Citations

Background Despite the introduction of the Centers for Disease Control and Prevention's opioid prescribing guidelines, studies indicate that a significant proportion of opioids prescribed at hospital discharge remains unused. Little is known if improved provider awareness of guidelines and metrics would facilitate rightsizing opioid prescriptions at hospital discharge. Our institution created opioid prescribing guidelines and a key metrics dashboard and subsequently disseminated these tools to our institution's leadership and prescribers. We aim to evaluate the effectiveness of these efforts in reducing hospital discharge opioid prescriptions, especially those for acute pain exceeding 100 morphine milligram equivalents (MME). Methods Following the development of practice-specific opioid prescribing guidelines in 2017, a key metrics dashboard was created in 2021 to display the percentage of hospital discharges with opioids prescribed and the percentage of discharges with opioids prescribed for acute pain exceeding 100 MME. These metrics were broken down into calendar years between 2018 and 2022, and by the 7 major practice regions across our institution spanning 5 U.S. States. Results From 2018 to 2022, all regions showed a decline in the percentage of hospital discharges with opioids prescribed (range 2.7%-9.4%). In the same period, 5 of 7 regions showed a decline in discharge opioid prescriptions exceeding 100 MME for acute pain (range 8.8%-23.2%). Two sites showed an increase of 2.4% and 2.7%. Conclusion Downward trends in hospital discharge opioid prescriptions were observed for most practice regions following the introduction of our institution's opioid prescribing guidelines and key metrics dashboard.

As artificial intelligence (AI) integrates within the intersecting domains of healthcare and computational biology, developing interpretable models tailored to medical contexts is met with significant challenges. Explainable AI (XAI) is vital for fostering trust and enabling effective use of AI in healthcare, particularly in image-based specialties such as pathology and radiology where adjunctive AI solutions for diagnostic image analysis are increasingly utilized. Overcoming these challenges necessitates interdisciplinary collaboration, essential for advancing XAI to enhance patient care. This commentary underscores the critical role of interdisciplinary conferences in promoting the necessary cross-disciplinary exchange for XAI innovation. A literature review was conducted to identify key challenges, best practices, and case studies related to interdisciplinary collaboration for XAI in healthcare. The distinctive contributions of specialized conferences in fostering dialogue, driving innovation, and influencing research directions were scrutinized. Best practices and recommendations for fostering collaboration, organizing conferences, and achieving targeted XAI solutions were adapted from the literature. By enabling crucial collaborative junctures that drive XAI progress, interdisciplinary conferences integrate diverse insights to produce new ideas, identify knowledge gaps, crystallize solutions, and spur long-term partnerships that generate high-impact research. Thoughtful structuring of these events, such as including sessions focused on theoretical foundations, real-world applications, and standardized evaluation, along with ample networking opportunities, is key to directing varied expertise toward overcoming core challenges. Successful collaborations depend on building mutual understanding and respect, clear communication, defined roles, and a shared commitment to the ethical development of robust, interpretable models. Specialized conferences are essential to shape the future of explainable AI and computational biology, contributing to improved patient outcomes and healthcare innovations. Recognizing the catalytic power of this collaborative model is key to accelerating the innovation and implementation of interpretable AI in medicine.