Literature DB >> 12578105

Using an anesthesia information management system to prove a deficit in voluntary reporting of adverse events in a quality assurance program.

M Benson1, A Junger, C Fuchs, L Quinzio, S Böttger, A Jost, D Uphus, G Hempelmann.   

Abstract

OBJECTIVE: A deficit is suspected in the manual documentation of adverse events in quality assurance programs in anesthesiology. In order to verify and quantify this, we retrospectively compared the incidence of manually recorded perioperative adverse events with automatically detected events.
METHODS: In 1998, data of all anesthetic procedures, including the data set for quality assurance of the German Society of Anaesthesiology and Intensive Care Medicine (DGAI), was recorded online with the Anesthesia Information Management System (AIMS) NarkoData4 (Imeso GmbH). SQL (Structured Query Language) queries based on medical data were defined for the automatic detection of common adverse events. The definition of the SQL statements had to be in accordance with the definition of the DGAI for perioperative adverse events: A potentially harmful change of parameters led to therapeutic interventions by an anesthesiologist.
RESULTS: During 16,019 surgical procedures, anesthesiologists recorded 911 (5.7%) adverse events manually, whereas 2966 (18.7%) events from the same database were detected automatically. With the exception of hypoxemia, the incidence of automatically detected events was considerably higher than that of manually recorded events. Fourteen and a half percent (435) of all automatically detected events were recorded manually.
CONCLUSION: Using automatic detection, we were able to prove a considerable deficit in the documentation of adverse events according to the guidelines of the German quality assurance program in anesthesiology. Based on the data from manual recording, the results of the quality assurance of our department match those of other comparable German departments. Thus, we are of the opinion that manual incident reporting seriously underestimates the true occurrence rate of incidents. This brings into question the validity of quality assurance comparisons based on manually recorded data.

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Year:  2000        PMID: 12578105     DOI: 10.1023/a:1009977917319

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   2.502


  11 in total

1.  [Quality documentation with an Anaesthesia Information Management System (AIMS)].

Authors:  A Junger; M Benson; L Quinzio; A Jost; C Veit; T Klöss; G Hempelmann
Journal:  Anaesthesist       Date:  1999-08       Impact factor: 1.041

2.  [Continuous improvement in anesthesiological quality documentation].

Authors:  A Junger; C Veit; T Klöss
Journal:  Anasthesiol Intensivmed Notfallmed Schmerzther       Date:  1998-11       Impact factor: 0.698

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Authors:  K V Sanborn; J Castro; M Kuroda; D M Thys
Journal:  Anesthesiology       Date:  1996-11       Impact factor: 7.892

5.  Factors influencing the reporting of adverse perioperative outcomes to a quality management program.

Authors:  R I Katz; R S Lagasse
Journal:  Anesth Analg       Date:  2000-02       Impact factor: 5.108

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  11 in total

1.  Automated detection of adverse events using natural language processing of discharge summaries.

Authors:  Genevieve B Melton; George Hripcsak
Journal:  J Am Med Inform Assoc       Date:  2005-03-31       Impact factor: 4.497

2.  Integrating incident reporting into an electronic patient record system.

Authors:  Guy Haller; Paul S Myles; Johannes Stoelwinder; Mark Langley; Hugh Anderson; John McNeil
Journal:  J Am Med Inform Assoc       Date:  2007-01-09       Impact factor: 4.497

3.  The reliability of manual reporting of clinical events in an anesthesia information management system (AIMS).

Authors:  Allan F Simpao; Eric Y Pruitt; Scott D Cook-Sather; Harshad G Gurnaney; Mohamed A Rehman
Journal:  J Clin Monit Comput       Date:  2012-05-22       Impact factor: 2.502

4.  Two open access, high-quality datasets from anesthetic records.

Authors:  David Cumin; Vanessa Newton-Wade; Michael J Harrison; Alan F Merry
Journal:  J Am Med Inform Assoc       Date:  2012-08-04       Impact factor: 4.497

Review 5.  Using real-time clinical decision support to improve performance on perioperative quality and process measures.

Authors:  Anthony Chau; Jesse M Ehrenfeld
Journal:  Anesthesiol Clin       Date:  2011-03

6.  Impact of intraoperative hypotension and blood pressure fluctuations on early postoperative delirium after non-cardiac surgery.

Authors:  J Hirsch; G DePalma; T T Tsai; L P Sands; J M Leung
Journal:  Br J Anaesth       Date:  2015-01-23       Impact factor: 9.166

7.  Hypotension after spinal anesthesia for cesarean section: identification of risk factors using an anesthesia information management system.

Authors:  F Brenck; B Hartmann; C Katzer; R Obaid; D Brüggmann; M Benson; R Röhrig; A Junger
Journal:  J Clin Monit Comput       Date:  2009-03-10       Impact factor: 2.502

8.  Computerize anesthesia record keeping in thoracic surgery--suitability of electronic anesthesia records in evaluating predictors for hypoxemia during one-lung ventilation.

Authors:  Jochen Sticher; Axel Junger; Bernd Hartmann; Matthias Benson; Andreas Jost; Martin Golinski; Stefan Scholz; Gunter Hempelmann
Journal:  J Clin Monit Comput       Date:  2002-08       Impact factor: 2.502

9.  A survey of user acceptance of electronic patient anesthesia records.

Authors:  Hyun Seung Jin; Myung Hee Kim; Suk Young Lee; Hui Yeon Jeong; Soo Joo Choi; Hye Won Lee
Journal:  Korean J Anesthesiol       Date:  2012-04-23

10.  Reporting critical incidents in a tertiary hospital: a historical cohort study of 110,310 procedures.

Authors:  Karin E Munting; Bas van Zaane; Antonius N J Schouten; Leo van Wolfswinkel; Jurgen C de Graaff
Journal:  Can J Anaesth       Date:  2015-09-25       Impact factor: 5.063

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