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Churn reduces medication adherence and disrupt continuity of care, and patients with coverage interruptions have more emergency department use.
Churn is a term used to describe when a person gains, loses, or changes their source of health insurance coverage. It is a dynamic that has plagued the insurance industry for decades and is usually considered an inevitable challenge. Every year, approximately 25% of the U.S. population switches out of their health plan (1). For people covered by Medicaid, churn is markedly higher. Medicaid’s complex eligibility requirements cause 50% of beneficiaries to lose coverage within 12 months of signing up, which can have particularly adverse effects on children (2).
Churn clearly increases member acquisition and administrative costs. What fewer people realize is the extent to which it also destabilizes care continuity and contributes to worse health outcomes. Churn has been shown to reduce medication adherence (more than 33% of people who change coverage skip doses or stop taking their medication altogether), disrupt continuity of care, and patients with coverage interruptions have more emergency department (ED) use and hospitalizations (1-3). A study of diabetics showed fivefold greater use of acute care services after coverage interruption compared to before the interruption regardless of age, sex, or diabetic complications (4).
High levels of churn also disincentivize long-term investments for innovative programs like Geisinger’s “Farmacy” or Boston Medical Center's housing investments (5,6). When executives know most coverage will only be held for a year or two, such investments make less sense, especially if the targeted population has churned into another plan where the ‘return’ ends up helping a competitor.
Improving patient engagement can help to achieve loyalty (7). Organizations that actively promote a person’s health can also gain their loyalty, an advantage that may prove difficult for competitors to dislodge. This can also create an enormous cost-of-care advantage. Engaged patients have better outcomes, irrespective of health status, age, sex, or income. They are less likely to have unmet medical needs, delay care, have clinical indicators outside the normal range, be hospitalized or use the ED. Moreover, their healthcare costs are 8–21% lower than their unengaged counterparts (8).
Fortunately, predictive analytics can help healthcare organizations (HCOs) reduce churn and retain their member and patient populations. AI-based models can predict individuals likely to disenroll and surface key factors to help understand why. These insights allow teams to craft personalized retention communication plans, and make retention initiatives a more integral part of care management efforts to promote engagement and improve health.
