Nursing Resources and Health Care-Associated Infections: Measures of Hospital Performance

护理资源和医疗保健相关感染：医院绩效衡量标准

• 批准号: 10199046
• 负责人: EDMUND R BECKER
• 金额: $ 40万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10199046
• 关键词: Nursing; Resources; Health; Care; Associated

PROJECT SUMMARY Healthcare-associated infections (HAIs) remain common, risking patient lives, adding to health care costs, and contributing to the major public health problem of antibiotic-resistant infections. Clinical interventions have been found effective in preventing HAIs under controlled conditions but not under usual care conditions. Infection prevention interventions have not focused on improving hospital-wide nurse work environments as recommended by the Institute of Medicine. The primary aim of this study is to determine whether nursing resources such as work environments and other modifiable features of nursing, including nurse staffing, skill mix, workforce stability, and education are associated with HAIs, with a goal of identifying promising hospital level strategies to facilitate infection reduction. Measures of these modifiable features of nursing will be derived by aggregating, to the hospital level, unique survey data collected in 2015-2016 from 27,319 nurses employed in 583 hospitals in four large states: California, Florida, New Jersey, and Pennsylvania. The hospitals in these large states account for close to a quarter of hospital discharges nationally. These hospital-level measures, together with measures of hospital size, teaching status, and technology derived from American Hospital Association Annual Survey data, will then be merged, separately, with infection data from two different sources; 1) patient-level data that combine information from the Medicare Provider and Analysis Review files (MEDPAR), the Medicare Outpatient Standard Analytic File, and the Medicare Carrier File (Provider Part B) and includes information essential for risk adjustment and enhanced measures of infections, and 2) hospital- level standardized infection ratios (SIR) using data from the Centers for Disease Control and Prevention (CDC) that are publicly reported on the Centers for Medicare and Medicaid Services (CMS) Hospital Compare website. Multilevel logit models and hierarchical linear models (for the patient level data) will be used to estimate the effects of the different nursing resource measures on the likelihoods of different groups of medical and surgical patients acquiring different infections, before and after adjusting for other hospital and patient characteristics. Ordinary least squares regression models (for the hospital level data) will be used to estimate these same nursing effects on the risk-adjusted standardized infection ratios, before and after controlling for other hospital characteristics. The large sample of representative (rather than volunteer) hospitals, the use of the Medicare Part B data to uncover infections in patients discharged to their home or a care facility that were not recognized during their hospital stay, and the use of unique data on work environments and other nursing characteristics will make it possible to capture significant effects of nursing which prior investigations have been unable to explore. The use of multiple data sources and multiple measures of infections, and the use of patient-level data which brings risk-adjustment under the investigators control, will also allow for the triangulation of results and identification of actionable recommendations with promise for reducing HAIs.

项目摘要 医疗保健相关感染（HAI）仍然很常见，危及患者生命，增加医疗保健成本， 导致了耐药性感染的主要公共卫生问题。临床干预措施 在受控条件下有效预防HAI，但在常规护理条件下无效。 感染预防干预措施并没有集中在改善医院范围内的护士工作环境， 由医学研究所推荐。本研究的主要目的是确定护理是否 资源，如工作环境和护理的其他可修改的功能，包括护士人员配置，技能 混合、劳动力稳定性和教育与HAI相关，目标是确定有前途的医院 一级的战略，以促进减少感染。护理的这些可修改的特征的措施将被导出 通过将2015-2016年从27，319名护士中收集的独特调查数据汇总到医院层面， 在四个大州的583家医院：加州、佛罗里达、新泽西和宾夕法尼亚。这些医院 大州占全国出院人数的近四分之一。这些医院层面的措施， 以及美国医院的医院规模、教学状况和技术等指标 协会年度调查数据，然后将合并，分别与感染数据，从两个不同的， 来源; 1）结合来自Medicare提供者和分析审查文件的联合收割机信息的患者级数据 （MEDPAR）、Medicare门诊患者标准分析文件和Medicare运营商文件（提供商部分B） 包括风险调整和加强感染措施所必需的信息，以及2）医院- 使用疾病控制和预防中心（CDC）的数据水平标准化感染率（SIR） 医疗保险和医疗补助服务中心（CMS）医院比较 网站将使用多水平logit模型和分层线性模型（用于患者水平数据）， 评估不同的护理资源措施对不同医疗群体的可能性的影响 手术患者获得不同的感染，调整前和调整后的其他医院和病人 特色将使用普通最小二乘回归模型（用于医院级数据）估计 这些相同的护理效果对风险调整的标准化感染率，在控制之前和之后， 其他医院特色大样本的代表性（而不是志愿者）医院，使用 医疗保险B部分数据，以揭示出院到家中或护理机构的患者的感染， 在住院期间未被识别，以及使用有关工作环境和其他护理的独特数据 这些特征将使我们有可能捕捉到护理的显著效果，而这些效果是先前的研究所发现的。 无法探索。使用多个数据源和多种感染措施，以及使用 在研究者控制下进行风险调整的患者水平数据也将允许 对结果进行三角测量，并确定有希望减少HAI的可行建议。

项目成果

Scalable and sustainable approaches to address the well-being of healthcare personnel.

可扩展且可持续的方法来解决医护人员的福祉。

• DOI: 10.1111/jan.15505
• 发表时间: 2023
• 期刊: Journal of advanced nursing
• 影响因子: 3.8
• 作者: Giordano,NicholasA;Swan,BethAnn;Johnson2nd,TheodoreM;Cimiotti,JeannieP;Muirhead,Lisa;Wallace,Michelle;Mascaro,JenniferS
• 通讯作者: Mascaro,JenniferS

Medicare meets the cloud: the development of a secure platform for the storage and analysis of claims data.

医疗保险与云的结合：开发用于存储和分析索赔数据的安全平台。

• DOI: 10.1093/jamiaopen/ooae007
• 发表时间: 2024
• 期刊: JAMIA open
• 影响因子: 2.1
• 作者: Simpson,RoyL;Lee,JosephA;Li,Yin;Kang,YuJin;Tsui,Circe;Cimiotti,JeannieP
• 通讯作者: Cimiotti,JeannieP