An AI-ready data playbook for Chief Data Officers who need ROI inside the existing stack - use-case-led selection, use-case-specific cleaning, and patient-identity discipline that ships AI without a platform rebuild.
Pick the data the AI use case actually needs. Not the data the team imagines might be useful. The scope is the use case, not the warehouse, and the program ships when the use case ships.
Cleaning is use-case-specific. The cleaning rules a fraud detection AI needs are different from what a clinical decision support AI needs. The overlay codifies the rules per use case so reproducibility lives with the use case, not with whoever wrote the notebook.
Identity is non-negotiable in healthcare AI. Every join across systems uses the network's MPI or, if no MPI exists, a use-case-scoped identity resolver with documented confidence rules. Wrong identity is the bias the model amplifies.
Pick the data the AI use case actually needs. Not the data the team imagines might be useful.
Cleaning is use-case-specific. The cleaning rules a fraud detection AI needs are different from what a clinical decision support AI needs.
Identity is non-negotiable in healthcare AI. Every join across systems uses the network's MPI or, if no MPI exists, a use-case-scoped identity resolver with documented confidence rules.
Ship the first use case into production behind a feature flag. Run a bias review on protected populations before the model is live to the network.
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