By Jeff Fuller, CipherHealth Vice President of Analytics Solutions
Consider this: in 2019, five percent of the population accounted for nearly half of all health spending in the United States. It’s somewhat of a binary picture: while a small sliver of patients incur massive costs, nearly half of patients incur very little cost, if any at all. There are very few people, in fact, who spend around the average. Health concerns and needs vary during an individual’s lifetime, and knowing what leads to this variation and developing upstream solutions to minimize the avoidable downstream expenses should be a primary goal for your care management team.
Effective population health management uses data to organize appropriate and effective care delivery programs that both optimize resource investment and engage patients in improving their own health. Coordinating care for the most in-need patients is complex, and is often manually driven with limited or sporadic information available to the care management teams. Teams spend resources and hours to ensure all points of access between the patient and the health system are uniform and easy to use to deliver the organization’s promise of providing the highest quality and experience of care at an optimal total cost of care. They also operationalize strategies for acquiring new patients and retaining existing patients in-network, which can be muddied when looking through a lens that fails to stratify patients according to risk.
Care management leaders, then, must efficiently segment patients into risk categories. Using a combination of clinical data from the EHR and conversational context about the patient’s experiences and preferences, risk stratification will accurately take both sources into account revealing the best approach for both clinical care and engagement for every segment of the patient population. Doing this well allows you to connect high risk patients with a care management team that communicates across the continuum to improve care coordination and decrease total cost of care while not losing engagement from the rest of the population. The primary goal is to manage high and rising risk patients to avoid unnecessary, higher-cost utilization and enable easy access to care for low risk patient management, aiming to keep patients healthy and connected to the system.
At the other end of the spectrum, the 50% of the population with low risk and total health spending below or equal to the 50th percentile accounted for only 3% of all health spending; the average cost of care for individuals in this group was $374 in 2019 (as contrasted by $61,000 average per person in the top 5%). Roughly 14% of the population had $0 in health expenditures. These large segments of patients may not need intensive levels of outreach. Care management interventions should focus on the highest risk and disengaged patients, while streamlining outreach for everyone else—making it more uniform, automated, and user-friendly. Reducing the volume of manual tasks is essential for care management staff that are already overburdened. Automated outreach enables staff to use their time more productively, increasing satisfaction and reducing turnover.
So what areas have the highest impact?
Automating communications to low-risk patients is especially effective in pre-care settings. Giving patients the tools to self-serve—through self-scheduling, appointment reminders, referral coordination, and virtual assistants—gives providers the capacity to allocate resources where they are most needed, while still providing a cohesive experience for all patients.
The alternative to automated outreach involves extensive hours and redundancy. Much of risk segmentation can be done through the EHR, but patient outreach and engagement can provide additional contextual data to paint a fuller picture of every patient and get a better understanding of patient preferences. It becomes a self-feeding cycle: engagement creates patient data, which is combined with clinical data. Taken together, providers have new visibility to optimize and improve care, engagement, satisfaction, and ultimately, outcomes.
Jeff Fuller is the Vice President of Analytics Solutions at CipherHealth– a leading healthcare technology company creating innovative patient engagement solutions that improve communication and satisfaction. With over 24 years of experience in health system operations and analytics, Jeff’s work is aimed at serving health transformation with a commitment to whole-person health and high-value care. As a subject matter expert in innovative analytics solutions, Jeff believes our industry is in a pivotal moment to elevate digital health engagement as a catalyst to achieve personalized, proactive, and convenient health conversations and relationships. Prior to joining CipherHealth, Jeff was the Executive Director of Analytical Solutions at UNC Health.
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