Clinical Productivity System - A Decision Support Model

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.