Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment
Casey Bennett, Thomas Doub, April Bragg, Jason Luellen, Christina Van, Regenmorter, Jennifer Lockman, Randall Reiserer

TL;DR
This study evaluates the predictive utility of the CDOI patient-reported outcome measure in a real-world mental health setting, demonstrating its potential for clinical decision support and personalized treatment, while analyzing implementation factors.
Contribution
It provides empirical evidence of CDOI's predictive capacity and explores factors influencing its adoption in clinical practice.
Findings
CDOI predicts outcome changes effectively based on baseline and early scores.
Implementation factors significantly affect adoption rates.
There is a disconnect between clinician intentions and actual use.
Abstract
The CDOI outcome measure - a patient-reported outcome (PRO) instrument utilizing direct client feedback - was implemented in a large, real-world behavioral healthcare setting in order to evaluate previous findings from smaller controlled studies. PROs provide an alternative window into treatment effectiveness based on client perception and facilitate detection of problems/symptoms for which there is no discernible measure (e.g. pain). The principal focus of the study was to evaluate the utility of the CDOI for predictive modeling of outcomes in a live clinical setting. Implementation factors were also addressed within the framework of the Theory of Planned Behavior by linking adoption rates to implementation practices and clinician perceptions. The results showed that the CDOI does contain significant capacity to predict outcome delta over time based on baseline and early change scores…
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