SICK Score Clinical Event Outcome Scoring System
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Solution Overview
Problem
Current clinical event outcome scoring systems fail to accurately assess the severity of illness by not considering the combination of adverse factors and their impact on patient outcomes, leading to inadequate decision-making in healthcare, particularly in Pay-for-Performance programs, which can exacerbate health care disparities.
Innovation Solution
A clinical event outcome scoring system that generates a Severity of Illness Clinical Key (SICK) score by analyzing historical patient data to create a statistical model for each clinical event, incorporating physiological, lifestyle, cultural, genetic, and behavioral attributes, and their weighted contributions to outcomes, enabling personalized Pre-Clinical Event Care Plans and improved resource planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional severity of illness indexes are used to assess patient risk, then the assessment process is simple and quick, but the accuracy of outcome prediction is insufficient because they only tabulate specific risk factors without accounting for the severity of disease processes
Solution Approach 1:
The system segments the assessment into multiple dimensions: (1) identification of adverse factors present in the patient, (2) determination of severity level for each adverse factor, and (3) calculation of a composite SICK score by combining weighted severity levels. This segmentation allows the system to capture both the presence and severity of multiple disease processes, significantly improving outcome prediction accuracy while maintaining a structured, manageable assessment framework
Solution Approach 2:
The system transforms traditional binary risk factor assessment into a multi-level severity scale. Each adverse factor is evaluated not just for presence/absence but for its severity level (e.g., mild, moderate, severe), and these severity parameters are then weighted and combined to produce the SICK score. This parameter change from simple tabulation to severity-weighted combination directly addresses the limitation of traditional indexes
2Reliability
If traditional risk assessment methods are used in Pay-for-Performance programs, then program implementation is straightforward, but the programs may penalize doctors for caring for the poorest and sickest patients, exacerbating health care disparities
Solution Approach 1:
The system performs preliminary assessment of the SICK score before clinical events occur, allowing healthcare providers to identify high-risk patients in advance. This preliminary action enables proactive care planning and resource allocation for the poorest and sickest patients, ensuring they receive appropriate attention before being evaluated in Pay-for-Performance programs, thereby preventing penalization of providers caring for complex populations
Solution Approach 2:
The SICK score provides feedback on the true severity of patient populations served by different providers. By accurately reflecting the combination and severity of adverse factors, the system gives feedback that adjusts for patient complexity, ensuring that Pay-for-Performance evaluations fairly account for the difficulty of caring for certain populations, thus reducing disparities
3Measurement precision
If generic statistical models are used for clinical events, then model development is efficient and resource-consuming is minimized, but the model cannot capture patient-specific attributes that may have greater impact on certain patient groups
Solution Approach 1:
The system applies local quality by allowing different weighting schemes and severity thresholds for different patient populations or clinical contexts. While the overall SICK score framework remains consistent, the specific weights assigned to adverse factors and the thresholds for severity levels can be customized for different patient groups, clinical events, or healthcare settings. This enables personalized outcome assessment that captures population-specific characteristics without requiring completely separate models for each group
Data Source
AI summary
A clinical event outcome scoring system and method are used to determine a Severity of Illness Clinical Key (SICK) score, which is a probable degree of successful outcome for a patient about to undergo a specific clinical event, such as for example, coronary bypass surgery, hip replacement, bariatric surgery, discharge from a hospital for home recovery, a course of chemotherapy, radiation, or other treatment protocol. The system and method analyzes historical patient data to generate a statistical model for each specific clinical event of interest, which can then be used to determine a SICK score for a patient about to undergo the same clinical event. In some embodiments, the statistical model can be “fine-tuned” to render subcategories of statistical models tailored for certain patient populations about to undergo the same clinical event. In some embodiments, the statistical model can be augmented to take into account “outliers,” who have extra challenges not taken into account with the primary statistical model.