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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