---
title: 6 Ways Health Plans Can Use Data Science and AI to Improve Behavioral Health Outcomes
description: In this article, learn six ways behavioral health plans can leverage data science and AI to gain insights and improve timely outcomes for their members.
image: https://insights.vitaldatatechnology.com/hubfs/AdobeStock_899465919.jpeg
---

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[October 29, 2024](https://insights.vitaldatatechnology.com/author/vital-data-technology) [Data Science](https://insights.vitaldatatechnology.com/tag/data-science), [intelligent automation](https://insights.vitaldatatechnology.com/tag/intelligent-automation), [Care Management](https://insights.vitaldatatechnology.com/tag/care-management), [Artificial Intelligence](https://insights.vitaldatatechnology.com/tag/artificial-intelligence), [Population Health](https://insights.vitaldatatechnology.com/tag/population-health), [Behavioral Health](https://insights.vitaldatatechnology.com/tag/behavioral-health)

# 6 Ways Health Plans Can Use Data Science and AI to Improve Behavioral Health Outcomes

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Behavioral health is a critical component of overall health and well-being, and health plans have a significant role to play in ensuring that individuals receive the care they need, when they need it – especially in today’s post-pandemic environment of whole-person care. For insurers, serious behavioral health problems are one of the greatest drivers of need for all types of medical care and associated costs. 

Fortunately, with the ongoing development of advanced data science and AI-driven tools in healthcare, behavioral health plans now have the opportunity to leverage data to gain insights and improve timely outcomes for their members. When actionable interventions are discovered immediately, automated coordination ensures timely action by all parties. 

In this article, we will explore six specific ways health plans can use data science and AI-driven tools to improve behavioral health outcomes.

## **1.  Predictive Analytics**

Health plans can use predictive analytics to identify individuals who are at risk of developing behavioral health conditions. Predictive analytics uses data to forecast future events, behaviors, and outcomes, and drive strategic decisions. By analyzing internal data from claims or electronic health records (EHRs), along with other sources of outside data such as social determinants of health (SDOH), health plans can identify patterns and risk factors that indicate an increased likelihood of developing conditions, including depression, anxiety, or substance abuse disorders.  

With this enriched view of patient behavior and comorbidities as input into predictive models, predictive analytics can help identify which individuals are at increased risk of developing certain behavioral health issues. They can then be reached out to proactively with preventive care programs before they become symptomatic or begin using costly services such as emergency room visits or hospitalizations unnecessarily because they lacked information and access during times when symptoms were most severe.

## **2.  Artificial Intelligence and Machine Learning**

While health plan data is valuable, it is also difficult to analyze due to its size and complexity. Artificial intelligence (AI) helps health plans make sense of this information by finding correlations between patient behaviors, outcomes, and other factors. This enables them to make better decisions about how they provide care for patients with mental health needs. One of the most powerful tools that health plans can use to make decisions is machine learning. Machine learning is a type of AI that can be used to analyze vast amounts of data and make predictions about future behavior or connections between different types of data. 

Machine learning algorithms can be trained to recognize patterns in large datasets - for example, they might learn which types of patients tend to have higher rates of depression, or whether certain medications are effective for treating certain conditions. These algorithms then apply those patterns when making predictions about future behavior or connections between different types of information like lab test results and prescriptions filled within a given period. 

For example, a large hospital system recently used machine learning algorithms to identify at-risk patients before they became symptomatic. This enabled clinicians to intervene earlier in the trajectory of their illness and improve treatment outcomes overall - especially among those who were most at risk of harming themselves or being readmitted following discharge from the emergency room. 

## **3.  Social Determinants of Health**

Data science can be used to identify social determinants of health (SDOH) that can significantly impact behavioral health outcomes. Looking at data on factors such as income levels, educational attainment, housing stability, employment status, and access to healthy food, health plans can identify individuals who may be at increased risk of developing behavioral health conditions. With this information, health plans can develop targeted interventions to address SDOH and improve overall health outcomes for their members. 

Moreover, addressing SDOH involves more than just recognizing risk factors; it requires a comprehensive approach that includes community engagement, policy advocacy, and collaboration with local organizations. Health plans can work alongside community leaders and policymakers to implement programs that enhance educational opportunities, improve housing conditions, and increase access to mental health resources. By doing so, they not only mitigate the immediate risks associated with SDOH but also foster long-term resilience and well-being within the communities they serve.

## **4.  Population Health Management**

Data science can be used to manage population health and improve behavioral health outcomes at a population level. By analyzing data on a large scale, including a population’s medical history, social determinants of health, and behavioral patterns, health plans can identify trends that can inform policy and programmatic interventions.  

This can include developing interventions to address specific behavioral health conditions and identifying strategies to improve overall health and well-being for entire populations. This approach can improve health outcomes and reduce costs by ensuring that resources are allocated to the most effective interventions.  However, it is important to keep in mind that data science models are not one size fits all. Population health requires models that address the unique dynamics of the health plan’s population, which varies by geography and lines of business.  Be wary of vendors touting off-the-shelf predictive models.  

## **5.  Data Sharing**

The process of efficiently and safely sharing behavioral health data remains a challenge due to ongoing siloes and inoperability across systems and users within the healthcare ecosystem. And even when data is available, it is often underreported because of the lingering stigma associated with seeking and disclosing mental health treatment. 

According to [Deloitte](https://www2.deloitte.com/us/en/insights/industry/health-care/future-of-behavioral-health.html), a robust data-sharing environment – one in which consumers are able to access and control their data, and safety protocols are in place to ensure privacy and data blinding - is key to increased access to behavioral health treatment and early intervention. Data science-enabled tools work to increase interoperability by creating a truly holistic record of member health based on the aggregation and standardization of electronic health records (EHRs) and other conventional and non-conventional data sources. Health plans can then apply AI to this data to predict early onset of certain mental health conditions and recommend interventions to improve outcomes. 

Changes needed to create this type of data sharing environment and support true interoperability are well underway, including open-source and cloud-based software solutions, and enhancements in database security. 

## **6.  Performance Monitoring**

Health plans can use data science to monitor the performance of behavioral health providers. By analyzing data on provider outcomes, including patient outcomes, treatment adherence, and utilization rates, health plans can identify providers who are delivering high-quality care as well as those who may need additional support or training. This can help health plans ensure that their members receive the best possible care and can help providers improve their performance over time, especially through more effective and efficient closing of care gaps. 

As demonstrated by these examples, data science has the potential to transform the way health plans approach behavioral health. By leveraging data to identify risk factors, address social determinants of health, manage population health,  promote data sharing, and monitor provider performance, among other things, health plans can improve outcomes for individuals with behavioral health conditions and promote overall health and well-being for their members. As data science evolves, health plans will have even more opportunities to use data to innovate, improve behavioral health outcomes, and lower costs. 

**\*\*\*** 

Vital Data Technology’s Affinitē platform uses data science and predictive modeling to increase workflow efficiency and improve care outcomes for [Behavioral Health Organizations](https://insights.vitaldatatechnology.com/hubfs/Flyer%20-%20Affinite%20Behavioral%20Health.pdf) – critical for success in today’s complex and rapidly shifting market. Affinitē represents the industry’s only cloud-native, end-to-end platform that seamlessly integrates data science with best-in-class medical management tools for plans, providers, and members.  

In conjunction with embedded, machine learning data science, Affinitē instantly risk stratifies, segments, and identifies actionable interventions to empower every stakeholder with a 360-degree view of member status, quality, and risk performance. With these sources available at the ready, health plans can uncover actionable insights that positively impact member health and increase collaboration with internal and external stakeholders. 

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