Categories by Heart: Shortcut Reasoning in a Cardiology Clinic

Patient descriptions

Describing someone is a matter of selection; there are a myriad of characteristics, aspects, or episodes for a single person, yet a given situation restricts what characteristics, aspects, or episodes are appropriate. Person-descriptions are tailored for the actual purposes at hand (Maynard, 1982). For “patient descriptions,” the most obvious descriptive element concerns the patient’s diagnostic category. In addition, almost everyone who talks about patients at the clinic establishes the categories age, sex, class, and ethnicity at an early stage. For example, a doctor I shadowed was called to the emergency room to examine a patient. The doctor wanted to admit the patient to the clinic for more thorough tests and on our way back to the ward we ran into the senior physician. The doctor told the senior physician that she needs a bed for a new patient. The following brief exchange occurred:

Sex, symptom, age, ethnicity are efficiently established in a matter of seconds. It is not immediately clear for what purpose the two doctors collaboratively categorize the patient in this way as neither the patient’s sex nor age, let alone ethnicity, is legitimate grounds for admission to the clinic. It may be fair to suggest that the staff simply want to “get a picture” of who they are dealing with, and for that purpose the most basic and prominent social categories that are applied are age, sex, class, and ethnicity (Baker, 2004). In the hospital, patients’ “class affiliation” is certainly indistinct and less conspicuous than the other categories; determining class or social status is trickier than the other categories. However, “class” seems to merge with “lifestyle” as a token of the impact of the epidemiological framework; bad lifestyle choices are linked to the lower classes, and vice versa. Thus, indications of class or social status and expected lifestyle behavior may be expressed by mentioning a patient’s occupation: “he’s a taxi driver,” “sport teacher,” or “the hockey pro.” I cannot recall ever hearing the staff mention a female patient’s occupation. More common than mentioning occupations was the explicit elements of “lifestyle” descriptions. These were included, for example, when patients were discussed during rounds: “And then we have Anders Svensson, a healthy person who exercises a lot.”

Pointing out a patient’s healthy and active lifestyle is a way of signaling an otherwise healthy status, but can also be used by the staff to stress the importance of helping the patient. A doctor argued for a more thorough examination of a patient as follows: “A woman who is very physically active: takes care of grandchildren and does aqua aerobics. I think we have to help her. She shouldn’t have to put up with this!” The description of the patient (physically active, grandchildren, aqua aerobics) was repeated on several occasions during the day: in the medical report, in face-to-face conversations with colleagues, and in a telephone conversation with a senior physician. The doctor paved the way for her colleagues to agree with her suggestion of a more comprehensive battery of tests and samples. In this and similar ways, age, sex, class, ethnicity, and lifestyle serve as contextualizing cues that direct the interpretation of the situation (Gumpertz, 1982). Such cues can be blunt and direct, but they can also be “a nudge to the inferential process” (Levinson, 2002, p. 27).

Patient descriptions that draw on the categories age, sex, class, or ethnicity often appear in the context of linking an individual patient to known generalized categories in order to establish how this particular patient may be expected to behave. Such descriptions can be swift and stereotypical, and they are sometimes explicitly framed as gross generalizations. For example, a doctor tells me about a patient of “immigrant origin” who had his second infarct. She continues: “If I were to generalize, they feel good for 10 years and then they stop taking the medicine” (with the unfortunate consequence that they become ill again). By stating that this claim is generalized, the doctor demonstrates an awareness that the “typical behavior” does not have to be valid for all immigrant patients. Yet, by mentioning the generalized knowledge of “immigrant patients,” she establishes an easy link between this patient category and any individual patient associated with the category. In establishing (or sometimes guessing at) an individual patient’s social background, the staff constructs an idea of what kind of patient they are dealing with and what to expect regarding patient compliance or typical behavior in general.

In addition to heredity, today’s coronary risk factors are widely thought to relate to lifestyle. Lifestyle factors concern what we eat and drink, whether we smoke, and how we use (or do not use) our bodies, which means that practically everyone is potentially at risk for coronary disease and should be constantly cautious (cf. Hunt & Emslie, 2001). “Risk factor epidemiologists” link these risks to various categories defined by sex, class, and ethnicity (Shim, 2002) using the categories as proxy indicators of harmful lifestyle habits (Heyman et al., 1998). This practice seems to have trickled down to the individual level. Simplified links between category and behavior may be used as “facts” or “evidence” when specific patients at the clinic are described. For example, one of the senior physicians seemed to have the habit of checking the internet for the patient’s residence using their postcode. During a conference he exclaimed district names—“Berga!” or “Stensby!”—when the patients were discussed. The tone was light and playful, and by way of explanation he stated: “The postcode is important for the prognosis.” The places he mentioned (false names) are primarily represented by council estates and considered “working-class areas” and/or areas with large proportions of immigrants, and by referring to them he implicitly invokes the epidemiological idea that people from lower classes generally do not lead healthy lifestyles. The readiness of staff at the cardiology clinic to accept and use such epidemiological links is most likely due to the constant demand for risk assessments of individual patients, and the emphasis on behavioral risk factors (Hunt & Emslie, 2001). Notably, the doctor’s playful tone of voice when searching for a patient’s postcode suggests a distanced approach to the link he establishes between a single patient and aggregated epidemiological results, leaving a line of retreat if someone accuses him of an overly dogmatic stance.

From a distance, all categorization work may seem blunt and stereotypical, and it is often pointed out as such by the categorizer (“One shouldn't generalize...”). However, it would be depreciating to see stereotyping only when people make use of categories in order to describe and explain reality. In any setting age, sex, class, and ethnicity are basic social categories applied by people to determine with whom they are dealing (Baker, 2004). In addition, each local setting attaches specific meanings, assumptions, and knowledge to these categories, which become visible when people invoke them in certain situations. In a judicial context, for example, age, sex, class, and ethnicity can carry connotations that differ from the connotations found in an educational context. At the cardiology clinic, the categories are “epidemiologically colored,” so to speak; they are linked to ideas about lifestyle habits, patient compliance, and discourses on health inequality, among others, and as such they work as contextualizing cues that guide the interpretation of the situation (Gumpertz, 1982).

Discussions about decision making

Patient and client descriptions in human service organizations are often incorporated into arguments for or against various decisions (Holstein, 2013), so how are the categories age, sex, class, ethnicity, and lifestyle made situationally relevant when emphasized in discussions for or against treatment decisions? As Holstein (2013) argues, professionals’ descriptive practices form the basis for making decisions and taking actions. The power of descriptions may be exemplified by the fact that, for a long time, the dominating picture of a typical cardiac patient in both professional and lay beliefs was that of a middle-aged man with chest pain. This picture was taught in a textbook widely used in U.S. medical training, consequently directing doctors’ perceptions, actions, and decisions (Lutfey & McKinlay, 2009). Throughout the Western world, researchers have pointed to the neglect of symptoms other than chest pain and the underdiagnosis of female cardiac patients (e.g., Lockyer & Bury, 2002; Perers, 2006).

The gender bias within cardiology has been debated widely in Swedish tabloids and newspaper articles with headlines such as “Anneli 60: The doctors missed my cardiac infarct—didn’t bother to take important blood tests.” ^iv The medical staff in my study discussed and related to this critique of gender bias by (mostly) opposing it. They disagree with the idea that women are discriminated against and are more likely to put forward “age” as being influential for decision making. Age, it seems, is less controversial than gender, probably because the notion of age as a biological category is not yet contested to the same degree as that of gender (but see Jönson & Siverskog, 2012; Bytheway, 1995). The last few decades bore witness to a displacement of the criterion “old age,” and highly advanced technology may now be offered to patients over the age of 90. Shim, Russ, and Kaufman (2006) analyzed how routine practices for life extension within cardiology change ideas about how old is “old” and link this development to risk. What was formerly seen as a risky population has now become a new class of medical subjects, as new technology means that risks are manageable. This view was expressed by a senior physician during a conference when the discussion concerned a patient named Mary who is in her 80s. Three medical candidates were present during the conference:

When no doubt or questioning of the decision is uttered, the senior physician raises the question himself: is it reasonable to do surgery on an “old lady”? The doctor invokes the categories age and sex and their adjoining discourses on risk and discrimination; old age is a risk factor for infarcts and higher risk exists for older patients undergoing invasive procedures (sometimes argued to be even riskier for women due to narrow vessels). Furthermore, the possible suspicion that age or sex would be discriminatory grounds for a negative treatment decision rather than legitimate medical grounds is effectively discarded by the demand to “look at the objective facts” and the listing of reasons for a positive treatment decision. Notably, his considerations may have been directed to me as an observer rather than to the candidates; prior to the round, the nurse I was shadowing had introduced me to David (it was the first time I met him) and I told him briefly about my project on medical decision making. Still, his rhetorical performance shows the staff’s readiness to discuss medical practice in the context of prevailing societal debates.

“Ethnicity” as a category can be acknowledged in the form of “cultural knowledge.” One example is the male doctor who announces his appearance outside the door in order to let the female patient put on her veil before he enters. In another example, ethnicity and gender are invoked in a case in which the doctors have to decide what kind of medical examination is the most appropriate for Fatima, a patient with chest pain. There are various methods with which to examine a patient’s coronary artery, such as a cardiac stress test (exercise bike), cardiac scintigraphy (a kind of x-ray), and the more precise surgical angiography. The two doctors discuss which method to use for Fatima, who suffers from kidney disease in addition to her cardiac symptoms, which means that it is too risky for her to go through angiography:

In a medical context, this vast and odd generalization can be framed as “cultural competence”—a specific kind of knowledge that is celebrated within health care.^v “Cultural” in this context means “ethnicity” and fits well with the epidemiological framework and its routine inclusion of demographic differences. The statement offers a logic that meets the request from medical staff to provide “culturally competent” medical care (Shim, 2002, p. 141).

In the context of risk assessment, another example of a rather crude application of “cultural competence” is found in Hughes and Griffiths’ (2004) analysis of case presentations by cardiac surgeons in a British study. The doctors discuss a candidate patient of South Asian origin and a surgeon says; “But he’s a grim prospect isn’t he? Is he still smoking? These Asians smoke like chimneys” (Hughes & Griffiths, 2004, p. 82). By implicitly referring to aggregated epidemiological knowledge of coronary artery disease in an ethnic minority group, the surgeon instructs the listener to be vigilant about all patients from that group with respect to risk assessment. Ethnicity (“these Asians”) is used as a short-cut to invoke an assemblage of (epidemiological) assumptions.

Finding explanations

Why do people end up in the cardiology clinic? Medical staff point out family history as one vital cause, but they commonly speak about lifestyle as the cause of cardiac disease (e.g., obesity, inactivity, smoking). The position that a healthy lifestyle is imperative to good health is often demonstrated at the clinic, not only in conversations with patients, but also between colleagues. For example, just before rounds the doctors chatted informally:

Not surprisingly, patients at the clinic are often concerned about what caused their illness, and they are prone to identify possible errors in their lifestyles: “Should I exercise even more?” asks one patient, and another suggests giving up the daily dessert. Dietary and exercise advice, as well as programs to help smokers quit, are offered to patients on a routine basis. When the staff face unlikely cases, they are preoccupied by finding explanations as to why a certain individual suffers from an infarct despite no apparent risk factors, such as heredity. Old age is an uncontested risk factor for cardiac disease and seems to work as a self-evident explanation in such a way that staff do not comment on age as a cause at all.^vi Yet, relatively young people may also suffer from cardiac infarct. These unlikely cases tend to be talked about and commented on by the staff in addition to the regular discussions over lab results, anamneses, and treatments. Discussions about unlikely cases often link to lifestyle factors (e.g., weight, smoking, exercise, etc.). From the field notes:

Nina is initially perplexed by having such a young person as an inpatient at the clinic. When she notices the patient’s weight, she utters “Ah, well” [in Swedish: “Jahaaa”] in a tone of voice that suggests that the weight in and of itself explains the patient’s misfortune. Her astonishment over the young patient was reduced by the knowledge of his heavy weight, and my effort to explain heavy weight with height is brushed aside (“But still”). A remarkable occurrence (a young person having heart problems) is turned into something not-so-remarkable when signs of a hazardous lifestyle is observed (heavy weight).

Lifestyle factors are not always found. During a report from the night shift to the morning shift, the nurses go through each patient. In one such report, the night nurse says about Paul, who has been treated for his second cardiac infarct:

The fact that patients who are compliant with the staff’s directions (take medicine, quit smoking, eat less fatty food, etc.) may still have another infarct is described as unfair. “There is no fairness in this”, says a senior physician in placing the patient in the “bad luck category,” which Davison and colleagues (1991) found in the lay epidemiology used by British interviewees discussing what kind of person gets heart trouble. In their study, chance, fate, and destiny accounted for the unexpected cases of coronary disease and death in young, healthy, non-smoking people. When plausible explanations are absent, such as lifestyle and heredity, or simply old age, professionals at the cardiology clinic refer to the unpredictable cases as unfair randomness.

“Paula is overweight”

I will end the analysis with a final example, which is not a clear-cut case of the previously separated analytical contexts of patient descriptions, decision making, or finding explanations. Instead, it is a fuzzy mixture of all these contexts. I think the following excerpts from the field notes show how person-descriptions guide the decision-making process, as well as the search (or non-search) for an explanation. This time the explanation concerns a symptom, not the disease itself, and it has vital implications for what course of action the doctors choose.

Paula is a patient who has stayed at the ward for almost a week with no signs of improvement. She has complained about breathlessness and she worries it may be something more in addition to her “regular” heart disease (which is why she is an in-patient at the clinic). The senior physician, Dan, has reassured Paula that it is not fibrillation, a heart rhythm disorder. During the week, Dan has repeatedly referred to her breathlessness as being caused by her being overweight, stating to his colleagues, “I guess she weighs 90 kilos, so. . .” Another example was observed during the rounds when we visit Paula’s bedside:

Dan actually used the English word “overweight” here; the rest of the excerpt is translated from Swedish. It is reminiscent of the way Swedish parents often switch to English or another language when they do not want their children to understand what they are talking about. This verbal procedure suggests moral sensitivity to the matter; a perceived blameworthiness that is withheld from Paula. Nonetheless, to the medical staff she is described as a person who is overweight, and this “lifestyle factor” explains Paula’s extra symptoms (i.e., the breathlessness). This description of Paula, among other things, guides Dan and the staff when they discuss her medical troubles. Towards the end of the week, during a conference, the doctors want to discharge Paula but a nurse’s objections alter the course of events:

When we walk the rounds later and leave Paula’s room, Dan says to his colleague, “I guess we shouldn’t ignore it.” Paula’s symptom was initially ignored because it was obviously caused by her being overweight, but by the end of the week her symptom had turned into a legitimate medical puzzle to solve. The continuous references to Paula as a patient with heavy weight worked as a morally loaded patient description. At other times “overweight” was put forward as an explicit and certain explanation of the symptom. “Overweight” as an explanatory lifestyle category rules out alternative explanations, leading the doctors to conclude that they cannot do more for Paula but send her home. When the nurse raises subtle objections to their decision, another description of Paula is presented. She is willing to be active (“she's trying to move about”), but she simply can't because she is too affected by her medical condition. The nurse backs up her statement with ECG images and lab results, convincing the doctors to look for other explanations than her merely being overweight. Eventually the doctors find a possible (and treatable) explanation in the diagnosis atrial tachycardia, initiating a range of tests to establish if their suspicion is correct. The initial decision to discharge the patient the same day is dropped and not mentioned again.

The case of Paula shows the close association between patient descriptions and the search for explanations, and ultimately their consequences on medical decision-making. This case also shows that the explanatory value of patient descriptions is not necessarily uncontested, but can be subject to objections during the course of medical reasoning between staff members.