Approaching the Challenges of Clinical Data

As you can see from the case of Mrs. N, organizing the patient's clinical data poses several challenges. The beginning student must decide whether to cluster the patient's symptoms and signs into one problem or into several problems. The amount of data may appear unmanageable. The quality of the data may be prone to error. Guidelines to help you address these challenges are provided in the following paragraphs.

Clustering Data Into Single Versus Multiple Problems. One of the greatest difficulties facing students is how to cluster the clinical data. Do selected data fit into one problem or several problems? The patient's age may help—young people are more likely to have a single disease, while older people tend to have multiple diseases. The timing of symptoms is often useful. For example, an episode of pharyngitis 6 weeks ago is probably unrelated to fever, chills, pleuritic chest pain, and cough that prompt an office visit today. To use timing effectively, you need to know the natural history of various diseases and conditions. A yellow penile discharge followed 3 weeks later by a painless penile ulcer suggests two problems: gonorrhea and primary syphilis. In contrast, a penile ulcer followed in 6 weeks by a maculopapular skin rash and generalized lymphadenopathy suggest two stages of the same problem: primary and secondary syphilis.

Involvement of different body systems may help you to cluster the clinical data. If symptoms and signs occur in a single system, one disease may explain them. Problems in different, apparently unrelated systems often require more than one explanation. Again, knowledge of disease patterns is necessary. You might decide, for example, to group a patient's high blood pressure and sustained apical impulse together with flame-shaped retinal hemorrhages, place them in the cardiovascular system, and label the constellation "hypertensive cardiovascular disease with hypertensive retinopathy." You would develop another explanation for the patient's mild fever, left lower quadrant tenderness, and diarrhea.

Some diseases involve more than one body system. As you gain knowledge and experience, you will become increasingly adept at recognizing multisystem conditions and building plausible explanations that link together their seemingly unrelated manifestations. To explain cough, hemoptysis, and weight loss in a 60-year-old plumber who has smoked cigarettes for 40 years, you probably even now would rank lung cancer high in your differential diagnosis. You might support your diagnosis with your observation of the patient's clubbed fingernails. With experience and continued reading, you will recognize that his other symptoms and signs can be linked to the same diagnosis. Dysphagia would reflect extension of the cancer to the esophagus, pupillary asymmetry would suggest pressure on the cervical sympathetic chain, and jaundice could result from metastases to the liver.

In another case of multisystem disease, a young man who presents with odynophagia, fever, weight loss, purplish skin lesions, leukoplakia, generalized lymphadenopathy, and chronic diarrhea is likely to have AIDS. Related risk factors should be explored promptly.

Sifting Through an Extensive Array of Data. It is common to confront a relatively long list of symptoms and signs, and an equally long list of potential explanations. One approach is to tease out separate clusters of observations and analyze one cluster at a time, as just described. You can also ask a series ofkey questions that may steer your thinking in one direction and allow you to temporarily ignore the others. For example, you may ask what produces and relieves the patient's chest pain. If the answer is exercise and rest, you can focus on the cardiovascular and musculoskeletal systems and set the gastrointestinal system aside. If the pain is substernal, burning, and occurs only after meals, you can logically focus on the gastrointestinal tract. A series of discriminating questions helps you form a decision tree or algorithm that is helpful in collecting and analyzing clinical data and reaching logical conclusions and explanations.

Assessing the Quality of the Data. Almost all clinical information is subject to error. Patients forget to mention symptoms, confuse the events of their illness, avoid recounting facts that are embarrassing, and often slant their stories to what the clinician wants to hear. Clinicians misinterpret patient statements, overlook information, fail to ask "the one key question," jump prematurely to conclusions and diagnoses, or forget an important part of the examination, such as the testicular examination in a young man with asymptomatic testicular carcinoma. You can avoid some of these errors by acquiring the habits of skilled clinicians, summarized below.

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