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Embracing the Uncertainty of Diagnosis

Morgenstern, Justin MD

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doi: 10.1097/01.EEM.0000513589.87778.89
    diagnosis, uncertainty
    diagnosis, uncertainty:
    diagnosis, uncertainty

    Doctors don't make definitive diagnoses. Our patients are certainly under the illusion that we do. A physician's job is to determine the probability of a disease, even in the best of times.

    We all inherently understand this. It is the central tenet of our discharge instructions: “If anything changes, come back to the emergency department.” But why would anything change if we already know the diagnosis? If this question seems silly, it is because we have all internalized the uncertainty of medicine. We know that what is clearly a viral illness could easily turn out to be early sepsis or pneumonia later.

    Unfortunately, the inherent uncertainty of medical diagnosis is easily obscured by disease labels. Tell a patient that he has gastroenteritis, and the diagnosis is made. Tell a doctor the same thing, and she will re-examine the patient's belly the next day to rule out appendicitis.

    This is why emergency physicians are taught to make diagnoses like “chest pain not yet diagnosed” instead of “costochondritis.” The pain might seem inflammatory, but costochondritis sounds too certain. We want to use terminology that conveys the inherent uncertainty of our diagnosis to the patient.

    Vague terminology like “shortness of breath not yet determined” is also problematic. Most of the time, I have a (highly) educated guess about the diagnosis. It would be a disservice to the patient and the rest of the health care team for me to ignore my diagnostic training. The label “SOB NYD” helps no one. The label “congestive heart failure,” even if uncertain, helps guide the patient's care. We seem to be stuck between two extremes. “SOB NYD” is too vague, but “congestive heart failure” is too specific. Both labels mask the nuance and probability of diagnostics.

    Imagine you admitted two patients to the hospital with congestive heart failure. One has a history of CHF, orthopnea, paroxysmal nocturnal dyspnea, no other respiratory conditions, B-lines on ultrasound and x-ray, crackles, and an elevated jugular venous pressure. The other has a history of COPD and CHF, a combination of wheezes and crackles on exam, and nondiagnostic imaging. After a few hours, you decide that CHF is the most likely diagnosis for both, but you are far from certain.

    Both patients will have the same admission diagnosis written on the chart. Both will leave the department with the same label. The nursing team will be told a patient is being admitted with CHF. The RT who calls at 3 a.m. will be told that both have CHF. The covering physician, as well as the team that assumes care the next day, will see the CHF diagnosis. But these two patients are not the same.

    All of your diagnostic expertise has been lost when you wrote this single diagnosis on the chart. You were almost certain of the diagnosis for one patient, and you could tell the RT at 3 a.m. as the patient deteriorates that CPAP or furosemide was the necessary treatment. The other patient's diagnosis was uncertain, however, so if he deteriorates in the middle of the night, the most appropriate intervention might be a repeat physical exam and further testing. Unfortunately, this information is lost behind a single diagnostic label.

    There must be a better way.

    What if we used probabilistic notations to indicate our level of certainty about a diagnosis? That a patient who I am 99 percent sure is short of breath from CHF receives the diagnosis of “CHF 99%,” but the other patient who might have CHF but could have other diagnoses might get the diagnosis of “CHF 55%.”

    A probabilistic notation would save the inpatient teams' time and effort in trying to read our minds or restarting the diagnostic process. It would guide care overnight when the physician is harder to reach. It could even empower members of the interdisciplinary team (who spend much more time with the patients than physicians do) to voice their observations because it is now clear to them that the diagnosis is uncertain.

    Our outpatient teams will also benefit. Orthopedic surgeons could triage patients based on either an “ACL tear 90%” or a “knee effusion, ACL tear 10%.” The time frame for follow-up with rheumatology might be different for “temporal arteritis 99%” versus “temporal arteritis 5%.”

    Probabilistic Notations

    I would love to see probabilistic notations on patients I am assessing in the ED. Imagine a patient with headaches. You are seeing her at 3 a.m. and don't have access to her old notes, but her neurologist has told her she has migraines. Wouldn't it be nice to know if this was a definitive diagnosis (migraine 100%) or a provisional diagnosis (migraine 60%)?

    This exercise could make us better clinicians. Rather than just writing “Salter-Harris type 1 fracture 25%” for a child with tenderness but normal x-rays, I might look into the actual base rate of the disease. Finding out that the true rate of Salter-Harris type 1 fractures based on MRI is only three percent might change my practice. (JAMA Pediatr 2016;170[1]:e154114.)

    It is difficult to admit a patient without a diagnosis. Discharged patients also want to know what you think is going on. A provisional diagnosis is fine in theory; we understand that it is probabilistic. But provisional diagnoses quickly become permanent diagnoses in practice.

    We emergency physicians are frequently blamed for misdiagnoses. These are labelled errors, but calling a change in a provisional diagnosis an error is wrong. Our job is to come up with the best guess, and for the most part, we do an excellent job of it. We take limited information and transform it into a provisional diagnosis that allows us to start empiric therapy. Unfortunately, the act of transcription into a chart has a way of transforming a provisional diagnosis into the final diagnosis. These are just some initials thoughts. I would not want this taken too far. The act of putting a number on our diagnoses might backfire and make them seem more certain than they really are. Nor should we start quibbling among ourselves about whether a diagnosis actually has a 90 percent or an 88 percent chance of being right.

    My current solution is inelegant. I am hoping someone out there can suggest a better way. Whatever the solution, we need to embrace the role of probability in all medical diagnoses.

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