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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Health Information Technology (HIT)
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Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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DEPLOYR:用于在电子病历中部署定制实时机器学习模型的技术框架.

Conor K Corbin1, Rob Maclay2, Aakash Acharya3

  • 1Department of Biomedical Data Science, Stanford, California, USA.

Journal of the American Medical Informatics Association : JAMIA
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概括

我们开发了DEPLOYR,这是一种技术框架,用于将机器学习模型部署到电子医疗记录系统中. 这一框架使实时监测和前性评估成为可能,解决了人工智能工具临床翻译方面的差距.

关键词:
人工智能的人工智能是人工智能.临床决策支持 临床决策支持计算基础设施计算基础设施医疗保健组织 医疗保健组织机器学习是机器学习.组织准备 组织准备

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科学领域:

  • 临床信息学 临床信息学
  • 医疗保健中的机器学习
  • 医疗信息技术 (IT) 是一种健康技术.

背景情况:

  • 医疗机构需要强大的框架来在临床工作流程中实施机器学习 (ML) 模型.
  • 现有的治理框架需要技术解决方案,以有效,安全和高质量的模型部署.
  • 弥合ML研究和临床应用之间的差距对于改善患者护理至关重要.

研究的目的:

  • 引入DEPLOYR,用于实时部署和监控由研究人员创建的ML模型的技术框架.
  • 为了使ML模型能够无集成到广泛使用的电子医疗记录 (EMR) 系统中.
  • 为在医疗保健机构中部署ML模型提供最佳实践信息.

主要方法:

  • DEPLOYR促进了由EMR行动和实时数据收集引发的模型部署,用于推断.
  • 它包括在临床医生工作流程中显示推断的机制,并随着时间的推移监测模型性能.
  • 该框架支持无声部署和部署模型的前性评估.

主要成果:

  • 使用DEPLOYR无声部署和评估12个ML模型,预测EMR系统中的实验室诊断结果.
  • 模型是由EMR中的临床医生操作 (按点击) 触发的.
  • 前性评估表明,与回顾性绩效估计的差异存在差异.

结论:

  • 由于追溯和前性估计之间的性能差异,在医疗保健中无声部署ML模型是可行的和必要的.
  • 预期性能测量应该指导模型部署的最终决策.
  • DEPLOYR旨在促进将ML模型转化为临床实践.