Table 2.

A practical implementation framework for integrating deep learning tools into DRG-based workflows.

Phase	Key steps and considerations
Problem and scope definition	Identify a high-cost or high-variance DRG to target. Define clear success metrics (e.g., reduce LOS, lower readmission penalties).
Data and infrastructure	Assess quality, accessibility, and completeness of required data (EHR, imaging, etc.). Evaluate existing IT infrastructure and computational resources.
Model development and validation	Select an appropriate model based on the problem and data type. Plan for rigorous internal and, crucially, external validation. Conduct fairness audits to identify and mitigate potential algorithmic bias.
Workflow integration	Co-design user interfaces with frontline clinicians to ensure usability. Provide comprehensive staff training on how to interpret and act on model outputs. Ensure seamless integration into the EHR to avoid disrupting clinical routines.
Governance and monitoring	Ensure full compliance with regulatory (e.g., HIPAA, GDPR) and ethical standards. Continuously track key clinical outcomes and financial metrics (e.g., ROI, cost-per-case) post-implementation.

DRG, diagnosis-related group; LOS, length of stay; EHR, electronic health record; HIPAA, health insurance portability and accountability act; GDPR, general data protection regulation; ROI, return on investment.