Hybrid Human Computer-Assisted Coding Workflow
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Solution Overview
Problem
The process of generating healthcare bills based on clinical reports is tedious, time-consuming, and error-prone due to complex billing code standards and a shortage of expert billing coders, leading to inefficiencies and inaccuracies in coding processes.
Innovation Solution
A computer system that utilizes a CAC module to generate initial billing codes and assess their accuracy, with a routing mechanism to determine if human review is necessary, involving both an initial and final human reviewer to ensure quality and accuracy, leveraging automated technology while maintaining human oversight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fully automated CAC systems are used to generate billing codes, then productivity increases, but accuracy and reliability deteriorate due to insufficient handling of complex charts
Solution Approach 1:
The patent segments the coding process into multiple stages: automated CAC generates initial codes, then human reviewers review and correct codes for complex charts. This segmentation allows the system to leverage automated speed for simple cases while applying human expertise only where needed, resolving the contradiction between productivity and accuracy.
Solution Approach 2:
The patent applies partial human review rather than complete manual review for all charts. Human reviewers focus only on complex charts that the CAC system identifies as needing review, providing excessive action (human review) only where necessary to maintain accuracy while preserving automated productivity for the majority of cases.
2Reliability
If expert billing coders are trained to handle complex coding scenarios, then coding accuracy improves, but training time and cost increase
Solution Approach 1:
The CAC system acts as an intermediary that handles the bulk of coding work, freeing expert coders from routine tasks. This allows the organization to maintain high accuracy by retaining fewer expert coders who focus on complex cases, reducing the overall training burden while preserving coding quality.
Solution Approach 2:
The CAC system captures and codifies expert coding knowledge into automated algorithms and rules. This copying of expert knowledge into the system allows less experienced coders to achieve higher accuracy by working alongside the CAC system, reducing the need for extensive training while maintaining reliability.
3Measurement precision
If CAC systems are tuned to improve accuracy for specific healthcare providers, then coding precision improves, but deployment time and complexity increase
Solution Approach 1:
The CAC system is designed with universal, configurable parameters that can be adjusted for different healthcare providers without requiring complete system reconfiguration. This allows the system to adapt to specific provider needs and improve precision while maintaining a standardized core architecture that limits complexity.
Solution Approach 2:
The system allows tuning of specific parameters (such as review thresholds, code selection criteria, and confidence levels) to optimize performance for different healthcare providers. These parameter changes enable customization and improved precision without fundamentally altering the system architecture, thus managing complexity.
4Reliability
If more human reviewers are involved in the coding process, then error rate decreases, but productivity decreases due to increased manual review requirements
Solution Approach 1:
The patent segments the review process so that human reviewers only examine complex charts identified by the CAC system, rather than reviewing all charts. This segmentation reduces the total volume of manual review required while maintaining low error rates through targeted human oversight of problematic cases.
Solution Approach 2:
The CAC system serves as an intermediary that pre-screens charts and identifies only those requiring human review. This filtering function reduces the workload on human reviewers, allowing more reviewers to be effectively utilized without proportionally reducing overall productivity, as they focus only on cases needing human judgment.
Data Source
AI summary
A computer system increases the efficiency with which billing codes may be generated based on a chart, such as a medical chart. The computer system provides the chart to a computer-assisted coding (CAC) module, which produces an initial set of billing codes and an initial assessment of the accuracy and/or completeness of the codes. The computer system decides whether to send the initial set of billing codes to an initial human reviewer. If the computer system sends the initial set of billing codes to the initial human reviewer, then the initial human reviewer reviews the chart and the output of the CAC module, and attempts to fix errors in the CAC output. The system provides the chart and the current (initial or modified) codes to a final human reviewer, who may be more highly skilled than the initial human reviewer, for final verification and modification.