Understanding bias and the implications for patient safety

Summary

There are many different types of bias, some more commonly known than others. This resource has been created to help explain different types of bias and to provide some practical examples of how some of these can impact patient safety.

The content has been developed following a Patient Safety Education Network session led by Samia Sakuma, lead Quality Governance Lead for Paediatrics at West Hertfordshire Teaching Hospitals NHS Trust.

Content

Types of bias and practical examples

Anchoring bias – Sticking with your initial impression.

• Example: "I was right the last time".

Aggregate bias –- Assuming evidence from population groups applies equally to an individual patient.

• Example one: A frailty pathway recommends conservative management for older adults with pneumonia. An individual patient who is usually very active and independent is not considered for escalation early, despite clinical deterioration.
• Example two: Pain assessment guidance based on average recovery patterns following surgery leads staff to underestimate significant postoperative pain experienced by one patient whose response differs from expected norms.

Ascertainment bias – Judgements influenced by prior expectations or contextual information.

• Example one: A patient known to attend frequently with abdominal pain is initially assessed as having another functional episode, delaying recognition of acute appendicitis.
• Example two: Documentation describing a patient as “anxious” influences subsequent assessments, resulting in physical symptoms initially being attributed to anxiety rather than investigated further.

Availability bias – Where people overestimate the importance or likelihood of events based on how easily examples come to mind.

• Example: A patient comes in with flu-like symptoms, it must be flu as its flu season. The patient had strep A infection that was unresolved but this was not treated as the flu diagnosis took precedence.

Base rate neglect – Ignoring how common or uncommon conditions are when making decisions.

• Example one: A a rare neurological diagnosis is prioritised in a patient with headache, while more common causes such as medication side effects or dehydration are considered later.
• Example two: Chest pain in a young adult is assumed to be musculoskeletal without structured assessment, despite cardiac conditions still occurring at a measurable background rate.

Commission bias – Preference for action rather than watchful waiting, even when intervention may not help.

• Example one: antibiotics are prescribed for likely viral infection because active treatment feels safer than observation, exposing the patient to avoidable side effects.
• Example two: Additional imaging is requested despite low clinical indication, contributing to unnecessary radiation exposure and incidental findings.

Confirmation bias/belief bias –  the tendency to search for, interpret, favour and recall information in a way that confirms or supports one's prior beliefs or values or decisions.

• Example: Labelling a child at handover as a ‘drama queen’, thus anything that child does is interpreted through this lens. The child’s abnormal saturations were felt due to her being anxious and hyperventilating, however there was a genuine medical nonanxiety related need for oxygen, the child then had a respiratory arrest.

Diagnostic momentum – A diagnostic label becomes accepted and passed along without reassessment.

• Example one: A patient admitted with a presumed urinary tract infection continues to be treated for this diagnosis despite lack of supporting results, delaying identification of sepsis from another source.
• Example two: An ambulance handover describing “stroke” leads teams to continue that pathway even after features inconsistent with stroke emerge.

Framing effect – Where people’s decisions are influenced more by how information is presented than by the information itself.

• Example: What order do you present things. The first things you discuss are what stick in peoples minds. The language you use also frames something in a particular way. Calling a follow up protocol “Active surveillance” as opposed to “watchful waiting” can really make a big difference in whether people agree to this or not.

Gamblers fallacy – The mistaken belief that past random events can influence the probability of future independent events.

• Example: sepsis is relatively rare. If you have treated two patients in a row with sepsis, when you see a third patient you don’t believe the sequence can continue so you will go out of your way to find a diagnosis that isn’t sepsis, whereas each patient should be assessed afresh.

Over valuing bias/endowment effect – Causes individuals to overvalue what they own, often irrationally.

• Example: Spending time reading in depth articles on a medical condition such as mesenteric adenitis and reviewing guidance on managing this. Therefore diagnosing patient as having mesenteric adenitis because of the time expended on gathering and reviewing information on this thereby potentially missing another diagnosis.

Psych-out error - Physical illness incorrectly attributed to mental health or behavioural causes.

• Example one: Agitation in a patient with known mental health needs is attributed to psychiatric relapse before delirium secondary to infection is recognised.
• Example two: Shortness of breath in a patient with anxiety history is initially managed as panic symptoms, delaying diagnosis of pulmonary embolism.

Sutton’s slip – Focusing on the most obvious or common explanation without adequate verification.

• Example one: a patient with recurrent falls is assumed to have mechanical instability, while medication-related hypotension is identified later.
• Example two: Hyperglycaemia in a person with diabetes is attributed to poor control, delaying recognition of steroid-induced glucose elevation.

Visceral bias – Emotional reactions influencing clinical judgement.

• Example one: Challenging interactions during previous admissions unintentionally influence the urgency of reassessment when the patient re-attends unwell.
• Example two: A highly likeable patient’s reassurance that they feel “fine” reduces concern despite abnormal observations requiring escalation.

Yin–yang out – Belief that a patient has already had extensive assessment, so further evaluation is unlikely to help.

• Example one: A patient with multiple previous admissions for chest pain receives limited reassessment because earlier investigations were normal, despite new symptoms.
• Example two: Repeated attendance with headaches leads to reduced diagnostic curiosity when new neurological signs develop.

Zebra retreat – Avoiding consideration of rare diagnoses after being discouraged or corrected previously.

• Example one: After earlier feedback about over-investigating rare conditions, clinicians hesitate to pursue an uncommon metabolic disorder despite suggestive features.
• Example two: A rare drug reaction is not revisited because previous similar concerns were felt to be unlikely, delaying recognition when it genuinely occurs.