CDI Scoring System Prioritizes Patient Charts
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Solution Overview
Problem
Current clinical documentation improvement (CDI) processes in healthcare facilities are inefficient, as CDI specialists lack the capacity to review all patient charts regularly and prioritize them inadequately, leading to missed opportunities for significant documentation improvements.
Innovation Solution
A CDI scoring system that generates a numerical value representing the likelihood of improvement in clinical documentation, using real-time clinical information to prioritize patient charts for review, incorporating factors like Diagnosis-Related Group (DRG) codes, documentation accuracy, and patient location, allowing CDI specialists to focus on charts with the most potential for improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If CDI specialists manually review patient charts using traditional prioritization methods, then they can identify documentation gaps, but they lack the capacity to review all charts regularly and miss significant improvement opportunities
Solution Approach 1:
The patent introduces an automated prioritization system that acts as an intermediary between patient charts and CDI specialists. This system uses machine learning models to analyze clinical documentation and generate prioritization scores, enabling specialists to focus their limited capacity on high-value charts while maintaining high documentation accuracy through targeted reviews.
Solution Approach 2:
The patent replaces the manual mechanical process of chart prioritization with an automated computational system. Machine learning algorithms process clinical data to generate prioritization scores, substituting the manual assessment and decision-making process with an automated system that can evaluate all charts simultaneously, thereby increasing review capacity without sacrificing accuracy.
2Ease of operation
If CDI specialists review charts in random order or using simple prioritization, then they can maintain a steady workflow, but they neglect charts with the highest potential for significant improvement
Solution Approach 1:
The patent transforms the prioritization parameter from simple metadata (like admission date) to a complex prioritization score generated by machine learning models. This score incorporates multiple clinical parameters and documentation features, enabling the system to identify charts with the highest improvement potential while maintaining ease of operation through automated scoring and ranking.
3Measurement precision
If CDI specialists focus on initial reviews of new patients, then they can catch documentation errors early, but they dedicate equal effort to re-reviews which have less opportunity for improvement
Solution Approach 1:
The patent applies partial action by directing CDI specialist attention only to charts that meet a certain prioritization threshold. Rather than reviewing all charts equally, the system identifies and focuses resources on the subset of charts with the highest likelihood of significant improvement, reducing time spent on low-yield reviews while maintaining documentation accuracy for high-priority cases.
4Reliability
If technology tools flag all patient charts for review, then they can ensure comprehensive coverage, but they cannot differentiate which charts are most important, leading to random neglect of high-value charts
Solution Approach 1:
The patent applies local quality by differentiating the treatment and prioritization of individual charts based on their specific characteristics and improvement potential. Rather than uniform flagging, the system generates unique prioritization scores for each chart based on local documentation features, clinical parameters, and machine learning predictions, enabling precise identification of high-value review opportunities.
Data Source
AI summary
A patient case may be evaluated whenever new information is received or as scheduled. Evaluation may include resolving a Diagnosis-Related Group (DRG) code and determining a CDI scoring approach based at least in part on a result from the resolving. Resolving a DRG code may include determining whether a DRG code is associated with the patient case. If no DRG code is found, the system may search for an International Classification of Diseases code or ask a user to select or assign a DRG code. Using the determined CDI scoring approach, a first score may be generated and adjusted by at least one of length of stay, documentation accuracy, payer, patient location, documentation novelty, review timing, case size, or documentation sufficiency. The adjusted score may be normalized and presented to a CDI specialist, perhaps with multiple CDI scores in a sorted order.