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

项目摘要 由于在病人护理过渡期间用药错误的风险增加， EHR药物核对功能，具有显著提高质量和患者安全的潜力， 降低医疗成本。然而，尽管联邦政策推动了EHR药物协调， 住院依从性落后的一个主要原因是医生的熟练程度低，部分原因是 缺乏关于由哪个医生负责管理患者药物清单的专业共识 以及药物协调作为提高护理质量的临床工具的价值。 GR Health最近在其整个卫生系统中推出了经过认证的EHR技术。像许多 在医院，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，在出院时无法完成EHR药物核对，导致 沮丧的患者，药物清单不完整，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系统， GR Health将EHR药物核对技术从"有限使用"发展到"有意义的使用"。 第1步（开发阶段）将增强现有SKN系统（Microsoft-Yammer）的能力， 促进EHR药物协调;并将该工具集成到GR Health的EHR工作流程中。步骤2 （试点阶段）将在门诊和住院环境中进行SKN系统的试点/可行性测试， GR健康。具体目标是检查：1）通过SKN用户调查和 SKN使用分析;以及2）"SKN使用"和"EHR有意义使用"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），以促进隐性知识的交流（实践- 基于知识）的兴趣问题。隐性知识的交流反过来，有望增加 实践者参与解决问题，促进学习，并实现实践变革（即，EHR 有意义的使用）。试点研究将包括6名SKN主持人（高级管理员和MD冠军）， 60名来自门诊和住院医疗服务的SKN用户（从业人员）。假设是主动的， SKN主持人定期"自上而下"努力促进EHR药物协调， SKN用户"自下而上"地交流隐性知识，以实现参与、学习和有意义的使用。 该项目对《行动纲要》作出了高度反应，因为它试图测试一项保健信息技术使用战略， 实施和设计，这反过来又与AHRQ的五个卫生IT研究重点中的三个相一致。如果 假设成立，可以鼓励联邦EHR供应商将SKN功能纳入EHR 系统.该研究将利用一系列定量、定性和社会网络分析技术， 检查SKN用户参与度、SKN可用性、SKN使用分析和EHR有意义的使用。

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.