A qualitative assessment of perceptions and attitudes toward postoperative pain and opioid use in patients undergoing elective knee arthroscopy.

BACKGROUND: Orthopedic surgeons routinely prescribe opioids to manage post-operative pain. In the face of an opioid epidemic, a one-size-fits-all approach to pain management is no longer appropriate. Patient-centred prescribing practices should be used by surgeons; however, little is known about what influences patient attitudes toward postoperative pain and its management to inform such practices. We sought to explore patient attitudes toward postsurgical pain management, including opioids. METHODS: We conducted qualitative, semistructured interviews of 11 opioid-naive patients (age 16-46 yr) who were scheduled to undergo arthroscopic knee surgery. Transcripts were analyzed thematically using a framework analysis that involved familiarization, developing a thematic framework, indexing, charting and mapping, and interpretation. RESULTS: Participant attitudes toward postoperative pain and opioids were influenced by perceived tolerance to pain based on personal experience, perceived predisposition to addiction based on personal assumptions regarding addictive personality traits and risk factors, and perceptions of opioid use shaped by external influences, including family, friends and the media's depiction of the opioid epidemic. Every patient expressed that preoperative counselling and education regarding postoperative pain management would be beneficial in improving their knowledge base, easing anxieties and clarifying misunderstandings. CONCLUSION: Surgeons can address the patient-reported factors identified in this study to help optimize a patient's perioperative experience without relying solely on prescribed analgesia. By improving accessibility to education and promoting safe, patient-centred prescribing practices, we may reduce reliance on opioids in orthopedic surgery.

Can precision medicine advance psychiatry?

Precision medicine is a new approach that considers differences in genes, environment, and lifestyle in an attempt to tailor treatments for individual patients. Psychiatry, as a discipline, has historically relied on clinical judgement and phenomenology-based diagnostic guidelines and has yet to take full advantage. This editorial provides an insight into the expanding role of precision medicine in psychiatry, both in research and clinical practice. It discusses the application of genetics and subgroup stratification in increasing response rates to therapeutic interventions, mainly focusing on major depressive disorder and schizophrenia. It presents an overview of machine learning techniques and how they are being integrated with traditional research methods within the field. In the context of these developments, while emphasizing the considerable potential for moving toward precision psychiatry, we also acknowledge the inherent challenges.