Machine-Learning Risk Prediction for Prioritized Patient Interventions
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Solution Overview
Problem
Traditional healthcare management systems are incompatible with modern, value-based approaches and fail to support patient populations like Medicaid recipients, particularly in rural or remote locations, lacking predictive power for individual patients and failing to incorporate diverse data sources effectively.
Innovation Solution
A system utilizing a machine learning model trained on enriched patient data, including EHRs, insurance claims, and patient engagement data, to generate personalized intervention lists and continuously update them based on feedback, enhancing predictive power for individual patients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fee-for-service software tools are used, then the system supports traditional healthcare delivery models, but it cannot support value-based healthcare approaches
Solution Approach 1:
The system is designed to support multiple healthcare delivery models simultaneously - both traditional fee-for-service and modern value-based approaches. The platform can handle capitated models, bundled payments, and traditional reimbursement structures through a unified architecture, allowing organizations to transition between models without requiring separate software systems.
2Measurement precision
If traditional population models are used, then the system can predict cost for aggregate populations, but it lacks predictive power for individual patients
Solution Approach 1:
The system transitions from uniform population-level predictions to localized individual-level predictions by incorporating patient-specific characteristics. Each patient's risk profile is customized based on their unique combination of clinical data, social determinants, behavioral factors, and engagement history, enabling precise individual predictions while maintaining the ability to aggregate results for population analysis.
3Measurement precision
If comprehensive data collection is implemented, then the system can improve predictive accuracy, but it increases system complexity
Solution Approach 1:
The system introduces standardized data intermediaries and integration layers that mediate between diverse data sources (EHRs, social services, behavioral health systems) and the predictive engine. These intermediaries normalize and harmonize data from different formats and sources, reducing integration complexity while enabling comprehensive data utilization for improved predictive accuracy.
4Reliability
If continuous model re-training is performed, then the system maintains up-to-date predictions, but it increases computational resource usage
Solution Approach 1:
The system implements periodic batch re-training cycles combined with incremental updates rather than continuous full re-training. The model is re-trained on accumulated new data at scheduled intervals, and incremental adjustments are applied between cycles based on incoming data streams, maintaining prediction reliability while managing computational resource consumption through rhythmic rather than continuous processing.
Data Source
AI summary
Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.