Literature DB >> 21415434

Evaluation of a mandatory quality assurance data capture in anesthesia: a secure electronic system to capture quality assurance information linked to an automated anesthesia record.

Robert A Peterfreund1, William D Driscoll, John L Walsh, Aparna Subramanian, Shaji Anupama, Melissa Weaver, Theresa Morris, Sarah Arnholz, Hui Zheng, Eric T Pierce, Stephen F Spring.   

Abstract

BACKGROUND: Efforts to assure high-quality, safe, clinical care depend upon capturing information about near-miss and adverse outcome events. Inconsistent or unreliable information capture, especially for infrequent events, compromises attempts to analyze events in quantitative terms, understand their implications, and assess corrective efforts. To enhance reporting, we developed a secure, electronic, mandatory system for reporting quality assurance data linked to our electronic anesthesia record.
METHODS: We used the capabilities of our anesthesia information management system (AIMS) in conjunction with internally developed, secure, intranet-based, Web application software. The application is implemented with a backend allowing robust data storage, retrieval, data analysis, and reporting capabilities. We customized a feature within the AIMS software to create a hard stop in the documentation workflow before the end of anesthesia care time stamp for every case. The software forces the anesthesia provider to access the separate quality assurance data collection program, which provides a checklist for targeted clinical events and a free text option. After completing the event collection program, the software automatically returns the clinician to the AIMS to finalize the anesthesia record.
RESULTS: The number of events captured by the departmental quality assurance office increased by 92% (95% confidence interval [CI] 60.4%-130%) after system implementation. The major contributor to this increase was the new electronic system. This increase has been sustained over the initial 12 full months after implementation. Under our reporting criteria, the overall rate of clinical events reported by any method was 471 events out of 55,382 cases or 0.85% (95% CI 0.78% to 0.93%). The new system collected 67% of these events (95% confidence interval 63%-71%).
CONCLUSION: We demonstrate the implementation in an academic anesthesia department of a secure clinical event reporting system linked to an AIMS. The system enforces entry of quality assurance information (either no clinical event or notification of a clinical event). System implementation resulted in capturing nearly twice the number of events at a relatively steady case load.
© 2011 International Anesthesia Research Society

Mesh:

Year:  2011        PMID: 21415434     DOI: 10.1213/ANE.0b013e31821207f0

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  7 in total

1.  Implementation of an Anesthesia Information Management System in an Ambulatory Surgery Center.

Authors:  Seshadri C Mudumbai
Journal:  J Med Syst       Date:  2015-11-04       Impact factor: 4.460

2.  Disruptive innovators in anaesthesia: data and devices.

Authors:  Tong Khee Tan
Journal:  Singapore Med J       Date:  2019-03       Impact factor: 1.858

3.  Efficacy and unintended consequences of hard-stop alerts in electronic health record systems: a systematic review.

Authors:  Emily M Powers; Richard N Shiffman; Edward R Melnick; Andrew Hickner; Mona Sharifi
Journal:  J Am Med Inform Assoc       Date:  2018-11-01       Impact factor: 4.497

4.  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

Review 5.  Enhancing Patient Safety Event Reporting. A Systematic Review of System Design Features.

Authors:  Yang Gong; Hong Kang; Xinshuo Wu; Lei Hua
Journal:  Appl Clin Inform       Date:  2017-08-30       Impact factor: 2.342

6.  An analysis of near misses identified by anesthesia providers in the intensive care unit.

Authors:  Angela K M Lipshutz; James E Caldwell; David L Robinowitz; Michael A Gropper
Journal:  BMC Anesthesiol       Date:  2015-06-17       Impact factor: 2.217

7.  Data acquisition from S/5 GE Datex anesthesia monitor using VSCapture: An open source.NET/Mono tool.

Authors:  John George Karippacheril; Tam Yuk Ho
Journal:  J Anaesthesiol Clin Pharmacol       Date:  2013-07
  7 in total

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