Automated Documentation Error Detection and Notification Improves Anesthesia Billing Performance

Author:

Spring Stephen F.1,Sandberg Warren S.2,Anupama Shaji3,Walsh John L.4,Driscoll William D.5,Raines Douglas E.6

Affiliation:

1. Administrative Director for Finance.

2. Assistant Professor of Anesthesia, Harvard Medical School, and Assistant Anesthetist, Department of Anesthesia and Critical Care, Massachusetts General Hospital.

3. Senior Programmer and Analyst.

4. Instructor of Anesthesia, Harvard Medical School, and Assistant Anesthetist, Department of Anesthesia and Critical Care, Massachusetts General Hospital.

5. Clinical Engineer, Department of Anesthesia and Critical Care, Massachusetts General Hospital.

6. Associate Professor, Harvard Medical School, and Associate Anesthetist, Department of Anesthesia and Critical Care, Massachusetts General Hospital.

Abstract

Background Documentation of key times and events is required to obtain reimbursement for anesthesia services. The authors installed an information management system to improve record keeping and billing performance but found that a significant number of their records still could not be billed in a timely manner, and some records were never billed at all because they contained documentation errors. Methods Computer software was developed that automatically examines electronic anesthetic records and alerts clinicians to documentation errors by alphanumeric page and e-mail. The software's efficacy was determined retrospectively by comparing billing performance before and after its implementation. Staff satisfaction with the software was assessed by survey. Results After implementation of this software, the percentage of anesthetic records that could never be billed declined from 1.31% to 0.04%, and the median time to correct documentation errors decreased from 33 days to 3 days. The average time to release an anesthetic record to the billing service decreased from 3.0+/-0.1 days to 1.1+/-0.2 days. More than 90% of staff found the system to be helpful and easier to use than the previous manual process for error detection and notification. Conclusion This system allowed the authors to reduce the median time to correct documentation errors and the number of anesthetic records that were never billed by at least an order of magnitude. The authors estimate that these improvements increased their department's revenue by approximately $400,000 per year.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Anesthesiology and Pain Medicine

Reference18 articles.

Cited by 94 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Information Management and Technology;Cottrell & Patel's Neuroanesthesia;2025

2. A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia;Cureus;2023-09-11

3. “People, We Have a Problem”;Anesthesiology;2023-05-09

4. Big data in anaesthesia: a narrative, nonsystematic review;European Journal of Anaesthesiology Intensive Care;2023

5. Opal: an implementation science tool for machine learning clinical decision support in anesthesia;Journal of Clinical Monitoring and Computing;2021-11-27

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