The Diagnostic Network
One aspect of the CLIPP software that makes it a uniquely effective teaching program is its incorporation of a clinical reasoning tool called the Diagnostic Network. This requires the student to develop a differential diagnosis based on the key clinical findings in the case and to justify the differential by showing graphically how the findings and the differential diagnosis relate. After completing this diagnostic network, students can compare theirs to the network created by the case author.
How It WorksAfter students have discovered findings in a case, they are prompted to create a diagnostic network. The findings are shown in lavender at the top of the screen. The student enters several hypotheses: diagnoses that might explain all or some of the key findings.
The student then weights each hypothesis, showing a positive or negative relationship between a finding and a hypothesis. A "+" means the finding argues for the hypothesis, a "++" means the finding argues strongly for the hypothesis, and a "+++" means that the finding proves that the hypothesis is the diagnosis.
Likewise, a "-" means the finding argues against the hypothesis, a "--" means the finding argues strongly against the hypothesis, and a "---" means the finding excludes the hypothesis as a diagnosis in the case.
When the student saves the network, the software adds the pluses and minuses and lists the hypothesis with the greatest number at the top of the list, the hypothesis with the next greatest number second, and so on in descending order.
An example of a completed network
The Highest-Ranked Diagnosis — A Caveat
Usually, the diagnosis with the greatest number is the most likely diagnosis in the case. However, because of the complexity of medicine and true clinical reasoning, it is possible that the diagnosis with the highest number is not the final diagnosis in the case. As students continue through the case, they are sometimes asked to refine their diagnostic network in light of new findings. After each network, students can compare their networks with those of the authors and read the author's explanations as to why each diagnosis is likely or unlikely in the case.