But not all AI is the same. The right AI-powered digital health solution will support multiple chronic conditions and comorbidities, employing data from diverse and comprehensive data sets to provide meaningful, personalized, and actionable data and insights. And when members receive real-time, personalized, AI-driven coaching, they tend to be more engaged, making more positive micro-decisions about their daily health. The same data sets can support better clinical decisions and enable health plans, systems, and other care ecosystems to understand the segments of their populations, build better chronic care programs, and optimize outcomes.
2.) IntegrationCritical questions: Can a new digital health solution complement — instead of replacing — existing chronic care programs? Will it require changes in existing processes, or can it be incorporated seamlessly? These are perhaps the most important questions to ask when deciding whether a digital health solution is —or is not — the right fit for your organization.
To find a solution that integrates seamlessly with your existing chronic care programs, look for a platform that can be tailored to support both your unique population and your unique organizational needs. Look for white labeling, multiple options to align with your chronic care program, and a consistent user experience with real-time, scalable content and data.
Further, the right digital health solution can address the ever-increasing administrative burdens that health plans face. But only if it integrates seamlessly into your operation with the right levels of support and training for internal staff.
Enhanced integration can also empower members to proactively self-manage their care, and close care gaps — while also ideally preventing acute complications of chronic disease — and the costs associated with them.
3.) Success MetricsIt’s clear that digital health solutions can help optimize health outcomes and manage costs. But achieving these goals requires clear planning, accountability, and success metrics.
While the most critical success metrics are clinical and financial outcomes, performance of a digital health solution should be measured along the way — throughout a member’s care journey.
Prior to onboarding, success metrics can be established by piloting a program with a population segment, then tailoring unique population-level performance metrics to a unique population.
During onboarding and throughout the member journey, key metrics to measure include activations, time on platform, data collected, engagement with relevant communications, net promoter score — as well as ongoing quality measures like cost savings, care utilization, A1C reduction, controlled blood glucose, drop in blood pressure, etc.
Measuring engagement is especially critical. At a population health level, “Patient engagement is highly predictive of both quality outcomes and health systems costs.”2
And finally, success can be measured by a digital health partners ability to consistently customize their approach to meet unique organizational needs, while also personalizing it for unique member needs. Questions to consider: Will this integrate into existing chronic care management programs? Will it provide a consistent user experience with real-time, scalable content and data? Can it be customized via multiple options for your brand, chronic care program, or ecosystem?
The right digital health solution will equip a health plan to track all these success metrics, and demonstrate their forward progress.
For health plans, the twin aim of improved member care and reduced costs is within sight. And with the right digital health solution—one built for scalability, integration, and the means to measure success —the destination may be closer than you think.
Learn more about the Welldoc® platform with our case study for health plans here.
1.Iglay K, Hannachi H, Joseph Howie P, Xu J, Li X, Engel SS, Moore LM, Rajpathak S. Prevalence and co-prevalence of comorbidities among patients with type 2 diabetes mellitus. Curr Med Res Opin. 2016 Jul;32(7):1243-52. doi: 10.1185/03007995.2016.1168291. Epub 2016 Apr 4. PMID: 26986190.
2. Murali NS, Deao CE. Patient Engagement. Prim Care. 2019 Dec;46(4):539-547. doi: 10.1016/j.pop.2019.07.007. Epub 2019 Jul 31. PMID: 31655750. https://www.sciencedirect.com/science/article/abs/pii/S0095454319300594?via%3Dihub