Patient Readmission Prediction Model Using Attribute Selection
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Solution Overview
Problem
Healthcare providers face challenges in identifying and preventing potentially preventable patient readmissions, which are a significant source of avoidable costs and affect quality of care, especially in value-based purchasing models.
Innovation Solution
A patient readmission prediction model is developed that selects meaningful patient attributes to predict the likelihood of post-discharge readmissions, using patient data to generate a predictive risk score, facilitating timely care management interventions and reducing overall healthcare costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a patient readmission prediction model is developed using selected patient attributes, then the ability to identify high-risk patients is improved, but the complexity of the prediction system increases
Solution Approach 1:
The patent extracts and selects only the most meaningful patient attributes from the complete set of available patient data. This selective extraction process identifies key predictors of readmission while discarding irrelevant information, thereby achieving accurate predictions without requiring the full complexity of all available data.
Solution Approach 2:
The prediction model applies different weighting and selection criteria to different patient attributes based on their local importance for predicting readmission. Not all patient attributes are treated equally; instead, the model identifies and emphasizes specific attributes that have higher predictive value for particular patient populations or conditions.
2Reliability
If patient data is analyzed to generate predictive risk scores, then the ability to prevent readmissions is improved, but the computational resources and time required increase
Solution Approach 1:
The patent performs preliminary analysis to identify and select the most meaningful patient attributes before building the prediction model. This advance preparation ensures that when the model is applied to new patients, only the critical attributes need to be processed, significantly reducing the time and computational resources required for prediction while maintaining high reliability.
3Loss of energy
If targeted interventions are implemented for high-risk patients, then cost savings from reduced readmissions are achieved, but the resources required for intervention increase
Solution Approach 1:
The patent applies targeted interventions based on locally identified high-risk patient groups rather than implementing uniform interventions across all patients. By selecting specific patient attributes that predict readmission risk and focusing resources on those who most need intervention, the system achieves cost savings while optimizing the allocation of intervention resources.
Solution Approach 2:
The prediction model enables healthcare providers to self-identify high-risk patients who would benefit most from intervention, allowing resources to be allocated efficiently without requiring external guidance or blanket protocols for all patients.
Data Source
AI summary
Facilities are provider herein for predicting potentially preventable patient readmissions after discharge from a health care provider. A patient readmission prediction model is built based on received patient data for one or more health care providers. Patient attributes discernible from that patient data are extracted and analyzed, and certain attributes are selected as being meaningful in predicting likelihood of post-discharge, potentially preventable patient readmission by patients newly admitted to a health care provider. Patient data for a newly admitted patient is obtained, and the readmission prediction model is applied against that patient data for the newly admitted patient to obtain a predictive risk score that is indicative of the likelihood that the newly admitted patient will experience a potentially preventable readmission post-discharge from the health care provider.