Reporting of near‐miss events for transfusion medicine: improving transfusion safety

Callum, Jeannie; Kaplan, Harold S.; Merkley, Lisa L.; Pinkerton, P. H.; Fastman, Barbara Rabin; Romans, Robert A.; Coovadia, Ahmed S.; Reis, Marciano D.

doi:10.1046/j.1537-2995.2001.41101204.x

Cited by 106 publications

(87 citation statements)

References 20 publications

(26 reference statements)

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“…The implementation of automated techniques improves standardization of methods and the quality of achieved results. Human errors in manual processing are significant causes of fatal transfusion complications [2][3][4][5][6]. We redesigned all immunohematological routine testing processes at the blood bank of the Clinic of the University of Munich in two branch sites according to GMP and GLP by introduction of a new automated immunohematological testing system.…”

Section: Introductionmentioning

confidence: 99%

Automation and Data Processing with the Immucor Galileo® System in a University Blood Bank

Wittmann¹,

Frank²,

Schramm³

et al. 2007

Transfus Med Hemother

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The implementation of automated techniques improves the workflow and quality of immunohematological results. The workflows of our university blood bank were reviewed during the implementation of an automated immunohematological testing system. Methods: Work impact of blood grouping and subgrouping, cross-matching and antibody search using the Immucor Galileo system was compared to the previous used standard manual and semi-automated methods. Results: The redesign of our workflow did not achieve a significant reduction of the specimen’s working process time, the operator’s time however was reduced by 23%. Corresponding results were achieved for blood grouping, Rhesus typing, antibody screen and for autocontrol when changing from two semi-automated to the Galileo system. Because of the higher sensitivity of the Immucor antibody detection system, the rate of the initial positive antibody screens rose from 4 to 6% Conclusion: The Immucor Galileo system automates routine blood bank testing with high reliability, specificity and higher sensitivity compared to our previous used standard manual and semi-automated methods.

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Section: Introductionmentioning

confidence: 99%

Automation and Data Processing with the Immucor Galileo® System in a University Blood Bank

Wittmann¹,

Frank²,

Schramm³

et al. 2007

Transfus Med Hemother

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show abstract

“…Initially limited to the nuclear (Phimister et al, 2004) and aviation industries (NASA, 2006), research on the design of effective NMSs has received much attention in a wide range of industries over the last couple of years (Wu et al, 2010;Gnoni et al, 2013;Andriulo & Gnoni, 2014;Goode et al, 2014), especially in the healthcare industry for improved patient safety (Barach & Small, 2000;Callum et al, 2001;Aspden et al, 2004;Fried, 2009). Most NMSs in use today are proprietary systems designed specifically for the organisation that uses them.…”

Section: Types Of Nmssmentioning

confidence: 99%

A near-miss management system architecture for the forensic investigation of software failures

Bella

Eloff

2016

Forensic Science International

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Abstract-Digital forensics has been proposed as a methodology for doing root-cause analysis of major software failures for quite a while. Despite this, similar software failures still occur repeatedly. A reason for this is the difficulty of obtaining detailed evidence of software failures. Acquiring such evidence can be challenging, as the relevant data may be lost or corrupt following a software system's crash. This paper proposes the use of near-miss analysis to improve on the collection of evidence for software failures. Near-miss analysis is an incident investigation technique that detects and subsequently analyses indicators of failures. The results of a near-miss analysis investigation are then used to detect an upcoming failure before the failure unfolds. The detection of these indicators -known as near misses -therefore provides an opportunity to proactively collect relevant data that can be used as digital evidence, pertaining to software failures. A Near Miss Management System (NMS) architecture for the forensic investigation of software failures is proposed. The viability of the proposed architecture is demonstrated through a prototype.

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“…The use of automated techniques reduces the impact of human errors in the blood bank and improves standardization and quality of achieved results [2][3][4]. Human errors due to manual processing are a significant cause of fatal transfusion complications [2,5,6]. In order to optimize the GMP processes at the blood bank of the University of Regensburg, an automation of laboratory methods for blood testing was implemented.…”

Section: Introductionmentioning

confidence: 99%

Automation and Data Processing in Blood Banking Using the Ortho AutoVue® Innova System

Dada

Beck

Schmitz³

2007

Transfus Med Hemother

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The use of automated techniques reduces the impact of human errors in blood banking and improves standardization and quality of achieved results. Work processes were analyzed to see where improvements could be made before installing an automated testing instrument. The suitability of automation and data processing in order to reduce the bulk of routine and emergency work was explored. Methods: The evaluation focused on blood group and subgroup typing, crossmatching, and antibody screening using the Ortho AutoVue Innova System compared to standard manual methods. Results: The redesigned layout of the workspace led to a reduction of the operator’s working process time of the specimen by 6%, whereas process optimizations resulted in shortening of the operator’s working process time by 53%. Using the manual DiaMed system and the automatic AutoVue system, corresponding results were achieved for blood group typing (100%), rhesus typing (100%), antibody screening (100%), and autocontrol (87%). For known antibodies from freeze samples this correspondence amounted to 82%. The turnaround time from receipt at blood bank to completion for standard cross-matching, blood typing, and antibody screening is decreased up to 17% using the automatic system due to the standardization and automation of testing. Conclusion: The Ortho AutoVue Innova System facilitates the automation of routine blood bank testing, enrolling blood typing, cross-matching, and antibody screening with high reliability, sensitivity and specificity compared to standard manual methods.

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Reporting of near‐miss events for transfusion medicine: improving transfusion safety

Cited by 106 publications

References 20 publications

Automation and Data Processing with the Immucor Galileo® System in a University Blood Bank

Automation and Data Processing with the Immucor Galileo® System in a University Blood Bank

A near-miss management system architecture for the forensic investigation of software failures

Automation and Data Processing in Blood Banking Using the Ortho AutoVue® Innova System

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