Using Social Knowledge Networking (SKN) Technology to Enable Meaningful Use of EHR Technology

项目总结 由于在病人护理过渡期间出现用药差错的风险增加， EHR药物调节功能，有可能显著提高质量和患者安全，以及 降低医疗成本。然而，尽管联邦政策推动了EHR药物调节， 住院依从性滞后有一个主要原因--医生参与度低--这在一定程度上源于 对于哪位医生负责管理患者的用药清单，缺乏专业共识 以及药物调节作为提高护理质量的临床工具的价值。 GR Health最近在其整个医疗系统中推出了经过认证的EHR技术。像许多人一样 医院，GR Health面临的一个关键挑战是，没有下原始订单的医生 患者的药物，在出院时拒绝完成EHR药物调节，导致 沮丧的患者，用药清单不完整，用药错误率高于预期。 该试点/可行性项目旨在实施社会知识网络(SKN)系统，以实现 GR Health将从EHR药物调节技术的“有限使用”发展到“有意义的使用”。 第一步(开发阶段)将增强现有的SKN系统(微软-Yammer的)能力，以 促进EHR药物对账；并将该工具集成到GR Health的EHR工作流程中。步骤2 (试验阶段)将在#年的门诊和住院设置中对SKN系统进行试点/可行性测试 GR Health。具体目标是通过SKN用户调查来检查：1)SKN系统的用户参与度 SKN使用分析；以及2)“SKN使用”和“EHR有意义的使用”之间的初步关联。 其基本原理是，SKN系统将汇集不同的从业者群体(医生， 护士、药剂师、IT专家和管理人员)，以促进默示知识的交流(实践- 基于知识)的兴趣问题。默会知识交流反过来，预计会增加 实践者在解决问题、促进学习和实现实践改变方面的参与(即，EHR 有意义的使用)。试点研究将包括6名SKN主持人(高级管理人员和MD冠军)和 来自门诊和住院医学服务的60名SKN用户(从业者)。假设是积极主动的， SKN主持人为促进EHR用药协调而进行的定期“自上而下”的努力将与 “自下而上”交换SKN用户提供的隐性知识，以支持参与、学习和有意义的使用。 该项目高度响应FOA，因为它寻求测试卫生信息技术使用的战略， 实施和设计，这反过来又与AHRQ的五个卫生信息技术研究优先事项中的三个保持一致。如果 假设成立，联邦电子病历供应商可以被鼓励将SKN功能整合到电子病历中 系统。该研究将利用一系列定量、定性和社交网络分析技术来 检查SKN用户参与度、SKN可用性、SKN使用情况分析和EHR有意义的使用情况。

PROJECT SUMMARY Owing to the heightened risk of medication errors during patient care transitions, the “meaningful use” of the EHR Medication Reconciliation functionality, has potential to significantly improve quality & patient safety, and reduce healthcare costs. Despite the federal policy impetus towards EHR Medication Reconciliation however, hospital adherence has lagged for one chief reason—low physician engagement—which in part, stems from lack of professional consensus about which physician is responsible for managing a patient's medication list and the value of medication reconciliation as a clinical tool for improving quality of care. GR Health has recently launched certified EHR technology throughout its health system. Like many hospitals, a key challenge faced by GR Health is that physicians who did not place the original order for a patient's medication, are resistant to completing EHR Medication Reconciliation at discharge, leading to frustrated patients with incomplete medication lists and higher-than-expected medication error rates. This pilot/feasibility project seeks to implement a Social Knowledge Networking (SKN) system to enable GR Health to progress from “limited use” of EHR Medication Reconciliation technology, to “meaningful use.” Step 1 (Development Phase) will be to enhance an existing SKN system (Microsoft-Yammer's) capability to promote EHR medication reconciliation; and integrate the tool into the EHR workflow at GR Health. Step 2 (Pilot Phase) will be to conduct a pilot/feasibility test of the SKN system in the outpatient & inpatient settings at GR Health. Specific aims are to examine: 1) user-engagement in the SKN system through SKN user surveys & SKN usage analytics; and 2) preliminary associations between “SKN use” and “EHR meaningful use.” The rationale is that an SKN system would bring together a diverse group of practitioners (physicians, nurses, pharmacists, IT specialists, and administrators), to facilitate the exchange of tacit knowledge (practice- based knowledge) on the problem of interest. Tacit knowledge exchange in turn, is expected to increase practitioners' engagement in solving the problem, promote learning, and enable practice change (i.e., EHR meaningful use). The pilot study would include 6 SKN Moderators (senior administrators & MD champions) and 60 SKN Users (practitioners) from the outpatient & inpatient medicine service. The hypothesis is that proactive, periodic “top-down” efforts of SKN Moderators to promote EHR Medication Reconciliation would synergize with “bottom-up” exchange of tacit knowledge by SKN Users, to enable engagement, learning, and meaningful use. The project is highly responsive to the FOA, since it seeks to test a strategy for health IT use, implementation, and design, which in turn is aligned with three of AHRQ's five health IT research priorities. If the hypothesis holds, federal EHR vendors could be encouraged to incorporate SKN features into EHR systems. The study will utilize a range of quantitative, qualitative, and social network analytic techniques to examine SKN user engagement, SKN usability, SKN usage analytics, and EHR meaningful use.