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Human error is not easy to define, as boundaries are often blurred between the actions or inactions of individuals and the deficiencies of the systems in which they work. However, it is important to define and classify different sorts of errors in medicine, largely because this may help us learn from incidents. We can think about errors in medicine in relation to the clinical processes involved – for example, prescribing errors or diagnostic errors – but perhaps it is also useful to look at the underlying psychological themes. In his analysis of different types of error, Reason²² divided them into two broad types of error: slips and lapses. These are errors of action and mistakes that are, broadly speaking, faults of knowledge or planning. He also discusses violations that, as distinct from errors, are intentional acts that, for one reason or another, deviate from the usual or expected course of action.

Table 11.1 International adverse events studies, showing data for older patients.

Sources: Mills⁶; Brennan, et al.⁷; Wilson, et al.⁸; Thomas, et al.⁹; Vincent, Neale, and Woloshynowych¹⁰; Davis, et al.¹¹; Baker, et al.¹²; Forster, et al.¹³; Michel, et al.¹⁴; Sari, et al.¹⁵; Sousa, et al.¹⁶; Rafter, et al.¹⁷; Nilsson, et al.¹⁸

Study	Year	No. of subjects	No. (proportion, %) of elderly subjects	Definition of elderly (years)	Overall adverse event rate (%)	Incidence in elderly (%)	Incidence in young (%)	Difference
California (Mills)	1977	20,864	3826 (18.34%)	≥65	4.65	7.22±0.82	4.07±0.30	p < 0.05
Harvard (Brennan)	1991	30,121	4980 (16.53%)	≥65	3.7	Standardized for DRG 5.7±0.6	2.6±0.2 (16–44 yrs)	p < 0.0001
Australia (Wilson)	1995	14,210	3945 (27.76%)	≥65	16.6	23.3	Mean 13.75	Not given
Utah and Colorado (Thomas)	2000	15,000	Not stated	≥65	2.9±0.2	All adverse events 5.29±0.37	All adverse events 2.80±0.18	p = 0.001
UK (Vincent)	2001	1014	342 (33.73%)	≥65	10.8	18.13 (62/342)	7.25 (48/662)	p < 0.001
New Zealand (Davis)	2002	6579	1967 (29.9%)	≥65	11.2	17.6 (346/1967)	10.93 (504/4612)	Not given
Canada (Ross‐Baker)	2004	3745	Not stated	Not stated	7.5	Mean age of patient with adverse events 64.9 (SD 16.7) vs. 62.0 (SD 18.4) yrs, p = 0.016
Ottawa (Forster)	2004	502	126 (25.1%)	>72	12.7	22.22 (28/126)	9.57 (36/376)	p < 0.001
France (Michel)	2007	8754	Not stated	Not stated	6.6 per 1000 days of hospitalization	Mean age of those experiencing adverse events = 63 yrs, 61.7 yrs for those who did not (p = 0.5)
UK (Sari)	2007	1006	332 (33.0%)	≥75	8.7	13.5 (95% CI 9.8–17.2)	6.2 (95% CI 4.4–8.0)	p < 0.001
Portugal (Sousa)	2014	1669	Not stated	>65	11.1	19.3 (59% of adverse events in >65 years)	8.2	Not stated
Ireland (Rafter)	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.