The following is a summary of “Clinicians diagnosing virtual patients with the classification algorithm for chronic pain in the ICD-11 (CAL-CP) achieve better diagnoses and prefer the algorithm to standard tools: An experimental validation study,” published in the April 2024 issue of Pain by Hay et al.

The ICD-11 offers a detailed chronic pain classification system with over 100 diagnoses, prompting the development of the Classification Algorithm for Chronic Pain (CAL-CP) algorithm to improve diagnostic accuracy.

Researchers conducted a retrospective study to assess the CAL-CP’s impact on chronic pain diagnosis accuracy, clinician preference, and usability.

They engaged 195 clinicians in an international online study, each diagnosing 4/8 fictitious patients. Clinicians chatted with virtual patients to gather data and review medical histories and examination findings. Patient cases varied in complexity. Simple cases had one chronic pain diagnosis, while complex cases had two. Using a 2 × 2 repeated-measures design with a tool (algorithm/standard browser) and diagnostic complexity (simple/complex) as factors, clinicians utilized either the algorithm or the ICD-11 browser for diagnoses. Following each case, clinicians identified pain diagnoses and evaluated the diagnostic process. The accuracy of assigned diagnoses and evaluations of the algorithm’s usefulness and ease of use were examined.

The results showed that the algorithm led to a higher percentage of accurate diagnoses for chronic primary and secondary pain. Clinicians favored the algorithm, making it easier and more beneficial than the ICD-11 browser. In particular, novice users found the algorithm advantageous.

Investigators found that the CAL-CP algorithm improved chronic pain diagnosis accuracy and gained clinician acceptance, suggesting its value for routine care and research.

Source: onlinelibrary.wiley.com/doi/full/10.1002/ejp.2274