Literature DB >> 11941572

The Medical Event Reporting System for Transfusion Medicine: will it help get the right blood to the right patient?

Harold S Kaplan1, Jeannie L Callum, Barbara Rabin Fastman, Lisa L Merkley.   

Abstract

The Medical Event Reporting System for Transfusion Medicine (MERS-TM) collects, classifies, and analyzes events that potentially could compromise the safety of transfused blood to facilitate system improvement. This system is designed to collect data on near misses as well as actual events. Near-miss events are a valuable source of data because they occur more frequently than, but share many characteristics and causes of, actual events. Further, although most current reporting efforts describe only what has occurred with little attention to what caused the event, MERS-TM includes a standardized method of causal analysis. The standardization provided by MERS allows users to compare their experience with that of other organizations, which speeds learning across the entire transfusion medicine community. Important features of the MERS-TM system are that it is able to capture threats, hazards, near misses, injuries, and deaths; characterizes failures and recoveries systematically; identifies and provides causal codes for the entire range of system defects including technical, organizational, cultural, and human factors; raises staff awareness about error management; is easily integrated with existing quality assurance programs; has a consistent and straightforward classification method; enables compliance with mandatory Food and Drug Administration reporting and accreditation requirements; has features to deal with a high volume of reports; supplies Web-based training, data entry, and analysis; and provides comparative benchmarks from comparable institutions. Copyright 2002, Elsevier Science (USA). All rights reserved.

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Year:  2002        PMID: 11941572     DOI: 10.1053/tmrv.2002.31459

Source DB:  PubMed          Journal:  Transfus Med Rev        ISSN: 0887-7963


  9 in total

1.  Finding clusters of similar events within clinical incident reports: a novel methodology combining case based reasoning and information retrieval.

Authors:  C Tsatsoulis; H A Amthauer
Journal:  Qual Saf Health Care       Date:  2003-12

2.  Coverage of patient safety terms in the UMLS metathesaurus.

Authors:  Aziz A Boxwala; Qing T Zeng; Anthony Chamberas; Luke Sato; Meghan Dierks
Journal:  AMIA Annu Symp Proc       Date:  2003

3.  Error management in blood establishments: results of eight years of experience (2003-2010) at the Croatian Institute of Transfusion Medicine.

Authors:  Tomislav Vuk; Marijan Barišić; Tihomir Očić; Ivanka Mihaljević; Dorotea Sarlija; Irena Jukić
Journal:  Blood Transfus       Date:  2012-02-22       Impact factor: 3.443

4.  Do expert assessments converge? An exploratory case study of evaluating and managing a blood supply risk.

Authors:  John Eyles; Nancy Heddle; Kathryn Webert; Emmy Arnold; Bronwen McCurdy
Journal:  BMC Public Health       Date:  2011-08-24       Impact factor: 3.295

5.  Web-based hazard and near-miss reporting as part of a patient safety curriculum.

Authors:  Leanne M Currie; Karen S Desjardins; Ellen Sunni Levine; Patricia W Stone; Rebecca Schnall; Jianhua Li; Suzanne Bakken
Journal:  J Nurs Educ       Date:  2009-12       Impact factor: 1.726

6.  Patients' positive identification systems.

Authors:  Pasqualepaolo Pagliaro; Rosalia Turdo; Enrico Capuzzo
Journal:  Blood Transfus       Date:  2009-10       Impact factor: 3.443

7.  Organization and representation of patient safety data: current status and issues around generalizability and scalability.

Authors:  Aziz A Boxwala; Meghan Dierks; Maura Keenan; Susan Jackson; Robert Hanscom; David W Bates; Luke Sato
Journal:  J Am Med Inform Assoc       Date:  2004-08-06       Impact factor: 4.497

8.  Towards the creation of a flexible classification scheme for voluntarily reported transfusion and laboratory safety events.

Authors:  Julie M Whitehurst; John Schroder; Dave Leonard; Monica M Horvath; Heidi Cozart; Jeffrey Ferranti
Journal:  J Biomed Semantics       Date:  2012-05-18

9.  Unit-based incident reporting and root cause analysis: variation at three hospital unit types.

Authors:  Cordula Wagner; Hanneke Merten; Laura Zwaan; Sanne Lubberding; Danielle Timmermans; Marleen Smits
Journal:  BMJ Open       Date:  2016-06-21       Impact factor: 2.692

  9 in total

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