RESUMO
PURPOSE OF REVIEW: In the present review, various categories of pain, clinician-observed pain scales, and patient-reported pain scales are evaluated to better understand factors that impact patient pain perceptions. Additionally, the expansion of areas that require further research to determine the optimal way to evaluate pain scale data for treatment and management are discussed. RECENT FINDINGS: Electronic health record (EHR) data provides a starting point for evaluating whether patient predictors influence postoperative pain. There are several ways to assess pain and choosing the most effective form of pain treatment. Identifying individuals at high risk for severe postoperative pain enables more effective pain treatment. However, there are discrepancies in patient pain reporting dependent on instruments used to measure pain and their storage in the EHR. Additionally, whether administered by a physician or another healthcare practitioner, differences in patient pain perception occur. While each scale has distinct advantages and limitations, pain scale data is a valuable therapeutic tool for assisting clinicians in providing patients with optimal pain control. Accurate assessment of patient pain perceptions by data extraction from electronic health records provides a potential for pain alleviation improvement. Predicting high-risk postoperative pain syndromes is a difficult clinical challenge. Numerous studies have been conducted on factors that impact pain prediction. Postoperative pain is significantly predicted by the kind of operation, the existence of prior discomfort, patient anxiety, and age.