In a quasi-experimental study decision support software was installed in three hospitals to study the ability to scale (spread) its use from one hospital on paper to three hospitals as software, and to examine the effect on 30 and 60-day readmissions. The Discharge Decision Support System (D2S2) software analyzes data collected by nurses on admission with a proprietary risk assessment tool, identifies patients in need of post-acute care, and alerts discharge planners. On six intervention units, with a concurrent comparison group of 76 units, we examined the implementation experience and compared readmission outcomes before and after implementation. The software implementation finished one month ahead of schedule, the software performed reliably. High-risk patients admitted in the experimental phase after implementation of D2S2 decision support had significantly fewer 30-day readmissions (a decrease 22.2% to 9.4%) When high and low risk patients were analyzed together, D2S2 achi8eved a 33% relative reduction in 30 day readmissions (13.1% to 8.8%) and sustained a 37% relative reduction at 60 days. The software, available commercially through RightCare Solutions, was adopted by the health system and remains in use after 22 months. The D2S2 risk assessment tool can be installed easily in existing EHR systems. Future research will focus on how the tool influences discharge decision-making and how its accuracy can be improved in specific settings.

Focus on readmission risk assessment tools has never been higher, and yet for all the time, resources, and attention spent developing and implementing these disparate models, readmission rates have barely budged. Fundamental flaws exist in most approaches in the areas of Data, Model Adaptability, and Clinical Workflow Integration. Many tools rely solely on historical patient data mined from the EHR or on disease-specific models that cannot be scaled to address all readmissions challenges. Models that rely on data collected at discharge are not timely enough to enable clinicians to take meaningful action, and ones that are not well-integrated into clinical workflow are not easily adopted.Finally, static prediction tools that do not adjust to a hospital's specific patient population deliver limited results over time. For a readmission risk assessment tool to achieve a meaningful and long-lasting impact, these common pitfalls must be avoided at all costs.

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