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Because of the salience of the outcome, the analysis is subject to hindsight bias.

Loss Control Insights

An incident-by-incident learning mode is reactive, based on specific characteristics of single events, and in most organizations consumes a major portion, if not the entirety, of the budget available for improving the system. An alternative proactive learning approach Reason, , at least with regard to adverse events and fatalities, is to collect data on large numbers of events; analyze the root causes; build a database of these causes; and then act upon the underlying patterns of causes, which are much more likely than single events to point to systemic or latent Reason, problems.

Indeed, some systemic or latent causes that can be uncovered through aggregate databases can be identified not at all, or not as efficiently, by analysis of single incidents. Given that the majority of adverse events occur infrequently, large incident databases may be necessary to provide sufficient examples for purposes of analyzing rare events such as gas embolism or anaphylaxis. If one wants to rise above the level of single events and their causes and base interventions on the most frequent and important root causes found in large databases, a root-cause taxonomy is needed.

The causal factors fed into these databases should be made comparable at a general, abstract level so that they are quantifiable.

Various aspects of the event will require different sub taxonomies:. Recovery root causes require a similar taxonomy. This taxonomy is likely to overlap somewhat with the categories of the failure taxonomy but will differ in some respects because of the more complex recovery phases of detection, diagnosis, and correction, each with their specific enablers Van der Schaaf and Kanse, Context variables , although not causal, provide additional useful background information, such as the who, what, when, where, and consequences of an event.

Context variables may well be largely domain specific, allowing analysis tailored to a specific reporting system. There is considerable overlap in the context variables collected for near-miss and adverse event analysis. These narratives should be stored with the analysis results, with consideration of requirements for deidentification, to allow for later, off-line analysis, especially by external researchers.

Van der Schaaf outlines four essential characteristics of near-miss systems:. Integration with other systems —Not only should a near-miss system contribute to and benefit from adverse event reporting systems, it should also be integrated, wherever possible, with other approaches used to measure, understand, and improve the performance of health care systems, such as audits of employee safety conducted by the National Institute for Occupational Safety and Health, total quality programs, environmental protection programs, maintenance optimization efforts, and logistics cost reduction programs.

Comprehensive coverage in a qualitative sense of possible inputs and outputs—The system should be able to handle not only safety-related near misses but also events with actual adverse consequences and with a range of different types of consequences i. It should cover not only negative deviations from normal system performance errors, failures, faults but also positive deviations successful recoveries. Model-based analysis —To the extent possible, a system model of health care work situations, including a suitable description of individual behaviors in a complex technical and organizational environment, should be the basis for the design of the information processing portion of the near-miss system.

Effective handling of the data encompasses 1 the required input data elements taken from free-text near-miss reports , 2 methods for analyzing a report to identify root causes, and 3 methods for interpreting the resulting database to generate suggestions to management for specific countermeasures. Rather, the emphasis should be on learning how to continuously improve patient safety by building feedback.

At the individual level, organizational learning can be improved by staff education and learning. In designing a near-miss system, two important dimensions are the medical domain it will cover and the level from local hospital department or primary care practice, to hospital, to nationwide at which it will function.

An example is shown in Table The four cells in this table can be divided into three levels of complexity of a near-miss system:. Ideally, the design of a near-miss system should progress from the lowest to the highest level of complexity. Doing so will ensure a continuous flow of voluntary reports, which can be expected to be produced mainly by the cell I systems; to be passed on to the aggregate intermediate-level systems; and finally to reach the highest, comprehensive level of cell IV.

Continued willingness to provide such input will depend greatly on its direct effects on those reporting, that is, insight into their work situation with regard to patient safety, specifically for their single-domain department.

Considering the need for root-cause taxonomies cited earlier, this approach to designing a near-miss system means that:. To the extent possible, all of these types and levels should have identical causal taxonomies for both failure and recovery factors and identical free-text structures for the original input narratives. Some basic context variables i. As long as standard terminologies and taxonomies are used, data can be reported and acted upon at different levels of granularity.

Safety Management: Near Miss Identification, Recognition, and Investigation

Coarser classification is necessary with the smaller collections available at the local level, but much finer granularity is possible when analyzing data from a large number of institutions. The strength of large-scale collections is that rare events can be well characterized.


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The nature of the information collected —It is obvious from arguments presented earlier in this chapter that descriptive reports are not sufficient; a causal analysis should be possible as well. A free-text description of an event will always be provided, sometimes guided by a standard set of questions e. The use of information in the database —There should be regular and appropriate feedback to personnel at all levels. It should be easy to generate summary statistics and clear examples from the database and to identify specific error reduction and recovery promotion strategies that can be proposed to management.

The level of help provided for collecting and analyzing the data —Analyst aids should be provided in the form of interview questions, flow charts, software, and the like. The nature of the organization of the reporting scheme —A local reporting system maintains close ties with reporters of events, but a central system may be more efficient in certain situations, for example, if there is widespread trust in the operation of the near-miss system.

Probably for all. Only in the case of certain well-defined, near-catastrophic events should there be a legal obligation to report. Whether the scheme is acceptable to all personnel —All of the above considerations should lead to a feeling of shared ownership.

Incident Investigation Training

Whether the data are best gathered by a well-known colleague most commonly in a local system or by an unknown outsider usually in a more central system again depends on the specific situation. Everyone involved should at least be familiarized with the purpose and background of the reporting scheme. The following specific problems involved in data collection Lucas, must be addressed to achieve a successful near-miss system:. Event focused —analyzing individual incidents rather than looking for general patterns of causes in a large database. The result is anecdotal reporting systems. Consequence driven —making the amount of attention and the resources devoted to investigation directly proportional to the severity of the outcome.

Technical myopia —a bias toward hardware rather than human failures. Variable quality —both within and between reporting systems, leading to incomparable investigation methods and results. Although the above discussion stems from experiences in high-tech industries and date from , by and large they still hold today and for health care as well. Here we focus on those aspects most relevant to the key issues in near-miss systems for health care—willingness to report, trust, and acceptance:.

The reporting threshold i. The opportunity, importance, and procedures of contributing to patient safety by voluntary reporting should be well known to all target groups. To this end, substantial investments must be made in publicizing, explaining, and discussing these issues before the formal launching of the near-miss system i.

Especially important is clear, continued, visible support by top management.

Safety Management--Article Index and Topic Introduction

Managers should be open and consistent in their communication about the importance, use, and accessibility of the data and their commitment to actually using the recommendations from the database analysis to choose, justify, and implement focused actions aimed at improving local performance on patient safety.

Optimum investments in system change depend not only on the scientific aspects of the root-cause analysis method and other tools employed but also on the more practical aspects of their usability and clarity and the training and support provided to the staff designated to carry out these analyses. Variability among individual analysts in identifying and then assigning classification codes to root causes should be checked at regular intervals using interrater reliability trials Wright, All of the above preparations and aspects should culminate in an optimal stream of frequent, meaningful, convincing, and therefore motivating feedback to all levels of staff and patients.

If prioritization requires a full root-cause analysis, the descriptive portion of the analysis not the classifications themselves should be fed back to the reporter for validation. After prioritization and analysis at the database level i. These visible changes in the system will serve as a major motivator, as will evaluation of their effects in a later phase.

Instituting and running a near-miss system should not burden an organization unduly. As noted in Chapter 6 , automated surveillance systems, augmented by other detection methods, will increase the number of detected adverse events that might warrant further analysis. Since near misses occur much more frequently than adverse events Bates et al. Once a near-miss system has been functioning for a while, it is crucial to establish selection criteria that can identify a manageable number of reported events with enough learning potential to warrant full root-cause analyses.

In addition to the criteria mentioned in Chapter 6 , likely candidates would include the novelty or surprise factor—new elements not seen before, even considered impossible. Another criterion could be potential fatal consequences or the realization that this event must have been latent in the organization for a long time, passing through many barriers that should have caught it earlier. Also, when an event is one that should have been prevented by a recent focused intervention, one would like to know why it still occurred.

Near misses are regarded as being on the same continuum as adverse events in terms of failure factors but differing in terms of the additional information they provide on recovery factors and in their significantly higher frequency of occurrence. Because the assumption of the causal continuum implies that the causes of near misses do not differ from those of adverse events, this leads to the claim that near misses are truly precursors to later potential adverse events and therefore valuable to report.

The primary focus for improving patient safety is on identifying and eliminating the system faults that can lead to adverse events. This objective can be approached by analyzing both adverse events and near misses to identify the system faults involved. A direct causal comparison between near misses and adverse events requires shared taxonomies for sets of events in terms of both root causes and.

After enough adverse events or other serious medical mishaps have been reported and analyzed to build a statistically sound database for a health care organization, the amount of overlap between the causes of near misses and adverse events should be examined. Doing so will not only clarify the relationship between these two sets of events but also demonstrate clearly and convincingly to all potential reporters the importance of near-miss systems.

In some cases, adverse event descriptions also encompass recovery actions that were obviously too late, too weak, or of the wrong type to have been successful. In these cases, such failed opportunities at recovery, or at least damage limitation, can be classified using the taxonomy for near-miss recovery factors and compared with successful recoveries to understand the predictors of success. Summarizing the main points for designing, implementing, and operating a near-miss system, Table uses a seven-module framework to describe what is required in each step of the processing of near-miss reports Van der Schaaf et al.

Detection —This module contains the registration mechanism, aiming at easy entry of complete or at least nonbiased , valid reporting 2 of all near-miss situations detectable by employees, patients, and others. Selection —A mature near-miss system will probably generate many duplications of earlier reports, increasing the workload of the safety staff coping with sizable piles of reports. To maximize the learning process using limited resources, a selection procedure is necessary to filter out the most interesting reports for further analysis in the subsequent modules.

Description —Any report selected for further processing should lead to a detailed, complete, neutral description of the course of events and situations resulting in the reported near miss, with appropriate deidentification. These causal elements should be shown in their logical order what caused. Computerized detection using a signal approach has not been as effective for detecting near misses as for detecting adverse events Jha et al. Increasingly, however, new technologies such as computerized order entry Bates et al. Compiling a database; performing periodic statistical analysis to uncover dominant causal factors.

Classification —As the most fundamental of causal elements, root causes should each be classified according to a suitable taxonomy. In this way, the fact that every incident usually has multiple causes is fully recognized, and each analyzed near miss thus adds a set of root causes to the database. Severity should also be assessed. Computation —In exceptional cases only e. Generally, however, the database is allowed to build up gradually over a certain period, after which a periodic statistical analysis of the entire or the most recent part of the database is performed, with the aim of identifying patterns of root causes instead of unique, nonrecurring symptoms.

Interpretation and implementation —Once the most dominant causes have been identified, a mechanism should be in place that suggests types of.

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Management can then select one or more focus areas on the basis of these model-based options for intervention and other dimensions, such as time to effect, cost, and regulator requirements. The associated interventions can then be implemented. Evaluation —Once the selected interventions have had some time to take effect, they should be monitored for their effectiveness in bringing about the expected change. Subsequent periodic database analyses should be used for this purpose by checking for decreased for failure factors or increased for recovery factors presence in the near-miss reports generated after implementation.