Articles | February 26, 2024

What We Get Wrong About Predicting Mental Health Outcomes

Incorporating mental health factors into predicting health outcomes can help plan sponsors improve interventions. Doing so successfully requires well-executed intervention, not merely identification and awareness.

Data reveals the importance of incorporating the order in which mental health and comorbidities are diagnosed, as well as gender differences, into the analyses and recommendations for chronic condition case management. Unfortunately, predictive models are not sophisticated enough to account for the succession of conditions.

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That’s why health plan sponsors should consider using a multidimensional approach when addressing mental health’s impact on participants.

In an article recently published in the First Quarter 2024 issue of Benefits Quarterly, we outline a seven-step strategy for prioritizing mental health interventions and unifying resources for a holistic, timely, participant-focused experience:

  1. Determine which resources address mental health directly.
  2. Consider whether the mental health spending ratio is optimal.
  3. Negotiate for high-quality predictive analytics.
  4. Discuss whether data input systems that predict outcomes are receiving all essential data in a timely manner.
  5. Examine the extent to which predictive analytics are siloed from other vendors.
  6. Find out whether outreach protocols address mental health conditions at the same time as medical conditions.
  7. Review reporting regularly to identify trends and areas for improvement.

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Download the article here, with permission from the International Society of Certified Employee Benefit Specialists, the publishers of Benefits Quarterly.

Interested in using predictive analytics to help prioritize mental health interventions?

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