BACKGROUND: Transfusion medicine lacks a standard method for the systematic collection and analysis of event reports. Review of event reports from the Food and Drug Administration (FDA) showed a relative paucity of information on event causation. Thus, a causal analysis method was developed as part of a prototype Medical Event Reporting System for Transfusion Medicine (MERS-TM). STUDY DESIGN AND METHODS: MERS-TM functions within existing quality assurance systems and utilizes descriptive coding and causal classification schemes. The descriptive classification system, based upon current FDA coding, was modified to meet participant needs. The Eindhoven Classification Model (Medical Version) was adopted for causal classification and analysis. Inter-rater reliability for the MERS-TM and among participating organizations was performed with the development group in the United States and with a safety science research group in the Netherlands. The MERS-TM was then tested with events reported by participants. RESULTS: Data from 503 event reports from two blood centers and two transfusion services are discussed. The data showed multiple causes for events and more latent causes than previously recognized. The distribution of causes was remarkably similar to that in an industrial setting outside of medicine that uses the same classification approach. There was a high degree of inter-rater reliability when the same events were analyzed by quality assurance personnel in different participating organizations. These personnel found the method practical and useful for providing new insights into conditions producing undesired events. CONCLUSION: A generally applicable and reliable method for identifying and quantifying problems that exist throughout transfusion medicine will be a valuable addition to event reporting activity. By using a common taxonomy, participants can compare their experience with that of others. If proven as readily implementable and useful as shown in initial studies, MERS-TM is a potential standard for transfusion medicine.
BACKGROUND: Transfusion medicine lacks a standard method for the systematic collection and analysis of event reports. Review of event reports from the Food and Drug Administration (FDA) showed a relative paucity of information on event causation. Thus, a causal analysis method was developed as part of a prototype Medical Event Reporting System for Transfusion Medicine (MERS-TM). STUDY DESIGN AND METHODS: MERS-TM functions within existing quality assurance systems and utilizes descriptive coding and causal classification schemes. The descriptive classification system, based upon current FDA coding, was modified to meet participant needs. The Eindhoven Classification Model (Medical Version) was adopted for causal classification and analysis. Inter-rater reliability for the MERS-TM and among participating organizations was performed with the development group in the United States and with a safety science research group in the Netherlands. The MERS-TM was then tested with events reported by participants. RESULTS: Data from 503 event reports from two blood centers and two transfusion services are discussed. The data showed multiple causes for events and more latent causes than previously recognized. The distribution of causes was remarkably similar to that in an industrial setting outside of medicine that uses the same classification approach. There was a high degree of inter-rater reliability when the same events were analyzed by quality assurance personnel in different participating organizations. These personnel found the method practical and useful for providing new insights into conditions producing undesired events. CONCLUSION: A generally applicable and reliable method for identifying and quantifying problems that exist throughout transfusion medicine will be a valuable addition to event reporting activity. By using a common taxonomy, participants can compare their experience with that of others. If proven as readily implementable and useful as shown in initial studies, MERS-TM is a potential standard for transfusion medicine.
Authors: James B Battles; Nancy M Dixon; Robert J Borotkanics; Barbara Rabin-Fastmen; Harold S Kaplan Journal: Health Serv Res Date: 2006-08 Impact factor: 3.402
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