Differential diagnosis generators. Differential diagnosis (DDx) generating software may benefit clinicians who rely on type 1 reasoning—a type of reasoning that can increase the likelihood of delayed or missed diagnoses because alternative diagnoses are not considered. DDx generators combine patient symptoms, physical exam findings, and other factors to suggest a list of possible diagnoses for consideration, thus minimizing the chance of error.15 Some of the currently available DDx generators include Isabel, DXplain, DiagnosisPro, and PEPID.15
However, few randomized controlled studies have investigated whether the use of a DDx generator reduces diagnostic error, and evidence is lacking to prove their usefulness in clinical practice. Furthermore, while an exhaustive list of possible diagnoses may be helpful, some proposed diagnoses may be irrelevant and may distract from timely attention being paid to more likely possibilities. Additionally, forming an extensive DDx list during every patient encounter would significantly add to the physician’s workload and could contribute to physician burnout.
Selective use? We believe that DDx generators would be best used selectively as a safeguard for the clinician who becomes aware of an increased risk of diagnostic error in a particular patient. As previously discussed, errors involving cognitive processes are more often errors of improper reasoning rather than of insufficient knowledge.3 The DDx generator then serves as a way of double-checking to ensure that additional diagnoses are being considered. This can be especially helpful when facing patients who display difficult behaviors or when the clinician’s cognitive reserve is depleted by other factors.
DDx generators may also help the physician expand his or her differential diagnosis when a patient is failing to improve despite appropriately treating the working diagnosis.
Another option worth studying? Future studies could also investigate whether discussing a case with another clinician is an effective way to reduce cognitive biases and diagnostic errors.
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