If only....: failed, missed and absent error recovery opportunities in medication errors.

BACKGROUND: Systematic analysis of error recovery can provide hospitals with important information to help them improve their ability to detect and correct errors. Because errors will always crop up and 100% safety can never be achieved, hospitals should be able to prevent patient harm by timely and effective error recovery. METHODS: In this study, failed, missed and absent recovery opportunities were identified in 52 medication errors which all resulted in severe patient harm or patient death. For all identified recovery opportunities, the underlying failure factors were identified and classified according to the Eindhoven classification model. Those failure factors represent negative influences on error recovery. RESULTS: The number of recovery opportunities per error ranged from 0 to 11; on average, 2.4 recovery opportunities were identified. Of 127 identified recovery opportunities, 94 (74%) were planned and 33 (26%) were unplanned or ad hoc. Most failure factors underlying the planned recovery opportunities were organisational failure factors; most failure factors underlying the unplanned recovery opportunities were human failure factors. CONCLUSIONS: From this study, it can be concluded that actual accidents can be used as an alternative data source to near misses for the analysis and understanding of error recovery. By using both sources, hospitals can enhance their resilience by reinforcing the positive influences on error recovery as well as reducing the negative ones. Together with traditional error reduction methodologies, which only concentrate on eliminating failure factors, hospitals thus have numerous opportunities to improve patient safety.

Feasibility and reliability of PRISMA-medical for specialty-based incident analysis.

AIMS AND OBJECTIVES: In this study, the feasibility and reliability of the Prevention Recovery Information System for Monitoring and Analysis (PRISMA)-Medical method for systematic, specialty-based analysis and classification of incidents in the neonatal intensive care unit (NICU) were determined. METHODS: After the introduction of a Neonatology System for Analysis and Feedback on Medical Events (NEOSAFE) in eight tertiary care NICUs and one paediatric surgical ICU, PRISMA-Medical was started to be used to identify root causes of voluntary reported incidents by multidisciplinary unit patient safety committees. Committee members were PRISMA-trained and familiar with the department and its processes. In this study, the results of PRISMA-analysis of incidents reported during the first year are described. At t = 3 months and t = 12 months after introduction, test cases were performed to measure agreement at three levels of root cause classification using PRISMA-Medical. Inter-rater reliability was determined by calculating generalised kappa values for each level of classification. RESULTS: During the study period, 981 out of 1786 eligible incidents (55%) were analysed for underlying root causes. In total, 2313 root causes were identified and classified, giving an average of 2.4 root causes for every incident. Although substantial agreement (kappa 0.70-0.81) was reached at the main level of root cause classification of the test cases (discrimination between technical, organisational and human failure) and agreement among the committees at the second level (discrimination between skill-based, rule-based and knowledge-based errors) was acceptable (kappa 0.53-0.59), discrimination between rule-based errors (the third level of classification) was more difficult to assess (kappa 0.40-0.47). CONCLUSION: With some restraints, PRISMA-Medical proves to be both feasible and acceptably reliable to identify and classify multiple causes of medical events in the NICU.

Prospective risk analysis of health care processes: a systematic evaluation of the use of HFMEA in Dutch health care.

The aim of this study was to evaluate the use of Healthcare Failure Mode and Effect Analysis (HFMEA) in Dutch health care by means of user feedback. Thirteen HFMEA analyses of various health care processes were successfully concluded and on average took 69 person-hours (excluding reporting). These results show that HFMEA can successfully be applied in Dutch health care. However, the user feedback also uncovered several perceived drawbacks, such as the fact that HFMEA is very time-consuming and that, particularly, the risk assessment part of HFMEA is difficult to carry out. Moreover, a lack of guidance with regard to the identification of failure mode causes and effective actions might influence the quality of the outcomes of an HFMEA analysis. Several suggestions are put forward to improve the perceived utility and acceptance of HFMEA. Nevertheless, future research is necessary to evaluate the actual effects of these recommendations. Error modelling and risk analysis, and their contribution to explaining human performance in socio-technical systems, traditionally belong to the field of ergonomics. The user feedback on HFMEA and the suggestions that are put forward may also be useful for (H)FMEA and hazard analysis and critical control point applications in sectors other than health care.

Specialty-based, voluntary incident reporting in neonatal intensive care: description of 4846 incident reports.

OBJECTIVES: To examine the characteristics of incidents reported after introduction of a voluntary, non-punitive incident reporting system for neonatal intensive care units (NICUs) in the Netherlands; and to investigate which types of reported incident pose the highest risk to patients in the NICU. DESIGN: Prospective multicentre survey. METHODS: Voluntary, non-punitive incident reporting was introduced in eight level III NICUs and one paediatric surgical ICU. An incident was defined as any unintended event which (could have) reduced the safety margin for the patient. Multidisciplinary, unit-based patient safety committees systematically collected and analysed incident reports, and assigned risk scores to each reported incident. Data were centrally collected for specialty-based analysis. This paper describes the characteristics of incidents reported during the first year. Bivariate logistic regression analysis was conducted to identify high-risk incident categories. RESULTS: There were 5225 incident reports on 3859 admissions, of which 4846 were eligible for analysis. Incidents with medication were most frequently reported (27%), followed by laboratory (10%) and enteral nutrition (8%). Severe harm was described in seven incident reports, and moderate harm in 63 incident reports. Incidents involving mechanical ventilation and blood products were most likely to be assigned high-risk scores, followed by those involving parenteral nutrition, intravascular lines and medication dosing errors. CONCLUSIONS: Incidents occur much more frequently in Dutch NICUs than has been previously observed, and their impact on patient morbidity is considerable. Reported incidents concerning mechanical ventilation, blood products, intravascular lines, parenteral nutrition and medication dosing errors pose the highest risk to patients in the NICU.

Error recovery in a hospital pharmacy.

A field study was performed in a hospital pharmacy aimed at identifying positive and negative influences on the process of detection of and further recovery from initial errors or other failures, thus avoiding negative consequences. Confidential reports and follow-up interviews provided data on 31 near-miss incidents involving such recovery processes. Analysis revealed that organizational culture with regard to following procedures needed reinforcement, that some procedures could be improved, that building in extra checks was worthwhile and that supporting unplanned recovery was essential for problems not covered by procedures. Guidance is given on how performance in recovery could be measured. A case is made for supporting recovery as an addition to prevention-based safety methods.

Identification and classification of the causes of events in 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.

The attributes of medical event-reporting systems: experience with a prototype medical event-reporting system for transfusion medicine.

OBJECTIVE: To design, develop, and implement a prototype medical event-reporting system for use in transfusion medicine to improve transfusion safety by studying incidents and errors. METHODS: The IDEALS concept of design was used to identify specifications for the event-reporting system, and a Delphi and subsequent nominal group technique meetings were used to reach consensus on the development of the system. An interdisciplinary panel of experts from aviation safety, nuclear power, cognitive psychology, artificial intelligence, and education and representatives of major transfusion medicine organizations participated in the development process. Setting.- Three blood centers and three hospital transfusion services implemented the reporting system. RESULTS: A working prototype event-reporting system was recommended and implemented. The system has seven components: detection, selection, description, classification, computation, interpretation, and local evaluation. Its unique features include no-fault reporting initiated by the individual discovering the event, who submits a report that is investigated by local quality assurance personnel and forwarded to a nonregulatory central system for computation and interpretation. CONCLUSIONS: An event-reporting system incorporated into present quality assurance and risk management efforts can help organizations address system structural and procedural weakness where the potential for errors can adversely affect health care outcomes. Input from the end users of the system as well as from external experts should enable this reporting system to serve as a useful model for others who may develop event-reporting systems in other medical domains.

Developing and using cognitive task typologies.

Based on experiences from case studies, some preliminary thoughts are presented regarding the why and how of developing a cognitive task typology model. Three different practical functions of such a typology are outlined: comparison, prediction, and design. These functions can be applied to sets of tasks both in the same and in different task worlds (e.g. artificial/laboratory tasks vs. real-life/case study tasks). Several existing techniques may be suitable for this purpose: hierarchical task analysis, verbal protocols, and the use of elementary cognitive modules. Finally two partly overlapping approaches to identify relevant task dimensions for a typology are outlined: one based on a macro-ergonomic view of human-machine systems, and the other centred around the cognitive concept of an operator's mental model of the task. In the Discussion a bottom-up strategy for typology development is advocated, and the relationship with task complexity is noted.