Conclusion
This project demonstrates a path for analyzing and anticipating where Colorado’s behavioral health need are likely to grow by pairing population projections with observed service use patterns. The result is a forward looking view that extends beyond current demand and highlights emerging pressure points to information planning and targeted investments.
Unsupervised techniques such as clustering and principal component analysis revealed groups of counties sharing similar age structures and demographic profiles. These patterns matter because counties with rapidly growing older adults poulations will require different services the counties dominated by youth and young adults. Recognizing these groupings early enables prioritization of the right kinds of care in the right places.
On the supervised side, Naives Bayes and Decision tress produced interpretable rules about when high usage and potential strain are most likely, while Support Vector Machines showed more complex and nonlinear boundaries. By leveraging region, payer, year, service type, and a small set of numeric indicators, a Neural Network differentiated high spend from lower spend cases on the test set, yielding a stronger data driven triage signal. Taken together, the methods tell a consistent story of current patterns combined with projected demographics can surface where demand and inefficiency are likely to rise.
The results help provide direction for identifying counties that most closely resemble today’s high need areas and planning capacity to match the likely demand, adjusting the classification threhold to strike the desired balance between false positives and false negatives in line with policy priorities and using transparent decision rules from the tree based models to ugide conversations with payers and providers about which service types and regions to target. Local expertise remains essential and the evidence presented provides a consistent data informed foundation for planning and resource allocation.
Further improvements like adding provider availability and historical utilization could help to enhance future projections and forcasting. Regular retraining will help keep the models current as conditions change. Attention to fairness and transparency is also critical to avoid unintended disadvantages for rural areas or vulnerables groups. Even with these cautions, the work shows that existing administratie and demographic data can be translated into practical guidance for investment and helping more Coloradans recieve quality mental health care.