Differential Diagnosis: Physician’s Personal Characteristics Interacts Accuracy of Patient Diagnoses

           Researchers are investigating the possible interaction between patient characteristics like age, gender, race, socioeconomic status, and physician characteristic like age, race, medical specialty, and experience to evaluate if they impact the number and type of diagnoses considered, the level of certainty adhering to them, and the types of tests that would be ordered. The research asks when considering a diagnosis does the patient’s age, gender, race, and socioeconomic status have an effect on the probability of the physician assigning a specific diagnosis and/or treatment course.  Do physician characteristics such as gender, race, medical specialty, and experience affect the probability of the patient being assigned a specific diagnosis and/or treatment course? Does social patterning found in the current research that suggests risk factors found within a certain population really reflect the true prevalence of illness within a given population or does it actually reflect unconscious bias, inaccurate misconceptions, and prejudices?

To accurately access the impact of patient-doctor interaction McKinlay, Lin, Freund, & Moskowitz (2002) designed an experiment using videotapes and an interview the survey which measured four patient factors, four physician factors, six pair-wise interactions between patient factors, six pair-wise interactions between physician factors, and sixteen pair-wise interactions between patient and physician factors. Using the data gathered like patient age, gender, race, and socioeconomic status, physician characteristics such as gender, race, medical specialty and experience, as well as, number and type of diagnoses considered, the level of certainty adhering to them, and the types of tests that would be ordered. One hundred and twenty-eight physicians who participated in the study were asked to watch videos of patients of differing gender, age, race, and socioeconomic status complaining of differing diagnosis’s with similar symptomatology and then asked to answer questions about their level of certainty  on the  given a diagnosis, another possible diagnosis, and a treatment course. The experiment videos included a patient depicting the presentation symptomatology consistent with polymyalgia rheumatica (PMR) and the second video presented symptomatology designed to be consistent with a diagnosis of depression. These two disorders were chosen by their prevalence within the elderly population, the overlapping symptomatology that leaves room for a variety of diagnosis and treatment outcomes, the differing etiologies, one is a medical diagnosis, while the other resonates from an emotional or psychological distress (McKinlay, Lin, Freund,  & Moskowitz, 2002).

After analyzing the data including the “primary diagnosis, defined as the diagnosis given the highest probability; the level of certainty attached to the primary diagnosis; and the number of diagnostic tests or procedures recommended by the physician subject” McKinlay, Lin, Freund,  & Moskowitz (2002) determined that subjects majority (or 65 %) of the 128 subjects viewing the video for PMR confused it with depression, and  the minority  ( 7 %) of the subjects accurately diagnosed PMR as PMR and less than half (40.6%) even listed PMR as a possible diagnosis. From this, the researchers determined that patient age, gender, race, and socioeconomic status do not affect the outcome and likely diagnosis, level of certainty attached to the diagnosis or number of tests ordered and the evidence does not support the expected conclusion that patient attributes linked to the epidemiology of disease affect physician decision-making when the diagnosis is being made (McKinlay, Lin, Freund, & Moskowitz, 2002).  

The study also finds that physician characteristics including gender, age, race, and medical specialty had no direct effect on the three variables studied, but when considering the influence of these physician characteristics against the number of tests ordered there was a significant interaction noted.  The first interaction noted was between specialty and age; while medical specialty had no direct effect on the number of tests orders in older doctors, in the younger doctor, ’s the opposite was found, differences in medical specialty  did impact the number of tests ordered (McKinlay, Lin, Freund,  & Moskowitz, 2002).  In other words, older doctors ordered less regardless of specialty, while younger doctors ordered more depending on their specialty. Race and medical specialty also showed a significant interaction, as it was found that African American physician with different medical specialties had differing number of tests ordered, as well as they were more likely to miss the diagnosis of PMR.  African American physicians order an average of 5.66 tests and internists averaged 3.22 tests, which was more than White physicians who also saw little differences between the number of tests ordered and medical specialty (McKinlay, Lin, Freund, & Moskowitz, 2002).  While evaluating the depression video, the researchers found the race of the physician also impacted the diagnosis, as white physicians were twice as likely to diagnose depression then black physicians. Medical specialty also affected the diagnosis of depression, as internists were found more likely (65%) to diagnose depression accurately than are family practitioners (42.8 %) (McKinlay, Lin, Freund,  & Moskowitz, 2002). Physician’s age and patient gender also affected the diagnosis of depression, meaning, older doctors we more likely to diagnose depression in women and younger doctors were more likely to give the diagnosis to men (McKinlay, Lin, Freund, & Moskowitz, 2002).

The social problems discussed could create a breeding ground for future illness by leaving patients true medical symptoms unattended, or often administering the wrong medical treatment to patients who need other types of treatments, sometimes this can result in further injury or even in the death of the patient. I think that this a social problem is likely created by inconsistencies or differences found in the education levels of physicians, internists as well as our overlapping nomenclature. Being those older doctors were taught older material that often was broader, and younger doctors are being taught newer material that is focused there is a fundamental difference in the levels of awareness between age and medical specialty. Therefore, I feel that the best way to remedy the problem is to focus on educating physicians to be aware of the inconsistencies found within the data, advocate for them to be aware of how their characteristics like race, age, and medical specialty can affect their own decisions on diagnosis, and work to remedies inconsistencies in education levels and disease awareness.

References

McKinlay, John B., Lin, Ting, Freund, Karen, & Moskowitz, Mark. (2002). The Unexpected influence of physician attributes on clinical decisions: Results of an experiment. Journal of Health and Social Behavior, 43(1), p. 92-106.