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.

Dangerously normalised

Fatigue in the NHS is a long-standing issue, one of the most persistent and often under-recognised. Many systems and rotas are built on the assumption that people will work long shifts, skip breaks, and pick up extra hours to make ends meet or fit around family life. For many staff, long shifts offer flexibility, but the cost can be impaired judgement leading to poorer care. Fatigue has become normalised, with staff continuing to work when exhausted, whereas in aviation and transport, strategies are in place to try to prevent fatigue impacting on safety.

There’s also a collective fatigue across the NHS — exhaustion from years of uncertainty and constant change. Culturally, rest is often frowned upon. Few proper rest spaces exist, and napping or having a ‘proper’ break is often felt to be unacceptable. Staff may even fear that admitting to fatigue will lead to questions about whether they are up to the job. Fatigue is not a human resource issue – it’s a patient safety issue.

Adding fatigue to risk registers

When exhausted staff are making complex decisions, the consequences and risk to patients can be serious. Fatigue isn’t just about the individual staff member; it’s a systemic problem. Every healthcare organisation should have fatigue on its risk register because of its undeniable impact on patient safety.

Part of the problem is that, unlike aviation or transport, healthcare still lacks a clear definition of fatigue. The Health and Safety Executive has one that could be used in the interim, so organisations do not need to wait before taking action. Regulation of fatigue risk management exists in other industries and should be part of regulation within the NHS.

The need for strong leadership

Addressing fatigue requires strong leadership and high-level backing. This isn’t about reminding individuals to ‘look after themselves’ — it needs a systems approach. Fatigue risk management should be treated like any other safety system, collecting and triangulating data from surveys, incident reports, and occupational health. Rostering and shift scheduling should support rest and recovery.  High-risk groups, including mental health trusts, must be included.

The General Medical Council (GMC) and the Nursing and Midwifery Council (NMC) both acknowledge fatigue as a risk factor in adverse events, yet systemic solutions are still lagging behind the scale of the problem.

Co-designing rostering solutions

The NHS would struggle to function if every staff member worked strictly within fatigue limits. But that can’t justify accepting the status quo. Crucially, any solution must be co-designed with staff. Fatigue is a human issue, and change must balance patient safety, wellbeing, and service provision. Co-design helps ensure any policy works in practice — for patients and professionals alike.

Healthcare support workers — who often work the longest hours for the lowest pay, especially need support. There also needs to be open thinking about creative solutions: could volunteers help reduce pressure if better supported? Are occupational health and wellbeing services being fully used?

Final thoughts

Fatigue is often one factor, in many, contributing to avoidable harm. The challenge of addressing the impact on patient safety is huge, but fatigue can be managed, mitigated, and made visible. We can start by formally recognising it as a risk, embedding fatigue management in safety culture, and highlighting it in Patient Safety Incident Investigations. Staff need to be empowered to speak up when they’re too fatigued to work safely.

Patient Safety Partners can help shine a light on staff fatigue, its impact on patient safety and call for it to be added to risk registers throughout the NHS.

How to join the Patient Safety Partners Network

The Patient Safety Partners Network meets monthly in a virtual capacity and now includes nearly 200 Patient Safety Partners. These meetings provide a supportive and safe space for Patient Safety Partners to:

• discuss barriers and opportunities
• share successes
• discuss how they can use their collective voice to make a difference for patient safety.

Only Patient Safety Partners working within NHS organisations in England can join, although experts are often invited to present or discuss specific topics.

If you are a Patient Safety Partner, you can find out more about the Patient Safety Partner Network, and how to join here.

Related reading

• Why we need to manage fatigue in the NHS – a blog from Nancy Redfern and Emma Plunkett
• Managing fatigue as part of a safety culture – a blog from Nancy Redfern, Emma Plunkett and Roopa McCrossan
• HSSIB Investigation report: The impact of staff fatigue on patient safety (24 April 2025)
• Fighting Fatigue Together campaign

Share your insights

Have you seen the impact of fatigue on patient safety? Have you personally been affected as a member of staff or a patient? Share your thoughts by commenting below (sign up here for free first) or you can contact our editorial team at content@pslhub.org. 

This study demonstrates challenges to patient safety associated with the tension between the necessity of workarounds and the desire to adhere to policy. This can create stress and anxiety among nurses. They experience a tension at the intersection of the necessity of workarounds to deliver care, to be a good nurse, and the desire to adhere to policy. The associated stress and anxiety can lead to burnout, professional disengagement, and attrition. The study proposes solutions to manage challenges associated with workarounds.

Intuitive thinking, though quick and often effective, can lead to cognitive biases like anchoring and availability heuristics. 

A C.A.R.E approach incorporating tools like the TWED checklist (Threat, What if I'm wrong? What else?, Evidence, Dispositional factors) and Shisa Kanko (Japanese method of pointing and calling) can help to improve decision-making and action precision in clinical settings.