Data processing is the clustering of data obtained from the history, physical examination, and laboratory and imaging studies. It is rare for patients to have a solitary symptom or sign of a disease. They more commonly complain of multiple symptoms, and the examiner may find several related signs during the physical examination. It is the job of the astute observer to fit as many of these clues together into a meaningful pathophysiologic relationship. This is data processing.

For example, suppose the interviewer obtains a history of dyspnea, cough, earache, and hemoptysis. Dyspnea, cough, and hemoptysis can be grouped together as symptoms suggestive of cardiopulmonary disease. Earache does not fit with the other three symptoms and may be indicative of another problem. For another patient who complains of epigastric burning relieved by eating and whose stool is found to contain blood, this symptom and this sign should be studied together. These data suggest an abnormality of the gastrointestinal tract, possibly a duodenal ulcer. Although patients usually have multiple symptoms or signs from a pathologic condition, they may not always manifest all the symptoms or signs of the disease being considered. For instance, the presence of polyuria and polydipsia in a patient with a family history of diabetes is adequate to raise the suspicion that a lateral rectus palsy may be related to diabetes, even if diabetes has not previously been diagnosed in this patient. In another patient, a 30-pound weight loss, anorexia, jaundice, and a left supraclavicular lymph node are suggestive of gastric carcinoma with liver metastasis to the porta hepatis. This illustrates the concept of data processing multiple symptoms into a single diagnosis. The process has sometimes been likened to the rule of Occam’s razor: The simplest theory is preferable—in this case, that all the symptoms can be explained by one diagnosis. Although it is a useful rule to keep in mind, it is not always applicable.

Problem list development results in a summary of the physical, mental, social, and personal conditions affecting the patient’s health. The problem list may contain an actual diagnosis or only a symptom or sign that cannot be clustered with other bits of data. The date on which each problem developed is noted. This list reflects the clinician’s level of understanding of the patient’s problems, which should be listed in order of importance. Table 24-1 is an example of a problem list.

The presence of a symptom or sign related to a specific problem is a pertinent positive finding. For example, a history of gout and increased uric acid level are pertinent positive findings in a man suffering from excruciating back pain radiating to his testicle. This patient may be suffering from renal colic secondary to a uric acid kidney stone. The absence of a symptom or sign that, if present, would be suggestive of a diagnosis is a pertinent negative finding. A pertinent negative finding may be just as important as a pertinent positive finding; the fact that a key finding is not present may help rule out a certain diagnosis. For example, the absence of tachycardia in a woman with weight loss and a tremor makes the existence of hyperthyroidism less likely; the presence of tachycardia would strengthen the likelihood of hyperthyroidism.

An important consideration in any database is the patient’s demographic information: sex, age, ethnicity, and area of residence. A man with a bleeding disorder dating from birth is likely to have hemophilia. A 65-year-old person with exertional chest pain is probably suffering, statistically, from coronary artery disease. A 26-year-old African-American patient with multiple episodes of severe bone pain may be suffering from sickle cell anemia. A person living in the San Joaquin Valley who has pulmonary symptoms may have coccidioidomycosis. This information is often suggestive of a unifying diagnosis, but the absence of a “usual” finding should never totally exclude a diagnosis.

It has been said, “Common diseases are common.” This apparently simplistic statement has great merit because it underlines the fact that the observer should not assume an exotic diagnosis if a common one accurately explains the clinical state. (In contrast, if a common diagnosis cannot account for all the symptoms, the observer should look for another, less common diagnosis.) It is also true that “Uncommon signs of common diseases are more common than common signs of uncommon diseases.”

Finally, “A rare disease is not rare for the patient who has the disease.” If a patient’s symptoms and signs are suggestive of an uncommon condition, that patient may be the 1 in 10,000 with the disease. Nevertheless, statistics based on population groups provide a useful guide in approaching clinical decision-making for individual patients.

Diagnostic Reasoning from Signs and Symptoms

Unfortunately, decisions in medicine can rarely be made with 100% certainty. Probability weights the decision. Only if the cluster of symptoms, signs, and test results is unequivocal can the clinician be certain of a diagnosis. This does not occur often. How, then, can the clinician make the “best” decision—best in light of current knowledge and research?

Laboratory tests immediately come to mind. But signs and symptoms obtained from the patient’s history and physical examination perform the same function as laboratory tests, and the information and results obtained from signs, symptoms, and tests are evaluated in the same way and are subject to the same rules and standards of evidence for diagnostic reasoning. Also, signs and symptoms actually account for more (90%) of the developing problem list than do laboratory test results (<10%).