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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.
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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人工智能机器学习驱动的门诊预约管理:关于可接受性的定性研究

Kerry V Wood1, Daniel Frings1, Chris Flood1

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此摘要是机器生成的。

人工智能 (AI) 预约提醒显示出减少错过门诊预约的希望. 然而,患者和工作人员的参与是关键的,为了在医疗保健中成功采用人工智能,需要解决隐私问题和整合挑战.

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任命管理管理的任命管理.可以接受的可接受性.人工智能的人工智能是人工智能.数字健康数字健康技术采用 技术采用

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 患者参与策略 患者参与策略

背景情况:

  • 管理门诊预约是一个重大挑战,错过预约导致医疗保健能力的大量浪费.
  • 由人工智能 (AI) 机器学习驱动的自动预约提醒是提高效率的潜在解决方案.
  • 成功实施人工智能提醒需要理解和解决患者和工作人员的参与,以及评估整体影响.

研究的目的:

  • 调查人工智能机器学习驱动的预约管理系统在患者和医疗保健工作人员中是否可接受.
  • 确定影响人工智能在门诊预约管理中的采用和有效性的关键障碍和促进者.
  • 为优化人工智能驱动的医疗保健解决方案收集见解.

主要方法:

  • 半结构面试对7名工作人员和12名患者进行了采访.
  • 专题分析被用来分析采访数据,涉及编码,分类,并通过研究人员讨论提炼主题.
  • 由于实际约束,对样本大小和概括性的局限性得到了承认.

主要成果:

  • 出现了五个关键主题:对于患者来说,道德问题,人工智能理解,提醒效果,用户满意度和可用性;对于员工来说,人工智能理解,犹,障碍,驱动器,技术经验,操作方面和可持续性.
  • 确定的障碍包括隐私问题,有限的互动性,分散的系统集成和运营挑战.
  • 辅导员感知到预测的准确性和提醒的有用性,患者重视方便性和可用性,而工作人员则专注于操作上的好处,例如减少DNA率.

结论:

  • 患者和工作人员对NHS预约管理中的AI的看法显示,需要提高整合性,清晰性,交互性和可访问性.
  • 尽管人们对数据安全有信心,但目前隐私问题和运营效率低下阻碍了人工智能的广泛采用.
  • 通过定制策略提高用户体验对于在医疗保健环境中成功实施人工智能至关重要.