Medical Coder Sorter Optimizing Task Assignment
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Solution Overview
Problem
Medical coding is a highly manual and error-prone process, requiring significant human intervention and multiple quality assurance steps, leading to inefficiencies and increased costs, with a need for a system to efficiently assign tasks to medical coders based on their abilities and objectives.
Innovation Solution
A medical coder sorter system that dynamically assigns findings to coders based on their abilities and quantifiable objectives, using exploration and exploitation modes to optimize task selection and generate reports, with competency determination to ensure accurate and efficient coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual medical coding process is used, then coder expertise and flexibility are maintained, but productivity is low and errors increase
Solution Approach 1:
The patent introduces an automated sorting system as an intermediary between medical findings and human coders. This system uses natural language processing and machine learning to pre-process and categorize findings, presenting only relevant cases to coders. This mediator handles routine sorting tasks, allowing coders to focus on complex coding decisions, thereby improving both accuracy and productivity.
Solution Approach 2:
The patent replaces the manual mechanical process of coding with an automated computational system for the sorting and prioritization phase. While final coding decisions remain human, the initial filtering, categorization, and prioritization of findings are performed by algorithms, reducing the manual burden and enabling faster processing of large volumes of medical data.
2Reliability
If multiple quality assurance steps are implemented, then coding reliability improves, but device complexity and cost increase
Solution Approach 1:
The patent extracts the quality assurance function from the manual coding process and embeds it into the automated sorting system. The system automatically applies filtering criteria, consistency checks, and priority assignments before presenting findings to coders. This extraction of QA steps into the automated workflow reduces the need for separate manual review layers, maintaining quality while simplifying the overall process structure.
3Productivity
If dynamic task assignment based on coder abilities is implemented, then productivity increases, but device complexity increases
Solution Approach 1:
The patent dynamically adjusts system parameters based on coder performance metrics, finding characteristics, and workload conditions. The sorting algorithm modifies priority assignments, categorization criteria, and task distribution parameters in real-time based on coded data about coder expertise and historical performance. This dynamic parameter adjustment enables adaptive task assignment that improves productivity without requiring complex manual management.
Data Source
AI summary
A sorter of medical findings for assessment by a medical coder is provided. In some embodiments, the sorter receives information about a user (coder), including identification, a role, and historical activity. The sorter determines whether to run in exploration or exploitation modes. Exploration is used to explore the scope of the findings and also identify variables that impact a finding. Exploitation is designed to maximize a goal (such as throughput or profitability). Lastly a finding is selected and provided to the user. The selection is based upon computing internal parameters when in exploration, or based upon optimizing for criteria when in exploitation. The sorter may also determine competency for the user, and cut them off from performing additional coding if they are found incompetent.