Literature DB >> 23063015

A systematic approach to the identification and classification of near-miss events on labor and delivery in a large, national health care system.

Steven L Clark1, Janet A Meyers, Donna R Frye, Kathryn McManus, Jonathan B Perlin.   

Abstract

We describe a systematic approach to the identification and classification of near-miss events on labor and delivery in a large, national health care system. Voluntary reports of near-miss events were prospectively collected during 2010 in 203,708 deliveries. These reports were analyzed according to frequency and potential severity. Near-miss events were reported in 0.69% of deliveries. Medication and patient identification errors were the most common near-miss events. However, existing barriers were found to be highly effective in preventing such errors from reaching the patient. Errors with the greatest potential for causing harm involved physician response and decision making. Fewer and less effective existing barriers between these errors and potential patient harm were identified. Use of a comprehensive system for identification of near-miss events on labor and delivery units have proven useful in allowing us to focus patient safety efforts on areas of greatest need.
Copyright © 2012 Mosby, Inc. All rights reserved.

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Year:  2012        PMID: 23063015     DOI: 10.1016/j.ajog.2012.09.011

Source DB:  PubMed          Journal:  Am J Obstet Gynecol        ISSN: 0002-9378            Impact factor:   8.661


  3 in total

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Authors:  Reza Dehnavieh; Hossein Ebrahimipour; Yasamin Molavi-Taleghani; Ali Vafaee-Najar; Somayeh Noori Hekmat; Hamid Esmailzdeh
Journal:  Glob J Health Sci       Date:  2014-12-25

2.  Finding Understudied Disorders Potentially Associated with Maternal Morbidity and Mortality.

Authors:  Laritza M Rodriguez; Dina Demner Fushman
Journal:  AJP Rep       Date:  2019-03-04

3.  Maternal 'near miss' collection at an Australian tertiary maternity hospital.

Authors:  Skandarupan Jayaratnam; Sonia Kua; Caroline deCosta; Richard Franklin
Journal:  BMC Pregnancy Childbirth       Date:  2018-06-11       Impact factor: 3.007

  3 in total

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