Hasil untuk "Other systems of medicine"

Reed Taylor Sutton, D. Pincock, D. Baumgart et al.

Computerized clinical decision support systems, or CDSS, represent a paradigm shift in healthcare today. CDSS are used to augment clinicians in their complex decision-making processes. Since their first use in the 1980s, CDSS have seen a rapid evolution. They are now commonly administered through electronic medical records and other computerized clinical workflows, which has been facilitated by increasing global adoption of electronic medical records with advanced capabilities. Despite these advances, there remain unknowns regarding the effect CDSS have on the providers who use them, patient outcomes, and costs. There have been numerous published examples in the past decade(s) of CDSS success stories, but notable setbacks have also shown us that CDSS are not without risks. In this paper, we provide a state-of-the-art overview on the use of clinical decision support systems in medicine, including the different types, current use cases with proven efficacy, common pitfalls, and potential harms. We conclude with evidence-based recommendations for minimizing risk in CDSS design, implementation, evaluation, and maintenance.

V. Kaul, Sarah Enslin, S. Gross

Artificial intelligence (AI) was first described in 1950; however, several limitations in early models prevented widespread acceptance and application to medicine. In the early 2000s, many of these limitations were overcome by the advent of deep learning. Now AI systems are capable of analyzing complex algorithms and self-learning, we enter a new age in medicine where AI can be applied to clinical practice through risk assessment models, improving diagnostic accuracy and improving workflow efficiency. This article presents a brief historical perspective on the evolution of AI over the last several decades and the introduction and development of AI in medicine in recent years. A brief summary of the major applications of AI in gastroenterology and endoscopy are also presented, which will be reviewed in further detail by several other articles in this issue of GIE.

V. Chandrakala, Valmiki Aruna, Gangadhara Angajala

Over the past few years, nanotechnology has been attracting considerable research attention because of their outstanding mechanical, electromagnetic and optical properties. Nanotechnology is an interdisciplinary field comprising nanomaterials, nanoelectronics, and nanobiotechnology, as three areas which extensively overlap. The application of metal nanoparticles (MNPs) has drawn much attention offering significant advances, especially in the field of medicine by increasing the therapeutic index of drugs through site specificity preventing multidrug resistance and delivering therapeutic agents efficiently. Apart from drug delivery, some other applications of MNPs in medicine are also well known such as in vivo and in vitro diagnostics and production of enhanced biocompatible materials and nutraceuticals. The use of metallic nanoparticles for drug delivery systems has significant advantages, such as increased stability and half-life of drug carrier in circulation, required biodistribution, and passive or active targeting into the required target site. Green synthesis of MNPs is an emerging area in the field of bionanotechnology and provides economic and environmental benefits as an alternative to chemical and physical methods. Therefore, this review aims to provide up-to-date insights on the current challenges and perspectives of MNPs in drug delivery systems. The present review was mainly focused on the greener methods of metallic nanocarrier preparations and its surface modifications, applications of different MNPs like silver, gold, platinum, palladium, copper, zinc oxide, metal sulfide and nanometal organic frameworks in drug delivery systems.

P. Jain, Xiaohua Huang, I. El-Sayed et al.

J. Braithwaite, K. Churruca, Janet C. Long et al.

BackgroundImplementation science has a core aim – to get evidence into practice. Early in the evidence-based medicine movement, this task was construed in linear terms, wherein the knowledge pipeline moved from evidence created in the laboratory through to clinical trials and, finally, via new tests, drugs, equipment, or procedures, into clinical practice. We now know that this straight-line thinking was naïve at best, and little more than an idealization, with multiple fractures appearing in the pipeline.DiscussionThe knowledge pipeline derives from a mechanistic and linear approach to science, which, while delivering huge advances in medicine over the last two centuries, is limited in its application to complex social systems such as healthcare. Instead, complexity science, a theoretical approach to understanding interconnections among agents and how they give rise to emergent, dynamic, systems-level behaviors, represents an increasingly useful conceptual framework for change. Herein, we discuss what implementation science can learn from complexity science, and tease out some of the properties of healthcare systems that enable or constrain the goals we have for better, more effective, more evidence-based care. Two Australian examples, one largely top-down, predicated on applying new standards across the country, and the other largely bottom-up, adopting medical emergency teams in over 200 hospitals, provide empirical support for a complexity-informed approach to implementation. The key lessons are that change can be stimulated in many ways, but a triggering mechanism is needed, such as legislation or widespread stakeholder agreement; that feedback loops are crucial to continue change momentum; that extended sweeps of time are involved, typically much longer than believed at the outset; and that taking a systems-informed, complexity approach, having regard for existing networks and socio-technical characteristics, is beneficial.ConclusionConstruing healthcare as a complex adaptive system implies that getting evidence into routine practice through a step-by-step model is not feasible. Complexity science forces us to consider the dynamic properties of systems and the varying characteristics that are deeply enmeshed in social practices, whilst indicating that multiple forces, variables, and influences must be factored into any change process, and that unpredictability and uncertainty are normal properties of multi-part, intricate systems.

R. Smith-Bindman, M. Kwan, Emily C. Marlow et al.

Importance Medical imaging increased rapidly from 2000 to 2006, but trends in recent years have not been analyzed. Objective To evaluate recent trends in medical imaging. Design, Setting, and Participants Retrospective cohort study of patterns of medical imaging between 2000 and 2016 among 16 million to 21 million patients enrolled annually in 7 US integrated and mixed-model insurance health care systems and for individuals receiving care in Ontario, Canada. Exposures Calendar year and country (United States vs Canada). Main Outcomes and Measures Use of computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, and nuclear medicine imaging. Annual and relative imaging rates by imaging modality, country, and age (children [<18 years], adults [18-64 years], and older adults [≥65 years]). Results Overall, 135 774 532 imaging examinations were included; 5 439 874 (4%) in children, 89 635 312 (66%) in adults, and 40 699 346 (30%) in older adults. Among adults and older adults, imaging rates were significantly higher in 2016 vs 2000 for all imaging modalities other than nuclear medicine. For example, among older adults, CT imaging rates were 428 per 1000 person-years in 2016 vs 204 per 1000 in 2000 in US health care systems and 409 per 1000 vs 161 per 1000 in Ontario; for MRI, 139 per 1000 vs 62 per 1000 in the United States and 89 per 1000 vs 13 per 1000 in Ontario; and for ultrasound, 495 per 1000 vs 324 per 1000 in the United States and 580 per 1000 vs 332 per 1000 in Ontario. Annual growth in imaging rates among US adults and older adults slowed over time for CT (from an 11.6% annual percentage increase among adults and 9.5% among older adults in 2000-2006 to 3.7% among adults in 2013-2016 and 5.2% among older adults in 2014-2016) and for MRI (from 11.4% in 2000-2004 in adults and 11.3% in 2000-2005 in older adults to 1.3% in 2007-2016 in adults and 2.2% in 2005-2016 in older adults). Patterns in Ontario were similar. Among children, annual growth for CT stabilized or declined (United States: from 10.1% in 2000-2005 to 0.8% in 2013-2016; Ontario: from 3.3% in 2000-2006 to -5.3% in 2006-2016), but patterns for MRI were similar to adults. Changes in annual growth in ultrasound were smaller among adults and children in the United States and Ontario compared with CT and MRI. Nuclear medicine imaging declined in adults and children after 2006. Conclusions and Relevance From 2000 to 2016 in 7 US integrated and mixed-model health care systems and in Ontario, rates of CT and MRI use continued to increase among adults, but at a slower pace in more recent years. In children, imaging rates continued to increase except for CT, which stabilized or declined in more recent periods. Whether the observed imaging utilization was appropriate or was associated with improved patient outcomes is unknown.

L. C. Matos, J. Machado, F. Monteiro et al.

Traditional Chinese medicine (TCM) is a systematic healthcare system developed from clinical experience based on a scientific model of regulation. TCM relies on unique theories and practices to treat diseases and enhance health. These practices include Chinese herbal medicine and dietetics, acupuncture and moxibustion, and other non-medication therapies such as Chinese bodywork or manual therapy, known as “Tuina”, and traditional biofeedback exercises, known as “Qigong” and “Taijiquan”. The integration of TCM in Western health systems and research requires a rational communicable theory, scientific proof of efficacy and safety, and quality control measures. Understanding the structural concepts of the TCM language makes possible the parallelism to Western physiology, and the inherent rational use of the reflex therapeutic systems, anti-inflammatory mechanisms and mental training involved, for example, in acupuncture and “Qigong”. The results of TCM clinical trials and fundamental research on its nature and mechanisms have encouraged the development and application of well-designed research strategies such as double blinding in acupucture to overcome limitations and resistances in integrating these practices into the existing biomedical paradigms of the West. This review aims to overview some TCM theoretical concepts and the evidence-based clinical application of TCM’s leading practices to create an easy-to-consult and condensed source of information, available for the healthcare community, facilitating the understanding and communication between conventional health professionals and TCM practitioners and acupuncturists.

N. Abul-Husn, E. Kenny

Personalized medicine has largely been enabled by the integration of genomic and other data with electronic health records (EHRs) in the United States and elsewhere. Increased EHR adoption across various clinical settings and the establishment of EHR-linked population-based biobanks provide unprecedented opportunities for the types of translational and implementation research that drive personalized medicine. We review advances in the digitization of health information and the proliferation of genomic research in health systems and provide insights into emerging paths for the widespread implementation of personalized medicine.

W. U. Jiashuo, Fangqing Zhang, L. Zhuangzhuang et al.

H. Hampel, Peng Gao, J. Cummings et al.

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer’s disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Vitali V. Maldonado, Neel Patel, Emma E Smith et al.

Mesenchymal stem/stromal cells (MSCs) have been carefully examined to have tremendous potential in regenerative medicine. With their immunomodulatory and regenerative properties, MSCs have numerous applications within the clinical sector. MSCs have the properties of multilineage differentiation, paracrine signaling, and can be isolated from various tissues, which makes them a key candidate for applications in numerous organ systems. To accentuate the importance of MSC therapy for a range of clinical indications, this review highlights MSC-specific studies on the musculoskeletal, nervous, cardiovascular, and immune systems where most trials are reported. Furthermore, an updated list of the different types of MSCs used in clinical trials, as well as the key characteristics of each type of MSCs are included. Many of the studies mentioned revolve around the properties of MSC, such as exosome usage and MSC co-cultures with other cell types. It is worth noting that MSC clinical usage is not limited to these four systems, and MSCs continue to be tested to repair, regenerate, or modulate other diseased or injured organ systems. This review provides an updated compilation of MSCs in clinical trials that paves the way for improvement in the field of MSC therapy.

Praveen Balakrishnan, Emy S. Surendran, Lisha S. Raj

Intervertebral disc prolapse (IVDP) or disc herniation is a moderately common disorder in males during their third to fifth decade of life. Conservative treatments are limited and surgery is an effective treatment option, but without much benefits in the long-term follow-up. Many claims of effects of Ayurveda interventions for IVDP are made, but evidence of effects of Ayurveda interventions in acute cases is meagre. This case report highlights the effects of Ayurveda management (following a multimodal approach with lepa, upanaha, pinda sweda, and vasti) within a short period of time of 26 days, in an acute case of IVDP. The Oswestry Disability Index reduced from 45 to zero and Functional Rating Index from 40 to 2. There was a linear reduction in pain and numbness in weekly follow up using the visual analogue scale. This case report highlights the role of Ayurveda treatments in the management of acute cases of intervertebral disc prolapse to achieve result within a short span of time.

Sevil Uygun İlikhan, Mahmut Özer, Hande Tanberkan et al.

The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.

A. Stenzinger, E. Moltzen, E. Winkler et al.

The technical development of high‐throughput sequencing technologies and the parallel development of targeted therapies in the last decade have enabled a transition from traditional medicine to personalized treatment and care. In this way, by using comprehensive genomic testing, more effective treatments with fewer side effects are provided to each patient—that is, precision or personalized medicine (PM). In several European countries—such as in England, France, Denmark, and Spain—the governments have adopted national strategies and taken “top‐down” decisions to invest in national infrastructure for PM. In other countries—such as Sweden, Germany, and Italy with regionally organized healthcare systems—the profession has instead taken “bottom‐up” initiatives to build competence networks and infrastructure to enable equal access to PM. In this review, we summarize key learnings at the European level on the implementation process to establish sustainable governance and organization for PM at the regional, national, and EU/international levels. We also discuss critical ethical and legal aspects of implementing PM, and the importance of access to real‐world data and performing clinical trials for evidence generation, as well as the need for improved reimbursement models, increased cross‐disciplinary education and patient involvement. In summary, PM represents a paradigm shift, and modernization of healthcare and all relevant stakeholders—that is, healthcare, academia, policymakers, industry, and patients—must be involved in this system transformation to create a sustainable, non‐siloed ecosystem for precision healthcare that benefits our patients and society at large.

A. Verma, Joshua Murray, R. Greiner et al.

CMAJ | AUGUST 30, 2021 | VOLUME 193 | ISSUE 34 E1351 M achine learning — the process of developing systems that learn from data to recognize patterns and make accurate predictions of future events1 — has considerable potential to transform health care. Machine-learned tools could support complex clinical decision-making and could automate many of the mundane tasks that may waste clinician time and lead to work dissatisfaction.2 Despite growing interest in and regulatory approval of such technologies, for example smartwatch algorithms to detect atrial fibrillation,3 to date machine-learned tools have had only limited use in routine clinical practice.4 Developing and implementing machine-learned tools in medicine requires infrastructure and resources that can be difficult to access, such as large, real-time clinical data sets, technical skills in data science, computing power and clinical informatics infrastructure. Other barriers to adoption include challenges in ensuring data security and privacy, poorly performing mathematical models, difficulty integrating tools into existing workflows, low acceptance of machine-learned solutions by clinician users, and uncertainty about how to evaluate them.4 In this article we outline an approach to developing and adopting machine-learned solutions in health care. Related articles discuss some of the caveats of using this technology5 and the evaluation of machine-learned tools.6 Developing machine-learned solutions for clinical use requires a strong understanding of clinical care, data science and implementation science. A number of excellent frameworks support data analytics and quality-improvement initiatives, including the Cross-Industry Standard Process for Data Mining (CRISP-DM),7 the Model for Improvement developed by the Institute for Healthcare Improvement 8 and the Knowledge to Action9 framework. However, there is no clear, comprehensive framework specifically focused on adoption of machine-learned tools in health care. We propose a 3-phase framework to develop and implement machine-learned solutions in clinical care, illustrated by a case example (Box 1). The framework comprises an exploration phase, a solution design phase, and an implementation and evaluation phase (Figure 1). It can be used for a range of solutions, including computer vision–based projects, automation and optimization projects, and predictive analytics. The framework can also be applied when organizations are implementing machine-learned solutions that were developed elsewhere because the steps, other than model development, remain the same. What are the key steps of the exploration phase?

Syeda Jannatul Ferdaus, Shyamal Kumar Paul, Syeda Anjuman Nasreen et al.

Chronic infection of <i>Helicobacter pylori</i> represents a key factor in the etiology of gastrointestinal diseases, with high endemicity in South Asia. The present study aimed to determine the prevalence of <i>H. pylori</i> among dyspeptic patients in north–central Bangladesh (Mymensingh) and analyze risk factors of infection and antimicrobial resistance (AMR) determinants in the pathogen. Endoscopic gastrointestinal biopsy samples were collected from dyspeptic patients for a one-year period from March 2022 and were checked for the presence of <i>H. pylori</i> via the rapid urease test and PCR and further analyzed for the status of virulence factors <i>vacA</i>/<i>cagA</i> and genetic determinants related to AMR via PCR with direct sequencing or RFLP. Among a total of 221 samples collected, 80 (36%) were positive for <i>H. pylori</i>, with the <i>vacA</i>+/<i>cagA</i>+ genotype being detected in almost half of them. <i>H. pylori</i> was most prevalent in the age group of 41–50-year-olds, with it being more common in males and rural residents with a lower economic status and using nonfiltered water, though the rates of these factors were not significantly different from those of the <i>H. pylori</i>-negative group. Relatively higher frequency was noted for the A2147G mutation in 23S rRNA, related to clarithromycin resistance (18%, 7/39). Amino acid substitutions in PBP-1A (T556S) and GyrA (N87K and D91N) and a 200 bp deletion in <i>rdxA</i> were detected in samples from some patients with recurrence after treatment with amoxicillin, levofloxacin, and metronidazole, respectively. The present study describes the epidemiological features of <i>H. pylori</i> infection in the area outside the capital in Bangladesh, revealing the spread of AMR-associated mutations.

Jackson Ishara, Marcellin C. Cokola, Ariel Buzera et al.

Abstract Background Located in the Eastern Democratic Republic of Congo (South-Kivu), Kalehe and Idjwi are two relatively unexplored territories with little to no research on edible insects even though anthropo-entomophagy practice is widespread. This study therefore aimed at exploring the biodiversity, perception, consumption, availability, host plants, harvesting techniques, and processing techniques of edible insects. Methods Data were collected through a field survey using three techniques, namely structured interviews, direct observations, and insect collection and taxonomy. A total of 260 respondents, 130 in each territory, were interviewed. The field survey focused on inventorying commonly edible insects as well as recording consumer preferences, preference factors, seasonal availability, host plants, harvesting techniques, and processing and preservation methods. Samples for taxonomic characterization were preserved in 70% alcohol. Results Nine edible insects, namely Ruspolia differens Serville 1838, Gryllotalpa Africana Palisot de Beauvois 1805, Locusta migratoria Linnaeus 1758, Macrotermes subhyalinus Rambur 1842, Gnathocera trivittata Swederus 1787, Rhynchophorus phoenicis Fabricius 1801, Vespula spp. Linnaeus 1758, Apis mellifera Linnaeus 1758, and Imbrasia oyemensis Rougeot 1955, were recorded as being consumed either as larvae, pupae, and adults. Ruspolia differens and M. subhyalinus were reported as the most preferred by consumers in the studied territories. A scatter plot of matrices and Pearson's correlations showed a negative correlation between preference based on taste, size, and shape, as well as perceived nutritional value. Their seasonal availability differs from one species to another and correlated with host plants availability. Harvesting techniques and processing and preservation methods depend on species, local knowledge, and practices. Conclusion The huge edible insect diversity observed in Kalehe and Idjwi is evidence of anthropo-entomophagy practices in the area. In addition to being an important delicacy and traditional foods, edible insects can contribute to food, environmental, and financial security through local business opportunities. Households can rely on edible insects to meet their nutritional needs instead of conventional livestock. Indigenous practices and technologies used for harvesting, processing, and preserving edible insects must be improved to meet international standards to increase the market and capitalize on the economic potential of edible insects.

Kanika K. Ahuja, Tanika Pundir

Andrew Myers, Catherine Ipsen, Krys Standley

IntroductionLack of transportation is a significant barrier to community participation for many disabled adults. Living in a rural area introduces additional transportation barriers, such as having to travel long distances to access services or socialize, and limited public transit options. While the importance of transportation access is clear, the mix of different transportation options used by people with disabilities to participate in their communities is less understood, particularly among those who do not or cannot drive.MethodsWe used data from the 2017 National Household Travel Survey to explore transportation behaviors among disabled adults in rural and urban areas and by four regions across the United States. We explored differences by transportation modalities (e.g., driver, passenger, public transportation, taxi/uber, walk) and trip purposes (e.g., social, independent living, healthcare, work). Our sample included 22,716 adults with travel-limiting disabilities.ResultsSeveral geographic differences emerged among non-drivers. Rural non-drivers were less likely to take any trip, particularly for social activities, and reported using less public transportation or walking/rolling than urban non-drivers. Further, respondents from the Northeast were more likely to report using public transportation and walking/rolling options, relative to the Midwest, South, and West. Overall, disabled rural adults reported lower odds of giving up driving, even after controlling for socio-demographic and health characteristics.DiscussionThese findings highlight the relative importance of different transportation modalities for participating in activities and the continued reliance upon personal vehicles, either as a driver or passenger, especially among rural disabled residents. Potential policy insights are discussed.

Chen Junren, Xie Xiaofang, Li Mengting et al.

Abstract Patchouli (“Guanghuoxiang”) or scientifically known as Pogostemon cablin Benth, belonging to the family Lamiaceae, has been used in traditional Chinse medicine (TCM) since the time of the Eastern Han dynasty. In TCM theory, patchouli can treat colds, nausea, fever, headache, and diarrhea. Various bioactive compounds have been identified in patchouli, including terpenoids, phytosterols, flavonoids, organic acids, lignins, glycosides, alcohols, pyrone, and aldehydes. Among the numerous compounds, patchouli alcohol, β-patchoulene, patchoulene epoxide, pogostone, and pachypodol are of great importance. The pharmacological impacts of these compounds include anti-peptic ulcer effect, antimicrobial effect, anti-oxidative effect, anti-inflammatory effect, effect on ischemia/reperfusion injury, analgesic effect, antitumor effect, antidiabetic effect, anti-hypertensive effect, immunoregulatory effect, and others.For this review, we examined publications from the previous five years collected from PubMed, Web of Science, Springer, and the Chinese National Knowledge Infrastructure databases. This review summarizes the recent progress in phytochemistry, pharmacology, and mechanisms of action and provides a reference for future studies focused on clinical applications of this important plant extract.