Dynamic Patient Attribution via Care Responsibility Curves
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Solution Overview
Problem
Conventional methods for patient attribution in Accountable Care Organizations do not provide real-time tracking and accurately reflect clinician responsibility, leading to inefficient management of patient care and misallocation of incentives for cost and quality of care.
Innovation Solution
A system and method for generating patient-attribution assignments based on clinicians' interactions with patients, using care responsibility curves that decay over time, to accurately rank and assign responsibility to the most involved clinicians, enabling real-time updates and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional patient attribution methods are used, then the system is simple to operate, but the attribution accuracy and real-time tracking capability deteriorate
Solution Approach 1:
The patent implements dynamic patient attribution by continuously updating clinician responsibility scores as new interactions occur. The system transitions from static attribution to dynamic re-attribution based on real-time interaction data, allowing the most responsible clinician to be identified at any point in time through continuous score recalculations.
Solution Approach 2:
The patent replaces conventional mechanical tracking methods with an information-based electronic system. Instead of manual tracking and assignment, the system uses electronic interaction records, automated score calculations, and computer-generated attributions to determine clinician responsibility, thereby improving accuracy while managing complexity through automation.
2Measurement precision
If real-time tracking is implemented, then the attribution accuracy improves, but the computational resources and processing time increase
Solution Approach 1:
The patent calculates responsibility scores for multiple clinicians simultaneously rather than sequentially evaluating each clinician individually. By computing scores for all potential responsible clinicians in parallel based on interaction data, the system achieves real-time attribution accuracy while optimizing computational efficiency through batch processing.
3Measurement precision
If interaction context is considered, then the responsibility assignment accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent transforms qualitative interaction context into quantitative responsibility scores through defined weighting parameters. Different interaction types (e.g., face-to-face visits, phone calls, messages) are assigned different weight values, allowing the system to process contextual information systematically and accurately determine clinician responsibility through mathematical calculation rather than subjective judgment.
Data Source
AI summary
Methods and systems for managing patient attributions are provided. Interaction events between clinicians and the patient are identified, and using times associated with each interaction, a time series of the interactions for each clinician may be constructed. Care responsibility curves measuring a clinician's care responsibility level over time may be generated using the time series, an initial care responsibility level assigned to each interaction and a rate of decay for each interaction, which may be based on the type of action and type of clinician who interacted with the patient. A care responsibility score for each clinician may be determined from the clinician's care responsibility curve, and a patient-attribution assignment record may be created to attribute the patient to one of the clinicians based on a ranking of the clinicians' care responsibility scores.