  Purpose: This goal of this study was to evaluate the effects of a data-driven
clinical productivity system that leverages Electronic Health Record (EHR) data
to provide productivity decision support functionality in a real-world clinical
setting. The system was implemented for a large behavioral health care provider
seeing over 75,000 distinct clients a year. Design/methodology/approach: The
key metric in this system is a "VPU", which simultaneously optimizes multiple
aspects of clinical care. The resulting mathematical value of clinical
productivity was hypothesized to tightly link the organization's performance to
its expectations and, through transparency and decision support tools at the
clinician level, affect significant changes in productivity, quality, and
consistency relative to traditional models of clinical productivity. Findings:
In only 3 months, every single variable integrated into the VPU system showed
significant improvement, including a 30% rise in revenue, 10% rise in clinical
percentage, a 25% rise in treatment plan completion, a 20% rise in case rate
eligibility, along with similar improvements in compliance/audit issues,
outcomes collection, access, etc. Practical implications: A data-driven
clinical productivity system employing decision support functionality is
effective because of the impact on clinician behavior relative to traditional
clinical productivity systems. Critically, the model is also extensible to
integration with outcomes-based productivity. Originality/Value: EHR's are only
a first step - the problem is turning that data into useful information.
Technology can leverage the data in order to produce actionable information
that can inform clinical practice and decision-making. Without additional
technology, EHR's are essentially just copies of paper-based records stored in
electronic form.
