Tamang S, Kopec D, Shagas G, Levy K
Department of Computer and Information Science, Brooklyn College, CUNY, USA.
Stud Health Technol Inform. 2005;114:93-104.
Chronic and terminally ill patients are disproportionately affected by medical errors. In addition, the elderly suffer more preventable adverse events than younger patients. Targeting system wide "error-reducing" reforms to vulnerable populations can significantly reduce the incidence and prevalence of human error in medical practice. Recent developments in health informatics, particularly the application of artificial intelligence (AI) techniques such as data mining, neural networks, and case-based reasoning (CBR), presents tremendous opportunities for mitigating error in disease diagnosis and patient management. Additionally, the ubiquity of the Internet creates the possibility of an almost ideal network for the dissemination of medical information. We explore the capacity and limitations of web-based palliative information systems (IS) to transform the delivery of care, streamline processes and improve the efficiency and appropriateness of medical treatment. As a result, medical error(s) that occur with patients dealing with severe, chronic illness and the frail elderly can be reduced.The palliative model grew out of the need for pain relief and comfort measures for patients diagnosed with cancer. Applied definitions of palliative care extend this convention, but there is no widely accepted definition. This research will discuss the development life cycle of two palliative information systems: the CONFER QOLP management information system (MIS), currently used by a community-based palliative care program in Brooklyn, New York, and the CAREN case-based reasoning prototype. CONFER is a web platform based on the idea of "eCare". CONFER uses XML (extensible mark-up language), a W3C-endorced standard mark up to define systems data. The second system, CAREN, is a CBR prototype designed for palliative care patients in the cancer trajectory. CBR is a technique, which tries to exploit the similarities of two situations and match decision-making to the best-known precedent cases. The prototype uses the opensource CASPIAN shell developed by the University of Aberystwyth, Wales and is available by anonymous FTP. We will discuss and analyze the preliminary results we have obtained using this CBR tool. Our research suggests that automated information systems can be used to improve the quality of care at the end of life and disseminate expert level 'know how' to palliative care clinicians. We will present how our CBR prototype can be successfully deployed, capable of securely transferring information using a Secure File Transfer Protocol (SFTP) and using a JAVA CBR engine.
慢性病患者和绝症患者受医疗差错的影响尤为严重。此外，老年人比年轻患者遭受更多可预防的不良事件。针对弱势群体进行全系统的“减少差错”改革，可显著降低医疗实践中人为差错的发生率和流行率。健康信息学的最新发展，特别是人工智能（AI）技术如数据挖掘、神经网络和基于案例的推理（CBR）的应用，为减少疾病诊断和患者管理中的差错带来了巨大机遇。此外，互联网的普及为医疗信息传播创造了近乎理想网络的可能性。我们探讨基于网络的姑息治疗信息系统（IS）在改变护理提供方式、简化流程以及提高医疗治疗效率和适当性方面的能力和局限性。这样一来，在处理严重慢性病和体弱老年人的患者中发生的医疗差错就可以减少。姑息治疗模式源于对癌症患者缓解疼痛和采取舒适措施的需求。姑息治疗的应用定义扩展了这一传统，但尚无广泛接受的定义。本研究将讨论两个姑息治疗信息系统的开发生命周期：纽约布鲁克林一个社区姑息治疗项目目前使用的CONFER QOLP管理信息系统（MIS），以及CAREN基于案例的推理原型。CONFER是一个基于“电子护理”理念的网络平台。CONFER使用XML（可扩展标记语言），这是一种由万维网联盟认可的标准标记来定义系统数据。第二个系统CAREN是为癌症病程中的姑息治疗患者设计的CBR原型。CBR是一种技术，它试图利用两种情况的相似性，并将决策与最知名的先例案例相匹配。该原型使用威尔士阿伯里斯特威斯大学开发的开源CASPIAN外壳，可通过匿名文件传输协议获取。我们将讨论并分析使用此CBR工具获得的初步结果。我们的研究表明，自动化信息系统可用于提高临终护理质量，并将专家级的“专业知识”传播给姑息治疗临床医生。我们将展示我们的CBR原型如何能够成功部署，能够使用安全文件传输协议（SFTP）安全地传输信息，并使用JAVA CBR引擎。
