Literature DB >> 17197858

Automated documentation error detection and notification improves anesthesia billing performance.

Stephen F Spring1, Warren S Sandberg, Shaji Anupama, John L Walsh, William D Driscoll, Douglas E Raines.   

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.

Mesh:

Year:  2007        PMID: 17197858     DOI: 10.1097/00000542-200701000-00025

Source DB:  PubMed          Journal:  Anesthesiology        ISSN: 0003-3022            Impact factor:   7.892


  21 in total

1.  Are anesthesia start and end times randomly distributed? The influence of electronic records.

Authors:  Litisha G Deal; Michael E Nyland; Nikolaus Gravenstein; Patrick Tighe
Journal:  J Clin Anesth       Date:  2014-05-20       Impact factor: 9.452

2.  Minimizing electronic health record patient-note mismatches.

Authors:  Adam B Wilcox; Yueh-Hsia Chen; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2011-04-12       Impact factor: 4.497

3.  Adoption of anesthesia information management systems by US anesthesiologists.

Authors:  Terrence L Trentman; Jeff T Mueller; Keith J Ruskin; Brie N Noble; Christine A Doyle
Journal:  J Clin Monit Comput       Date:  2011-07-05       Impact factor: 2.502

4.  A substitution method to improve completeness of events documentation in anesthesia records.

Authors:  Antoine Lamer; Julien De Jonckheere; Romaric Marcilly; Benoît Tavernier; Benoît Vallet; Mathieu Jeanne; Régis Logier
Journal:  J Clin Monit Comput       Date:  2015-01-30       Impact factor: 2.502

5.  The effect of requesting a reason for non-adherence to a guideline in a long running automated reminder system for PONV prophylaxis.

Authors:  Fabian O Kooij; Toni Klok; Benedikt Preckel; Markus W Hollmann; Jasper E Kal
Journal:  Appl Clin Inform       Date:  2017-03-29       Impact factor: 2.342

6.  Near real-time notification of gaps in cuff blood pressure recordings for improved patient monitoring.

Authors:  Bala G Nair; Mayumi Horibe; Shu-Fang Newman; Wei-Ying Wu; Howard A Schwid
Journal:  J Clin Monit Comput       Date:  2013-01-03       Impact factor: 2.502

7.  Innovation & market consolidation among electronic health record vendors: an acute need for regulation.

Authors:  J Wanderer; P Mishra; J Ehrenfeld
Journal:  J Med Syst       Date:  2014-01       Impact factor: 4.460

8.  Information needs for the OR and PACU electronic medical record.

Authors:  V Herasevich; M A Ellsworth; J R Hebl; M J Brown; B W Pickering
Journal:  Appl Clin Inform       Date:  2014-07-16       Impact factor: 2.342

9.  Classification of Current Procedural Terminology Codes from Electronic Health Record Data Using Machine Learning.

Authors:  Michael L Burns; Michael R Mathis; John Vandervest; Xinyu Tan; Bo Lu; Douglas A Colquhoun; Nirav Shah; Sachin Kheterpal; Leif Saager
Journal:  Anesthesiology       Date:  2020-04       Impact factor: 7.892

Review 10.  A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems.

Authors:  Allan F Simpao; Jonathan M Tan; Arul M Lingappan; Jorge A Gálvez; Sherry E Morgan; Michael A Krall
Journal:  J Clin Monit Comput       Date:  2016-08-16       Impact factor: 2.502

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