Hospital Efficiency Changes from Health Information Exchange Participation

描述（由申请人提供）：背景：由于人口老龄化，慢性病患病率高，以及最近通过《平价医疗法案》扩大了保险范围，美国医院对医疗保健的需求正在增加。越来越困难的财务环境使医院满足这一需求的能力变得复杂。许多政策制定者和实践者认为，在整个医疗保健系统中交换临床数据是帮助医院满足这一需求的关键一步，因为它能够提高医院的效率。然而，现有的研究未能评估效率 随着时间的推移，医院参与健康信息交换（HIE）的变化。该项目通过评估医院参与HIE的效率随时间的变化来解决这一差距。该提案是对特别强调通知（NOT-HS-13-011）的回应，该通知要求将R36赠款集中在卫生信息技术上，并特别解决了对研究成果领域的影响。方法学：这项研究使用了一个国家的样本，所有急性护理，非联邦医院在市场上至少有一家医院积极参与HIE。数据来源包括2009-2012年美国医院协会（AHA）年度调查和信息技术（IT）补充，以及2009-2012年医疗保险和医疗补助服务中心（CMS）公共数据文件。将使用Malmquist算法（一种数据包络分析）来衡量效率变化。Malmquist模型给出了三个衡量效率变化的指标：全要素生产率（TFP）、技术变化（TC）和技术效率变化（TEC）。将这三个指数的几何平均值分为五分位数，并用作有序逻辑回归方程的因变量。感兴趣的独立变量将包括医院是否曾经参加过HIE，累计参与年数，以及交换伙伴的组织联系。将使用逆概率权重校正选择偏倚。将使用倾向评分调整校正HIE参与的内变量。初步分析确定了1,303家符合纳入标准的医院。影响：本研究的结果可以帮助政策制定者，因为他们试图评估的影响，在高收入企业的公共投资。医院管理者寻求证明他们的私人HIE投资决策也可以从这项研究中受益。使用Malmquist算法的三个指标也有助于医院识别产生效率变化的具体机制。最后，这项研究的结果将有助于研究人员继续评估新的健康信息技术的影响。

DESCRIPTION (provided by applicant): Background: Demand for healthcare from hospitals in the United States is increasing due to an aging population with a high prevalence of chronic conditions, and the recent expansion of insurance coverage through the Affordable Care Act. Complicating the ability for hospitals to meet this demand is an increasingly difficult financial environment. Many policy makers and practitioners argue that the exchange of clinical data across the health care system is a key step in helping hospitals meet this demand due to its ability to improve hospital efficiency. However, existing research fails to evaluate the efficiency changes over time from hospital participation in health information exchanges (HIEs). This project addresses this gap by evaluating the efficiency changes over time from hospital participation in HIE. This proposal is in response to the special emphasis notice (NOT-HS-13-011) that calls for R36 grants focusing on health information technology, and specifically addresses the impact on outcomes area of research. Methodology: This research uses a national sample of all acute-care, non-federal hospitals in a market with at least one hospital actively participating in an HIE. Data sources include the 2009-2012 American Hospital Association (AHA) annual surveys and information technology (IT) supplements, and the 2009-2012 Center for Medicare and Medicaid Services (CMS) public data files. Efficiency change will be measured using the Malmquist algorithm, a type of Data Envelopment Analysis. The Malmquist yields three measures of efficiency change: total factor productivity (TFP), technological change (TC), and technical efficiency change (TEC). The geometric means of each of these three indices will be divided into quintiles and used as the dependent variable in ordinal logistic regression equations. Independent variables of interest will include whether the hospital has ever participated in HIE, cumulative years participation, and organizational affiliation of exchange partners. Selection bias will be corrected for using inverse probability weights. Endogeneity of HIE participation will be corrected for using propensity score adjustments. Preliminary analysis identified 1,303 hospitals that fit the inclusion criteria. Implications: The results of this study can assist policymakers as they attempt to evaluate the impact of the public investment in HIEs. Hospital administrators seeking to justify their private HIE investment decisions can also benefit from this study. The use of the three indices from the Malmquist algorithm also helps hospitals identify the specific mechanism that produce efficiency changes. Finally, the results of this study will be useful to researchers as they continue to evaluate the impact of new health information technologies.