Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment

• Main Authors: Bennett, C., Doub, T., Bragg, A., Luellen, J., Van Regenmorter, C., Lockman, J., Reiserer, R.
• Format: Conference Proceeding
• Language: English
• Subjects: Accuracy; CDOI; Clinical Decision Support Systems; Data mining; Data models; Electronic Health Records; Implementation; Medical treatment; Patient-Reported Outcomes; Predictive models; Reliability; Theory of Planned Behavior
• DOI: 10.1109/HISB.2011.20

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 in a large, real-world clinical setting, as suggested in previous research. The implementation analysis revealed a number of critical factors affecting successful implementation and adoption of the CDOI outcome measure, though there was a notable disconnect between clinician intentions and actual behavior. Most importantly, the predictive capacity of the CDOI underscores the utility of direct client feedback measures such as PROs and their potential use as the basis for next generation clinical decision support tools and personalized treatment approaches.