Doctors and Decisions: a

closer look at Errors and

Risk Communication

Nipa Shah, MD

University of Florida at Jacksonville

Department of Community Health and

Family Medicine

Goals

• Understand the basics of medical decision making

• Know how to discuss risk with patients

• Learn of the most common errors doctors make in their thinking processes (leading to poor decisions)

• Be more aware that personal emotions play in guiding/misguiding decisions relevant to patient care

• As a patient, what language works?

Medical Decision Making

• Optimal strategies for patient care and policy decision making

• Understanding individual and group decision-making processes

• Outcomes of decisions, their measurement, and valuation

• Risk communication, risk attitudes, and judgment

• Methods to teach about and improve actual decisions

• Methods for technology assessment, literature synthesis, and evidence-based decision making in specialties

RISK

• Much of the medical decision-making

process involves evaluating risk.

• Risk is defined in many ways:

– “actual people considering real threats to their

(or others’) well-being

– http://www.tufts.edu/~skrimsky/PDF/Emerg_Risk.PDF

accessed 11/16/11

Goal of explaining risk

• Patients ought to make health care

decisions that are consistent with their

own values and beliefs.

A basic example

• Taking a statin for hyperlipidemia for an

obese 28 yo female

– What are the risks?

– How do you explain them?

RISK and ARR

The absolute risk reduction (ARR) (or, in the case of harm, absolute risk increase)

• most straightforward measure of risk

• defined as the absolute difference in the proportion of treated and untreated patients who experience an outcome

• E.g. Mortality in controls is 30%, and 20% with treatment, ARR is 10% (30-20=10)

RISK and NNT

• Another risk descriptor preferred by many is number needed to treat (NNT)

– the reciprocal of the absolute risk reduction

– represents the total number of patients who must be treated to produce the target outcome in one patient (1/ARR)

• If ARR is 10%, the NNT is 1/10%=1/0.10=10

Condition Treatment Outcome* NNT

Prevention

Hypertension in patients with type 2

diabetes

Hypertension treatment Diabetes-related death over 10 years 15

Hyperlipidemia (secondary prevention) Various versus placebo Heart attack or stroke over five years 16

Deep venous thrombosis Warfarin (Coumadin; target INR = 1.5

to 2.0) versus placebo for one year

Venous thromboembolism over one

year

22

Heart failure (New York Heart

Association class I or II)

Enalapril (Vasotec) versus placebo Death at one year 100

Hyperlipidemia (primary prevention) Simvastatin (Zocor) versus no

treatment

Death over one year 163

Treatment

Helicobacter pyloriinfection Triple therapy Eradication 1.1

Peptic ulcer Helicobacter pylori eradication therapy

versus acid suppression treatment for

six to eight weeks

Cure at one year 1.8

Migraine Sumatriptan (Imitrex) versus placebo Headache relief at two hours 2.6

Bacterial conjunctivitis Topical antibiotics versus placebo Early clinical remission (three to five

days)

5

ABLE 1

Selected NNTs for Prevention and Treatment

http://www.medicine.ox.ac.uk/bandolier/band50/b50-8.html

RISK and RRR

• Relative risk ratio (RRR)

– difference in the proportions of untreated and treated

patients who experience a particular outcome relative

to the proportion of untreated patients who would

experience the outcome.

• 30-20/30=33%

• Compares one risk to another

• The major problem? Relative risk measures tend

to exaggerate small differences in effect

Same numbers in treatment/control

groups….but…

• ARR: 10%

• RRR: 30%

• NNT: 10

• Which will get the headline?

• Which number will sell the

product/promote the intervention?

If a lipid lowering med resulted in……% patients

who will take the medicine

• 34% reduction in heart attacks (relative risk reduction)

• 1.4% fewer patients having heart attacks (the equivalent absolute rate reduction)

• 71 subjects would have to be treated for five years to prevent one heart attack (the equivalent NNT)

• 88%

• 42%

• 31%

• http://mdm.sagepub.com/content/15/2/152.full.pdf

accessed 11/14/11

INNUMERACY

• Illiteracy in how to think and understand

using numbers

When 12.86% of death was

more risky than 24.14% Research shows that if death rates are presented:

“1,286 out of 10,000” Or

“24.14 out of 100”

Which death rate was rated as more risky?

• http://www.ky.hum.titech.ac.jp/kimihiko/articles/yamagishi(1997)_acp.pdf

accessed 11/14/11

Answer our patients’ question:

“How likely is this to

happen?”

Data on communicating risk

• Recommendation from medical societies:

– numerical and visual formats for

communicating risk

• Reality: verbal communication only

• Color coding and simple charts work better

• http://www.biomedcentral.com/content/pdf/1471-

2296-12-15.pdf

HTTP://WWW.NNTONLINE.NET

Cates plots

• Cates Plot for statins

Risk Characteristic Theater

• Visual risk representation

– A theater filled with 1000 people

– Cross off the ones affected

– Rifkin-and-Bouwer graphic showing mortality

statistics represented as seats in an

auditorium.

– Rifkin, Bouwer. The Illusion of Certainty – Health Benefits and Risks.

– http://theillusionofcertainty.com/RCT/RCT.html accessed 11/16/11

Cholesterol

• http://theillusionofcertainty.com/RCT/RCT.html accessed 11/15/11

Difference in mortality rates for 50 year old

non-smokers (top) and 50 year old smokers

Difference in mortality rates for 60 year old

non-smokers (top) and 60 year old smokers

• Patient Decision Aids

– Contraception

General Guidelines re: risk

• Understand commonly used measures of risk, including merits and drawbacks

• Communicate risk using absolute measures, such as number needed to treat (or harm) and absolute risk reduction (or increase)

• Generally speaking, avoid discussion of relative risk measures, due to their potential for misinterpretation

• Probabilities should be described as natural frequencies instead of percentages

• Commonplace words such as “likely” and “uncommon,” if used, should be defined as explicitly as possible

• Visual aids, such as the Risk Characterization Theater and Cates Plots, may be particularly helpful in conveying quantitative concepts more intuitively.

Context

• Patients are sensitive to the ways in which risk information might be framed

• Saying what the patient might lose by not having a screening test more persuasive than framing involving potential gains

• E.g. flu vaccine

• Saying positive terms (for example, chance of survival) is more likely to persuade patients to accept risky options than information presented in negative terms (chance of death)

Mnemonic RISK

• R – Relate relevant evidence

• I – Individualize the message, using a flexible approach

• S – Seek the patient’s perspectives and share the decision-making

• K-checK the patient’s understanding, monitor, and review decisions – asking a patient to teach back what was just

explained is one of the simplest ways to find out whether or not they truly comprehend the message.

– http://www.nature.com/nrrheum/journal/v3/n3/full/ncprheum0397.html accessed 11/16/11

-

http://www.wolfescape.com/Humour/Patients.htm accessed 10/28/09

Error

• An error doesn't become a mistake until

you refuse to correct it.

---Orlando A. Battista

Our role

• Admit our errors to ourselves

• Analyze them

• Keep them accessible at all

times

• Don’t lose the open mind

First impressions

• Physicians tend to go with their first

impressions

• From a large pool of data, we SEIZE

apparently positive findings (lipase slightly

elevated; must be pancreatitis)

• Once this is done, we ignore anything that

may be contradictory to our initial bias

• Simple awareness of this is KEY!

Pattern Recognition

• A key factor in making as assessment and

plan

• Depends on “eyeballing” the patient

• Doctors rely on stereotypes and pattern

recognition .

• Patients and their families need to be

aware of this

Losing the Open Mind

• Don’t accept the word of

another physician at face

value

• DOUBT ALL!

Cognitive Mistakes

• Ignore selective information

• Put patients in a narrow

framework (makes our job easier)

• We tend to choose what’s

convenient for diagnosis and

treatment

Time is the greatest luxury

• “A doctor can’t think with one eye on the

clock and another on the computer

screen.”

• “Working in haste and cutting corners are

the quickest routes to cognitive errors.” • Jerome Groopman, MD

HEURISTICS

• Shortcuts taken by experience

• an adjective for experience-based techniques that help in problem solving, learning and discovery

• Response to uncertainty and demands of the situation

• Not taught in medical school, where they want you to approach patient care systematically (e.g. trial and error)

HEURISTICS

• Necessity and power of it

Miseducation

• Human patients are not

textbook cases

Affective error

• E.g. You get to bond with a patient, and

perhaps don’t want them to go through the

agony of turning over in bed so you could

examine them, or putting them through an

endoscopy.

• You tend to believe and hope for the best

outcome for them.

EMOTIONS

• Can effect a doctor’s perception and

judgment, and actions/reactions

• Can lead to mistakes in thinking

• E.g.

– A patient that disgusts you

• A drug addict, an alcoholic

• Do you tend to dismiss other diagnoses?

E.g. Obese patients

• Higher the BMI of patients, the less respect physicians have for them.

• When physicians respect their patients, patients get more information from their doctors

• Some patients who don't feel respected may avoid the health care system altogether

– http://www.eurekalert.org/pub_releases/2009-10/jhmi-phl102109.php accessed 11/15/11

• “The secret of the care of the patient is in

caring for the patient.”

– Dr. Francis Weld Peabody

• Harvard Medical School 1925

• This poses a paradox

• If we erase our emotions, we fail to care

for the patient

• Feelings may blind us in determining

what’s wrong with him/her

• BE AWARE OF THIS POTENTIAL

SOURCE FOR ERROR!

Yerkes-Dodson Law

• On task performance

• Bell shaped curve

• Peak= productive anxiety=optimum level of tension and anxiety=sharply focuses mind and triggers quick reactions

• http://en.wikipedia.org/wiki/Yerkes-Dodson_law

Yerkes-Dodson Law

• Example--rhabdomyolysis

• A person screaming for pain medicine,

and is given the wrong treatment solely to

calm him down, i.e. IV morphine instead of

high amounts of IV fluids

Attribution error

• people predominantly presume that the actions

of others are indicative of the "kind" of person

they are, rather than the kind of situations that

compels their behavior • Example: I stub my toe. It is the object's fault, not mine. (External

Cause)

• Someone else stubs their toe. It is because they weren't paying

attention. (Internal Cause)

– Classic demonstration study: Jones and Harris (1967)

– http://psychology.wikia.com/wiki/Fundamental_attribution_error accessed 11/16/11

What can patients say?

• “I sense that we aren’t communicating well.”

• “May I retell my story, in case either you or me

overlooked something?”

• “I’m most worried about ____________”

• “What else could it be?”

• “Is there anything that doesn’t fit?”

• “Is it possible that I have more than one

problem?” – Groopman, J. (2007) “How Doctors Think” NYC, NY: Houghton.

What can patients say?

• “Don’t save me from an unpleasant test

just because we are friends.”

• “We appreciate the care you give. We

understand that you may need to do things

that cause discomfort or pain.”

• Groopman, J. (2007) “How Doctors Think” NYC, NY: Houghton.

What can patients say?

• Yes, I realize it appears I am just a moody,

perimenopausal woman, but what I am

feeling is more than just menopause.

– Could have a pheochromocytoma

– Groopman, J. (2007) “How Doctors Think” NYC, NY: Houghton.

Decisions based on Clinical intuition

– Through practice

– Through remembering when we were wrong

– Through receiving feedback

• Helps you understand misguided decisions

• Helps you understand your technical errors

• Groopman, J. (2007) “How Doctors Think” NYC, NY: Houghton.

References

• Medical Decision Making Journals

• How Doctors Think by Jerome Groopman,

MD

• Various websites/journals/original articles

as noted on the slides

Thank you!

Questions?