hospital pharmacy

医院药房
  • 文章类型: Journal Article
    这篇文章探讨了人工智能对科学写作的影响,特别关注其在医院药学中的应用。它分析了增强信息检索的人工智能工具,文献分析,书写质量,和手稿起草。像共识这样的聊天机器人,以及Scite和SciSpace等平台,在科学数据库中实现精确搜索,提供基于证据的回应和参考。SciSpace有助于生成比较表和制定有关研究的查询,而ResearchRabbit绘制科学文献以识别趋势。DeepL和ProWritingAid等工具通过纠正语法来提高写作质量,风格,和抄袭错误。A.R.I.A.加强参考管理,和珍妮AI协助克服作家的障碍。像langchain这样的Python库支持高级语义搜索和代理的创建。尽管他们的好处,人工智能引发了包括偏见在内的伦理问题,错误信息,和抄袭。强调了负责任的使用和专家严格审查的重要性。在医院药房,人工智能可以提高研究和科学交流的效率和精度。药剂师可以使用这些工具来保持更新,提高出版物的质量,优化信息管理,促进临床决策。总之,人工智能是医院药学的有力工具,只要负责任地和道德地使用它。
    The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyses artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer\'s block. Python libraries such as langchain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasised. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimise information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically.
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  • 文章类型: Journal Article
    医院的药房服务旨在满足所有患者的需求。这无疑是医院提供的最复杂的服务之一。在政府医院,包括埃塞俄比亚的人,药房主要用作药店和药房。据我们所知,这项研究是首次评估和确保埃塞俄比亚医院药学服务更好的质量保证.因此,本文的目的是评估医院药学服务质量保证的现状。
    进行了横断面调查。使用KoboCollect移动应用程序从医院药房负责人处收集数据,然后导出到社会科学统计软件包(SPSS)版本25进行分析。分类变量的描述性统计以百分比和频率表示。
    所有(100%)被研究的医院药房都有合格的药房主任,并在药房全职工作。只有40%的人拥有药物信息中心和功能性药物处方委员会。所有药房都有自己的处置设施,但没有定期处置过期或不合适的药物。结果显示,所有医院药房都没有委托负责任的机构进行质量评估。
    这项研究的结果清楚地表明,医院药房的质量保证服务受到损害。研究结果可用于确定需要改进的领域,并制定提高医院药学服务质量的策略。
    UNASSIGNED: Pharmacy services in hospitals are designed to meet the needs of all patients. This is undoubtedly one of the most complex services provided by hospitals. In government hospitals, including those in Ethiopia, pharmacies mainly serve as drug stores and dispensaries. To the best of our knowledge, this study is the first to assess and ensure better quality assurance for hospital pharmacy services in Ethiopia. Therefore, the objective of this article was to assess the current status of the quality assurance of pharmacy services in hospitals.
    UNASSIGNED: A cross-sectional survey was conducted. The data were collected from hospital pharmacy heads using the Kobo Collect mobile application and then exported to the Statistical Package for Social Science (SPSS) version 25 for analysis. Descriptive statistics for categorical variables are presented as percentages and frequencies.
    UNASSIGNED: All (100%) of the studied hospital pharmacies had a qualified pharmacy director and worked in the pharmacy full-time. Only 40% had a drug information center and a functional drug formulary committee. All pharmacies had their own disposal facilities but did not regularly dispose of expired or unfit medications. The results revealed that all hospital pharmacies did not have a responsible body delegated for quality evaluation.
    UNASSIGNED: The findings of this study clearly show that quality assurance services in hospital pharmacies are compromised. The findings can be used to identify areas of improvement and develop strategies to enhance the quality of hospital pharmacy services.
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  • 文章类型: Journal Article
    当患者出院时,必须告知其全科医生(GP)和社区药剂师其药物的变化。这需要医院和初级保健临床医生之间进行有效的沟通和信息共享。
    确定优先药物移交问题和解决方案,以告知共同设计和开发多方面的干预措施。
    使用了一种改进的标称组技术来就药物移交优先领域达成共识。互动2小时工作坊的第一个小时,重点是对从文献中得出的预先确定的问题进行排名。在第二个小时,参与者确定了解决方案,然后通过在线平台从最高优先级到最低优先级进行排名。使用描述性统计数据来分析车间数据。
    共有32名参与者参加了研讨会,其中包括医院医生(n=8,25.0%),全科医生和医院药剂师(各n=6,18.8%),消费者和社区药剂师(各n=4,12.5%),医院和老年护理机构护士(n=2,各6.3%)。从23个问题的列表中,排名最高的问题是高工作量和时间压力影响放电过程(22/32).从36个解决方案列表中,参与者确定了两个同样排名最高的解决方案(各12/27).他们要求病人带着出院总结出院,包括药物和解信息和,开发一个综合信息技术系统,在该系统中,初级药物摘要和笔记可以访问,二级和三级保健提供者。
    共识过程强调了医院程序中的挑战,其中可能通过多方面干预措施的共同设计来实施潜在的解决方案,以提高药物移交质量。
    UNASSIGNED: When a patient is discharged from hospital it is essential that their general practitioner (GPs) and community pharmacist are informed of changes to their medicines. This necessitates effective communication and information-sharing between hospitals and primary care clinicians.
    UNASSIGNED: To identify priority medicine handover issues and solutions to inform the co-design and development of a multifaceted intervention.
    UNASSIGNED: A modified nominal group technique was used to reach consensus on medicine handover priority areas. The first hour of an interactive 2-hr workshop focused on ranking pre-identified issues drawn from literature. In the second hour, participants identified solutions that they then ranked from highest to lowest priority through an online platform. Descriptive statistics were used to analyse workshop data.
    UNASSIGNED: In total 32 participants attended the workshop including hospital doctors (n = 8, 25.0%), GPs and hospital pharmacists (n = 6 each, 18.8%), consumers and community pharmacists (n = 4 each, 12.5%), and both hospital and aged care facility nurses (n = 2 each 6.3%). From the list of 23 issues, the highest ranked issue was high workload and time pressures impacting the discharge process (22/32). From the list of 36 solutions, the participants identified two solutions that were equally ranked highest (12/27 each). They were mandating that patients leave hospital with a discharge summary, including medication reconciliation information and, developing an integrated information technology system where medication summary and notes are accessible for primary, secondary and tertiary health provider.
    UNASSIGNED: The consensus process highlighted challenges in hospital procedures where potential solutions may be implemented through co-design of a multifaceted intervention to improve medicine handover quality.
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  • 文章类型: Journal Article
    背景:尽管欧洲医院药剂师协会(EAHP)于2017年发布了欧洲范围的医院药房能力框架,但并非所有国家都采用并实施了这样的框架。
    目的:本研究旨在开发和验证奥地利定制的国家医院药房能力框架,以支持医院药房劳动力的发展。
    方法:分三个阶段进行了多方法研究。(I)对48个医疗保健相关协会网站和6个科学数据库进行了系统的文献综述,确定能力框架,指南和相关文件。(II)在针对欧洲共同培训框架(CTF)的“患者护理和临床药学技能”领域进行映射之前,由三名研究人员审查了提取的行为能力的背景国家适用性。(III)专家小组(n=4;奥地利医院药剂师协会(AAHP)董事会成员)讨论了对由此产生的临床技能能力框架草案的验证。调查结果的报告与报告卫生专业能力框架发展的建议(CONFERD-HP指南)和PRISMA2020清单一致。
    结果:系统评价(SR)产生了28个框架,准则和相关文件,以及379项行为能力的识别,其中19个映射到CTF的“患者护理和临床药学技能”域(删除重复项之后)。专家小组讨论导致了建议的变化,以确保背景国家适当性。
    结论:这项研究导致了奥地利第一个临床国家药学能力框架的开发和验证。未来的研究应侧重于成功实施该计划所需的政治和实践结构。
    BACKGROUND: Despite the publication of a European wide competency framework for hospital pharmacy by the European Association of Hospital Pharmacist (EAHP) in 2017, not all countries have adopted and implemented such a framework.
    OBJECTIVE: This study aimed to develop and validate a bespoke national hospital pharmacy competency framework for Austria that supports the hospital pharmacy workforce development.
    METHODS: A multi-method study was carried out in three phases. (I) A systematic literature review across 48 websites of healthcare-related associations and six scientific databases was conducted, identifying competency frameworks, guidelines and related documents. (II) Extracted behaviour competencies were reviewed for contextual national appropriateness by three researchers prior to mapping against the \"Patient Care and Clinical Pharmacy Skills\" domain of European Common Training Framework (CTF). (III) Validation of the resultant draft clinical skills competency framework took place by an expert panel (n = 4; Austrian Association of Hospital Pharmacists (AAHP) board members) discussion. Reporting of findings is aligned with the recommendations for reporting Competency Framework Development in health professions (CONFERD-HP guidelines) and the PRISMA 2020 checklist.
    RESULTS: The systematic review (SR) resulted in 28 frameworks, guidelines and related documents and the identification of 379 behaviour competencies, with nineteen mapped to the \"Patient Care and Clinical Pharmacy Skills\" domain of the CTF (after removal of duplicates). Expert panel discussion resulted in suggested changes to ensure contextual national appropriateness.
    CONCLUSIONS: This study resulted in the development and validation of the first clinical national pharmacy competency framework for Austria. Future studies should focus on political and practical structures necessary for its successful implementation.
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  • 文章类型: Journal Article
    背景与目的作用于中枢神经系统的药物具有引起药物相关问题(DRPs)的高潜力。临床药剂师通过跨学科医疗团队内的协作努力可以预防,检测,并解决DRP,从而有助于促进药物安全和改善受护理个人的生活质量。这项研究旨在评估2016年2月至2019年11月在三级医院神经内科病房中发现的DRPs。方法这是一项描述性研究,采用横断面和回顾性设计,涉及从药学服务(PC)记录中收集的次要数据。学生的t检验,皮尔逊相关系数,泊松模型,和逻辑回归模型用于分析年龄之间的关联,药物的数量和类型,住院时间,以及DRP的发生。结果共纳入130例患者,共检测到266个DRPs,93例患者经历了一个以上的DRP,37例未出现任何DRP。与必要性相关的DRP是最普遍的(46.6%)类型,其次是安全相关的DRP(28.6%)。60岁以上人群的安全相关DRPs患病率较高(p<0.001)。值得注意的结论,84.6%的药剂师建议解决DRP的干预措施被医疗团队接受。发现的大量DRP突显了药剂师的临床作用和跨专业合作在神经系统患者护理中的重要性,特别是在老年人的药物随访中。
    Background and objective Drugs that act on the central nervous system have a high potential to cause drug-related problems (DRPs). A clinical pharmacist aided by collaborative efforts within an interdisciplinary healthcare team can prevent, detect, and resolve DRPs, thereby contributing to the promotion of medication safety and improving the quality of life of individuals under care. This study aimed to assess DRPs identified in the neurology ward of a tertiary hospital from February 2016 to November 2019. Methods This was a descriptive study with a cross-sectional and retrospective design involving secondary data collected from pharmaceutical care (PC) records. Student\'s t-tests, Pearson correlation coefficients, Poisson models, and logistic regression models were used to analyze the associations between age, number and type of medications, duration of hospitalization, and the occurrence of DRPs. Results A total of 130 patients were included in the study, and a total of 266 DRPs were detected, with 93 patients experiencing more than one DRP and 37 not presenting any DRPs. Necessity-related DRPs were the most prevalent (46.6%) type, followed by safety-related DRPs (28.6%). The prevalence of safety-related DRPs was higher in individuals older than 60 years (p<0.001). Conclusions Of note, 84.6% of the interventions suggested by pharmacists to resolve DRPs were accepted by the healthcare team. The high number of DRPs found underscores the importance of the clinical role of the pharmacist and interprofessional collaboration in the care of neurological patients, especially in the pharmaceutical follow-up of elderly individuals.
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  • 文章类型: Journal Article
    人工智能是一个广泛的概念,包括研究计算机执行通常需要人类智能干预的任务的能力。通过利用大量的医疗保健数据,人工智能算法可以识别模式并预测结果,这可以帮助医疗机构及其专业人员做出更好的决策并取得更好的结果。机器学习,深度学习,神经网络,或自然语言处理是最重要的方法之一,允许系统从数据中学习和改进,而不需要显式编程。人工智能已经被引入生物医学,加速进程,提高准确性和效率,改善病人护理。通过使用人工智能算法和机器学习,医院药剂师可以分析大量的患者数据,包括医疗记录,实验室结果,和药物简介,帮助他们识别潜在的药物相互作用,评估药物的安全性和有效性,并提出明智的建议。人工智能整合将提高药学服务质量,优化流程,促进研究,部署开放式创新,促进教育。掌握人工智能的医院药剂师将在这一转变中发挥至关重要的作用。
    Artificial intelligence is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, Artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks, or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. Artificial intelligence has been introduced in biomedicine, accelerating processes, improving accuracy and efficiency, and improving patient care. By using Artificial intelligence algorithms and machine learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. Artificial intelligence integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master Artificial intelligence will play a crucial role in this transformation.
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  • 文章类型: Journal Article
    人工智能(AI)是一个广泛的概念,包括研究计算机执行通常需要人类智能干预的任务的能力。通过利用大量的医疗保健数据,人工智能算法可以识别模式并预测结果,这可以帮助医疗机构及其专业人员做出更好的决策并取得更好的结果。机器学习,深度学习,神经网络或自然语言处理是最重要的方法之一,允许系统从数据中学习和改进,而不需要显式编程。人工智能已经被引入生物医学,加速进程,提高安全性和效率,改善病人护理。通过使用AI算法和机器学习,医院药剂师可以分析大量的患者数据,包括医疗记录,实验室结果,和药物简介,帮助他们识别潜在的药物相互作用,评估药物的安全性和有效性,并提出明智的建议。人工智能整合将提高药学服务质量,优化流程,促进研究,部署开放式创新,促进教育。掌握AI的医院药剂师将在这一转变中发挥至关重要的作用。
    Artificial intelligence (AI) is a broad concept that includes the study of the ability of computers to perform tasks that would normally require the intervention of human intelligence. By exploiting large volumes of healthcare data, artificial intelligence algorithms can identify patterns and predict outcomes, which can help healthcare organizations and their professionals make better decisions and achieve better results. Machine learning, deep learning, neural networks or natural language processing are among the most important methods, allowing systems to learn and improve from data without the need for explicit programming. AI has been introduced in biomedicine, accelerating processes, improving safety and efficiency, and improving patient care. By using AI algorithms and Machine Learning, hospital pharmacists can analyze a large volume of patient data, including medical records, laboratory results, and medication profiles, aiding them in identifying potential drug-drug interactions, assessing the safety and efficacy of medicines, and making informed recommendations. AI integration will improve the quality of pharmaceutical care, optimize processes, promote research, deploy open innovation, and facilitate education. Hospital pharmacists who master AI will play a crucial role in this transformation.
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  • 文章类型: Journal Article
    医院药房提供的药物信息(DI)旨在促进合理和安全的药物治疗。虽然建议对此任务进行质量评估,需要更多关于决定质量的因素的知识。我们旨在评估不同因素对医院药房向医疗保健专业人员提供的DI质量的影响。回顾过去,对有关德国医院药房五年来的年度DI测试的虚构查询的答案进行了评估,以满足内容相关和结构要求。对查询复杂性的影响进行了多变量分析,DI组织(专门的DI中心;每天负责的药剂师;DI在其他日常任务之上),和质量措施(第二次看;在DI/on病房回答药剂师的经验;使用文档数据库)。在2017-2021年,有45、71、79、118和122家医院药房参加。查询复杂性对内容相关质量有统计上的显著影响,结果不佳,复杂性更高(2018/2021年,OR0.25/0.04,p<0.01)。与每天负责的药剂师(OR0.76/p=0.65)或除常规任务外的DI(OR0.35/p=0.02)相比,DI中心在内容相关质量方面取得了更好的结果。DI中心在结构质量方面得分较高。从第二个角度来看,总体趋势是与内容相关的质量和结构质量更好。总之,建议采用专门的DI中心和二次看点作为提高质量措施。应加强回答复杂询问的培训。
    Drug information (DI) provided by hospital pharmacies aims to promote rational and safe drug therapy. While quality assessment for this task is recommended, more knowledge on the factors determining the quality is needed. We aimed to evaluate the impacts of different factors on the quality of DI provided by hospital pharmacies to healthcare professionals. Retrospectively, answers on fictitious enquiries about annual DI tests for German hospital pharmacies over five years were evaluated for content-related and structural requirements. Multivariate analysis was performed for the impact of the enquiry complexity, DI organization (specialized DI center; pharmacist responsible per day; DI on top of other routine tasks), and quality measures (second look; experience of answering pharmacist in DI/on ward; use of documentation database). In 2017-2021, 45, 71, 79, 118, and 122 hospital pharmacies participated. The enquiry complexity had a statistically significant impact on the content-related quality, with poor results for a higher complexity (years 2018/2021, OR 0.25/0.04, p < 0.01). The DI centers achieved better results regarding content-related quality than for a pharmacist responsible per day (OR 0.76/p = 0.65) or DI on top of routine tasks (OR 0.35/p = 0.02). The DI centers scored better in structural quality. The second look showed an overall trend of a better content-related and structural quality. In conclusion, specialized DI centers and second looks are recommended as quality-improving measures. Training for answering complex enquiries should be intensified.
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  • 文章类型: Journal Article
    目的:药品不良反应(ADR)是一个主要的药品安全问题,也是医院药品信息服务部门经常查询的话题。我们的目标是分析这些关于背景的询问,复杂性,ADR的性质,并涉及药物类,以提高院内药物安全性。
    方法:回顾,对德国一所大学医院2018-2022年药房药物信息的ADR查询进行了关于查询者的分析(职业,医学专业)和查询详情(药物,可疑的ADR/药物开始前的询问,ADR系统器官类,确定可能的原因,和查询复杂性)。
    结果:在543个查询中,医生询问了516人(95%),493(91%)患者特异性,390(71%)可疑ADR,药物开始前153(28%)。查询经常来自内科(74/13.6%),儿科(71/13.1%),神经病学(70/12.9%),和血液肿瘤学(62/11.4%)。最常见的ADR是血液学(94/17%)和肝(72/13%)。每次查询的药物数量中位数为3种(范围为0-37种),209次(38%)查询涉及一种特定药物,165(30%)有关≥11种药物。在75例(36%)关于一种药物和155例(94%)≥11种药物的查询中,确定了可疑ADR的可能原因。最常见的药物是抗肿瘤(54/25.8%),神经系统药物(42/20.1%),和抗感染药物(40/19.1%)。大多数查询(342/63%)是复杂的(多个/专家资源)。
    结论:询问询问通常由医生参考特定临床情况下的可疑不良反应。在许多情况下,确定了可能的原因,指出对患者护理的直接积极影响。应鼓励在药物开始之前进行查询,以增加药物安全性。有关主要ADR影响和药物类别的信息有助于有针对性的咨询。
    OBJECTIVE: Adverse drug reactions (ADRs) are a major drug safety concern and a frequent topic of enquiries to hospital drug information services. Our goal was to analyse these enquiries regarding background, complexity, nature of ADR, and involved drug classes to improve in-hospital drug safety.
    METHODS: Retrospectively, ADR enquiries to a German university hospital pharmacy drug information 2018-2022 were analysed regarding enquirer (profession, medical specialty) and enquiry details (drugs, suspected ADR/enquiry prior to drug initiation, ADR system organ class, probable cause identified, and enquiry complexity).
    RESULTS: Of 543 enquiries, 516 (95%) were asked by physicians, 493 (91%) patient-specific, 390 (71%) on suspected ADRs, and 153 (28%) prior to drug initiation. Enquiries originated frequently from internal medicine (74/13.6%), paediatrics (71/13.1%), neurology (70/12.9%), and haemato-oncology (62/11.4%). Most frequent ADRs were haematologic (94/17%) and hepatic (72/13%). The median number of drugs per enquiry was three (range 0-37), 209 (38%) enquiries referred to one specific drug, 165 (30%) concerned ≥11 drugs. A probable cause for suspected ADRs was identified in 75 (36%) enquiries concerning one drug and 155 (94%) with ≥11 drugs. Most frequent drugs were antineoplastic (54/25.8%), nervous-system-drugs (42/20.1%), and anti-infective (40/19.1%). Most enquiries (342/63%) were complex (multiple/specialist resources).
    CONCLUSIONS: Enquiries were usually asked by physicians referring to suspected ADRs in specific clinical situations. A probable cause was identified in many cases pointing to a direct positive impact on patient care. Enquiries prior to drug initiation should be encouraged to increase drug safety. Information on main ADR effects and drug classes helps with targeted counselling.
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  • 文章类型: Journal Article
    这篇文章探讨了人工智能对科学写作的影响,特别关注其在医院药学中的应用。它分析了增强信息检索的人工智能工具,文献分析,书写质量,和手稿起草。像共识这样的聊天机器人,以及Scite和SciSpace等平台,在科学数据库中实现精确搜索,提供基于证据的回应和参考。SciSpace有助于生成比较表和制定有关研究的查询,而ResearchRabbit绘制科学文献以识别趋势。DeepL和ProWritingAid等工具通过纠正语法来提高写作质量,风格,和抄袭错误。A.R.I.A.加强参考管理,和珍妮AI协助克服作家的障碍。诸如LangChain之类的Python库支持高级语义搜索和代理的创建。尽管他们的好处,人工智能引发了包括偏见在内的伦理问题,错误信息,和抄袭。强调了负责任的使用和专家严格审查的重要性。在医院药房,人工智能可以提高研究和科学交流的效率和精度。药剂师可以使用这些工具来保持更新,提高出版物的质量,优化信息管理,促进临床决策。总之,人工智能是医院药学的有力工具,只要负责任地和道德地使用它。
    The article examines the impact of artificial intelligence on scientific writing, with a particular focus on its application in hospital pharmacy. It analyzes artificial intelligence tools that enhance information retrieval, literature analysis, writing quality, and manuscript drafting. Chatbots like Consensus, along with platforms such as Scite and SciSpace, enable precise searches in scientific databases, providing evidence-based responses and references. SciSpace facilitates the generation of comparative tables and the formulation of queries regarding studies, while ResearchRabbit maps the scientific literature to identify trends. Tools like DeepL and ProWritingAid improve writing quality by correcting grammatical, stylistic, and plagiarism errors. A.R.I.A. enhances reference management, and Jenny AI assists in overcoming writer\'s block. Python libraries such as LangChain enable advanced semantic searches and the creation of agents. Despite their benefits, artificial intelligence raises ethical concerns including biases, misinformation, and plagiarism. The importance of responsible use and critical review by experts is emphasized. In hospital pharmacy, artificial intelligence can enhance efficiency and precision in research and scientific communication. Pharmacists can use these tools to stay updated, enhance the quality of their publications, optimize information management, and facilitate clinical decision-making. In conclusion, artificial intelligence is a powerful tool for hospital pharmacy, provided it is used responsibly and ethically.
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