Checkpoint Identification Rules for Detecting Care Gaps
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Solution Overview
Problem
Existing systems struggle to ensure that medical care standards are consistently met due to diverse and dynamic medical data sources, equipment, and temporal compliance issues, leading to potential care gaps and missed treatment opportunities.
Innovation Solution
A computer system for check point identification through in silico modeling, which identifies conditions and contacts associated with a subject, applies decision rules to model care plans, and communicates notifications via a network to address care gaps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple diverse medical data sources are integrated to comprehensively evaluate standard of care compliance, then the completeness of medical data increases, but the complexity of data processing and system architecture increases
Solution Approach 1:
The system segments medical data into multiple modalities (structured EHR data, unstructured physician notes, medical test results, imaging data) and processes each through specialized modules. Decision rules are applied separately to each data type, and results are integrated to form comprehensive care gap identifiers, reducing overall processing complexity while maintaining data completeness
Solution Approach 2:
The patent introduces intermediary components including a FHIR server that standardizes data exchange between diverse sources, a care gap identifier that mediates between raw data and clinical decisions, and a notification system that bridges the gap between system analysis and provider action. These intermediaries simplify integration of heterogeneous data sources
2Measurement precision
If decision rules are frequently updated to reflect changing standards of care, then the accuracy of compliance monitoring improves, but the system stability and maintenance difficulty worsen
Solution Approach 1:
The system implements dynamic decision rules that can be updated to reflect changing standards of care. The architecture allows rules to be modified, added, or removed without system redesign, enabling accurate compliance monitoring with evolving guidelines while maintaining system stability through modular rule management
Solution Approach 2:
The system performs preliminary actions by pre-defining decision rules based on current standards of care before compliance evaluation begins. This allows the system to be proactive in identifying care gaps based on updated guidelines rather than requiring reactive system reconfiguration, simplifying maintenance
3Reliability
If comprehensive care pathway analysis is performed to identify all potential care gaps, then the thoroughness of medical review increases, but the time required for processing and potential loss of time for clinicians increases
Solution Approach 1:
The system applies partial action by focusing decision rule evaluation on specific clinically relevant time periods (e.g., evaluating colonoscopy compliance within a 5-year window, pacemaker follow-up within 1 year) rather than analyzing all historical data. This provides thorough review where needed while limiting processing time through strategically selected evaluation windows
Solution Approach 2:
The system extracts and prioritizes only the most critical care gaps that meet decision rule criteria, separating these from routine care elements. By extracting only actionable care gaps rather than presenting all analyzed data, the system maintains thoroughness while reducing the time burden on clinicians
4Measurement precision
If detailed subject-specific conditions and biomarkers are considered to personalize care recommendations, then the precision of personalized medicine increases, but the complexity of data analysis and computational requirements increase
Solution Approach 1:
The system applies local quality by tailoring decision rule application to individual subject characteristics. Different decision rules are activated based on subject-specific factors such as age, disease stage, biomarker presence, and comorbidities. This enables precise personalized care recommendations while managing computational complexity through selective rule activation rather than evaluating all rules for all subjects
Data Source
AI summary
Computer systems for check point identification for a subject through in silico modeling are provided. One or more conditions and a plurality of contacts associated with the subject are identified in a data repository. A subset of decision rules is discovered from among a plurality of decision rules through alignment of a condition in the one or more conditions against the decision rules. The subset of decision rules models a response to the first condition at a first entity. A first decision rule in the subset of decision rules is activated using an evaluation module when a corresponding triggering condition for the decision rule arises in the data repository. A notification rule actionable upon the activating of the first decision rule is identified. A notification is communicated using a computer network to a notification path, consisting of a subset of the plurality of contacts, in accordance with the notification rule.