The Financial Impact of Not Utilizing Evidence-Based Medicine


The financial consequences of poor prescribing habits, as detailed in Which Drug Should I Prescribe?, are simply enormous. Recall that just one physician, making poor prescribing decisions for only 500 patients, could unnecessarily add nearly a quarter of a billion dollars in cost to the healthcare system in just 10 years. In the case of prescribing habits, physicians personally gain from their poor choices only by receiving adoration from drug reps along with free lunches and dinners. But what about other treatment decisions? How much cost is added to the system when physicians deviate from evidence-based practice guidelines, and is there a financial benefit to them for doing so?

Let’s look at a practical example.

Imagine a small Ob/Gyn practice with four providers. The four providers see 1,000 young women a year between the ages of 17-20 who desire birth control (that is, each physician sees 250 of these patients). The evidence-based approach to treating these patients would be to give them a long-acting reversible contraceptive (LARC) and to not do a pap smear (of course, they should still have appropriate screenings for mental health, substance abuse, etc. as well as testing for gonorrhea and chlamydia and other interventions as needed).

If each of these women presented initially at age 17 or 18 desiring birth control and received a three-year birth control device (e.g, Nexplanon or Skyla), then it is likely that a majority of them would not return for yearly visits until the three year life of the birth control had passed. It is also likely that only 1-3 of the 1000 women would become pregnant over the three year period while using a LARC. Gynecologists for decades have tied the pap smear to the yearly female visit. As a consequence, most young women, when they learn that they don’t need a pap every year, don’t come back for a visit unless they need birth control or have some problem.

The most common need, of course, is birth control. But when women have a LARC and know that they don’t need a yearly pap smear, they are even less likely to return just for a screening visit. Admittedly, gynecologists need to do a better job of “adding value” to the yearly visit and focus on things that truly improve the quality of life for patients – like mental health and substance abuse counseling – but these things take time and it’s much quicker just do an unnecessary physical exam and move on. Physicians use the need for a birth control prescription – and the lie that they need a pap smear in order to get a prescription for birth control – in order to force women back to the office each year. What’s worse, many young women don’t go to a gynecologist in the first place for birth control because they do dread having a pelvic exam; they then become unintentionally pregnant after relying on condoms or coitus interruptus.

In the following example, the evidence-based (EB) physician sees an 18-year-old patient who desires birth control; he charges her for a birth control counseling visit and charges her for the insertion of a Nexplanon, which will last three years and provides extraordinary efficacy against pregnancy. He sees her each year afterwards for screening for STDs and other age-appropriate counseling, but he does not do a pap smear until she turns 21.

The non-EB physician sees the same 18-year-old patient who desires birth control and prescribes a brand-name birth control pill (at an average cost of $120 per month and with an 8.4% failure rate). He insists that she first have a pap smear since she is sexually active and he plans to continue doing pap smears each year when she returns for a birth control refill. He tells her scary stories about a patient he remembers her age who was having sex and got cervical cancer but thankfully he caught it with a pap smear (of course, that patient didn’t have cervical caner  – she had dysplasia – but these little white lies are common in medicine). He doesn’t adequately screen her for mood disorders, substance abuse, or other issues relevant to her age group, but he does make a lot of money off of her; and he could care less about the psychological harm he might have caused her with his scary stories.

About 30% of women in this age group will have an abnormal pap smear if tested, and about 13.5% would have a pap smear showing LSIL, HSIL, or ASCUS with positive high risk HPV. The non-EBM physician would then do colposcopies on these 13.5% of women, along with short interval repeat pap smears (e.g, every 4-6 months) on the women with abnormal pap smears. About 15% of the women who receive colposcopy will have a biopsy with a moderate or severe dysplastic lesion, and the non-EBM physician will then do an unnecessary and potentially sterilizing LEEP procedure on those patients.

Since he gave these women birth control pills, 84 of the 1,000 patients will become pregnant each year, and this will lead to significant costs for caring for these pregnant women and delivering these babies. In fact, 252 of the 1000 women will become pregnant over the next three years (compared to 1-3 women who received a LARC). Ironically, the very reason why the patients came to the doctor in the first place is the most neglected, with nearly 1 in 4 becoming pregnant because of the poor decisions of the non-EB physician. The chart below shows the financial repercussions for the first year of care for these patients and for three years of care (since that is the useful life of a Nexplanon or Skyla LARC):


The differences are dramatic: the evidence-based physician has perhaps 3 pregnancies per 1000 patients and no issues with pap smears since none were done. I have no doubt that many of the women returned dissatisfied with the Nexplanon and some likely discontinued and switched methods, and I have allowed for return visits each year. A greater number of women who received birth control pills will return with problems and a desire to switch. But over 3 years, 252 of the women who were given birth control pills will become pregnant.

These young women came to get birth control and were instead victimized. The effects of unintended pregnancy on young women are profound. Women who have children under age 18 are nearly twice as likely to never graduate high school, nearly two-thirds live in poverty and receive government assistance, three-quarters never receive child support, and their children grow up underperforming in school. These are dramatic societal costs that must be considered a consequence of physicians ignoring evidence-based guidelines.

So what about the money? The physician who follows evidence-based guidelines will collect about $350,000 over the three year period; whereas the physician who does not will collect nearly $1.3 million in the same time. The physicians are financially incentivized to hurt young women. These extra monetary gains for the physician cost our healthcare system nearly 6.3 million dollars more. This story can be repeated for a variety of other medical problems in every speciality.

Another Ob/Gyn example: a physician who treats abnormal uterine bleeding with a hysterectomy when a lesser alternative might have sufficed (such as a Mirena IUD, birth control pills, or an endometrial ablation). The physician is financially incentivized to perform a hysterectomy because her fee is higher for that service compared to other, more appropriate services; but in order for the physician to make a few hundred dollars more, the healthcare system is drained of many thousands of dollars (the hospital charges for a hysterectomy can be enormous) and the patient is exposed to a higher risk procedure when a lower risk intervention was likely to work just as well.

The current model of financially incentivizing physicians to provide as much care as possible (what is called fee for service) is bankrupting our healthcare system and harming patients with too many interventions, too many prescriptions, and too much care. No good alternative to this system has been proposed. But fee for service provides no incentive for physicians to choose lower cost treatments, to prescribe less expensive drugs, or to perform expensive interventions only when truly needed. No good alternative to fee for service has been proposed, but every important player in healthcare realizes that the model needs to be changed in order to improve the quality of care and reduce costs.

The financial services industry faces a similar crisis. Currently, most financial advisors make money off of selling products, collecting fees for the transactions they conduct and the financial products that they sell, and, in some cases, residual fees from future earnings. This incentivizes advisors to sell products (stocks, bonds, equities, etc.) that produce the largest fees or the biggest return for them, not for the client. New federal rules are attempting to address this problem by requiring financial advisors to comply with fiduciary standards, meaning that they must put the clients’ needs above their own. How is this incentivized? A fiduciary advisor makes more money only if the client does; the fiduciary rules outlaw transactional fee (like selling an investment product) and instead tie the advisor’s fees to future client earnings. Full disclosure of conflicts of interest are also required. Essentially, the financial services industry is transitioning from a fee for service model (how many investment products can I sell?) to a fee for better outcomes model (I’ll only make more money if my client does).

In theory, physicians should comply with fiduciary rules for patient care because of our professional oath; doing what is best for patients without exploiting them is the most essential ethic of any medical professional. But professionalism among medical doctors is a true rarity, and the proof of this is the vast majority of physicians who choose the unethical approach of rejecting evidence-based guidelines and instead exploiting patients for financial gain.

Now I’m not suggesting that most physicians who don’t follow evidence-based guidelines do so because they have consciously chosen to financially exploit the patient, but in the example above of a 1,000 patients in a four physician group, if those physicians choose to follow evidence-based guidelines, they would likely not need the fourth physician and they wouldn’t be able to pay her. The finances of medical practice dictate far too many decisions, and sometimes this influence is subconscious (though many times it is not). Physicians are smart, and they are too good at couching their decisions to over-utilize interventions as if it’s a benefit to those patients: it’s never because physicians want to make more money doing it, it’s always because they are doing what’s best for patients. But the example above, which is typical for similar issues in many specialities, shows that outcomes are not better and the cost of not following evidence-based guidelines are extraordinary.

Physicians need to be financially incentivized to follow ethical, fiduciary principles. The Hippocratic Oath isn’t cutting it. Financial services advisors under fiduciary guidelines are paid more only if the client does well, and so too doctors should make more money only if their patients do well. In a $55 visit, I can choose to prescribe an inexpensive generic drug for a problem (at a cost of $48 a year) or I can choose to prescribe an expensive branded drug (at a cost of $4200 a year). Insurance companies need to recognize this and reward the physician who makes good decisions. It is much better to pay the physician another $100 to have the time to talk to the patient and educate her than it is to pay $4200 a year for the poor decision. Why not pay physicians as much for IUD insertions as they are payed for hysterectomies? Yes, I am quite serious. You would see so many IUDs being inserted that it would make your head spin, but the cost of healthcare overall would drop dramatically and patient outcomes would improve. High school graduation rates would increase. Poverty would decrease. Paychecks would go up as less money was spent on health insurance premiums. Why not pay more for vaginal deliveries than cesarean deliveries? In general, paying more for the good care that physicians provide and less for the bad care physicians provide is the solution. But what is good care and what is bad care?  That’s the struggle.

I suggest three things.

  • First, the total cost of care for a diagnosis, including pharmacy costs, must be part of the equation. Less expensive care must result in financial gain for the doctor (and the hospital if appropriate).
  • Second, adherence to evidence-based guidelines must result in financial benefit, and deviations from EBM must be explained in detail before it’s payed for by the payor.
  • Third, physicians must be financially rewarded for improved patient outcomes over a long term.

A physician who does 10 hysterectomies per year should, in most cases, be paid more than a physician who does 100 hysterectomies per year. The number of unnecessary surgeries and interventions in all disciplines of medicine is staggering, and it results in high-cost, low-quality healthcare that is bankrupting the US system. I wish that the Hippocratic Oath were enough and that physicians always did what was best for their patients even if it meant making less money, but this wish is a pipe dream. We need appropriate financial incentives.

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Which Drug Should I Prescribe?


Once the diagnosis is made correctly, the treatments begin. Selection of prescription drugs is one of the important (and costly) things that physicians do. How do we decide which drugs to use? Or, a better question, How should we decide?

First, let’s consider a patient who represents an almost typical case these days: a diabetic who has neuropathy, hypertension, hyperlipidemia, GERD, hypothyroidism, bipolar depression, and an overactive bladder. Even if this might not represent the problem list of your typical patient, it likely represents the problem list of any two patients (the average American between ages 19 and 64 takes 12 prescription drugs per years – many of which are unnecessary).

For each issue on the problem list, let’s select two common treatments. Doctor A tries to use lower cost drugs and generics when available. Doctor B favors newer drugs and believes that he is providing world-class medicine (at least that’s what the drug reps keep telling him). Let’s see what it costs Doctor A and Doctor B to treat this patient each year. Also, we’ll see what the ten year cost of treatment is and then magnify that times 500 patients (to represent a typical treatment panel of patients that a primary care doctor might have).


The difference in the two approaches is dramatic: Doctor B has spent nearly a 1/4 billion dollars more than Doctor A in just 10 years on just 500 patients. Add to this the increased costs of poorly selected short-term medications (like choosing Fondaparinux at $586/day versus Lovenox at $9.25/day, or choosing Benicar at $251/day rather than Cozaar at $8/day), unnecessarily prescribed medications (like antibiotics for earaches, sore throats, and sinus infections), unnecessary tests (like CT scans for headaches or imaging for low back pain), and the cost differences between Doctor A and Doctor B soar, all based upon decisions that the two doctors make in different ways.

In a career, Doctor B may cost the healthcare system easily $1 billion more than Doctor A. The cost of prescription drugs (and more importantly, the way doctors decide to use them) more than explains the run-away costs and low value of US healthcare. The top 100 brand-name prescription drugs in the US (by dollar amounts) netted $194 billion in sales a year as of 2015 (plus pharmacy mark-ups, etc.). This list of drugs includes Lyrica, Januvia, Dexilant, Benicar, Victoza, Synthroid, and Abilify (all mentioned above) plus Crestor (it recently went generic so I have substituted the similarly priced, non-generic Livalo), Vesicare (similarly priced to the Enablex listed above), and Pristiq (which is actually more expense than the Viibryd I used on the list – Viibryd is one of the top 100 prescribed branded drugs, just not a top 100 money maker). So Doctor B’s choice of drugs is representative for the choices many doctors are making in the US.

Now in fairness, the most prescribed drugs by number of prescriptions are generics, but because they are so inexpensive, they do not make the money list. Doctor A’s choice of Metformin, Levothyroxine, and Omeprazole (generic Prilosec) are all among the Top 10 most prescribed generics. Unfortunately, the most prescribed drug of all (with 123 million prescriptions in 2015) was generic Lortab.

So who’s right, Dr. A or Dr. B? Let’s go through a process to determine what drug we should prescribe.

Is a drug treatment necessary?

Just because a patient has a diagnosis doesn’t mean she necessarily needs a drug to match it. Physicians tend to overestimate the benefits of most treatments and do a poor job of conveying the potential benefits (and risks) to patients. Non-medical treatments (like lifestyle changes, counseling, etc.) are undervalued and perceived as less effective or just too much work. Providers often feel like they have to write a prescription or they will have an unsatisfied patient (in other words, they feel that if a patient presents with a complaint, it must be addressed with an intervention rather than just education and reassurance). Even worse, many clinicians believe that if they prescribe a medication during a visit, they can bill for a higher level visit, so they feel financially rewarded for prescribing. For example,

  • Psychotropic medications are notoriously over-prescribed (rather than utilizing more time-consuming interventions like psychotherapy), particularly antipsychotics, which are heavily marketed and often prescribed by non-psychiatrists. Stimulant medications, too, have become heavily abused and over-prescribed.
  • The elderly tend to accumulate diagnoses over time and are vulnerable to polypharmacy, often treating the side effects of one drug with another drug. An Austrian study found that 36% of elderly internal medicine patients were receiving at least one unnecessary drug.
  • Antibiotics are incredibly over-prescribed. A 2016 study revealed that at least 1 in 3 antibiotic prescriptions was unnecessary.

In general, overuse of prescription medications happens for the following reasons:

  • A drug is used for a condition that should first be treated with lifestyle changes or other non-pharmacological interventions (e.g. mild hypertension, obesity, mild hyperlipidemia, insomnia, anxiety, mild depression, etc.).
  • A drug is used for a condition that is actually a side-effect of another medication (e.g. constipation due to anticholinergics, sexual dysfunction due to antihypertensives or SSRIs, or insomnia due to a variety of drugs).
  • A drug is used for a condition that would get better without treatment (e.g. viral URIs, distal urethritis, musculoskeletal sprains and pains, earaches, etc.).
  • A second drug is used for a condition when another drug is already being used. Many times, this is necessary, but all too often, two drugs are used when only one would do.
  • A drug is used for a condition but offers almost no benefit for the condition (e.g. using SSRIs to treat premature ejaculation, even though the time from penetration to ejaculation is only lengthened by a few seconds).
  • A drug is prescribed with a proven benefit but the measured benefit is not considered worthwhile by the patient (e.g. a healthy 45 year old woman taking a statin drug to reduce her risk of a non-fatal heart attack by 1%age point over ten years).

Once it is decided that a drug is necessary for treatment, the next question is, Which one?

Which drug is appropriate?

In order to determine this, we must first decide what the goal of treatment is in order to select a medication that satisfies that goal. This sounds obvious, but it isn’t always done appropriately. The next part is trickier. In general, we need to select the lowest-cost medication that fulfills this treatment goal. Dr. A has done this in the above example quite well. Dr. B would rationalize his choices by adding that he has also picked either the most effective drug and/or the one with the least side effects. This rationale sounds attractive, and drug companies prey on the desire of physicians to use the best drug with the fewest side effects (and the ego-boosting idea that they are being innovative). But it is the wrong strategy. Here’s why.

Let’s say that Drug A is 90% effective at treating the desired condition and carries with it a 5% risk of an undesirable side effect. Meanwhile, Drug B is 95% effective at treating the condition and carries only a 2.5% risk of the side effect. Drug B is then marketed as having half the number of treatment failures as Drug A and half the number of side effects as Drug A. Drug A costs $4 while Drug B costs $350. So which drug should we use? If 100 people use Drug B, then approximately 92 will be treated successfully without the side effect at a cost of $386,400 per year. Eight people will remain untreated. If 100 people use Drug A, then approximately 85 people will be treated successfully without the side effect at a cost of $4,080 per year. After failing Drug A, an additional 7 people will use Drug B successfully at a cost of $29,400 per year, with 8 people still untreated. This try-and-fail or stepwise approach, rather than the “Gold Standard” approach, treated just as many people successfully but saved $352,920 per 100 people per year.

This scenario assumes that such dramatic differences between the two drugs even exist. The truth is that this is rarely the case. Such dramatic differences in side effects and efficacy do not exist for the drugs listed in the scenario above. What’s worse, in many cases the more expensive drug is actually inferior. HCTZ has long had superior mortality benefit data compared to ACE-Is and ARBs, but due to clever marketing, has never been utilized as much as it should be. Metformin is simply one of the best (and cheapest) drugs for diabetes, yet no one markets it and there are no samples in the supply closet. Fondaparinux is an important drug for the rare person who has heparin-induced thrombocytopenia (HIT), but the incidence of HIT among users of prophylactic Lovenox is less than 1/1000, hardly justifying the 63-fold increased price of fondaparinux. The clinical differences between Cozaar and Benicar can hardly begin to justify the 31-fold price difference. To decide by policy that every patient with overactive bladder should receive Vesicare ($300), Detrol LA ($320), or Enablex ($350) as a first line drug when most patients are satisfied with Oxybutynin ($4) is the attitude that is bankrupting US healthcare. In some cases, physicians prescribe the exact same drug at a costlier price (e.g. Sarafem for $486 instead of the chemically identical fluoxetine for $6).

A word of caution about interpreting drug comparison trials: drug companies have ten of billions of dollars at stake when marketing new drugs. The trials that are quoted to you are produced and funded by those drug companies. They only publish the studies that show positive results. Drug studies are, by far, the most biased of all publications so be incredibly careful in believing the bottom line from the studies. Here’s a howardism,

If the drug were really that good, it wouldn’t need to be marketed so aggressively.

Even when one drug is substantially better than another, it still may not be appropriate to prescribe it. Many times, the prohibitive cost of the perceived “better” drug prevents patients from actually getting and maintaining use of the drug, leading to significantly poorer outcomes than would have existed with the “inferior” drug. The best drug is the one the patient can afford to take.

Thus, pick the drug that is designed to achieve the desired goal that is least expensive (or at least pick a drug that is available as a generic), then specifically decide why that drug is not an option before picking a more expensive one (e.g. the patient is allergic to the drug, the patient previously failed the drug, etc.). (Wonder how much drugs cost? All the prices here are taken from

What influences determine the drugs that clinicians prescribe?

There are two facts about drug company marketing. The first is the physicians do not believe that they are influenced by drug company advertisements, representatives, free lunches and dinners, pens, sampling, CME-events, etc. The second is that drug company marketing is the number one influence on clinicians’ prescribing habits. In 2012, US drug companies spent $3 billion marketing to consumers while spending $24 billion marketing directly to doctors. They spend that money because they know it yields returns. Remember that just one Dr. B is worth nearly 1/4 of a billion dollars in a ten year period to pharmaceutical companies. They are more than happy to buy as many meals as it takes. But doesn’t this money support research? It’s true that most drug companies spend billions per year on research and development, but the total R&D expenditures are usually about half of marketing expenses alone, and obviously a fraction of the $350+ billion revenues. But don’t samples help me take care of my poor patients? No. They help influence your prescribing habits and they give you the impression that the drug is a highly desirable miracle that would be wonderful to give away to the needy because it’s just that good. What can help you take care of your patients are $4 generic drugs. Read more about the financials of the industry and the rising costs of drugs here.

These problems would be much worse than they are if insurance companies (“the evil insurance companies”) didn’t have prescribing tiers that force patients to ask their doctors for cheaper alternatives and if they didn’t deny coverage entirely for many brand-name drugs. But it is still an enormous problem, resulting in hundreds of billions of dollars of excess cost to the US healthcare system each year.

Simply by being more like Dr. A and less like Dr. B, physicians could rapidly and dramatically reduce the cost of US healthcare.

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Debiasing Strategies

CH 13267

All men make mistakes, but a good man yields when he knows his course is wrong, and repairs the evil. The only crime is pride.

— Sophocles, Antigone

(Thanks to my friend Michelle Tanner, MD who contributed immensely to this article).

In the post Cognitive Bias, we went over a list of cognitives biases that may affect our clinical decisions. There are many more, and sometimes these biases are given different names. Rather than use the word bias, many authors, including the thought-leader in this field, Pat Croskerry, prefer the term cognitive dispositions to respond (CDR) to describe many situations where clinicians’ cognitive processes might be distorted, including the use of inappropriate heuristics, cognitive biases, logical fallacies, and other mental errors. The term CDR is thought to carry less of a negative connotation, and indeed, physicians have been resistant to interventions aimed at increasing awareness of and reducing errors due to cognitive biases.

After the publication of the 2000 Institute of Medicine Report To Err is Human, which attributed up to 98,000 deaths per year to medical errors, many efforts were made to reduce errors in our practices and systems. Development of multidisciplinary teams, computerized order entry, clinical guidelines, and quality improvement task forces have attempted to lessen medical errors and their impact on patients. We have seen an increased emphasis on things like medication safety cross-checking, reduction in resident work hours, using checklists in hospital order sets or ‘time-outs’ in the operating room. But most serious medical errors actually stems from misdiagnosis. Yes, every now and again a patient might have surgery on the wrong side or receive a medication that interacts with another medication, but at any given time, up to 15% of patients admitted to the hospital are being treated for the wrong diagnosis – with interventions that carry risk – while the actual cause of their symptoms remains unknown and likely untreated. To Err Is Human noted that postmortem causes of death were different from antemortem diagnoses 40% of the time in autopsies! How many of those deaths might have been prevented if physicians had been treating the correct diagnosis?

Most of these failures of diagnosis (probably two-thirds) are related to CDRs and lot of work has been done since 2000 to elucidate various causes and interventions, but physicians have been resistant to being told that there might be a problem with how they think. Physicians love to blame medical errors on someone or something else – thus the focus has been on resident’s lack of sleep or medication interaction checking. Seeking to reduce physicians resistance due to a feeling of being criticized is a prime reason why Croskerry and others prefer to use the term cognitive disposition to respond rather than negative words like bias or fallacy. I’m happy with either term because I’m not sure that relabeling will change the main problem: physicians tend to be a bit narcissistic and therefore resistant to the idea that all of us are biased and all of us have to actively work to monitor those biases and make decisions that are overly-influenced by them.

We make poor decisions for one of two reasons: either we lack information or we don’t apply what we know correctly. Riegleman, in his 1991 book Minimizing medical mistakes: the art of medical decision making, called this ‘errors of ignorance’ and ‘errors of implementation.’ One of the goals of To Err is Human was to create an environment where medical errors were attributed to systematic rather than personal failures, hoping to make progress in reducing error by de-emphasizing individual blame. Our focus here, of course, is to evaluate the errors of implementation. Graber et al., in 2002, further categorized diagnostic errors into three types: No-fault errors, system errors, and cognitive errors. No-fault errors will always happen (like when our hypothetical physician failed to diagnose mesenteric ischemia despite doing the correct work-up). System errors have been explored heavily since the publication of To Err is Human. But the cognitive errors remain and understanding our CDRs (our biases, etc.) is the first step to reducing this type of error.

Croskerry divides the CDRs into the following categories:

  • Errors of overattachment to a particular diagnosis
    • Anchoring, confirmation bias, premature closure, sunk costs
  • Errors due to failure to consider alternative diagnoses
    • Multiple alternatives bias, representativeness restraint, search satisfying, Sutton’s slip, unpacking principle, vertical line failure
  • Errors due to inheriting someone else’s thinking
    • Diagnosis momentum, framing effect, ascertainment effect, bandwagon effect
  • Errors in prevalence perception or estimation
    • Availability bias, ambiguity effect, base-rate neglect, gambler’s fallacy, hindsight bias, playing the odds, posterior probability error, order effects
  • Errors involving patient characteristics or presentation context
    • Fundamental attribution error, gender bias, psych-out error, triage cueing, contrast effect, yin-yang out
  • Errors associated with physician affect, personality, or decision style
    • Commission bias, omission bias, outcome bias, visceral bias, overconfidence, vertical line failure, belief bias, ego bias, sunk costs, zebra retreat

Some additional biases mentioned above include the bandwagon effect (doing something just because every one else does, like giving magnesium to women in premature labor), ambiguity effect (picking a diagnosis or treatment because more is known about it, like the outcome), contrast effect (minimizing the treatment of one patient because, in contrast, her problems pale in comparison to the last patient), belief bias (accepting or rejecting data based on its conclusion or whether it fits with what one already believes rather than on the strength of the data itself), ego bias (overestimating the prognosis of your patients compared to that of others’ patients), and zebra retreat (not pursuing a suspected rare diagnosis out of fear of being view negatively by colleagues or others for wasting time, resources, etc.).

We are all vulnerable to cognitive dispositions that can lead to error. Just being aware of this is meaningful and can make us less likely to make these mistakes, but we need to do more. We need to actively work to de-bias ourselves. Let’s look at some strategies for this (adapted from Croskerry):

Develop insight/awareness: Education about CDRs is a crucial first step to reducing their impact on our clinical thinking, but it cannot stop with reading an article or a book. We have to look for examples of them in our own practices and integrate our understanding of CDRs into our quality improvement processes.  We need to identify our biases and how they affect our decision-making and diagnosis formulation. An analysis of cognitive errors (and their root causes) should be a part of every peer review process, quality improvement meetings, and morbidity and mortality conferences. Most cases that are reviewed in these formats are selected because a less than optimal outcome occurred; the root cause (or at least a major contributor) in most cases was a cognitive error.  

Consider alternatives: We need to establish forced consideration of alternative possibilities, both in our own practices and in how and what we teach; considering alternatives should be a part of how we teach medicine routinely. Always ask the question, “What else could this be?” Ask yourself, ask your learner, ask your consultant. The ensuing conversation is perhaps the most educational thing we can ever do. Even when the diagnosis is obvious, always ask the question. This needs to become part of the culture of medicine. 

Metacognition: We all need to continually examine and reflect on our thinking processes actively, and not just when things go wrong. Even when things go right, it is a meaningful and important step to consider why things went right. We focus to much on negative outcomes (this is a form of bias); consequently, we develop a skewed sense of what contributed to the negative outcome. So try thinking about what went right as well, reinforcing the good things in our clinical processes. 

Decrease reliance on memory: In the pre-computer days, a highly valued quality in a physician was a good memory. Unfortunately, medical schools today still emphasizes this skill, selecting students who might excel in rote memorization but lag behind in critical thinking skills. In the 1950s, memory was everything: there was no quick way of searching the literature, of comprehensively checking drug interactions, of finding the latest treatment guidelines, etc. But today, memory is our greatest weakness. Our memories are poor and biased, and there is more data that we need to have mastery of than ever before in order to be a good doctor. So stop relying on your memory. We need to encourage the habitual use of cognitive aids, whether that’s mnemonics, practice guidelines, algorithms, or computers. If you don’t treat a particular disease every week, then look it up each time you encounter it. If you don’t use a particular drug all the time, then cross check the dose and interactions every time you prescribe it. Even if you do treat a particular disease every day, you should still do a comprehensive literature search every 6 months or so (yearly at the very least).

Many physicians are sorely dated in their treatment. What little new information they learn often comes from the worst sources: drug and product reps, throw-away journals, popular media, and even TV commercials. Education is a life-long process. Young physicians need to develop the habits of life-long learning early. Today, this means relying on electronic databases, practice guidelines, etc. as part of daily practice. I, for one, use Pubmed at least five times a day (and I feel like I’m pretty up-to-date in my area of expertise).

Our memory, as a multitude of psychological experiments have shown, are among our worst assets. Stop trusting it.

Specific training: We need to identify specific flaws in our thinking and specific biases and direct efforts to overcome them. For example, the area that seems to contribute most to misdiagnosis relates to a poor understanding of Bayesian probabilities and inference, so specific training in Bayesian probabilities might be in order, or learning from examples of popular biases, like distinguishing correlation from causation, etc. 

Simulation: We should use mental rehearsal and visualization as well as practical simulation/videos exhibiting right and wrong approaches. Though mental rehearsal may sound like a waste of your time, it is a powerful tool. If we appropriately employ metacognition, mental rehearsal of scenarios is a natural extension. Remember, one of our goals is to make our System 1 thinking better by employing System 2 thinking when we have time to do so (packing the parachute correctly). So a practical simulation in shoulder dystocia, done in a System 2 manner, will make our “instinctual” responses (the System 1 responses) better in the heat of the moment when the real shoulder dystocia happens. A real shoulder dystocia is no time to learn; you either have an absolute and definitive pathway in your mind of how you will deliver the baby before it suffers permanent injury or you don’t. But this is true even for things like making differential diagnoses. A howardism: practice does not make perfect, but good practice certainly helps get us closer. A corollary of this axiom is that bad practice makes a bad doctor; unfortunately, a lot of people have been packing the parachute incorrectly for many years and they have gotten lucky with the way the wind was blowing when they jumped out of the plane. 

Cognitive forcing strategies: We need to develop specific and general strategies to avoid bias in clinical situations. We can use our clinical processes and approaches to force us to think and avoid certain biases, even when we otherwise would not. Always asking the question, “What else could this be?” is an example of a cognitive forcing strategy. Our heuristics and clinical algorithms should incorporate cognitive forcing strategies. For example, an order sheet might ask you to provide a reason why you have elected not to use a preoperative antibiotic or thromboembolism prophylaxis. It may seem annoying to have to fill that out every time, but it makes you think. 

Make tasks easier: Reduce task difficulty and ambiguity. We need to train physicians in the proper use of relevant information databases and make these resources available to them. We need to remove as many barriers as possible to good decision making. This may come in the form of evidence-based order sets, clinical decision tools and nomograms, or efficient utilization of evidence-based resources. Bates et al. list “ten commandments” for effective clinical decision support. 

Minimize time pressures: Provide sufficient quality time to make decisions. We fall back to System 1 thinking when we are pressed for time, stressed, depressed, under pressure, etc. Hospitals and clinics should promote an atmosphere where appropriate time is given, so that System 2 critical thinking can occur when necessary, without further adding to the stress of a physician who already feels over-worked, under-appreciated, and behind. I won’t hold my breath for that. But clinicians can do this too. Don’t be afraid to tell a patient “I don’t know” or “I’m not sure” and then get back to them after finding the data you need to make a good decision. We should emphasize this idea even on simple decisions. Our snap, instinctive answers are usually correct (especially if we have been packing the parachute well) but we need to always take the time to do something if it is the right thing to do. For example, in education, you might consider always using a form of the One-minute preceptorThis simple tool can turn usually non-educational patient “check-outs” into an educational process for both you and your learner. 

Accountability: Establish clear accountability and follow-up for decisions. Physicians too often don’t learn from cases that go wrong. They circle the wagons around themselves and go into an ego-defense mode, blaming the patient, nurses, the resident, or really anyone but themselves. While others may have some part in contributing to what went wrong, you can really only change yourself. We have to keep ourselves honest (and when we don’t, we need honest and not-always-punitive peer review processes to provide feedback). Physicians, unfortunately, often learn little from “bad cases,” or the “crashes,” but they also learn very little from “near-misses.” Usually, for every time a physician has a “crash,” there have been several near-misses (or, as Geroge Carlin called them, “near-hits”). Ideally, we would learn as much from a near-miss as we might from a crash, and, in doing so, hopefully reduce the number of both. We cannot wait for things to go wrong to learn how to improve our processes.

Using  personal or institutional databases for self-reflection is one way to be honest about outcomes. I maintain a database of every case or delivery I do; I can use this to compare any number of metrics to national, regional, and institutional averages (like primary cesarean rates, for example). We also need to utilize quality improvement conferences, even in nonacademic settings. Even when things go right, we can still learn and improve. 

Feedback: We should provide rapid and reliable feedback so that errors are appreciated, understood, and corrected, allowing calibration of cognition. We need to do this for ourselves, our peers, and our institutions. Peer review processes should use modern tools like root-cause analysis, and utilize evidence-based data to inform the analysis. Information about potential cognitive biases should be returned to physicians with opportunities for improvement. Also, adverse situations and affective disorders that might lead to increased reliance on CDRs should be assessed, including things like substance abuse, sleep deprivation, mood and personality disorders, levels of stress, emotional intelligence, communications skills, etc. 

Leo Leonidas has suggested the following “ten commandments” to reduce cognitive errors (I have removed the Thou shalts and modified slightly):

  1. Reflect on how you think and decide.
  2. Do not rely on your memory when making decisions.
  3. Have an information-friendly work environment.
  4. Consider other possibilities even though you are sure you are right.
  5. Know Bayesian probability and the epidemiology of the diseases (and tests) in your differential.
  6. Rehearse both the common and the serious conditions you expect to see in your speciality.
  7. Ask yourself if you are the right person to the make this decision.
  8. Take time when deciding; resist pressures to work faster than accuracy allows.
  9. Create accountability procedures and follow-up for decisions you have made.
  10. Use a database for patient problems and decisions to provide a basis for self-improvement.

Let’s implement these commandments with some examples:

1. Reflect on how you think and decide.

Case: A patient presents in labor with a history of diet-controlled gestational diabetes. She has been complete and pushing for the last 45 minutes. The experienced nurse taking care of the patient informs you that she is worried about her progress because she believes the baby is large. You and the nurse recall your diabetic patient last week who had a bad shoulder dystocia. You decide to proceed with a cesarean delivery for arrest of descent. You deliver a healthy baby weighing 7 lbs and 14 ounces.

What went wrong?

  • Decision was made with System 1 instead of System 2 thinking.
  • Ascertainment bias, framing effect, hindsight bias, base-rate neglect, availability, and probably a visceral bias all influenced the decision to perform a cesarean. 
  • This patient did not meet criteria for an arrest of descent diagnosis. Available methods of assessing fetal weight (like an ultrasound or even palpation) were not used and did not inform the decision. Negative feelings of the last case influenced the current case.

2. Do not rely on your memory when making decisions.

Case: A patient is admitted with severe preeclampsia at 36 weeks gestation. She also has Type IIB von Willebrand’s disease. Her condition has deteriorated and the consultant cardiologist has diagnosed cardiomyopathy and recommends, among other things, diuresis. You elect to deliver the patient. Worried about hemorrhage, you recall a patient with von Willebrand’s disease from residency, and you order DDAVP. She undergoes a cesarean delivery and develops severe thrombocytopenia and flash pulmonary edema and is transferred to the intensive care unit where she develops ARDS (and dies). 

What went wrong?

  • Overconfidence bias, commission bias. The physician treated an unusual condition without looking it up first, relying on faulty memories. 
  • DDAVP is contraindicated in patients with cardiomypathy/pulmonary edema. DDVAVP may exacerbate severe thrombocytopenia in Type IIB vWD. It also may increase blood pressure in patients with preeclampsia.

3. Have an information-friendly work environment.

Case: You’re attending the delivery of a 41 weeks gestation fetus with meconium stained amniotic fluid (MSAF). The experienced nurse offers you a DeLee trap suction. You inform her that based on recent randomized trials, which show no benefit and potential for harm from deep-suctioning for MSAF, you have stopped using the trap suction, and that current neonatal resuscitation guidelines have done away with this step. She becomes angered and questions your competence in front of the patient and tells you that you should ask the Neonatal Nurse Practitioner what she would like for you to do.

What went wrong?

  • Hindsight bias, overconfidence bias on the part of the nurse.
  • The work environment is not receptive to quality improvement based on utilizing data, and instead values opinion and anecdotal experience. This type of culture likely stems from leadership which does not value evidence based medicine, and institutions that promote ageism, hierarchy, and paternalistic approaches to patient care. An information-friendly environment also means having easy access to the appropriate electronic information databases; but all the databases in the world are useless if the culture doesn’t promote their routine utilization. 

4. Consider other possibilities even though you are sure you are right.

Case: A previously healthy 29 weeks gestation pregnant woman presents with a headache and she is found to have severe hypertension and massive proteinuria. You start magnesium sulfate. Her blood pressure is not controlled after administering the maximum dose of two different antihypertensives. After administration of betamethasone, you proceed with cesarean delivery. After delivery, the newborn develops severe thrombocytopenia and the mother is admitted to the intensive care unit with renal failure. Later, the consultant nephrologist diagnoses the mother with new onset lupus nephritis.

What went wrong?

  • Anchoring, availability, confirmation bias, premature closure, overconfidence bias, Sutton’s slip or perhaps search satisfying. In popular culture, these biases are summed up with the phrase, If your only tool is hammer, then every problem looks like a nail.
  • The physician failed to consider the differential diagnosis. 

5. Know Bayesian probability and the epidemiology of the diseases (and tests) in your differential.

Case: A 20 year old woman presents at 32 weeks gestation with a complaint of leakage of fluid. After taking her history, which sounds likes the fluid was urine, you estimate that she has about a 5% chance of having ruptured membranes. You perform a ferning test for ruptured membranes which is 51.4% sensitive and 70.8% specific for ruptured membranes. The test is positive and you admit the patient and treat her with antibiotics and steroids. Two weeks later she has a failed induction leading to a cesarean delivery. At that time, you discover that her membranes were not ruptured.

What went wrong?

  • Premature closure, base-rate neglect, commission bias.
  • The physician has a poor understanding of the positive predictive value of the test that was used. The PPV of the fern test in the case is very low, but when the test came back positive, the patient was treated as if the PPV were 100%, not considering what the post-test probability of the hypothesis was. 

6. Rehearse both the common and the serious conditions you expect to see in your speciality.

Case: You are attending the delivery of a patient who has a severe shoulder dystocia. Your labor and delivery unit has recently conducted a simulated drill for managing shoulder dystocia and though the dystocia is difficult, all goes well with an appropriate team response from the entire staff, delivering a healthy newborn. You discover a fourth degree laceration, which you repair, using chromic suture to repair the sphincter. Two months later, she presents with fecal incontinence.

What went wrong?

  • Under-emphasis of the seemingly less important problem. This is a form of contrast bias. We are biased towards emphasizing “life and death” scenarios sometimes at the expense of other unusual but less important problems. Simulation was a benefit to the shoulder dystocia but rehearsal could have been a benefit too for the fourth degree laceration.

7. Ask yourself if you are the right person to the make this decision.

Case: Your cousin comes to you for her prenatal care. She was considering a home-birth because she believes that the local hospital has too high a cesarean delivery rate. She says she trusts your judgment. While in labor, she has repetitive late decelerations with minimal to absent variability starting at 8 cm dilation. You are conflicted because you know how important a vaginal delivery is to her. You allow her to continue laboring and two hours later she gives birth to a newborn with Apgars of 1 and 4 and a pH of 6.91. The neonate seizes later that night.

What went wrong?

  • Visceral bias.
  • In this case, due to inherent and perhaps unavoidable bias, the physician made a poor decision. This is why we shouldn’t treat family members, for example. But this commandment also applies to the use of consultants. Physicians need to be careful not to venture outside their scope of expertise (overconfidence bias).

8. Take time when deciding; resist pressures to work faster than accuracy allows.

Case: A young nurse calls you regarding your post-operative patient’s potassium level. It is 2.7. You don’t routinely deal with potassium replacement. You tell her that you would like to look it up and call her back. She says, “Geez, it’s just potassium. I’m trying to go on my break.” Feeling rushed, you order 2 g of potassium chloride IV over 10 minutes (this is listed in some pocket drug guides!). The patient receives the dose as ordered and suffers cardiac arrest and dies.

What went wrong?

  • Overconfidence bias.
  • Arguably, the physician’s main problem is a lack of knowledge, but feeling pressured, he deviated from what should have been his normal habit and did not look it up (if this scenario seems far-fetched, it was taken from a case report from Australia).

9. Create accountability procedures and follow-up for decisions you have made.

Case: Your hospital quality review committee notes that you have a higher than average cesarean delivery wound infection rate. It is also noted that you are the only member of the department who gives prophylactic antibiotics after delivery of the fetus. You change to administering antibiotics before the case, and see a subsequent decline in wound infection rates.

What went wrong?

  • Nothing went wrong in this case. Peer review worked well, but it required the physician being receptive to it and being a willing participant in continuous quality improvement processes. It also required the non-malignant utilization of peer review. The situation might have been avoided if the physician had better habits of continuous education. 

10. Use a database for patient problems and decisions to provide a basis for self-improvement.

Case: You track all of your individual surgical and obstetric procedures in a database which records complications and provides statistical feedback. You note that your primary cesarean delivery rate is higher than the community and national averages. Reviewing indications, you note that you have a higher than expected number of arrest of dilation indications. You review current literature on the subject and decide to reassess how you decide if a patient is in active labor (now defining active labor as starting at 6 cm) and you decide to give patients 4 hours rather than 2 hours of no change to define arrest. In the following 6 months, your primary cesarean delivery rate is halved.

What went wrong?

  • Again nothing went wrong. This type of continuous quality improvement is the hallmark of a good physician. But it must be driven by data (provided from the database) rather than a subjective recall of outcomes. We must promote a culture of using objective data rather than memory and perception to judge the quality of care that we provide. Additionally, we must be open to the idea that the way we have always done things might not the be the best way and look continuously for ways to improve. This is another skill that is strengthened with metacognition. 

Trowbridge (2008) offers these twelve tips for teaching avoidance of diagnostic errors:

  1. Explicitly describe heuristics and how they affect clinical reasoning.
  2. Promote the use of ‘diagnostic timeout’s.
  3. Promote the practice of ‘worst case scenario medicine’.
  4. Promote the use of a systematic approach to common problems.
  5. Ask why.
  6. Teach and emphasize the value of the clinical exam.
  7. Teach Bayesian theory as a way to direct the clinical evaluation and avoid premature closure.
  8. Acknowledge how the patient makes the clinician feel.
  9. Encourage learners to find clinical data that doesn’t fit with a provisional diagnosis; Ask ‘‘What can’t we explain?’’
  10. Embrace Zebras.
  11. Encourage learners to slow down.
  12. Admit one’s own mistakes.

The Differential Diagnosis as a Cognitive Forcing Tool

I believe that the differential diagnosis can be one of our most powerful tools in overcoming bias in the diagnostic process. But the differential diagnosis must be made at the very beginning of a patient encounter to provide mental checks and raise awareness of looming cognitive errors before we are flooded with sources of bias. The more information that is learned about the patient, the more biased we potentially become. The traditional method of making a differential diagnosis is one of forming the differential as the patient’s story unfolds, usually after the history and physical; yet this may lead to multiple cognitive errors. Triage cueing from the patient’s first words may lay the ground work for availability, anchoring, confirmation bias, and premature closure. The most recent and common disease processes will easily be retrieved from our memory, limiting the scope of our thinking merely by their availability.

With bias occurring during the patient interview, by default – through system 1 thinking – we may begin to anchor on the first and most likely diagnosis without full consideration of other possibilities. This causes us to use the interviewing process to seek confirmation of our initial thoughts and it becomes harder to consider alternatives. Scientific inquiry should not seek confirmation of our hypothesis (or our favored diagnosis), but rather proof for rejection of other possibilities. Once we’ve gathered enough data to confirm our initial heuristic thinking, we close in quickly, becoming anchored to our diagnosis. A simple strategy to prevent this course of events is to pause before every patient interview and contemplate the full scope of possibilities; that is, to make the differential diagnosis after learning the chief complaint but before interviewing the patient. By using the chief complaint given on the chart, a full scope of diagnostic possibilities can be considered including the most likely, the most common, the rare and the life threatening. This will help shape the interview with a larger availability of possibilities and encourage history-taking that works to exclude other diagnoses. Here a howardism,

You can’t diagnosis what you don’t think of first.

Having taught hundreds of medical students how to make differential diagnoses, I have always been impressed how easy it is to bias them to exclude even common and likely diagnoses. For example, a patient presents with right lower quadrant pain. The student is biased (because I am a gynecologist), so the differential diagnosis focuses only on gynecologic issues. When taking the history, the student then fails to ask about anorexia, migration of the pain, etc., and fails to consider appendicitis as a likely or even a possible diagnosis. The history and physical was limited because the differential was not broad enough. In these cases, triage cueing becomes devastating.

If bias based merely on my speciality is that profound, imagine what happens when the student opens the door and sees the patient (making assumptions about class, socioeconomic status, drug-dependency, etc.), then hears the patient speak (who narrows the complaint down to her ovary or some other source of self-identified pain), then takes a history too narrowly focused (not asking broad review of system questions, etc.). I have considered lead poisoning as a cause of pelvic/abdominal pain every time I have ever seen a patient with pain, but, alas, I have never diagnosed it nor have I ever tested a patient for it. But I did exclude it as very improbable based on history.

For further reading:

  • Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003; 78(8):775-780.
  • Wachter R. Why diagnostic errors don’t get any respect–and what can be done about them. Health Aff. 2010. 29(9):1605-10.
  • Newman-Toker DE, Pronovost PJ. Diagnostic Errors: the next frontier for patient safety. JAMA. 2009; 301(10):1060-2.
  • Croskerry P. Cognitive forcing strategies in clinical decision making. Ann of Emerg Med. 2003; 41(1).
  • Graber M, et al. Cognitive interventions to reduce diagnostic error: a narrative review. BMJ Qual Saf. 2012;21:535-557.
  • Corskerry P. A universal model of diagnostic reasoning. Acad Med. 2009; 84(8):1022-28.
  • Redelmeier, DA. The cognitive psychology of missed diagnoses. Ann of Int Med. 2005; 142:115-120.

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Cognitive Bias


(This cartoon and nine more similar ones are here).

Our human reasoning and decision-making processes are inherently flawed. Faced with so many decisions to be made every day, we take short-cuts (called heuristics) that help us make “pretty good” decisions with little effort. These “pretty good” decisions are not always right and often compromise and exchange our best decision for one that is just good enough. These heuristics carry with them assumptions which may not be relevant to the individual decision at hand, and if these assumptions are not accurate for the current problem, then a mistake may be made. We call these assumptions “cognitive biases.” Thus,

When a heuristic fails, it is referred to as a cognitive biasCognitive biases, or predispositions to think in a way that leads to failures in judgment, can also be caused by affect and motivation. Prolonged learning in a regular and predictable environment increases the successfulness of heuristics, whereas uncertain and unpredictable environments are a chief cause of heuristic failure (Improving Diagnosis in Healthcare).

More than 40 cognitive biases have been described which specifically affect our reasoning processes in medicine. These biases are more likely to occur with quicker decisions than with slower decisions. The term Dual Process Theory has been used to describe these two distinct ways we make decision. Daniel Kahneman refers to these two processes as System 1 and System 2 thinking.

System 1 thinking is intuitive and largely unintentional; it makes heavy use of heuristics. It is quick and reasoning occurs unconsciously. It is effortless and automatic. It is profoundly influenced by our past experiences, emotions, and memories.

System 2 thinking, on the other hand, is slower and more analytic. System 2 reasoning is conscious and operates with effort and control. It is intentional and rational. It is more influenced by facts, logic, and evidence. System 2 thinking takes work and time, and therefore is too slow to make most of the decisions we need to make in any given day.

A System 1 decision about lunch might be to get a double bacon cheeseburger and a peanut butter milkshake (with onion rings, of course). That was literally the first meal that popped into my head as I started typing, and each of those items resonates with emotional centers in my brain that recall pleasant experiences and pleasant memories. But not everything that resonates is reasonable.

As the System 2 part of my brain takes over, I realize several things: I am overweight and diabetic (certainly won’t help either of those issues); I have to work this afternoon (if I eat that I’ll probably need a nap); etc. You get the idea. My System 2 lunch might be kale with a side of Brussel sprouts. Oh well.

These two ways of thinking actually utilize different parts of our brains; they are distinctly different processes. Because System 1 thinking is so intuitive and so affected by our past experiences, we tend to make most cognitive errors with this type of thought. Failures can occur with System 2 thinking to be sure, and not just due to cognitive biases but also due to logical fallacies, or just bad data; but, overall, System 2 decisions are invariably more correct than System 1 decisions.

We certainly don’t need to overthink every decision. We don’t have enough time to make System 2 decisions about everything that comes our way. Yet, the more we make good System 2 decisions initially, the better our System 1 decisions will become. In other words, we need good heuristics or algorithms, deeply rooted in System 2 cognition, to make the best of our System 1 thoughts. Thus the howardism:

The mind is like a parachute: it works best when properly packed.

The packing is done slowly and purposefully; the cord is pulled automatically and without thinking. If we thoroughly think about where to eat lunch using System 2 thinking, it will have a positive effect on all of our subsequent decisions about lunch.

How does this relate to medicine? We all have cognitive dispositions that may lead us to error.

First, we need to be aware of how we make decisions and how our brains may play tricks on us; a thorough understanding of different cognitive biases can help with this. Second, we need to develop processes or tools that help to de-bias ourselves and/or prevent us from falling into some of the traps that our cognitive biases have laid for us.

Imagine that you are working in a busy ER. A patient presents who tells the triage nurse that she is having right lower quadrant pain; she says that the pain is just like pain she had 6 months ago when she had an ovarian cyst rupture. The triage nurse tells you (the doctor) that she has put the patient in an exam room and that she has pain like her previous ruptured cyst. You laugh, because you have already seen two other women tonight who had ruptured cysts on CT scans. You tell the nurse to go ahead and order a pelvic ultrasound for suspected ovarian cyst before you see her. The ultrasound is performed and reveals a 3.8 cm right ovarian cyst with some evidence of hemorrhage and some free fluid in the pelvis. You quickly examine and talk to the patient, confirm that her suspicious were correct, and send her home with some non-narcotic pain medicine and ask her to follow-up with her gynecologist in the office.

Several hours later, the patient returns, now complaining of more severe pain and bloating. Frustrated and feeling that the patient is upset that she didn’t get narcotics earlier, you immediately consult the gynecologist on-call for evaluation and management of her ovarian cyst. The gynecologist performs a consult and doesn’t believe that there is any evidence of torsion because there is blood flow to the ovary on ultrasound exam. He recommends reassurance and discharge home.

The next day she returns in shock and is thought to have an acute abdomen. She is taken to the OR and discovered to have mesenteric ischemia. She dies post-operatively.

While this example may feel extreme, the mistakes are real and they happen every day.

When the patient told the nurse that her ovary hurt, the nurse was influenced by this framing effect. The patient suffered from triage cueing because of the workflow of the ER. The physician became anchored to the idea of an ovarian cyst early on. He suffered from base-rate neglect when he overestimated the prevalence of painful ovarian cysts. When he thought about his previous patients that night, he committed the gambler’s fallacy and exhibited an availability bias. When the ER doctor decided to get an ultrasound, he was playing the odds or fell victim to Sutton’s slip. When the ultrasound was ordered for “suspected ovarian cyst,” there was diagnosis momentum that transferred to the interpreting radiologist.

When the ultrasound showed an ovarian cyst, the ER physician was affected by confirmation bias. The ER doctor’s frequent over-diagnosis of ovarian cysts was reinforced by feedback sanction. When he stopped looking for other causes of pain because he discovered an ovarian cyst, he had premature closure. When he felt that the patient’s return to the ER was due to her desire for narcotics, the ER doctor made a fundamental attribution error. When he never considered mesenteric ischemia because she did not complain of bloody stools, he exhibited representativeness restraint. When he consulted a gynecologist to treat her cyst rather than explore other possibilities, he was exploited by the sunk costs bias.

Each of these are examples of cognitive biases that affect our reasoning (see definitions below). But what’s another way this story could have played out?

The patient presents to the ER. The nurse tells the doctor that the patient is in an exam room complaining of right lower quadrant pain (she orders no tests or imaging before the patient is evaluated and she uses language that does not make inappropriate inferences). The doctor makes (in his head) a differential diagnosis for a woman with right lower quadrant pain (he does this before talking to the patient). While talking to the patient and performing an exam, he gathers information that he can use to rule out certain things on his differential (or at least decide that they are low probability) and determines the pretest probability for the various diagnoses on his list (this doesn’t have to be precise – for example, he decides that the chance of acute intermittent porphyria is incredibly low and decides not to pursue the diagnosis, at least at first).

After assessing the patient and refining his differential diagnosis, he decides to order some tests that will help him disprove likely and important diagnoses. He is concerned about her nausea and that her pain seems to be out of proportion to the findings on her abdominal exam. He briefly considered mesenteric ischemia but considers it lower probability because she has no risk factors and she has had no bloody stools (he doesn’t exclude it however, because he also realizes that only 16% of patients with mesenteric ischemia present with bloody stools). Her WBC is elevated. He does decide to order a CT scan because he is concerned about appendicitis.

When the CT is not consistent with appendicitis or mesenteric ischemia, he decides to attribute her pain to the ovarian cyst and discharges her home. When the patient returns later with worsened pain, he first reevaluates her carefully and starts out with the assumption that he has likely misdiagnosed her. This time, he notes an absence of bowel sounds, bloating, and increased abdominal pain on exam. He again considers mesenteric ischemia, even though the previous CT scan found no evidence of it, realizing that the negative predictive value of a CT scan for mesenteric ischemia in the absence of a small bowel obstruction is only 95% – meaning that 1 in 20 cases are missed. This time, he consults a general surgeon, who agrees that a more definitive test needs to be performed and a mesenteric angiogram reveals mesenteric ischemia. She is treated with decompression and heparin and makes a full recovery.

These two examples represent an extreme of very poor care to very excellent care. Note that even when excellent care occurred, the rare diagnosis was still initially missed. But the latter physician was not nearly as burdened by cognitive biases as the former physician and the patient is the one who benefits. The latter physician definitely used a lot of System 1 thinking, at least initially, but when it mattered, he slowed down and used System 2 thinking. He also had a thorough understanding of the statistical performance of the tests he ordered and he considered the pre-test and post-test probabilities of the diseases on his differential diagnosis. He is comfortable with uncertainty and he doesn’t think of tests in a binary (positive or negative) sense, but rather as increasing or decreasing the likelihood of the conditions he’s interested in. He used the hypothetico-deductive method of clinical diagnosis, which is rooted in Bayesian inference.

Let’s briefly define the most common cognitive biases which affect clinicians.

  • Aggregate bias: the belief that aggregated data, such as data used to make practice guidelines, don’t apply to individual patients.
    • Example: “My patient is special or different than the ones in the study or guideline.”
    • Consequence: Ordering pap smears or other tests when not indicated in violation of the guideline (which may unintentionally lead to patient harm).
  • Anchoring: the tendency to lock onto the salient features of a diagnosis too early and not modify the theory as new data arrives.
    • Example: “Hypoglycemia with liver inflammation is probably acute fatty liver of pregnancy.”
    • Consequence: Ignoring or rationalizing away the subsequent finding of normal fibrinogen levels (which would tend to go against the diagnosis).
  • Ascertainment bias: this occurs when thinking is shaped by prior expectations, such as stereotyping or gender bias.
    • Example: “She has pain because she is drug-seeking again.”
    • Consequence: Not conducting appropriate work-up of pain.
  • Availability: the tendency to believe things are more common or more likely if they come to mind more easily, usually leading to over-diagnosis (it may also lead to under-diagnosis).
    • Example: “Ooh, I saw this once in training and it was a twin molar pregnancy!”
    • Consequence: Not considering statistically more probable diagnoses.
  • Base-rate neglect: the tendency to ignore the true prevalence of diseases, distorting Bayesian reasoning. May be unintentional or deliberate (for example, when physicians always emphasize the “worst case scenario”).
    • Example: “Its probably GERD but we need to rule out aortic dissection.”
    • Consequence: Ordering unnecessary tests with high false positive rates and poor positive predictive values. 
  • Commission bias: the tendency to action rather than inaction, believing action is necessary to prevent harm. More common in over-confident physicians.
    • Example: “This trick always works in my patients for that problem” or “It’s just a cold, but she made an appointment so she’ll be unhappy if I don’t give her antibiotics” or “I want you to be on strict bedrest since you are having bleeding in the first trimester to prevent a miscarriage.”
    • Consequence: Overuse of potentially risky or unnecessary therapeutics and perhaps guilt-commissioning (if, for example, the patient miscarries when she gets up to tend to her crying baby).
  • Confirmation bias: the tendency to look for supporting evidence to confirm a diagnosis rather than to look for data to disprove a diagnosis.
    • Example: “Aha! That’s what I suspected.”
    • Consequence: Incorrect diagnosis. We should always look for data to disprove our diagnosis (our hypothesis).
  • Diagnosis momentum: the effect of attaching diagnoses too early and making them stick throughout interactions with patient, nurses, consultants, etc. and then biasing others. 
    • Example: “This is probably an ectopic pregnancy” and writing suspected ectopic on the ultrasound requisition form.
    • Consequence: Radiologist reads corpus luteal cyst as ectopic pregnancy.
  • Feedback sanction: diagnostic errors may have no consequence because of a lack of immediate feedback or any feedback at all, particularly in acute care settings where there is no patient follow-up, which reinforces errors in diagnosis or knowledge.
    • Example: “I saw this girl with back pain due to a UTI.”
    • Consequence: Positive reinforcement of diagnostic errors (such as belief that UTIs are a common cause of back pain).
  • Framing effect: how outcomes or contingencies are framed (by family, nurses, residents, or even the patient) influences decision making and diagnostic processes.
    • Example: “The patients says that her ovary has been hurting for a week.”
    • Consequence: Focusing on ovarian or gynecological sources of pelvic pain rather than other more likely causes.
  • Fundamental attribution error: the tendency to blame patients for their illnesses (dispositional causes) rather than circumstances (situational factors).
    • Example: “Her glucose is messed up because she is noncompliant.”
    • Consequence: Ignoring other causes of the condition (e.g. infection leading to elevated glucose).
  • Gambler’s fallacy: the belief that prior unrelated events affect the outcome of the current event (such as a series of coin tosses all showing heads affecting the probability that next coin toss will heads).
    • Example: “My last three diabetics all had shoulder dystocias!!”
    • Consequence: Leads to inappropriate treatment of the current patient, based on facts that are irrelevant. 
  • Gender (racial) bias: the belief that gender or race affects the probability of a disease when no such link exists pathophysiologically.
    • Example: “We need to think about osteoporosis since she’s white.”
    • Consequence: Under- or over-diagnosing diseases. Two-thirds of published racial predilections, for example, in major text books are not supported.
  • Hindsight bias: knowledge of the outcome affects perception of past events and may lead to an illusion of failure or an illusion of control.
    • Example: “Last time I had this, she got better because I gave her ___.”
    • Consequence: Perpetuates error and encourages anecdotal medicine. For example, it is merely an assumption that the intervention affected the outcome, either positively or negatively. 
  • Multiple alternative bias: a multiplicity of diagnostic options leads to conflict and uncertainty and then regression to well-known diagnoses.
    • Example: “Well let’s just focus on what it probably is and not worry about all that other stuff for now.”
    • Consequence: Ignoring other important alternative diagnoses. 
  • Omission bias: the tendency towards inaction, the opposite of a commission bias, and more common than commission biases.
    • Example: “Group B strep infections in neonates are really rare, so I don’t see the point in the antibiotic for this mom.”
    • Consequence: May result in rare but serious harms.
  • Order effects: the tendency to focus on the beginning and the end and fill in the middle part of the story (creating a false narrative or constructing false associations), worsened by tendencies like anchoring. This bias is important to consider in patient hand-offs and presentations.
    • Example: “She had a fever but got better when we treated her for a UTI.”
    • Consequence: Leads to inappropriate causation biases, etc. (The patient got better. This may be due to antibiotics given for a possible UTI or the actual cause of her fever may still be unknown). 
  • Outcome bias: the tendency to pick a diagnosis that leads to good outcomes rather than a bad outcome, a form of a value bias.
    • Example: “I’m sure it’s just a panic attack and not a pulmonary embolism.”
    • Consequence: Missing potentially serious diagnoses.
  • Overconfidence bias: the belief that we know more than we do, leading to a tendency to act on incomplete information, hunches, or intuitions.
    • Example: “I see this all the time, just send her home.”
    • Consequence: Grave harm may occur because of missed diagnosis.
  • Playing the odds: the tendency to opt for more benign diagnoses or simpler courses of action when uncertainty in the diagnosis exists.
    • Example: “I’m sure that this ovarian cyst is benign; it’s gotta be.”
    • Consequence: Potentially devastating when coupled with an omission bias (e.g., not following-up with a repeat ultrasound in a few weeks for a questionable cyst). 
  • Posterior probability error: the tendency to believe that what has gone on before for a patient changes the probability for future events for the patient.
    • Example: “Every time she comes in her bleeding has just been from her vaginal atrophy.”
    • Consequence: Biases current evaluation and work-up (e.g., ignoring post-menopausal bleeding).
  • Premature closure: the tendency to accept a diagnosis before it has actually been confirmed when scant data supports the anchored diagnosis, often leading to treatment failures. 
    • Example: “We know what it is, she just hasn’t responded to treatment yet.”
    • Consequence: Ignoring alternative theories; evidenced by this famous phrase in medicine: When the diagnosis is made, the thinking stops.
  • Psych-out error: this occurs when serious medical problems (e.g., hypoxia, head injuries, etc) are misattributed to psychiatric diagnoses.
    • Example: “She just acts that way because she is bipolar.”
    • Consequence: Ignoring potentially catastrophic physical ailments (e.g, vascular disease or brain tumor). 
  • Search satisfying: the tendency to stop looking once something satisfying is found, both on the patient or in the medical literature (a form of premature closure).
    • Example: “This article says I’m right!” or “That’s where she’s bleeding!”
    • Consequence: Ignoring other causes of symptoms or other contradictory evidence or literature.
  • Sutton’s slip: the tendency to go where the money is, that is, to diagnosis the most obvious things, ignoring less-likely diagnoses.
    • Example: “I rob banks because that’s where the money is,” (Willie Sutton’s response when the judge asked him why he robbed banks).
    • Consequence: Under-utilizing System 2 thinking and ignoring diseases or presentations that are less common.
  • Sunk costs: the more investment that is made in a particular diagnosis or treatment, the less likely one is to release from it and consider alternatives.
    • Example: “I’ve just got to be right, I don’t know why this treatment isn’t working!”
    • Consequence: Further delay in pursuing the right treatment/diagnosis. This also results in a lot of false case reports (We present a case of such-and-such that was refractory to usual treatments but responded to some other crazy treatment or We present a case of such-and-such that presented in some weird nontypical way – in both cases, the diagnosis was likely wrong to begin with). 
  • Triage cueing: the biasing that results from self-triage or systematic triage of patients or presentations, creating tunnel vision.
    • Example: “I need a Gyn consult because she’s a female with pain.”
    • Consequence: Ignoring other organ systems or causes of pain.
  • Unpacking principle: the failure to elicit all relevant information in establishing a differential, particularly when a prototypical presentation leads to anchoring.
    • Example: “Anorexia and right lower quadrant pain is classic appendicitis.”
    • Consequence: Not considering all causes of each symptom individually (or collectively). As an aside, Occam’s razor and other cognitive processes that favor simplicity over complexity are usually wrong but feel comfortable to human imagination (it’s much simpler to blame the MMR vaccine for autism than it is to consider a polygenetic, multifactorial causation theory). 
  • Vertical line failure: this results from routine, repetitive processes that emphasize economy, efficacy, and utility (as opposed to lateral thinking).
    • Example: “I always do a diabetes screen on all pregnant women” or “When I see x I always do y.”
    • Consequence: Deemphasizes lateral thinking (e.g., What else might this be?).
  • Visceral bias: Results from countertransference and other visceral arousal leading to poor decision making.
    • Example: “That patient is just a troll” or “She is so sweet.”
    • Consequence: Leads to cognitive distortion and augments biases.
  • Yin-yang out: the tendency to stop looking or to stop trying once efforts have seemingly been exhausted even though the case hasn’t been satisfied.
    • Example: “We’ve worked her up to the yin-yang.”
    • Consequence: Leads to errors of omission.

I’m sure you can think of many other examples for these biases, and there are many other biases that have been described apart from those on the list. There is an emerging scientific literature which is examining the effects of bias on diagnostic and therapeutic outcomes and on medical error. The 2015 Institute of Medicine Report, Improving Diagnosis in Healthcare, is a good place to start exploring some of the implications of bias in the diagnostic process.

Next we will explore some strategies to mitigate our bias.

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