首页 > 最新文献

Journal of Medical Systems最新文献

英文 中文
A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction. 医学 LLM 的新型评估框架:结合模糊逻辑和 MCDM 以提取医学关系和临床概念
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-31 DOI: 10.1007/s10916-024-02090-y
A H Alamoodi, Omar Zughoul, Dianese David, Salem Garfan, Dragan Pamucar, O S Albahri, A S Albahri, Salman Yussof, Iman Mohamad Sharaf

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( A 4 ) "GatorTron S 10B" had the 1st rank as compared to ( A 1 ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.

人工智能(AI)已成为现代科技的重要组成部分,尤其是在医疗保健领域,这一点从大型语言模型(LLM)的不断发展中就能明显看出,这些模型被广泛应用于包括医疗在内的各个领域。然而,在医疗领域使用这些 LLMs 时,需要一个评估平台来确定其适用性并推动未来的开发工作。为此,本研究旨在通过开发一种专门用于评估医学 LLM 的综合多标准决策(MCDM)方法来解决这一问题。人工智能(尤其是 LLM)在医疗保健领域的成功取决于其有效性、安全性和伦理合规性。因此,必须有一个强大的评估框架,以便将其融入医疗环境。本研究建议使用模糊加权零不一致(FWZIC)方法扩展到 p, q-quasirung orthopair 模糊集(p, q-QROFS)来权衡评价标准。这一扩展可处理医疗决策过程中固有的不确定性。这种方法通过结合模糊逻辑原理,适应了现实世界中医疗数据和标准的不精确性和多面性。在本研究的案例研究中,采用了多分配理想-真实比较分析(MAIRCA)方法来评估医学 LLM。研究结果显示,"医学关系提取 "标准及其子级别的重要性(0.504)高于 "临床概念提取 "标准的重要性(0.495)。就所评估的 LLM 而言,在 6 个备选方案中,(A 4)"GatorTron S 10B "排名第一,而(A 1)"GatorTron 90B "排名第六。本研究的意义超出了学术讨论的范围,直接影响到医疗实践和患者的治疗效果。所提出的框架可以帮助医护人员在医疗环境中采用和使用 LLM 时做出更明智的决定。
{"title":"A Novel Evaluation Framework for Medical LLMs: Combining Fuzzy Logic and MCDM for Medical Relation and Clinical Concept Extraction.","authors":"A H Alamoodi, Omar Zughoul, Dianese David, Salem Garfan, Dragan Pamucar, O S Albahri, A S Albahri, Salman Yussof, Iman Mohamad Sharaf","doi":"10.1007/s10916-024-02090-y","DOIUrl":"https://doi.org/10.1007/s10916-024-02090-y","url":null,"abstract":"<p><p>Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that \"Medical Relation Extraction\" criteria with its sub-levels had more importance with (0.504) than \"Clinical Concept Extraction\" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( <math><mrow><mi>A</mi> <mn>4</mn></mrow> </math> ) \"GatorTron S 10B\" had the 1st rank as compared to ( <math><mrow><mi>A</mi> <mn>1</mn></mrow> </math> ) \"GatorTron 90B\" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142108163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities. 用于伤口评估和监测的移动应用程序:局限、进步与机遇。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-24 DOI: 10.1007/s10916-024-02091-x
Muhammad Ashad Kabir, Sabiha Samad, Fahmida Ahmed, Samsun Naher, Jill Featherston, Craig Laird, Sayed Ahmed

With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.

随着伤口评估应用程序在各种应用程序商店中大量涌现,以及人工智能(AI)在医疗保健应用程序中的不断融入,人们对综合评估系统的需求日益增长。目前的应用程序缺乏足够的循证可靠性,因此有必要进行系统评估。本研究的目的是评估伤口评估和监测应用程序,找出其局限性,并概述未来应用程序开发的机遇。我们在两大应用商店(Google Play 商店和苹果应用商店)进行了电子搜索,并由三名独立评分员对所选应用进行评分。共发现了 170 个应用程序,根据一系列纳入和排除标准,选出了 10 个进行审查。通过修改现有量表,我们创建了伤口评估应用程序评级量表,并用于评估所选的 10 款应用程序。我们的评分表主要评估应用程序的功能和软件质量特征。根据我们的评估,应用程序商店中的大多数应用程序都不符合伤口监测和评估的总体要求。我们评测的所有应用程序都主要面向从业人员和医生。根据我们的评估,ImitoWound 应用程序的平均得分最高,为 4.24 分。但在 11 项功能标准中,该应用程序只有 7 项标准。最后,我们建议未来有机会利用先进技术,特别是涉及人工智能的技术,来增强伤口评估应用程序的功能和功效。这项研究为未来的开发人员和研究人员提供了宝贵的资源,帮助他们改进基于伤口评估的应用程序的设计,包括软件质量和功能的改进。
{"title":"Mobile Apps for Wound Assessment and Monitoring: Limitations, Advancements and Opportunities.","authors":"Muhammad Ashad Kabir, Sabiha Samad, Fahmida Ahmed, Samsun Naher, Jill Featherston, Craig Laird, Sayed Ahmed","doi":"10.1007/s10916-024-02091-x","DOIUrl":"10.1007/s10916-024-02091-x","url":null,"abstract":"<p><p>With the proliferation of wound assessment apps across various app stores and the increasing integration of artificial intelligence (AI) in healthcare apps, there is a growing need for a comprehensive evaluation system. Current apps lack sufficient evidence-based reliability, prompting the necessity for a systematic assessment. The objectives of this study are to evaluate the wound assessment and monitoring apps, identify limitations, and outline opportunities for future app development. An electronic search across two major app stores (Google Play store, and Apple App Store) was conducted and the selected apps were rated by three independent raters. A total of 170 apps were discovered, and 10 were selected for review based on a set of inclusion and exclusion criteria. By modifying existing scales, an app rating scale for wound assessment apps is created and used to evaluate the selected ten apps. Our rating scale evaluates apps' functionality and software quality characteristics. Most apps in the app stores, according to our evaluation, do not meet the overall requirements for wound monitoring and assessment. All the apps that we reviewed are focused on practitioners and doctors. According to our evaluation, the app ImitoWound got the highest mean score of 4.24. But this app has 7 criteria among our 11 functionalities criteria. Finally, we have recommended future opportunities to leverage advanced techniques, particularly those involving artificial intelligence, to enhance the functionality and efficacy of wound assessment apps. This research serves as a valuable resource for future developers and researchers seeking to enhance the design of wound assessment-based applications, encompassing improvements in both software quality and functionality.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11344716/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142046805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Evaluating EHR-Integrated Digital Technologies for Medication-Related Outcomes and Health Equity in Hospitalised Adults: A Scoping Review. 评估电子病历集成数字技术对住院成人用药相关结果和健康公平的影响:范围审查。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-23 DOI: 10.1007/s10916-024-02097-5
Sreyon Murthi, Nataly Martini, Nazanin Falconer, Shane Scahill

The purpose of this scoping review is to identify and evaluate studies that examine the effectiveness and implementation strategies of Electronic Health Record (EHR)-integrated digital technologies aimed at improving medication-related outcomes and promoting health equity among hospitalised adults. Using the Consolidated Framework for Implementation Research (CFIR), the implementation methods and outcomes of the studies were evaluated, as was the assessment of methodological quality and risk of bias. Searches through Medline, Embase, Web of Science, and CINAHL Plus yielded 23 relevant studies from 1,232 abstracts, spanning 11 countries and from 2008 to 2022, with varied research designs. Integrated digital tools such as alert systems, clinical decision support systems, predictive analytics, risk assessment, and real-time screening and surveillance within EHRs demonstrated potential in reducing medication errors, adverse events, and inappropriate medication use, particularly in older patients. Challenges include alert fatigue, clinician acceptance, workflow integration, cost, data integrity, interoperability, and the potential for algorithmic bias, with a call for long-term and ongoing monitoring of patient safety and health equity outcomes. This review, guided by the CFIR framework, highlights the importance of designing health technology based on evidence and user-centred practices. Quality assessments identified eligibility and representativeness issues that affected the reliability and generalisability of the findings. This review also highlights a critical research gap on whether EHR-integrated digital tools can address or worsen health inequities among hospitalised patients. Recognising the growing role of Artificial Intelligence (AI) and Machine Learning (ML), this review calls for further research on its influence on medication management and health equity through integration of EHR and digital technology.

本范围综述旨在确定和评估有关研究,这些研究探讨了电子健康记录(EHR)集成数字技术的有效性和实施策略,旨在改善用药相关结果并促进住院成年人的健康公平。利用实施研究综合框架(CFIR)对研究的实施方法和结果进行了评估,并对方法学质量和偏倚风险进行了评估。通过对 Medline、Embase、Web of Science 和 CINAHL Plus 的检索,从 1,232 篇摘要中发现了 23 项相关研究,这些研究跨越 11 个国家,时间跨度从 2008 年到 2022 年,研究设计各不相同。电子病历中的警报系统、临床决策支持系统、预测分析、风险评估以及实时筛查和监控等综合数字工具在减少用药错误、不良事件和用药不当方面具有潜力,尤其是在老年患者中。所面临的挑战包括警报疲劳、临床医生的接受程度、工作流程整合、成本、数据完整性、互操作性以及算法偏差的可能性,并呼吁对患者安全和健康公平结果进行长期和持续的监控。本综述以 CFIR 框架为指导,强调了基于证据和以用户为中心的实践设计医疗技术的重要性。质量评估发现了影响研究结果可靠性和普遍性的资格和代表性问题。本综述还强调了一个重要的研究缺口,即电子病历集成数字工具是否能解决或恶化住院患者的健康不平等问题。鉴于人工智能(AI)和机器学习(ML)的作用越来越大,本综述呼吁进一步研究其通过整合电子病历和数字技术对药物管理和健康公平的影响。
{"title":"Evaluating EHR-Integrated Digital Technologies for Medication-Related Outcomes and Health Equity in Hospitalised Adults: A Scoping Review.","authors":"Sreyon Murthi, Nataly Martini, Nazanin Falconer, Shane Scahill","doi":"10.1007/s10916-024-02097-5","DOIUrl":"10.1007/s10916-024-02097-5","url":null,"abstract":"<p><p>The purpose of this scoping review is to identify and evaluate studies that examine the effectiveness and implementation strategies of Electronic Health Record (EHR)-integrated digital technologies aimed at improving medication-related outcomes and promoting health equity among hospitalised adults. Using the Consolidated Framework for Implementation Research (CFIR), the implementation methods and outcomes of the studies were evaluated, as was the assessment of methodological quality and risk of bias. Searches through Medline, Embase, Web of Science, and CINAHL Plus yielded 23 relevant studies from 1,232 abstracts, spanning 11 countries and from 2008 to 2022, with varied research designs. Integrated digital tools such as alert systems, clinical decision support systems, predictive analytics, risk assessment, and real-time screening and surveillance within EHRs demonstrated potential in reducing medication errors, adverse events, and inappropriate medication use, particularly in older patients. Challenges include alert fatigue, clinician acceptance, workflow integration, cost, data integrity, interoperability, and the potential for algorithmic bias, with a call for long-term and ongoing monitoring of patient safety and health equity outcomes. This review, guided by the CFIR framework, highlights the importance of designing health technology based on evidence and user-centred practices. Quality assessments identified eligibility and representativeness issues that affected the reliability and generalisability of the findings. This review also highlights a critical research gap on whether EHR-integrated digital tools can address or worsen health inequities among hospitalised patients. Recognising the growing role of Artificial Intelligence (AI) and Machine Learning (ML), this review calls for further research on its influence on medication management and health equity through integration of EHR and digital technology.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341601/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Comparing ChatGPT and a Single Anesthesiologist's Responses to Common Patient Questions: An Exploratory Cross-Sectional Survey of a Panel of Anesthesiologists. 比较 ChatGPT 和单个麻醉医师对常见患者问题的回答:麻醉医师小组的探索性横断面调查。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-22 DOI: 10.1007/s10916-024-02100-z
Frederick H Kuo, Jamie L Fierstein, Brant H Tudor, Geoffrey M Gray, Luis M Ahumada, Scott C Watkins, Mohamed A Rehman

Increased patient access to electronic medical records and resources has resulted in higher volumes of health-related questions posed to clinical staff, while physicians' rising clinical workloads have resulted in less time for comprehensive, thoughtful responses to patient questions. Artificial intelligence chatbots powered by large language models (LLMs) such as ChatGPT could help anesthesiologists efficiently respond to electronic patient inquiries, but their ability to do so is unclear. A cross-sectional exploratory survey-based study comprised of 100 anesthesia-related patient question/response sets based on two fictitious simple clinical scenarios was performed. Each question was answered by an independent board-certified anesthesiologist and ChatGPT (GPT-3.5 model, August 3, 2023 version). The responses were randomized and evaluated via survey by three blinded board-certified anesthesiologists for various quality and empathy measures. On a 5-point Likert scale, ChatGPT received similar overall quality ratings (4.2 vs. 4.1, p = .81) and significantly higher overall empathy ratings (3.7 vs. 3.4, p < .01) compared to the anesthesiologist. ChatGPT underperformed the anesthesiologist regarding rate of responses in agreement with scientific consensus (96.6% vs. 99.3%, p = .02) and possibility of harm (4.7% vs. 1.7%, p = .04), but performed similarly in other measures (percentage of responses with inappropriate/incorrect information (5.7% vs. 2.7%, p = .07) and missing information (10.0% vs. 7.0%, p = .19)). In conclusion, LLMs show great potential in healthcare, but additional improvement is needed to decrease the risk of patient harm and reduce the need for close physician oversight. Further research with more complex clinical scenarios, clinicians, and live patients is necessary to validate their role in healthcare.

患者对电子病历和资源的访问量增加,导致向临床人员提出的健康相关问题增多,而医生的临床工作量不断增加,导致他们没有更多时间对患者的问题做出全面、周到的回答。由大型语言模型(LLM)驱动的人工智能聊天机器人(如 ChatGPT)可以帮助麻醉医生高效地回复患者的电子问询,但其能力尚不明确。我们开展了一项基于横断面探索性调查的研究,其中包括 100 个与麻醉相关的患者问题/回复集,这些问题/回复集基于两个虚构的简单临床场景。每个问题都由独立的麻醉医师和 ChatGPT(GPT-3.5 模型,2023 年 8 月 3 日版本)回答。回答是随机的,并由三位盲人麻醉医师通过调查对各种质量和移情措施进行评估。在 5 点李克特量表中,ChatGPT 获得了相似的总体质量评分(4.2 vs. 4.1,p = .81)和显著更高的总体移情评分(3.7 vs. 3.4,p = .81)。
{"title":"Comparing ChatGPT and a Single Anesthesiologist's Responses to Common Patient Questions: An Exploratory Cross-Sectional Survey of a Panel of Anesthesiologists.","authors":"Frederick H Kuo, Jamie L Fierstein, Brant H Tudor, Geoffrey M Gray, Luis M Ahumada, Scott C Watkins, Mohamed A Rehman","doi":"10.1007/s10916-024-02100-z","DOIUrl":"https://doi.org/10.1007/s10916-024-02100-z","url":null,"abstract":"<p><p>Increased patient access to electronic medical records and resources has resulted in higher volumes of health-related questions posed to clinical staff, while physicians' rising clinical workloads have resulted in less time for comprehensive, thoughtful responses to patient questions. Artificial intelligence chatbots powered by large language models (LLMs) such as ChatGPT could help anesthesiologists efficiently respond to electronic patient inquiries, but their ability to do so is unclear. A cross-sectional exploratory survey-based study comprised of 100 anesthesia-related patient question/response sets based on two fictitious simple clinical scenarios was performed. Each question was answered by an independent board-certified anesthesiologist and ChatGPT (GPT-3.5 model, August 3, 2023 version). The responses were randomized and evaluated via survey by three blinded board-certified anesthesiologists for various quality and empathy measures. On a 5-point Likert scale, ChatGPT received similar overall quality ratings (4.2 vs. 4.1, p = .81) and significantly higher overall empathy ratings (3.7 vs. 3.4, p < .01) compared to the anesthesiologist. ChatGPT underperformed the anesthesiologist regarding rate of responses in agreement with scientific consensus (96.6% vs. 99.3%, p = .02) and possibility of harm (4.7% vs. 1.7%, p = .04), but performed similarly in other measures (percentage of responses with inappropriate/incorrect information (5.7% vs. 2.7%, p = .07) and missing information (10.0% vs. 7.0%, p = .19)). In conclusion, LLMs show great potential in healthcare, but additional improvement is needed to decrease the risk of patient harm and reduce the need for close physician oversight. Further research with more complex clinical scenarios, clinicians, and live patients is necessary to validate their role in healthcare.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017797","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Case-Study of Metoclopramide Prescription Error : A Grim Reminder. 甲氧氯普胺处方错误案例研究 :一个严峻的提醒。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-22 DOI: 10.1007/s10916-024-02099-3
Florent Wallet, Charlotte Doudet, Alexandre Theissen, Arnaud Friggeri, Charles-Hervé Vacheron

The integration of Computerized Provider Order Entry (CPOE) systems in hospitals has been instrumental in reducing medication errors and enhancing patient safety. This study examines the implications of a software oversight in a CPOE system : Metoclopramide had a concentrated formulation (100 mg) delisted (and then not manufactured) in 2014 due to safety concerns. Despite this, the CPOE system continued to accept prescriptions for this formulation because it was not removed from the medication library by the pharmacist. The objective of our study was to describe this specific prescription error related to an outdated the medication library of the CPOE. We analyzed all metoclopramide prescriptions from 2014, to 2023. Our findings showed that errors involving 100 mg or more dosages were relatively rare, at 2.98 per 1000 prescriptions (34 errors in 11,372 prescriptions). Notably, 47.1% of these errors occurred during on-call shifts, and 68% of these errors led to actual administration. These errors correlated with periods of higher nurse workload. The findings advocate for the integration of dedicated pharmacists into ICU teams to minimize medication errors and enhance patient outcomes, and a proactive medication management in healthcare.

医院整合计算机化医嘱输入系统(CPOE)在减少用药错误和提高患者安全方面发挥了重要作用。本研究探讨了 CPOE 系统软件疏忽的影响:出于安全考虑,甲氧氯普胺的浓缩制剂(100 毫克)于 2014 年退市(随后不再生产)。尽管如此,CPOE 系统仍继续接受这种制剂的处方,因为药剂师并未将其从药物库中删除。我们的研究旨在描述这种与 CPOE 药物库过时有关的特殊处方错误。我们分析了从 2014 年到 2023 年的所有甲氧氯普胺处方。我们的研究结果表明,涉及 100 毫克或以上剂量的错误相对较少,每 1000 张处方中只有 2.98 例(11372 张处方中出现 34 例错误)。值得注意的是,47.1% 的错误发生在值班期间,其中 68% 的错误导致了实际用药。这些错误与护士工作量较大的时期有关。研究结果提倡将专职药剂师纳入重症监护室团队,以最大限度地减少用药错误,提高患者的治疗效果,并在医疗保健中积极主动地进行用药管理。
{"title":"A Case-Study of Metoclopramide Prescription Error : A Grim Reminder.","authors":"Florent Wallet, Charlotte Doudet, Alexandre Theissen, Arnaud Friggeri, Charles-Hervé Vacheron","doi":"10.1007/s10916-024-02099-3","DOIUrl":"10.1007/s10916-024-02099-3","url":null,"abstract":"<p><p>The integration of Computerized Provider Order Entry (CPOE) systems in hospitals has been instrumental in reducing medication errors and enhancing patient safety. This study examines the implications of a software oversight in a CPOE system : Metoclopramide had a concentrated formulation (100 mg) delisted (and then not manufactured) in 2014 due to safety concerns. Despite this, the CPOE system continued to accept prescriptions for this formulation because it was not removed from the medication library by the pharmacist. The objective of our study was to describe this specific prescription error related to an outdated the medication library of the CPOE. We analyzed all metoclopramide prescriptions from 2014, to 2023. Our findings showed that errors involving 100 mg or more dosages were relatively rare, at 2.98 per 1000 prescriptions (34 errors in 11,372 prescriptions). Notably, 47.1% of these errors occurred during on-call shifts, and 68% of these errors led to actual administration. These errors correlated with periods of higher nurse workload. The findings advocate for the integration of dedicated pharmacists into ICU teams to minimize medication errors and enhance patient outcomes, and a proactive medication management in healthcare.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11341648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142017796","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Mixed Reality in the Operating Room: A Systematic Review. 手术室中的混合现实技术:系统综述。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-15 DOI: 10.1007/s10916-024-02095-7
Renato Magalhães, Ana Oliveira, David Terroso, Adélio Vilaça, Rita Veloso, António Marques, Javier Pereira, Luís Coelho

Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information. When integrated with voice control mechanisms, gestures and even iris movement, it becomes a valuable tool for medicine. These features are particularly appealing for the operating room and surgical learning, where access to information and freedom of hand operation are fundamental. This study examines the most significant research on mixed reality in the operating room over the past five years, to identify the trends, use cases, its applications and limitations. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to answer the research questions established using the PICO (Population, Intervention, Comparator and Outcome) framework. Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes. The articles' conclusions highlight the potential benefits of these innovations in surgical practice while acknowledging the challenges that must be addressed. Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The need for more flexible approaches and comprehensive studies is underscored by the specificity of procedures and limited samples sizes. The integration of imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.

混合现实技术因其独特的信息获取和可视化能力而备受关注。当与语音控制机制、手势甚至虹膜移动相结合时,它将成为一种宝贵的医疗工具。这些功能对于手术室和外科学习尤其具有吸引力,因为在手术室和外科学习中,信息的获取和手部操作的自由是至关重要的。本研究审查了过去五年中有关手术室中混合现实技术的最重要研究,以确定其趋势、用例、应用和局限性。研究按照《系统综述和元分析首选报告项目》(PRISMA)指南进行了系统综述,以回答使用 PICO(人群、干预、比较者和结果)框架确定的研究问题。虽然在手术室实施混合现实应用会带来一些挑战,但如果使用得当,也会产生显著效果。它可以让学习变得更容易,使若干程序的学习曲线变得更平缓,并促进手术过程的各个方面。文章的结论强调了这些创新在外科实践中的潜在好处,同时也承认了必须应对的挑战。技术复杂性、设备成本和陡峭的学习曲线是广泛采用混合现实技术和计算机辅助评估的重大障碍。由于手术的特殊性和样本量有限,因此需要更灵活的方法和更全面的研究。成像模式和创新功能的整合为临床应用带来了希望。然而,必须考虑与可用性、偏差和统计分析相关的问题。混合现实技术具有显著的优势,但仍存在一些挑战,如人体工程学问题、有限的视野和电池自主性等,这些问题必须得到解决,以确保其被广泛接受。
{"title":"Mixed Reality in the Operating Room: A Systematic Review.","authors":"Renato Magalhães, Ana Oliveira, David Terroso, Adélio Vilaça, Rita Veloso, António Marques, Javier Pereira, Luís Coelho","doi":"10.1007/s10916-024-02095-7","DOIUrl":"10.1007/s10916-024-02095-7","url":null,"abstract":"<p><p>Mixed Reality is a technology that has gained attention due to its unique capabilities for accessing and visualizing information. When integrated with voice control mechanisms, gestures and even iris movement, it becomes a valuable tool for medicine. These features are particularly appealing for the operating room and surgical learning, where access to information and freedom of hand operation are fundamental. This study examines the most significant research on mixed reality in the operating room over the past five years, to identify the trends, use cases, its applications and limitations. A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines to answer the research questions established using the PICO (Population, Intervention, Comparator and Outcome) framework. Although implementation of Mixed Reality applications in the operations room presents some challenges, when used appropriately, it can yield remarkable results. It can make learning easier, flatten the learning curve for several procedures, and facilitate various aspects of the surgical processes. The articles' conclusions highlight the potential benefits of these innovations in surgical practice while acknowledging the challenges that must be addressed. Technical complexity, equipment costs, and steep learning curves present significant obstacles to the widespread adoption of Mixed Reality and computer-assisted evaluation. The need for more flexible approaches and comprehensive studies is underscored by the specificity of procedures and limited samples sizes. The integration of imaging modalities and innovative functionalities holds promise for clinical applications. However, it is important to consider issues related to usability, bias, and statistical analyses. Mixed Reality offers significant benefits, but there are still open challenges such as ergonomic issues, limited field of view, and battery autonomy that must be addressed to ensure widespread acceptance.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11327191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141982525","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery. 人工智能(AI)在临床决策支持系统和护理服务中的有效性。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-12 DOI: 10.1007/s10916-024-02098-4
Khaled Ouanes, Nesren Farhah

This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screening, full-text articles were assessed for methodological quality and adherence to inclusion criteria. Data extraction focused on study design, AI technologies employed, reported outcomes, and evidence of AI-CDSS impact on patient and clinical outcomes. Thematic analysis was conducted to synthesise findings and identify key themes regarding the effectiveness of AI-CDSS. The screening of the articles resulted in the selection of 26 articles that satisfied the inclusion criteria. The content analysis revealed four themes: early detection and disease diagnosis, enhanced decision-making, medication errors, and clinicians' perspectives. AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations. Using AI in CDSSs can potentially improve patient outcomes by enhancing diagnostic accuracy, optimising treatment selection, and reducing medical errors.

本综述旨在评估人工智能驱动的 CDSS 对患者预后和临床实践的有效性。我们在 PubMed、MEDLINE 和 Scopus 上进行了全面检索。2018年1月至2023年11月期间发表的研究符合纳入条件。在对标题和摘要进行筛选后,对全文进行了方法学质量和是否符合纳入标准的评估。数据提取的重点是研究设计、采用的人工智能技术、报告的结果以及人工智能-CDSS对患者和临床结果影响的证据。对研究结果进行了主题分析,并确定了有关 AI-CDSS 效果的关键主题。经过筛选,共有 26 篇文章符合纳入标准。内容分析揭示了四个主题:早期发现和疾病诊断、加强决策、用药错误和临床医生的观点。研究发现,基于人工智能的 CDSS 可通过提供患者特定信息和循证建议来改善临床决策。在 CDSS 中使用人工智能可提高诊断准确性、优化治疗选择并减少医疗失误,从而改善患者的治疗效果。
{"title":"Effectiveness of Artificial Intelligence (AI) in Clinical Decision Support Systems and Care Delivery.","authors":"Khaled Ouanes, Nesren Farhah","doi":"10.1007/s10916-024-02098-4","DOIUrl":"https://doi.org/10.1007/s10916-024-02098-4","url":null,"abstract":"<p><p>This review aims to assess the effectiveness of AI-driven CDSSs on patient outcomes and clinical practices. A comprehensive search was conducted across PubMed, MEDLINE, and Scopus. Studies published from January 2018 to November 2023 were eligible for inclusion. Following title and abstract screening, full-text articles were assessed for methodological quality and adherence to inclusion criteria. Data extraction focused on study design, AI technologies employed, reported outcomes, and evidence of AI-CDSS impact on patient and clinical outcomes. Thematic analysis was conducted to synthesise findings and identify key themes regarding the effectiveness of AI-CDSS. The screening of the articles resulted in the selection of 26 articles that satisfied the inclusion criteria. The content analysis revealed four themes: early detection and disease diagnosis, enhanced decision-making, medication errors, and clinicians' perspectives. AI-based CDSSs were found to improve clinical decision-making by providing patient-specific information and evidence-based recommendations. Using AI in CDSSs can potentially improve patient outcomes by enhancing diagnostic accuracy, optimising treatment selection, and reducing medical errors.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141916965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Intelligent Clinic Nurse Scheduling Considering Nurses Paired with Doctors and Preference of Nurses. 考虑到护士与医生配对和护士偏好的智能诊所护士调度。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-12 DOI: 10.1007/s10916-024-02092-w
Yu-Chung Tsao, Danny Chen, Feng-Jang Hwang, Vu Thuy Linh

The nurse scheduling problem (NSP) has been a crucial and challenging research issue for hospitals, especially considering the serious deterioration in nursing shortages in recent years owing to long working hours, considerable work pressure, and irregular lifestyle, which are important in the service industry. This study investigates the NSP that aims to maximize nurse satisfaction with the generated schedule subject to government laws, internal regulations of hospitals, doctor-nurse pairing rules, shift and day off preferences of nurses, etc. The computational experiment results show that our proposed hybrid metaheuristic outperforms other metaheuristics and manual scheduling in terms of both computation time and solution quality. The presented solution procedure is implemented in a real-world clinic, which is used as a case study. The developed scheduling technique reduced the time spent on scheduling by 93% and increased the satisfaction of the schedule by 21%, which further enhanced the operating efficiency and service quality.

护士排班问题(NSP)一直是医院面临的一个至关重要且极具挑战性的研究课题,尤其是考虑到近年来由于服务行业中护士工作时间长、工作压力大、生活不规律等原因导致的护士短缺现象严重恶化。本研究探讨了在政府法律、医院内部规定、医护配对规则、护士的轮班和休息日偏好等条件下,以最大化护士对生成的时间表的满意度为目标的 NSP。计算实验结果表明,我们提出的混合元启发式在计算时间和求解质量方面都优于其他元启发式和人工排班。所提出的求解程序在一个真实世界的诊所中实施,该诊所被用作案例研究。所开发的调度技术减少了 93% 的调度时间,提高了 21% 的调度满意度,从而进一步提高了运营效率和服务质量。
{"title":"Intelligent Clinic Nurse Scheduling Considering Nurses Paired with Doctors and Preference of Nurses.","authors":"Yu-Chung Tsao, Danny Chen, Feng-Jang Hwang, Vu Thuy Linh","doi":"10.1007/s10916-024-02092-w","DOIUrl":"https://doi.org/10.1007/s10916-024-02092-w","url":null,"abstract":"<p><p>The nurse scheduling problem (NSP) has been a crucial and challenging research issue for hospitals, especially considering the serious deterioration in nursing shortages in recent years owing to long working hours, considerable work pressure, and irregular lifestyle, which are important in the service industry. This study investigates the NSP that aims to maximize nurse satisfaction with the generated schedule subject to government laws, internal regulations of hospitals, doctor-nurse pairing rules, shift and day off preferences of nurses, etc. The computational experiment results show that our proposed hybrid metaheuristic outperforms other metaheuristics and manual scheduling in terms of both computation time and solution quality. The presented solution procedure is implemented in a real-world clinic, which is used as a case study. The developed scheduling technique reduced the time spent on scheduling by 93% and increased the satisfaction of the schedule by 21%, which further enhanced the operating efficiency and service quality.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141916966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Supercharge Your Academic Productivity with Generative Artificial Intelligence. 利用生成式人工智能提高您的学术生产力。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-08 DOI: 10.1007/s10916-024-02093-9
Hannah Lonsdale, Vikas N O'Reilly-Shah, Asif Padiyath, Allan F Simpao
{"title":"Supercharge Your Academic Productivity with Generative Artificial Intelligence.","authors":"Hannah Lonsdale, Vikas N O'Reilly-Shah, Asif Padiyath, Allan F Simpao","doi":"10.1007/s10916-024-02093-9","DOIUrl":"10.1007/s10916-024-02093-9","url":null,"abstract":"","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11457929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141901970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Electromagnetic Compatibility Issues in 400-MHz-Band Wireless Medical Telemetry Systems and Their Management Using Simplified Methods for Safe Operation. 400-MHz 频段无线医疗遥测系统的电磁兼容性问题及其使用简化方法进行安全操作的管理。
IF 3.5 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES Pub Date : 2024-08-05 DOI: 10.1007/s10916-024-02096-6
Kai Ishida, Kiyotaka Fujii, Eisuke Hanada

Wireless medical telemetry systems (WMTSs) are typical radio communication-based medical devices that monitor various biological parameters, such as electrocardiograms and respiration rates. In Japan, the assigned frequency band for WMTSs is 400 MHz. However, the issues accounting for poor reception in WMTS constitute major concerns. In this study, we analyzed the effects of electromagnetic interferences (EMIs) caused by other radio communication systems, the intermodulation (IM) effect, and noises generated from electrical devices on WMTS and discussed their management. The 400-MHz frequency band is also shared by other radio communication systems. We showed the instantaneous and impulsive voltages generated from the location-detection system for wandering patients and their potential to exhibit EMI effects on WMTS. Further, we presented the IM effect significantly reduces reception in WMTS. Additionally, the electromagnetic noises generated from electrical devices, such as light-emitting diode lamps and security cameras, can exceed the 400 MHz frequency band as these devices employ the switched-mode power supply and/or central processing unit and radiate wideband emissions. Moreover, we proposed and evaluated simple and facile methods using a simplified spectrum analysis function installed in the WMTS receiver and software-defined radio for evaluating the electromagnetic environment.

无线医疗遥测系统(WMTS)是典型的基于无线电通信的医疗设备,用于监测各种生物参数,如心电图和呼吸频率。在日本,WMTS 的指定频段为 400 MHz。然而,导致 WMTS 接收不良的问题是人们关注的主要问题。在这项研究中,我们分析了其他无线电通信系统造成的电磁干扰(EMI)、互调(IM)效应以及电气设备产生的噪音对 WMTS 的影响,并讨论了如何处理这些问题。其他无线电通信系统也共享 400-MHz 频段。我们展示了流浪病人位置检测系统产生的瞬时电压和脉冲电压,以及它们对 WMTS 产生电磁干扰效应的可能性。此外,我们还介绍了 IM 效应会大大降低 WMTS 的接收能力。此外,电气设备(如发光二极管灯和监控摄像头)产生的电磁噪声可能会超过 400 MHz 频段,因为这些设备采用开关模式电源和/或中央处理单元,并辐射宽带发射。此外,我们还提出并评估了一些简单易行的方法,利用 WMTS 接收器和软件定义无线电中安装的简化频谱分析功能来评估电磁环境。
{"title":"Electromagnetic Compatibility Issues in 400-MHz-Band Wireless Medical Telemetry Systems and Their Management Using Simplified Methods for Safe Operation.","authors":"Kai Ishida, Kiyotaka Fujii, Eisuke Hanada","doi":"10.1007/s10916-024-02096-6","DOIUrl":"https://doi.org/10.1007/s10916-024-02096-6","url":null,"abstract":"<p><p>Wireless medical telemetry systems (WMTSs) are typical radio communication-based medical devices that monitor various biological parameters, such as electrocardiograms and respiration rates. In Japan, the assigned frequency band for WMTSs is 400 MHz. However, the issues accounting for poor reception in WMTS constitute major concerns. In this study, we analyzed the effects of electromagnetic interferences (EMIs) caused by other radio communication systems, the intermodulation (IM) effect, and noises generated from electrical devices on WMTS and discussed their management. The 400-MHz frequency band is also shared by other radio communication systems. We showed the instantaneous and impulsive voltages generated from the location-detection system for wandering patients and their potential to exhibit EMI effects on WMTS. Further, we presented the IM effect significantly reduces reception in WMTS. Additionally, the electromagnetic noises generated from electrical devices, such as light-emitting diode lamps and security cameras, can exceed the 400 MHz frequency band as these devices employ the switched-mode power supply and/or central processing unit and radiate wideband emissions. Moreover, we proposed and evaluated simple and facile methods using a simplified spectrum analysis function installed in the WMTS receiver and software-defined radio for evaluating the electromagnetic environment.</p>","PeriodicalId":16338,"journal":{"name":"Journal of Medical Systems","volume":null,"pages":null},"PeriodicalIF":3.5,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141889471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
期刊
Journal of Medical Systems
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1