Machine Learning Model for Automated Palliative Care Selection
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Solution Overview
Problem
Current healthcare systems face challenges in efficiently managing end-of-life care, often leading to unnecessary hospitalizations and high costs, due to the confusion between palliative care and hospice programs, which can disrupt care continuity and are expensive.
Innovation Solution
A computer system that uses a machine learning model to generate expiration likelihood estimates for database entries, allowing for the automated selection of palliative care interventions by training on historical data, determining expiration dates, and generating feature lists to identify the most impactful parameters for each patient, thereby optimizing care management and reducing costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional palliative care programs are implemented, then end-of-life care management is provided, but care continuity is disrupted and costs increase
Solution Approach 1:
The machine learning model performs preliminary identification of patients who would benefit from palliative care by analyzing historical data and generating probability scores before care decisions are made. This advance prediction allows for proactive care planning that maintains continuity and prevents costly last-resort interventions
Solution Approach 2:
The system enables automated patient identification and prioritization through the machine learning model, which self-evaluates patient data and generates care recommendations without requiring manual review of each patient record, thereby maintaining care continuity while reducing operational costs
2Measurement precision
If machine learning models are used to identify patients, then prediction accuracy improves, but computational resources and data processing requirements increase
Solution Approach 1:
The system extracts only the most relevant features from patient data that have been proven to correlate with palliative care benefits, rather than processing all available data. This feature selection process maintains high prediction accuracy while significantly reducing computational resource requirements
Solution Approach 2:
The machine learning model uses probability score thresholds and feature importance weighting to optimize the balance between prediction accuracy and computational efficiency. By adjusting these parameters, the system can achieve high accuracy with reduced computational burden
Data Source
AI summary
A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training a machine learning model to generate an entity expiration likelihood output, obtaining a set of multiple database entities, and processing, by the machine learning model, feature vector inputs associated with each database entry to generate an entity expiration likelihood output. The instructions include determining a subset of the database entities having the highest entity expiration likelihood outputs, and, for each database entity in the subset, determining output impact scores for parameters of the feature vector input associated with the database entity, generating a feature list based on the determined output impact scores, and automatically selecting an executable sequence according to the entity expiration likelihood output associated with the database entity. The feature list is specific to the database entity and includes one or more of the parameters having the highest output impact scores.