Model deployment, where the focus is on integrating the model into the healthcare system's workflow ensuring it meets operational standards.

Data pre-processing, which involves cleaning and preparing the patient data for analysis, including handling missing values and outliers.

Problem formulation, where the specific healthcare problem is defined, objectives are set, and success criteria are established in alignment with regulatory and interpretability requirements.

Model development, focusing on selecting and training algorithms that can predict readmission risks accurately while being interpretable.

None of the above.