There are data problems with behavioral health. Health plan sits on the solution

Health programs have been pouring resources into data analytics for many years. They mapped chronic disease trends, carefully studied prescription data, and chased cost savings in every corner of the healthcare system. However, the same health plan is often blind when it comes to behavioral health.

It doesn't mean that the data does not exist. Health programs have been on years of claims, pharmacies and clinical data that they can tell them who are at risk of severe behavioral health disorders and are currently being treated as effective as a practical approach. The question is how the health plan sees all the data.

Or, more importantly, it tries to tell them what.

Behavioral health has long been an afterthought in the healthcare system. For decades, it has been carved, reorganized and deprived. Now, the United States finds itself in a behavioral health crisis, and even a large amount of pandemic-era investments in mental health cannot be curbed. This is a particularly serious crisis for young people and does not seem to disappear.

That's because visiting alone is not enough to attract the care people need. Health programs have many programs and benefits specifically designed to address behavioral health crises. What they historically lack is the ability to identify, measure and interpret data across populations. Without this, in the face of the ongoing crisis, they and their members have caused huge crises, and the health plan is in a neutral state.

In 2024 alone, behavioral health status was estimated to receive an estimated $3.5 billion in overuse. This is not a sign of a functional system. This is a symptom of the data interpretation problem.

This is not a new health care problem

Twenty years ago, pharmaceutical companies faced similar challenges: They had products that could help specific patient groups, but they rely almost entirely on doctors to connect these points. Consumers are not even part of the equation – sales representatives are the main messengers, providing clinicians with medication information in hopes the prescription will drip.

When regulations move, the drug pivoted. The industry adopts a direct-to-consumer model that focuses on understanding exactly who needs treatment, exactly where on the journey, and what information will inspire them to seek scripts.

Today, a newly diagnosed rheumatoid arthritis patient has different information than someone who has managed the condition for 30 years. This complex segmentation model changes the attractiveness of medical organizations. The target no longer expands access. This is to drive action.

Health programs can bring the same approach and precision to responsibly and ethically the behavioral health trends in the member population – not in the market, but to help their members participate in services they already have access to, through benefits they have paid for.

A health plan has all the necessary data to understand which members need help, when and which plans or benefits are best suited to meet member needs. They don't need any data. They need ways to infer, arrange and interpret it. They need a way to transform them from claims, charts, prescriptions, wearables and other sources into smart ways. Members have expected their plans to analyze their data for drug interactions or supplemental reminders. The same expectations extend to behavioral health.

Think of this: All other aspects of health are so rigorously analyzed that complications, hospitalizations, and costs can be accurately predicted. However, in terms of behavioral health status, most health plans and providers stop analyzing after the screening phase.

For example, if the enrollment rate of diabetes management programs is high and only 50% of drug adherence steadily climbs, would this indicate basic or undetected social determinants or behavioral problems? Depression is a hidden driver of non-compliance, while an analytics engine powered by artificial intelligence can identify risk cohorts and pool data to better understand blind spots over time.

Furthermore, for example, annual depression screening completion rate is often considered a measure of success. But mental health is not a phenomenon every year. It is dynamic, fluid and easy to fluctuate according to social and environmental factors.

I can do screening today without red flags for my health plan or primary care physician. My world may be reversed tomorrow, and neither of them knows. This is happening in communities across the country. People are developing depression after the last screening, weeks or months, and the healthcare system doesn’t know.

But people keep sharing their health care information in ways that go far beyond the questionnaire. We use apps to record mood and lifestyle, wear devices that track health and fitness metrics, and outside of the year our doctor dates. All of this data is stitched together, providing a complete picture – or at least enough to satisfy the health plan to connect points.

Opportunities before a health plan are not about collecting more data. Instead, it’s about applying strategic lenses to data they already have.

Behavioral Health Intelligence is the missing layer in the analysis stack that can see past annual screenings, intermittent hospitalizations, and one-time treatment appointments to show predictive risks, reveal procedural gaps, and show patterns of actual interventions. Whether powered by traditional population health analysis, predictive modeling, or AI and machine learning, tools to do so are easily available. What is needed now is the willingness to apply them.

The bet is too high to behavioural health analysis and cannot keep the black box. It’s time for the wellness program to start treating it using the same analytical discipline as physical health.

Photo: Pixelliebe, Getty Images

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