Patient Safety Achieving a New Standard for Care (2004) / Chapter Skim
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7 Near-Miss Analysis
7 Near-Miss Analysis
Pages 226-246
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From page 226... ...
Since near misses and adverse events are thought to be part of the same causal con 226
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From page 227... ...
According to the incident causation model, near misses are the immediate precursors to later possible adverse events. Examining near misses provides two types of information relevant for patient safety: (1)
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From page 228... ...
Health care professionals are continually detecting, arresting, and deflecting potential adverse events, sometimes even subconsciously. Data on recovery processes represent valuable patient safety information, a fact that often goes unrecognized.
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From page 229... ...
As compared with adverse events, the added advantage of the recovery component should enable a more balanced view of how patient safety can be improved, focused not only on preventative measures to address the failure factors identified but also on means of building in or strengthening the recovery factors that come into play once errors have occurred. • Trending -- to gain a quantitative insight into the relative distribution of failure and recovery factors by building a database of underlying root causes of a large number of near misses.
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From page 230... ...
The claim in the health care domain that addressing the causes of near misses will also aid in preventing actual adverse events and fatalities will have to based on more than anecdotal evidence if that claim is to be widely accepted and therefore worth acting upon. Currently available databases could be used to test the causal continuum assumption in health care.
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From page 231... ...
were similar. The Dual Pathway One aspect of near-miss versus adverse event reporting that is relatively unknown but highly valued in practice is that near-miss reporting provides a dual pathway to improved system performance: • The direct, analytical pathway, which near-miss and adverse event systems have in common, is based on collecting incident data; analyzing root causes; and acting upon the most important causes, thereby gradually improving the system and achieving better (safety)
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From page 232... ...
A complete system also entails covering the entire range of consequences, from very minor, easily corrected near misses to catastrophic adverse events and fatalities. Learning from Databases, Not Just from Single Incidents One of the consequences of the traditional focus on incidents in which patients were actually harmed in the belief that such incidents can yield more fundamental lessons is a lack of data at lower levels of the health care system.
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From page 233... ...
taxonomies: • Failure root causes require a generic, fixed taxonomy, which should be identical over all medical/health care domains so that the system can be optimized overall, rather than within each domain This taxonomy should also acknowledge that patients themselves sometimes contribute to near misses and adverse events. • Recovery root causes require a similar taxonomy.
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From page 234... ...
, the output of a near-miss system should never lead to assigning blame to or punishing individual employees or even be used to evaluate them. Rather, the emphasis should be on learning how to continuously improve patient safety by building feedback
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From page 235... ...
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)
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From page 236... ...
IMPLEMENTATION AND OPERATIONAL CONSIDERATIONS An overview of systems for the collection of human performance data in industry (Lucas, 1987) identifies five practical aspects that contribute significantly to such a system's success or failure and must be addressed when defining data standards: • 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.
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From page 237... ...
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: • Input optimal in terms of both quantity and quality may be facilitated by providing multiple channels for reporting, including forms, computer linkup, and telephone; at multiple locations, including the nurses' station, the doctors' meeting room, from the patient's bedside, and from home; by multiple groups, not just medical staff but also lab technicians, administra
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From page 238... ...
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.
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From page 239... ...
Integration with Adverse Event Systems 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.
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From page 240... ...
These causal elements should be shown in their logical order (what caused 2Computerized detection using a signal approach has not been as effective for detecting near misses as for detecting adverse events (Jha et al., 1998)
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From page 241... ...
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.
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Care should be taken to ensure maximum overlap between such near-miss standards and those for adverse events. Where possible, tested definitions and models from both within and outside the medical field should be preferred.
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From page 243... ...
• Description -- A concise description of all relevant elements, from root causes to the reported event, in their chronological and logical (i.e., cause– effect) order demands tree-like techniques.
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The matrix should be based on accepted safety management models. Management should be supplied with this advice in a form that supports optimal decision making on the allocation of resources to patient safety improvement actions and then monitored with regard to whether these improvement programs have been implemented.
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From page 245... ...
Personal communication to Institute of Medicine's Committee on Data Standards for Patient Safety. Kaplan, H
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