Interprofessional online learning for primary healthcare: findings from a scoping review

Objectives: This article presents the findings from a scoping review which explored the nature of interprofessional online learning in primary healthcare. The review was informed by the following questions: What is the nature of evidence on online postgraduate education for primary healthcare interprofessional teams? What learning approaches and study methods are used in this context? What is the range of reported outcomes for primary healthcare learners, their organisations and the care they deliver to patients/clients? Setting: The review explored the global literature on interprofessional online learning in primary healthcare settings. Results: The review found that the 23 included studies employed a range of different e-learning methods with contrasting course durations, use of theory, participant mix, approaches to accreditation and assessment of learning. Most of the included studies reported outcomes associated with learner reactions and positive changes in participant attitudes/perceptions and improvement in knowledge/skills as a result of engagement in an e-learning course. In contrast, fewer studies reported changes in participant behaviours, changes in organisational practice and improvements to patients/clients. Conclusions: A number of educational, methodological and outcome implications are be offered. E-learning can enhance an education experience, support development, ease time constraints, overcome geographic limitations and can offer greater flexibility. However, it can also contribute to the isolation of learners and its benefits can be negated by technical problem

Artificial intelligence technologies and compassion in healthcare: A systematic scoping review

BackgroundAdvances in artificial intelligence (AI) technologies, together with the availability of big data in society, creates uncertainties about how these developments will affect healthcare systems worldwide. Compassion is essential for high-quality healthcare and research shows how prosocial caring behaviors benefit human health and societies. However, the possible association between AI technologies and compassion is under conceptualized and underexplored.ObjectivesThe aim of this scoping review is to provide a comprehensive depth and a balanced perspective of the emerging topic of AI technologies and compassion, to inform future research and practice. The review questions were: How is compassion discussed in relation to AI technologies in healthcare? How are AI technologies being used to enhance compassion in healthcare? What are the gaps in current knowledge and unexplored potential? What are the key areas where AI technologies could support compassion in healthcare?Materials and methodsA systematic scoping review following five steps of Joanna Briggs Institute methodology. Presentation of the scoping review conforms with PRISMA-ScR (Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews). Eligibility criteria were defined according to 3 concept constructs (AI technologies, compassion, healthcare) developed from the literature and informed by medical subject headings (MeSH) and key words for the electronic searches. Sources of evidence were Web of Science and PubMed databases, articles published in English language 2011–2022. Articles were screened by title/abstract using inclusion/exclusion criteria. Data extracted (author, date of publication, type of article, aim/context of healthcare, key relevant findings, country) was charted using data tables. Thematic analysis used an inductive-deductive approach to generate code categories from the review questions and the data. A multidisciplinary team assessed themes for resonance and relevance to research and practice.ResultsSearches identified 3,124 articles. A total of 197 were included after screening. The number of articles has increased over 10 years (2011, n = 1 to 2021, n = 47 and from Jan–Aug 2022 n = 35 articles). Overarching themes related to the review questions were: (1) Developments and debates (7 themes) Concerns about AI ethics, healthcare jobs, and loss of empathy; Human-centered design of AI technologies for healthcare; Optimistic speculation AI technologies will address care gaps; Interrogation of what it means to be human and to care; Recognition of future potential for patient monitoring, virtual proximity, and access to healthcare; Calls for curricula development and healthcare professional education; Implementation of AI applications to enhance health and wellbeing of the healthcare workforce. (2) How AI technologies enhance compassion (10 themes) Empathetic awareness; Empathetic response and relational behavior; Communication skills; Health coaching; Therapeutic interventions; Moral development learning; Clinical knowledge and clinical assessment; Healthcare quality assessment; Therapeutic bond and therapeutic alliance; Providing health information and advice. (3) Gaps in knowledge (4 themes) Educational effectiveness of AI-assisted learning; Patient diversity and AI technologies; Implementation of AI technologies in education and practice settings; Safety and clinical effectiveness of AI technologies. (4) Key areas for development (3 themes) Enriching education, learning and clinical practice; Extending healing spaces; Enhancing healing relationships.ConclusionThere is an association between AI technologies and compassion in healthcare and interest in this association has grown internationally over the last decade. In a range of healthcare contexts, AI technologies are being used to enhance empathetic awareness; empathetic response and relational behavior; communication skills; health coaching; therapeutic interventions; moral development learning; clinical knowledge and clinical assessment; healthcare quality assessment; therapeutic bond and therapeutic alliance; and to provide health information and advice. The findings inform a reconceptualization of compassion as a human-AI system of intelligent caring comprising six elements: (1) Awareness of suffering (e.g., pain, distress, risk, disadvantage); (2) Understanding the suffering (significance, context, rights, responsibilities etc.); (3) Connecting with the suffering (e.g., verbal, physical, signs and symbols); (4) Making a judgment about the suffering (the need to act); (5) Responding with an intention to alleviate the suffering; (6) Attention to the effect and outcomes of the response. These elements can operate at an individual (human or machine) and collective systems level (healthcare organizations or systems) as a cyclical system to alleviate different types of suffering. New and novel approaches to human-AI intelligent caring could enrich education, learning, and clinical practice; extend healing spaces; and enhance healing relationships.ImplicationsIn a complex adaptive system such as healthcare, human-AI intelligent caring will need to be implemented, not as an ideology, but through strategic choices, incentives, regulation, professional education, and training, as well as through joined up thinking about human-AI intelligent caring. Research funders can encourage research and development into the topic of AI technologies and compassion as a system of human-AI intelligent caring. Educators, technologists, and health professionals can inform themselves about the system of human-AI intelligent caring

Exogenous glucosamine globally protects chondrocytes from the arthritogenic effects of IL-1β

The effects of exogenous glucosamine on the biology of articular chondrocytes were determined by examining global transcription patterns under normal culture conditions and following challenge with IL-1β. Chondrocytes isolated from the cartilage of rats were cultured in several flasks either alone or in the presence of 20 mM glucosamine. Six hours later, one-half of the cultures of each group were challenged with 10 ng/ml IL-1β. Fourteen hours after this challenge, RNA was extracted from each culture individually and used to probe microarray chips corresponding to the entire rat genome. Glucosamine alone had no observable stimulatory effect on the transcription of primary cartilage matrix genes, such as aggrecan, collagen type II, or genes involved in glycosaminoglycan synthesis; however, glucosamine proved to be a potent, broad-spectrum inhibitor of IL-1β. Of the 2,813 genes whose transcription was altered by IL-1β stimulation (P < 0.0001), glucosamine significantly blocked the response in 2,055 (~73%). Glucosamine fully protected the chondrocytes from IL-1-induced expression of inflammatory cytokines, chemokines, and growth factors as well as proteins involved in prostaglandin E(2 )and nitric oxide synthesis. It also blocked the IL-1-induced expression of matrix-specific proteases such as MMP-3, MMP-9, MMP-10, MMP-12, and ADAMTS-1. The concentrations of IL-1 and glucosamine used in these assays were supraphysiological and were not representative of the arthritic joint following oral consumption of glucosamine. They suggest, however, that the potential benefit of glucosamine in osteoarthritis is not related to cartilage matrix biosynthesis, but is more probably related to its ability to globally inhibit the deleterious effects of IL-1β signaling. These results suggest that glucosamine, if administered effectively, may indeed have anti-arthritic properties, but primarily as an anti-inflammatory agent

African-American inflammatory bowel disease in a Southern U.S. health center

<p>Abstract</p> <p>Background</p> <p>Inflammatory Bowel Diseases (IBD) remain significant health problems in the US and worldwide. IBD is most often associated with eastern European ancestry, and is less frequently reported in other populations of African origin e.g. African Americans ('AAs'). Whether AAs represent an important population with IBD in the US remains unclear since few studies have investigated IBD in communities with a majority representation of AA patients. The Louisiana State University Health Sciences Center in Shreveport (LSUHSC-S) is a tertiary care medical center, with a patient base composed of 58% AA and 39% Caucasian (W), ideal for evaluating racial (AA vs. W) as well and gender (M vs. F) influences on IBD.</p> <p>Methods</p> <p>In this retrospective study, we evaluated 951 visits to LSUHSC-S for IBD (between 2000 to 2008) using non-identified patient information based on ICD-9 medical record coding (Crohn's disease 'CD'-555.0- 555.9 and ulcerative colitis 'UC'-556.0-556.9).</p> <p>Results</p> <p>Overall, there were more cases of CD seen than UC. UC and CD affected similar ratios of AA and Caucasian males (M) and females (F) with a rank order of WF > WM > AAF > AAM. Interestingly, in CD, we found that annual visits per person was the highest in AA M (10.7 ± 1.7); significantly higher (* -p < 0.05) than in WM (6.3 ± 1.0). Further, in CD, the female to male (F: M) ratio in AA was significantly higher (*- p < 0.05) (1.9 ± 0.2) than in Caucasians (F:M = 1.3 ± 0.1) suggesting a female dominance in AACD; no differences were seen in UC F: M ratios.</p> <p>Conclusion</p> <p>Although Caucasians still represent the greatest fraction of IBD (~64%), AAs with IBD made up >1/3 (36.4%) of annual IBD cases from 2000-2008 at LSUHSC-S. Further studies on genetic and environments risks for IBD risk in AAs are needed to understand differences in presentation and progression in AAs and other 'non-traditional' populations.</p

Lead-Time Trajectory of CA19-9 as an Anchor Marker for Pancreatic Cancer Early Detection

Background & Aims There is substantial interest in liquid biopsy approaches for cancer early detection among subjects at risk, using multi-marker panels. CA19-9 is an established circulating biomarker for pancreatic cancer; however, its relevance for pancreatic cancer early detection or for monitoring subjects at risk has not been established. Methods CA19-9 levels were assessed in blinded sera from 175 subjects collected up to 5 years before diagnosis of pancreatic cancer and from 875 matched controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. For comparison of performance, CA19-9 was assayed in blinded independent sets of samples collected at diagnosis from 129 subjects with resectable pancreatic cancer and 275 controls (100 healthy subjects; 50 with chronic pancreatitis; and 125 with noncancerous pancreatic cysts). The complementary value of 2 additional protein markers, TIMP1 and LRG1, was determined. Results In the PLCO cohort, levels of CA19-9 increased exponentially starting at 2 years before diagnosis with sensitivities reaching 60% at 99% specificity within 0 to 6 months before diagnosis for all cases and 50% at 99% specificity for cases diagnosed with early-stage disease. Performance was comparable for distinguishing newly diagnosed cases with resectable pancreatic cancer from healthy controls (64% sensitivity at 99% specificity). Comparison of resectable pancreatic cancer cases to subjects with chronic pancreatitis yielded 46% sensitivity at 99% specificity and for subjects with noncancerous cysts, 30% sensitivity at 99% specificity. For prediagnostic cases below cutoff value for CA19-9, the combination with LRG1 and TIMP1 yielded an increment of 13.2% in sensitivity at 99% specificity ( P = .031) in identifying cases diagnosed within 1 year of blood collection. Conclusion CA19-9 can serve as an anchor marker for pancreatic cancer early detection applications