Feedback

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 The Feedback portion of the Data Science Methodology emphasizes the importance of incorporating user feedback to refine and assess the model's performance and impact. Here's a summary of the key steps involved in the feedback process:


Define Review Process: The review process is defined and put into place, with clinical management executives having overall responsibility for measuring the results of the model.


Track Intervention Outcomes: Congestive heart failure patients receiving intervention are tracked, and their readmission outcomes are recorded.


Measure Intervention Effectiveness: The effectiveness of the intervention is measured to determine its impact on reducing readmissions. This is done by comparing readmission rates before and after the implementation of the model.


Review Impact After First Year: After the first year of implementing the intervention program, the impact on readmission rates is reviewed. The model is then refined based on the data compiled and knowledge gained during these stages.


Incorporate Additional Data: Data collection may be refined to incorporate additional information, such as participation in the intervention program or detailed pharmaceutical data, based on feedback and practical experience with the model.


Continued Refinement: Other refinements may be identified during the feedback stage, and intervention actions and processes are reviewed and refined based on initial deployment experience.


Redeployment and Continued Feedback: The refined model and intervention actions are redeployed, with the feedback process continued throughout the life of the intervention program.


Overall, the feedback process ensures that the model remains relevant and effective, with continuous refinement based on user experience and outcomes data. This cyclical approach to refinement ensures ongoing improvement at each stage of the data science methodology.





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