Evaluating Billing Code Distributions in the Emergency Department Following the Implementation of the New Documentation Guidelines.
JACEP Open(2025)
Abstract
Objectives:Changes to the Current Procedural Terminology (CPT) evaluation and management (E/M) documentation guidelines implemented on January 1, 2023, were primarily meant to address dissatisfaction with the prior system; however, it was not known how the changes might alter billing distributions. In this study, we compare the proportion of visits for each E/M code before and after the enactment of the changes across 5 emergency departments (EDs) to determine the effects on billing. Methods:This was a retrospective, observational analysis of all ED visits for patients over 18 years across 5 EDs from January 1 to March 31 in the years 2021, 2022, and 2023. In the primary analysis, we compared the distribution of visits for each of the studied CPT E/M codes in the 3 months before and after the enactment of the changes, utilizing a multivariate mixed-effect Poisson regression model. In our secondary analysis, we aimed to determine if the results differed when looking at academic and community sites separately. Results:Across all hospitals, visits coded as level 4 and level 5 comprised a significantly higher proportion of all visits in the postimplementation period (relative risk = 1.40 for level 4 and relative risk = 1.17 for level 5). The proportion of visits coded as levels 1, 2, and 3 significantly decreased in the postimplementation period, while those coded as critical care did not change. The same general trends were found in both academic and community settings separately, although with less statistical significance, particularly at the academic sites. Conclusion:In this observational analysis, we found that overall CPT E/M levels increased after the implementation of the new documentation guidelines, relieving apprehension that the documentation changes may lead to a decrease in reimbursement.
MoreTranslated text
Key words
documentation,billing,coding,emergency medicine
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined