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 |