Reimbursement Calculation System Integrating Clinical Quality Metrics
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Solution Overview
Problem
Current healthcare reimbursement systems focus on billing codes and ignore clinical data, leading to unreasonably high costs and inefficient care, as they do not account for patient conditions, care quality, or appropriateness, resulting in generalized reimbursement standards rather than effective high-quality care.
Innovation Solution
A computer-implemented method and system that records healthcare events by acquiring metadata and raw data, including patient and practitioner identifiers, medical reasons, and quality metrics from medical devices, to calculate reimbursement based on procedures, devices, and quality data, integrating digital and DICOM data for accurate and verifiable reimbursement processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If reimbursement systems focus on billing codes and ignore clinical data, then reimbursement processing is simplified, but reimbursement accuracy and care quality assessment deteriorate
Solution Approach 1:
The patent merges billing code data with clinical data from electronic health records into a unified reimbursement processing system. This integration allows the system to simultaneously process both administrative billing information and clinical quality metrics, resolving the contradiction between processing simplicity and reimbursement accuracy by combining previously separate data streams into a coordinated workflow.
Solution Approach 2:
The system implements multi-functional processing capabilities that handle both traditional billing code evaluation and clinical quality assessment within a single reimbursement platform. This universal approach enables the system to perform multiple functions (billing verification, clinical quality measurement, fraud detection) simultaneously, maintaining operational simplicity while improving reimbursement accuracy through comprehensive data utilization.
2Productivity
If reimbursement standards are based on generalities rather than specific clinical circumstances, then reimbursement processing is faster, but care quality assessment deteriorates
Solution Approach 1:
The system performs preliminary organization and categorization of clinical data before reimbursement processing, pre-structuring electronic health record information into standardized formats that can be quickly evaluated. This preliminary action enables fast processing speeds while maintaining the ability to assess specific clinical circumstances, as the data is already prepared and tagged for efficient retrieval and analysis during reimbursement evaluation.
Solution Approach 2:
The system dynamically adjusts evaluation parameters based on the specific clinical circumstances of each case. Rather than applying fixed generalized standards, the system modifies assessment criteria according to patient-specific factors, procedure complexity, and clinical outcomes, thereby maintaining high processing productivity through automated parameter adjustment while improving reliability through customized quality assessment.
3Measurement precision
If clinical data is integrated into reimbursement systems, then reimbursement accuracy and care quality assessment improve, but system complexity increases
Solution Approach 1:
The patent introduces intermediary components including standardized data interfaces, centralized data repositories, and mediation software that bridge electronic health record systems with reimbursement processing systems. These intermediaries manage the complexity of data integration by providing standardized connection protocols and data transformation layers, allowing accurate reimbursement processing through integrated clinical data while containing system complexity within modular intermediary components.
Solution Approach 2:
The system segments the integrated reimbursement system into distinct functional modules: data acquisition from electronic health records, data validation and standardization, clinical quality assessment engines, and reimbursement calculation components. This segmentation allows each module to handle specific tasks independently, improving overall reimbursement accuracy through comprehensive data integration while managing system complexity through modular architecture that enables independent development, testing, and maintenance of each component.
4Reliability
If quality metrics from medical devices are collected and verified, then fraud detection improves, but data acquisition and processing time increase
Solution Approach 1:
The system implements continuous data collection from medical devices during patient care delivery, rather than requiring separate post-procedure data entry. Quality metrics are captured in real-time as procedures are performed, enabling fraud detection through continuous verification of actual care delivery against billed services. This continuous action approach improves fraud detection reliability while minimizing additional time loss, as data acquisition occurs concurrently with care provision rather than as a separate sequential step.
Data Source
AI summary
A method and system is provided for recording a health care event, optimizing medical procedures and calculating reimbursement. The method includes: acquiring metadata comprising a patient identifier, a practitioner identifier, a health care site identifier, an entry time and a medical reason for the health care event; receiving a reimbursement request; generating a procedures list based on the medical reason; selecting a procedure; generating a list of required data types and a list of required quality data for the procedure; acquiring raw data comprising the procedure, a medical device identifier, an entry time and one or more quality data from the medical device for the procedure; and calculating a reimbursement for the health care event based on the procedure, the medical device identifier, the required quality data and the quality data from the medical device. The method implements iterative learning using the collected data to determine optimal health care procedures.