Behavioral health crises rarely manifest overnight. For health plans, the warning signs are hiding in plain sight – as data points scattered across disconnected systems, locked in outdated records, or altogether missing.
When it comes to physical health, health plans have the analytics capabilities to predict complications, hospitalizations, costs, and other outcomes with remarkable accuracy, but behavioral health analytics have not historically had that same level of rigor. Claims data, EHR records, and patient-reported outcomes can all provide valuable insights into the picture of a member’s behavioral health and the quality of the care they’re receiving, but they’re almost always retrospective. Moreover, those insights often aren’t integrated, painting an incomplete – or sometimes completely inaccurate – picture of a patient’s care and outcomes.
For health plans managing high-risk populations, that reactive position can be costly. The ability to know if a member is receiving the right level of care for the appropriate duration, and that the care being delivered is high quality, is priceless; it empowers health plans to intervene before a member disengages from care or experiences a crisis.
For example, an AI model could analyze existing claims data to identify high-risk members receiving insufficient care. The algorithm might detect that a member with a history of multiple psychiatric hospitalizations is only attending monthly therapy sessions, when similar high-risk profiles typically require weekly sessions to prevent a crisis. With that insight, the plan can then proactively authorize additional care before the member experiences another costly hospitalization.
Obtaining these kinds of insights is entirely possible with advances in AI and machine learning models for behavioral health, but they raise a pivotal question for health plans: how should these models be developed and managed? Should they be bought from partners, or built in-house?
Option A: Buying Behavioral Health Data Models
Some will inevitably decide on the latter, and given that homegrown analytics models have not typically prioritized behavioral health, this approach has a lot of merit. However, many prebuilt models on the market promise instant risk forecasts and off-the-shelf usability that isn’t realistic when you take into consideration the complexity and diversity of population health at a national scale. While a neatly-packaged model is appealing, there are latent costs, namely limited visibility into how a model was trained, what data it relies on, and under what conditions it will be reliable. Without that understanding, health plans are essentially betting their care strategies on a black box.
Behavioral health is notoriously dynamic. Even at a population level, social conditions and economic realities can shift quickly. A predictive model that’s accurate today may underperform tomorrow if it’s not maintained and retrained in real time. Health plans that choose to buy risk being locked into static models that could very quickly become antiquated. When that happens, they won’t just lack the ability to adapt the tool – they’ll lack the ability to explain its shortcomings. When the model gets it wrong (which every model will, at some point), those health plans must be ready to explain why to its stakeholders. If they can’t articulate what went wrong, or how they intend to fix it, trust will instantaneously erode.
There’s an additional future-proofing element here, too. Regulatory frameworks for AI and ML in healthcare are almost certainly coming down the pike. While it’s impossible to know exactly what shape that oversight will take, it’s safe to assume it will demand greater transparency, explainability, and accountability.
Option B: Building Behavioral Health Data Models
The decision to build comes with its own barriers to entry, but it’s also the surest way to retain ownership of your models.
Ownership is crucial, but it’s not just about propriety – it’s about ensuring the model design is aligned with organizational purpose. When health plans control their predictive models, they have ultimate control over the priorities that are baked into them. They can audit for bias and retrain models on new evidence. They can ensure it’s tuned not just for predictive accuracy, but for actionable outcomes that reflect a population’s needs, provider networks, and member engagement strategies.
This is more than just technical guardrailing. It’s a commitment to the people the data represent. Investment in that commitment can be the difference between a tool that identifies risk on paper and one that actually changes the trajectory of someone’s life.
Many health plans aren’t ready to do this fully in-house today. Those plans should seek partners with expertise that understand the importance of model ownership and are able to ensure in-house teams are prepared to maintain it over time.
The Best of Build and Buy: Consultative Analytics
Predictive models are strategic assets that must be nurtured. The work health plans do now to build, own, and continuously refine them will determine not only their ability to manage risk and costs, but their ability to adapt to the future of healthcare. By maintaining ownership over their predictive models, health plans will be ready to meet that future head-on. More importantly, they’ll be positioned to ensure these powerful models serve the best interests of their members.
That’s the approach NeuroFlow takes with its BHIQ analytics solution. Rather than offering cookie-cutter algorithms, NeuroFlow works directly with health plans to customize machine learning models trained to identify hidden behavioral health risks, and tailors the algorithms for their populations, provider networks, and data environments.
This consultative approach ensures model sensitivity, accuracy, and data security, while giving health plans full transparency into how the algorithms function. Health plans work hand-in-hand with NeuroFlow data scientists to shape the model inputs, ensure clinical relevance, and continuously monitor performance through real-time feedback loops. The result is predictive intelligence that is not only explainable, but instantly actionable.
Learn more about how BHIQ can help your plan build predictive models you can trust.
