Measuring the Value of Improving Access to Community Care

背景：及时提供医疗保健一直是退伍军人管理局的长期目标，现已重新 护理委员会强调的。为了改善获得护理的机会，退伍军人管理局实施了退伍军人选择计划 计划于2014年8月推出，该计划为符合条件的退伍军人提供接受社区护理的选择 供应商由退伍军人管理局支付。目前，关于这一选择的影响的科学证据存在很大差距 计划和其他退伍军人管理局社区关怀计划，特别是在计划拥有的学位方面 改善了获得护理的机会。更广泛地说，非退伍军人管理局的文献考察了更大的提供商选项的影响 关注使用率的变化，但没有评估改善患者选择的价值。这个 开发获取更多提供商选择的价值的措施在方法上具有挑战性 因为患者的偏好没有被直接观察到，而且价值包含了许多方面的访问 (例如，旅行距离、预约等待时间、提供者质量等)。为了解决这些证据差距，我们 建议开发和审查获取提供商选项价值的新措施，以 退伍军人使用最先进的计量经济学方法。更多科学证据帮助退伍军人事务部 退伍军人通过Choice计划和未来的退伍军人社区关怀计划进行的选择与 VHA 2018-2019财年运营计划。这项研究针对的是全美学习卫生保健系统 优先领域和高铁的接入和医疗保健系统改变了主要的优先领域。 目标：本研究的目标是：1)开发新的计量经济学方法应用于 从退伍军人的角度定量衡量更多接触提供者的价值和2) 考察当地和提供者特征在确定退伍军人价值时的相对重要性 改善了对供应商的访问。 方法：这项观察性研究将检查退伍军人管理局的管理数据和现有的公共数据 门诊部。在目标1中，我们将使用退伍军人管理局的数据来确定：1)符合退伍军人资格的退伍军人 2016年的CHOICE计划，2)退伍军人事务部和CHOICE门诊提供者和3)门诊服务的利用 通过退伍军人事务部的设施和选择计划。我们将分析退伍军人对提供者的显露偏好 使用计量经济学随机效用模型。这些模型假设患者选择产生 考虑到所有可用的选择，这是最大的好处。我们将经验地估计退伍军人对提供者的选择 使用嵌套多项Logit模型(NMNL)的随机效用框架。然后我们将使用参数 来自NMNL模型的估计和预测利润率，以计算更大提供商选项的价值 通过Choice计划。具体地说，计量经济模型将计算退伍军人的支付意愿 (WTP)，这代表理论上个人为更大的提供商支付的最大美元金额 选择。在目标2中，我们将把计量经济学分解方法应用于目标1中开发的模型，以评估 关键供应商和当地特征对决定价值的影响。值得注意的是，我们将利用 退伍军人管理局管理数据和公共使用数据之间的新颖数据链接，捕获了广泛的提供商集 特点。 关于下一步的声明：我们将开发一个为非研究人员设计的模拟工具，其中包括 评估一组特定提供者选项(即社区护理)对退伍军人的价值的研究结果 网络)。这一工具将使业务伙伴能够评估社区护理的充分性。 网络，并建立“假设”情景的业务案例。利益相关者将能够适应变化 通过模拟社区护理中提供者的假设加法和减法来确定条件 网络。此模拟功能将促进未来的分析，以确保社区护理网络包括HIGH 最符合退伍军人喜好的优质供应商。

Background: Providing timely access to health care has been a long-standing VA goal that has been re- emphasized by the Commission on Care. To improve access to care, VA implemented the Veterans Choice Program in August 2014, which provides eligible Veterans the option of receiving care from community providers paid for by VA. Currently, there is a substantial gap in scientific evidence on the effect of the Choice Program and other VA community care programs, particularly in regards to the degree the program has improved access to care. More generally, non-VA literature examining the effect of greater provider options has focused on changes in utilization, but has not assessed the value of improved patient choice. The development of measures that capture the value of greater provider options is methodologically challenging because patients’ preferences are not directly observed and value encompasses many dimensions of access (e.g., travel distance, appointment wait times, provider quality, etc.). To address these evidence gaps, we propose the development and examination of new measures capturing the value of provider options to Veterans using state-of-the art econometric methods. Greater scientific evidence to help VA provide enhanced choice for Veterans through the Choice Program and future VA community care programs is consistent with the VHA FY 2018-2019 Operational Plan. This study addresses the ORD-wide Learning Health Care System priority area and HSR&D’s Access and Health Care Systems Change major priority domains. Objectives: The objectives of this study are to: 1) develop new econometric method applications to quantitatively measure the value of greater access to providers from the perspective of Veterans and 2) examine the relative importance of local area and provider characteristics in determining Veterans’ value of having improved access to providers. Methods: This observational study will examine VA administrative data and existing public data characterizing outpatient providers. In Aim 1, we will use VA administrative data to identify: 1) Veterans eligible for the VA Choice Program in 2016, 2) VA and Choice outpatient providers and 3) utilization of outpatient services from VA facilities and through the Choice Program. We will analyze Veterans’ revealed preference for providers using econometric random utility models. These models assume patients select the provider that yields the greatest benefit, given all available options. We will empirically estimate Veterans’ choice of provider within the random utility framework using a nested multinomial logit model (NMNL). We will then use parameter estimates and predictive margins from the NMNL model to calculate the value of greater provider options through the Choice Program. Specifically, econometric models will calculate Veterans’ willingness to pay (WTP), which represents the maximum dollar amount an individual would theoretically pay for greater provider options. In Aim 2, we will apply econometric decomposition methods to models developed in Aim 1 to assess the influence of key provider and local area characteristics in determining value. Notably, we will leverage novel data linkages between VA administrative data and public use data capturing an extensive set of provider characteristics. Statement on Next Steps: We will develop a simulation tool designed for non-researchers that incorporates study results to estimate the value to Veterans of a specified set of provider options (i.e. a community care network). This tool will provide the ability for operational partners to assess the adequacy of community care networks and establish the business case of “what-if” scenarios. Stakeholders will be able to adapt to changing conditions through simulating the hypothetical addition and subtraction of providers within a community care network. This simulation feature will facilitate future analyses to ensure community care networks include high quality providers that best match Veterans’ preferences.