About
We bring the psych back to analytics.
At Colorado Psychology LLC, we believe that good decisions about human behavior require more than clinical intuition — they require measurement. We specialize in "psychoanalytics": data services for mental health practices that combine rigorous outcomes and predictive assessment using statistical methods that are transparent, interpretable, and grounded in what the research actually supports. More importantly, our methodologies understand the multilayered, paradoxical, and mysterious creatures that people are. Our work sits at the intersection of applied psychology and data science, translating complex behavioral patterns into information that practitioners and organizations can genuinely use, without overreaching.
Our outcomes assessment work draws on validated standardized measures and Feedback-Informed Treatment approaches to track client progress over time, identify early signals of deterioration, and support clinical decision-making within the context of strong therapeutic relationships, but with real data rather than impression alone. We help therapists use these tools responsibly and collaboratively, and train them to resist the temptation to 'overuse the numbers'. There is a world of difference between FIT and "measurement based care" and we are staunch advocates of the former as a critically humanistic and client-centered version of the latter.
On the organizational side, we apply psychometric and statistical methods to help practices understand the behavioral and interpersonal profiles of the clinicians they hire and retain — with an emphasis on fit, culture, and long-term outcomes rather than categorical or personality judgments. Our reports are calibrated against a growing normative pool of mental health professionals, contextualized for the realities of clinical work, and written to be readable by practice owners who are not statisticians. Across everything we do, the standard is the same: accurate assessment, honest interpretation, and conclusions that stay within the boundaries of what the data can support.