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Predict Member and Patient Churn

Churn reduces medication adherence and disrupt continuity of care, and patients with coverage interruptions have more emergency department use.

Problem

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).

Size of the Problem

  • 25% of the U.S. population switches health plan each year (1).
  • 33% of people who change coverage skip medications or stop taking them altogether (3).
  • 5x greater use of acute care services after coverage interruption for diabetics (4).

Why it matters

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).

Solution

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.

Datasources

  • Medical Claims: Data extracted from health insurance medical claims with details about dates and place of service, diagnosis codes, key procedures, use of medical equipment, and provider specialties.
  • Lab Test Results: Data about important risk markers from tests used for diagnosis, monitoring therapy, or screening, with details about specific results and abnormal indicators.
  • Operations & Services: Data from health plans, clinics and service providers that capture details about service interactions, billing and payment, technical issues, and complaints.

Citations

  1. Sommers BD, Gourevitch R, Maylone B, Blendon RJ, Epstein AM. Insurance Churning Rates For LowIncome Adults Under Health Reform: Lower Than Expected But Still Harmful For Many. Health Affairs. 2016;35(10):1816-1824. doi:10.1377/hlthaff.2016.0455
  2. Swartz K, Short PF, Graefe DR, Uberoi N. Reducing Medicaid Churning: Extending Eligibility For Twelve Months Or To End Of Calendar Year Is Most Effective. Health Affairs. 2015;34(7):1180-1187. doi:10.1377/hithaff.2014.1204
  3. Banerjee R, Ziegenfuss JY, Shah ND. Impact of discontinuity in health insurance on resource utilization. BMC Health Services Research. 2010;10(1). doi:).1186/1472-6963-10-195
  4. Rogers MAM, Lee JM, Tipirneni R, Banerjee T, Kim C. Interruptions In Private Health Insurance And Outcomes In Adults With Type 1 Diabetes: A Longitudinal Study. Health Affairs. 2018;37(7):1024-1032. doi:10.1377/hlthaff.2018.0204
  5. Feinberg A, Slotkin J, Hess A, Erskine A. How Geisinger Treats Diabetes by Giving Away Free, Healthy Food. Harvard Business Review. Published online October 25, 2017.
  6. BMC. Boston Medical Center to Invest $6.5 Million in Affordable Housing to Improve Community Health and Patient Outcomes, Reduce Medical Costs. Boston Medical Center. Published December 7, 2017.
  7. Heath S. Patient Experience Drives Patient Loyalty Over Standard Marketing. PatientEngagementHIT. Published online December 18, 2018. Accessed March 25, 2021.
  8. Hibbard JH, Greene J, Overton V. Patients With Lower Activation Associated With Higher Costs; Delivery Systems Should Know Their Patients” “Scores.” Health Affairs. 2013:32(2):216-222. doi:10.1377/hlthaff.2012:1064

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