Serious Illness Score Calculation for Clinical Queue Prioritization
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Solution Overview
Problem
Current risk models for serious illnesses often focus on prognosis or acute healthcare utilization, leading to delayed identification of individuals who need proactive care, resulting in unnecessary suffering and increased healthcare costs, as they prioritize those with poor prognoses or high acute care usage, thereby missing individuals who could benefit from early intervention.
Innovation Solution
A system and method that calculate a serious illness score for individuals based on healthcare data, stored in network-based databases, to prioritize healthcare interventions by converting healthcare data into functional scores across categories, tracking changes over time, and automatically assigning individuals to clinical queues for proactive care.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current risk models focus on prognosis or acute healthcare utilization, then identification of high-risk individuals is improved, but time to intervention is delayed and many individuals needing proactive care are missed
Solution Approach 1:
The system performs preliminary risk assessment by continuously monitoring healthcare data and calculating risk scores before acute events occur. This allows proactive identification of individuals at risk of serious illness, enabling early intervention before crisis points are reached, thus resolving the contradiction between accurate identification and timely intervention
Solution Approach 2:
The population is segmented into different risk strata based on calculated risk scores, allowing differentiated intervention strategies. High-risk individuals are identified and prioritized for proactive care, while lower-risk individuals receive appropriate levels of care, improving both identification accuracy and timeliness of intervention
2Productivity
If risk models prioritize individuals with poor prognosis or high acute care usage, then resource allocation to severe cases is improved, but early intervention opportunities are lost and healthcare costs increase
Solution Approach 1:
The system enables preliminary intervention by identifying at-risk individuals before they experience acute events or require expensive acute care. By calculating risk scores from routine healthcare data and intervening proactively, the system prevents costly hospitalizations and emergency care, thus improving resource allocation efficiency while reducing overall healthcare costs
Solution Approach 2:
The system converts routine healthcare data that would otherwise go unnoticed into valuable risk prediction information. By analyzing patterns in常规 healthcare utilization and converting them into actionable risk scores, the system transforms ordinary data into a benefit that prevents expensive acute care events
Data Source
AI summary
Systems and methods for serious illness identification and stratification are provided. In one embodiment, a method includes calculating functional scores in a plurality of categories based on healthcare data for an individual, calculating a total serious illness score for the individual based on the functional scores, measuring a serious illness score change for the individual based on the total serious illness score and historical serious illness scores for the individual, updating a clinical queue of a plurality of clinical queues to include the individual based on the serious illness score change and the total serious illness score, wherein each clinical queue comprises a list of individuals prioritized for healthcare intervention, and transmitting the updated clinical queue to a client device associated with a clinician. In this way, healthcare data may be leveraged to characterize the health status of an individual over time, and to prioritize serious illness care accordingly.