Bias in Healthcare

Week 1 L. Johnson Lecture

racial and ethnic bias

Providers may

unconsciously provide different levels of care

based on a patient's race or ethnicity, leading

to disparities in diagnoses, treatment, and

outcomes

racial and ethnic bias

Minority patients are often under-

prescribed pain medication compared to white

patients, which can lead to unmanaged pain

and worsened health outcomes

gender bias

Discrimination based on a

patient's gender can influence medical

decision-making, leading to either under- or

over-diagnosis of certain conditions

gender bias

Women are often undertreated for

pain, with their pain being perceived as less

severe compared to men’s, leading to

inadequate pain management

age related bias

involves

prejudices or assumptions about a patient's

age affecting their diagnosis or treatment.

This can result in either over- or under-

medication

age related bias

Older adults are often undertreated

for pain, as healthcare providers may believe

that pain is a normal part of aging and not

address it adequately

socioeconomic bias

Bias based on a

patient’s economic status or insurance

coverage can impact the quality of care they

receive, with wealthier patients often receiving

better treatment

socioeconomic bias

Patients from lower socioeconomic

backgrounds may experience biased

decision-making regarding opioid

prescriptions, with providers assuming they

may misuse medications

cultural bias

Differences in cultural norms and practices may lead healthcare providers to misinterpret a patient's symptoms or preferences, affecting their care

cultural bias

Cultural differences in how pain is

expressed or perceived may lead to biased

treatment decisions, with healthcare providers

underestimating the severity of pain in certain

cultural groups

conformation bias

When healthcare

providers rely too heavily on initial diagnoses

or assumptions, ignoring evidence that

contradicts their beliefs

conformation bias

A physician may continue to treat a

patient for depression without considering

alternative diagnoses like thyroid disorders,

based on an initial assessment

treatment bias

Healthcare providers may

favor certain treatments or medications over

others due to personal preference, outdated

practices, or biases toward specific methods

treatment bias

A provider might favor prescribing

opioids over non-opioid pain management

treatments due to familiarity or convenience,

even if it's not the best option for the patient

body size bias

Negative attitudes or

stereotypes toward patients based on their

body size or weight, which can lead to

inappropriate medical advice or treatment

body size bias

Obese patients might be dismissed

as “lazy” or “unmotivated” and have their

complaints attributed solely to their weight,

potentially overlooking other health

conditions

disability bias

Healthcare providers may

hold negative beliefs or misconceptions about

people with disabilities, leading to reduced

quality of care or a lack of accommodations

disability bias

A person with a disability may have

their pain or discomfort dismissed as "just part

of their condition," rather than being given the

appropriate treatment or investigation into

other underlying causes

implicit bias

Unconscious attitudes or

stereotypes about individuals or groups that

influence healthcare providers’ decisions

without their awareness

implicit bias

A physician might assume a Black

patient is less likely to follow treatment

recommendations, leading to less aggressive

treatment

affinity bias

favors patients or colleagues that share similar characteristics, interests, or backgrounds, leading to preferential treatment

affinity bias

a doctor might feel more comfortable prescribing a certain treatment to a patient who shares the same cultural background, assuming the patient will understand the treatment better or be more likely to adhere to it

attribution bias

make judgements about a patient’s behavior or condition based on their own perceptions rather than considering the full context

attribution bias

if a patient with diabetes doesn’t show up for their appointments, a healthcare provider might attribute their to irresponsibility or lack of motivation, rather than considering external factors like financial constraints or transportation issues

likability bias

to give preferential treatment to patients or colleagues whom one finds more likable or who share similar personality traits

likability bias

a provider might be more willing to give more attention or a more thorough examination to a patient they find personable and easy to communicate with, while giving less attention to a patient they find difficult or unpleasant, regardless of clinical need

performance bias

the influence of external factors on the way healthcare providers perform tasks, based on the expectations or perceptions of certain patient groups

performance bias

a doctor might provide more aggressive treatment to a young, healthy-looking patient and less aggressive treatment to an older, frail patient, even though both have the same medical condition, due to assumptions about the potential for recovery

confirmation bias

the tendency to seek out, interpret, or prioritize information that confirms one’s pre-existing beliefs or hypotheses while disregarding contradictory evidence

confirmation bias

a doctor who suspects a patient is abusing substances might focus solely on signs that support this assumption, ignoring other potential explanations for the patient’s symptoms and overlooking a correct diagnosis

anchoring bias

relies on the first piece of information they encounter, often leading to judgments or decisions based on that initial piece of information, even if it is irrelevant or insufficient

anchoring bias

if a doctor first hears that a paitnet has a history of asthma, they might focus on treating respiratory symptoms based on that anchor, potentially overlooking other conditions like pneumonia that may also be causing the patient’s symptoms

physiological bias

Differences in organ function (e.g., liver, kidney) across individuals based on genetic, racial, or environmental factors can lead to biased clinical decisions

pain perception bias

Neurological or sensory processing biases in pain perception may lead to differential pain management practices. Research suggests that certain groups may be perceived as having a higher or lower tolerance to pain, leading to under-or overprescription of pain medications

population prescribing bias

the unequal distribution of medications or treatments across different patient populations, often influenced by demographic characteristics such as race, ethnicity, gender, socioeconomic status, and age

receptor pharmacology bias

A healthcare provider prescribes a standard dose of opioid analgesics to a 50-year-old Hispanic male patient for post-surgical pain, assuming that the patient will metabolize the drug similarly to other patients in the population. However, research shows that individuals from certain ethnic groups, including Hispanics, may metabolize opioids more slowly due to genetic variations in enzyme activity. This oversight could result in the patient experiencing inadequate pain relief or increased side effects. This scenario exemplifies which type of bias in pharmacology?

anchoring bias

A healthcare provider is more likely to prescribe stronger analgesics to a pediatric patient after seeing a similar child in the past respond well to the treatment. This is an example of:

socioeconomic bias

A healthcare provider assumes that a patient from a lower socioeconomic background will not be able to afford pain management medications and thus prescribes a less expensive alternative. This is an example of:

population prescribing bias

A 60-year-old African American male patient with chronic pain is prescribed a lower dose of opioids compared to a 60-year-old white male patient, even though both have similar pain levels and medical histories. The prescribing provider assumes that the African American patient may be at a higher risk of opioid misuse, despite there being no evidence to support this assumption. This scenario is an example of which type of bias in pharmacology?