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PillPack Pharmacy Simplified. Amazon Second Chance Pass it on, trade it in, give it a second life. This slower, less visible, but fundamental and long-lasting cultural pathway is even regarded by some health care managers as more valuable in the long term than the straightforward analytical path Joustra, As stated above, the dependence of near-miss systems on voluntary reporting by health care staff affects staff attitudes much more profoundly than is the case with systems not dependent on such personal commitment.
However, playing an active role in detecting risks to patient safety is not necessarily limited to staff; patients themselves may be put in a position to contribute, for example, by being encouraged to ask questions about their care. In some cases, patients may help monitor their daily medications or medical treatment procedures, provided this information is supplied to them in an accessible format.
In this sense, patients and by extension their family and friends may be viewed as an extra, highly motivated line of defense. At the same time, involving patients in monitoring their own care clearly must be approached with caution and must be additional to, not a substitute for, the monitoring provided by systems and individual caregivers. Where patients provide an. Moreover, many patients may be unable to contribute anything toward monitoring their own care because they lack the required information or have impaired cognitive or sensory skills. In general, however, patients and their family and friends are a vastly underutilized resource for identifying things that go wrong in health care.
Where possible, they should be encouraged to report incidents, especially those in which they averted potentially harmful consequences e. To fulfill the goals outlined above, near-miss systems should be integrated into complete systems capable of capturing, analyzing, and disseminating information about patient safety.
They should be able to support management decisions on how and where to invest in safety-oriented system improvements. They should describe the failure and recovery mechanisms behind the reported incidents; analyze the root causes of failures; and recommend specific actions, based on the root causes most prominent in the database, within a prioritization strategy. A complete system also entails covering the entire range of consequences, from very minor, easily corrected near misses to catastrophic adverse events and fatalities.
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. Rarely if ever do errors or failures end up causing severe damage to a patient in any single hospital department or primary care practice. Inevitably, such occasions receive a great deal of media attention. The result is often massive investments designed to prevent such possibly very rare mishaps from recurring, at least in part because of the attention they attract and the desire of hospital managers to be seen as acting swiftly.
Because of the salience of the outcome, the analysis is subject to hindsight bias. 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.
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. 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. 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. 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.