Introduction: Many rural hospitals and health systems in the USA lack sufficient resources to treat COVID-19. St Lawrence Health (SLH) developed a system for managing inpatient COVID-19 hospital admissions in St Lawrence County, an underserved rural county that is the largest county in New York State. Methods: SLH used a hub-and-spoke system to route COVID-19 patients to its flagship hospital. It further assembled a small clinical team to manage admitted COVID-19 patients and to stay abreast of a quickly changing body of literature and standard of care. A review of clinical data was completed for patients who were treated by SLH's inpatient COVID-19 treatment team between 20 March and 22 May 2020. Results: Twenty COVID-19 patients were identified. Sixteen patients (80%) met National Institutes of Health criteria for severe or critical disease. One patient died. No patients were transferred to other hospitals. Conclusion: During the first 2 months of the pandemic, the authors were able to manage hospitalized COVID-19 patients in their rural community. Development of similar treatment models in other rural areas should be considered.
The damage done by the war on opioids: the pendulum has swung too far Timothy J Atkinson,1 Michael E Schatman,2 Jeffrey Fudin1,3–51PGY2 Pain and Palliative Care Pharmacy Residency, Stratton VA Medical Center, Albany, NY, 2Foundation for Ethics in Pain Care, Bellevue, WA, 3School of Pharmacy, University of Connecticut, Storrs, CT, 4Western New England University College of Pharmacy, Springfield, MA, 5Buffalo College of Pharmacy, State University of New York, Buffalo, NY, USAIn the United States, patterns of opioid use for the management of pain have drastically changed over the past 30 years. In the 1980s, the American pain medicine landscape was characterized by opiophobia, the fear to prescribe opioids. Around the turn of the millennium, however, we witnessed a fairly rapid shift to opiophilia, or the "overprescribing" of opioids. The ubiquitous undertreatment of pain was the catalyst for clinicians and pain societies to successfully lobby for increased use of opioids for all pain types, including non-cancer pain. The approval of new standards for pain management incorporating pain as the "fifth vital sign" by the Joint Commission on Accreditation of Healthcare Organizations (JCAHO)1 seemingly fueled this increase in opioid prescription. From 1991–2009, prescriptions for opioid analgesics tripled, with emergency department visits related to non-medical use of prescription opioid overdoses doubling from 2005–2009.2 In 2010, accidental overdose deaths associated with opioids increased for the eleventh consecutive year, highlighting the drastic shift in opioid use.3 The figurative pendulum began to swing toward opiophobia following the publication of data that demonstrated that the risk of addiction associated with chronic opioid use was likely underestimated.4 Guidelines for the use of controlled substances released by the Federation of State Medical Boards of the US in 1998 reflected this change in attitude.5 At present, there is a general consensus that opioids are over-prescribed and education among health care providers is sorely lacking, with considerable debate on how to appropriately address the issue not yet resulting in a balance between treating legitimate pain patients, and mitigating abuse, overdoses, and related deaths. In this environment, physicians and non-physician prescribers, health systems, regulatory agencies, and insurers are seeking tangible targets for intervention.
Abstract The rise of civilization is synonymous with the creation of tools that extend the intellectual and physical reach of human beings [133]. The pinnacle of such endeavours is to replicate the flexible reasoning capacity of human intelligence within a machine, making it capable of performing useful work on command, despite the complexity and adversity of the real world. In order to achieve such Artificial Intelligence (AI), a new approach is required: traditional symbolic AI has long been known to be too rigid to model complex and noisy phenomena and the sample-driven approach of Deep Learning cannot scale to the long-tailed distributions of the real world. In this book, we describe a new approach for building a situated system that reflects upon its own reasoning and is capable of making decisions in light of its limited knowledge and resources. This reflective reasoning process addresses the vital safety issues that inevitably accompany open-ended reasoning: the system must perform its mission within a specifiable operational envelope.
Description: In August 2021, leadership within the U.S. Department of Veterans Affairs (VA) and U.S. Department of Defense (DoD) approved a joint clinical practice guideline (CPG) for the management of substance use disorders (SUDs). This synopsis summarizes key recommendations. Methods: In March 2020, the VA/DoD Evidence-Based Practice Work Group assembled a team to update the 2015 VA/DoD Clinical Practice Guideline for the Management of Substance Use Disorders that included clinical stakeholders and conformed to the National Academy of Medicine's tenets for trustworthy CPGs. The guideline panel developed key questions, systematically searched and evaluated the literature, created two 1-page algorithms, and distilled 35 recommendations for care using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) system. This synopsis presents the recommendations that were believed to be the most clinically impactful. Recommendations: The scope of the CPG is broad; however, this synopsis focuses on key recommendations for the management of alcohol use disorder, use of buprenorphine in opioid use disorder, contingency management, and use of technology and telehealth to manage patients remotely.
In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games. This competition fills a gap in existing game AI competitions that have typically focussed on traditional card/board games or modern video games with graphical interfaces. By providing a platform for evaluating agents in text-based adventures, the competition provides a novel benchmark for game AI with unique challenges for natural language understanding and generation. This paper summarises the three competitions ran in 2016, 2017, and 2018 (including details of open source implementations of both the competition framework and our competitors) and presents the results of an improved evaluation of these competitors across 20 games.
Can we not work together to help family practitioners become more effective pain managers? Jeffrey Fudin,1,2,3 Timothy J Atkinson,4 Mena Raouf,4 Michael E Schatman5 1Stratton VA Medical Center, Albany, NY, USA; 2Albany College of Pharmacy and Health Sciences, Albany, NY, USA; 3Scientific and Clinical Affairs, Remitigate LLC, Delmar, NY, USA; 4VA Tennessee Valley Healthcare System, Murfreesboro, Nashville, TN, USA; 5US Pain Foundation, Bellevue, WA, USASnyder et al recently published a review in American Family Physician titled, “Treating Painful Diabetic Peripheral Neuropathy: An Update”, which provided an overview of pharmacologic treatment options for providers; however, some of the recommendations made by the authors were concerning.1 Recommendations that caught our attention included statements around pregabalin adjustment for renal impairment, using selective serotonin reuptake inhibitors (SSRIs) in the treatment of diabetic peripheral neuropathy (DPN), classification of tramadol, tapentadol, and oxycodone in DPN.
This study sought to formulate a consolidation of guidelines representing best practices related to office-based opioid treatment (OBOT) of opioid use disorder (OUD) using buprenorphine. It also demonstrates how a set of evidence-based guidelines may be linked with claims data to leverage analytic techniques that drive cost-effective, positive health outcomes.Literature review of US and international guidelines for OBOT using buprenorphine for OUD.The study conducted a review of currently available US and several international guidelines from 2009 to 2018 published on OUD and the use of buprenorphine in OBOT. Guidelines were consolidated based on common elements. The process of correlating common elements with available commercial and state Medicaid claims data is described, including which elements are amenable to analysis along with relative complexity.Seven guidelines met inclusion criteria and are presented as 3 tables, organized by clinical themes and phase of care related to OBOT use of buprenorphine for OUD. Themes included establishing care, monitoring treatment stability and engagement, and nonpharmacologic treatment to improve outcomes. Areas of agreement and divergence between guidelines are highlighted. Specific components are identified as they relate to metrics of interest to public and private payers.Among US and international guidelines for treatment of OUD, common themes are readily identified and may indicate agreement in regard to interventions. Linking pharmacy and medical billing claims data to evidence-supported best practices provides public and private payers the ability to track individual patients, facilitate high-quality care, and monitor outcomes.
We conducted a discourse analysis of meaningful work from the perspective of healthcare workers in an academic medical center where we previously observed relatively high levels of personal burnout (52.7%) and work-related burnout (47.5%), all based on the Copenhagen Burnout Inventory survey. Burnout is often studied as psychological condition characterized by exhaustion, depersonalization, and feelings of inefficacy or lack of career achievement, but as demonstrated in this analysis, burnout loses its meaning because healthcare professionals provide a robust account of what makes work meaningful to them despite their prevalence of burnout. Healthcare professionals exhibit a higher level of burnout compared to workforce members in other organizations. Physicians specifically are at high risk for exhibiting symptoms of burnout and work-life imbalances. In addition, burnout manifests itself early in the physician’s career compared other occupations, and in our sample was prevalent among nurses, too. In this discourse analysis of written answers to the survey question, In ten words or less describe what makes your work meaningful? healthcare professionals provide an account of meaningful work that maintains its value in this environment despite the level of burnout, especially when healthcare professionals can use their hard-earned knowledge to make a difference in the lives of people, and observe the results of their work, which is beyond just taking care of patients. Nurses accounted for meaningful work in terms of close connections with patients, while being closely focused on ability to provide professional care and experiencing the outcomes associated with that care, and knowing that they have done a good job. Physicians were patient focused, and they expressed meaningful work in terms of making a difference, and using their abilities to help patients. Basic scientists accounted for meaningful work in terms of their training and abilities to use science for the betterment of others in society.
This paper introduces Bayesian Flow Networks (BFNs), a new class of generative model in which the parameters of a set of independent distributions are modified with Bayesian inference in the light of noisy data samples, then passed as input to a neural network that outputs a second, interdependent distribution. Starting from a simple prior and iteratively updating the two distributions yields a generative procedure similar to the reverse process of diffusion models; however it is conceptually simpler in that no forward process is required. Discrete and continuous-time loss functions are derived for continuous, discretised and discrete data, along with sample generation procedures. Notably, the network inputs for discrete data lie on the probability simplex, and are therefore natively differentiable, paving the way for gradient-based sample guidance and few-step generation in discrete domains such as language modelling. The loss function directly optimises data compression and places no restrictions on the network architecture. In our experiments BFNs achieve competitive log-likelihoods for image modelling on dynamically binarized MNIST and CIFAR-10, and outperform all known discrete diffusion models on the text8 character-level language modelling task.
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