LITMIR.BIZ

Скачать книгу

2016 1574 Not stated Not stated 10.3 Mean age of those experiencing adverse events = 61.8, vs. 55.4 P < 0.001 Sweden (Nilsson) 2018 64971 Not stated >65 11.7 12.9 9.6 P < 0.001

Table 11.2 Methods of measuring errors and adverse events.

      Source: Adapted from Thomas and Petersen¹⁹.

Study method	Advantages	Disadvantages
Administrative data analysis	Uses readily available data	May rely on incomplete and inaccurate data
	Inexpensive	The data are divorced from clinical context
Record review/chart review	Uses readily available data	Judgements about adverse events not reliable
	Commonly used	Medical records are incomplete Hindsight bias
Review of electronic medical records	Inexpensive after initial investment Monitors in real time Integrates multiple data sources	Susceptible to programming and/or data‐entry errors Expensive to implement
Observation of patient care	Potentially accurate and precise Provides data otherwise unavailable Detects more active errors than other methods	Time‐consuming and expensive Difficult to train reliable observers Potential concerns about confidentiality Possible to be overwhelmed with information
Active clinical surveillance	Potentially accurate and precise for adverse events	Time‐consuming and expensive

Table 11.3 Methods of understanding errors and adverse events.

      Source: Adapted from Thomas and Petersen¹⁹.

Study method	Advantages	Disadvantages
Morbidity and mortality conferences and autopsy	Can suggest contributory factors Familiar to healthcare providers	Hindsight bias Reporting bias Focused on diagnostic errors Infrequently used
Case analysis/root cause analysis	Can suggest contributory structured systems approach Includes recent data from interviews	Hindsight bias Tends to focus on severe events Insufficiently standardized in practice
Claims analysis	Provides multiple perspectives (patients, providers, lawyers)	Hindsight bias Reporting bias Non‐standardized source of data
Error‐reporting systems	Provide multiple perspectives over time Can be a part of routine operations	Reporting bias Hindsight bias

Delays and errors in clinical decision‐making are particularly critical in medicine, and there is extensive literature about the complexities of medical decision‐making.²³ In our daily clinical practice, we use heuristics, which are simple but approximate rules to aid decision‐making by simplifying the situation and decision to be made. Particularly during times of fatigue, stress, or time pressure, these heuristics can become biases, leading to faulty clinical decision‐making and undesirable consequences.²⁴ Some of these, with common clinical examples, are given in Table 11.4.

      System and organizational factors

Errors and human behaviour cannot be understood in isolation but only in relation to the context in which people are working. Clinical staff are influenced by the nature of the task they are carrying out, the team they work with, their working environment, and the wider organizational context; these are the system factors. The systems in which we work have inbuilt defences and barriers, and it is only when these defences are simultaneously breached that adverse events occur. This concept forms the basis of Reason’s Swiss cheese model, shown in Figure 11.1.

Table 11.4 Examples of some cognitive biases and heuristics that commonly affect clinical reasoning.

      Source: Adapted from Redelmeier²⁴.

Name	Definition	Example
Availability heuristic	Making judgements based on cases that spring easily to mind	‘The last time I saw a patient with fever and a headache, it was only flu, so it is likely to be so in this case too’ (actually meningitis)
Anchoring heuristic	Sticking with initial impressions	The confused elderly patient who has a ‘UTI’ on admission (despite a negative MSU), whose severe constipation goes unnoticed
Framing effects	Making a decision based on how the information is presented to you	‘A&E referred this patient with fever and haemoptysis as “pneumonia”, so that is the most likely diagnosis even though the CXR is normal’ (actually a PE)
Blind obedience	Showing undue deference to seniority or technology – ‘they must be right, and I must be wrong’!	‘My consultant said that this patient could go home, so I am going to ignore concerns raised by nursing staff’; ‘The blood results show a normal haemoglobin even though this patient looks clinically anaemic – the blood results must be right’
Premature closure	Being satisfied too easily with an explanation	In a patient with staphylococcal sepsis, assuming the source of sepsis is their cellulitic leg and missing their underlying endocarditis

Figure

Скачать книгу