Behavioral health crises do not occur suddenly; they often present warning signs that are scattered across various systems and records. Health plans have the capability to predict physical health complications accurately, but behavioral health analytics have not traditionally been as robust. Claims data, electronic health records, and patient-reported outcomes can offer valuable insights, but they are usually retrospective and disconnected, leading to an incomplete understanding of a patient’s care.
For health plans managing high-risk populations, being proactive is crucial to prevent costly crises. Utilizing AI models to analyze claims data can help identify members at high risk who are not receiving adequate care. By proactively authorizing additional care, health plans can prevent expensive hospitalizations.
The question health plans face is whether to buy prebuilt behavioral health data models or build them in-house. Buying prebuilt models may seem convenient, but they can lack transparency and may not be adaptable to changing social and economic conditions. Moreover, regulatory frameworks for AI and machine learning in healthcare are evolving, requiring greater transparency and accountability.
Building behavioral health data models in-house gives health plans ownership and control over the models. This allows them to prioritize their organizational goals, audit for bias, and ensure the models are tailored to their specific needs. By investing in building their models, health plans can make a real impact on the lives of their members.
For health plans not ready to build models in-house, partnering with experts who understand the importance of model ownership is key. A consultative approach, like the one offered by NeuroFlow with their BHIQ analytics solution, can provide customized machine learning models that are tailored to the health plan’s population and data environment.
By working closely with data scientists, health plans can ensure model sensitivity, accuracy, and data security. This collaborative approach allows for full transparency into how the algorithms function and enables real-time monitoring and feedback. The result is predictive intelligence that is not only explainable but actionable, helping health plans make informed decisions to improve patient care and outcomes.
In conclusion, investing in building predictive models is crucial for health plans to effectively manage risk and costs in the ever-changing landscape of healthcare. By prioritizing ownership and transparency, health plans can ensure that their models serve the best interests of their members, ultimately leading to better overall outcomes.
