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中文摘要
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描述(由申请人提供):在过去的二十年里,减少差错和不良事件已成为医疗保健系统的中心重点。然而,精神障碍患者被系统地排除在这项研究之外。因此,以医院为基础的精神卫生服务中的患者安全事件(不良事件和差错)的流行病学仍然未知。目前的应用程序寻求通过对宾夕法尼亚州综合医院38个住院精神病室的随机样本中的11,000名医疗补助患者病历进行记录审查,来评估患者安全事件的发生率、性质和可预防性。我们将通过对单位领导层的详细调查来补充这些信息,以确定影响、促成和/或防止发生不良事件和/或错误的患者、提供者和精神科单位/医院因素。对来自十家医院的关键信息提供人的深入定性访谈将有助于解释这些量化结果,并更深入地了解患者、提供者和单位因素相互作用并导致伤害和错误的机制,着眼于干预发展。 公共卫生相关性:推进住院精神卫生保健患者安全的流行病学方法是公共卫生议程的组成部分,该议程利用经验证据改善以医院为基础的护理。我们的发现将对确定预防医疗差错和不良事件的机会和战略具有重要的政策和实践意义。这种干预措施有可能影响每年从住院精神病室出院的100多万名患者,并提高这一弱势群体的整体安全和护理质量。
英文摘要
DESCRIPTION (provided by applicant): Reducing errors and adverse events have become a central focus of the health care system over the last two decades. However, patients with mental disorders have been systematically excluded from this research. As a result, the epidemiology of patient safety events (adverse events and errors) in hospital based mental health services remains unknown. The current application seeks to assess the incidence, nature and preventability of patient safety events via a record review of 11,000 Medicaid patient medical charts in a random sample of 38 inpatient psychiatric units of general hospitals in Pennsylvania. We will supplement this information with detailed surveys of unit leadership to define the patient, provider, and psychiatric unit/hospital factors that influence, contribute to, and/or protect against the commission of adverse events and/or errors. In-depth qualitative interviews with key informants from ten hospitals will be used to help interpret these quantitative findings and more deeply understand the mechanisms by which patient, provider and unit factors interact and contribute to cause harm and error with an eye towards intervention development. PUBLIC HEALTH RELEVANCE: Advancing an epidemiological approach to patient safety for inpatient mental health care is integral to a public health agenda that uses empirical evidence for improving hospital- based care. Our findings will have significant policy and practice implications with regard to targeting opportunities and strategies to prevent medical errors and adverse events. Such interventions have the potential to impact over a million patients discharged from inpatient psychiatric units each year and enhance the overall safety and quality of care for this vulnerable population.
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Administrative Data Transfer Masking, Access, and Ethics Core
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
  • 批准号:
    10277514
  • 项目类别:
  • 资助金额:
    $78.42万
  • 财政年份:
    2021
  • 负责人:
    STEVEN C MARCUS
  • 依托单位:
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
  • 批准号:
    10462646
  • 项目类别:
  • 资助金额:
    $72.62万
  • 财政年份:
    2021
  • 负责人:
    STEVEN C MARCUS
  • 依托单位:
Development and clinical interpretation of machine learning emergency department suicide prediction algorithms using electronic health records and claims
  • 批准号:
    10631239
  • 项目类别:
  • 资助金额:
    $69.24万
  • 财政年份:
    2021
  • 负责人:
    STEVEN C MARCUS
  • 依托单位:
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