Enhancing information retrieval in electronic health records through collaborative filtering

通过协作过滤增强电子健康记录中的信息检索

• 批准号: 9922983
• 负责人: XIA NING
• 金额: $ 54.16万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-01 至 2023-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9922983
• 关键词: Accident and Emergency department; Address; Admission activity; Adult; Algorithms; Attitude; Books; Chest Pain; Clinical; Cluster randomized trial; Cognitive; Computerized Patient Records; Data; Development; Electronic Health Record; Emergency Department Physician; Evolution; Fast Healthcare Interoperability Resources; Feedback; Focus Groups; Goals; Health; Healthcare; Home environment; Indiana; Information Retrieval; Intervention; Length of Stay; Manuals; Measures; Methods; Nature; Online Systems; Outcome; Patient Care; Patient Rights; Patients; Pattern; Perception; Physicians; Procedures; Process; Process Measure; Randomized; Recommendation; Scheme; Source; Suggestion; Testing; Time; Visit; Walking; Work; base; clinical practice; design; experience; experimental study; high risk; improved; innovation; interest; trial design; usability; user centered design; virtual; web site

Project Abstract When we consider buying a book on Amazon's Website, we often benefit from items listed in a section called "Customers also viewed." These recommendations, generated by a method called collaborative filtering (CF), suggest items of possible interest based on what other customers have viewed and purchased. However, when clinicians search the electronic health record (EHR) with regard to a particular patient problem, the EHR does not make suggestions for potentially useful information. Instead, it requires clinicians to go through the same manual, cumbersome and laborious process of searching for and retrieving information for similar patients/problems every single time. This limitation is magnified in high-risk situations, such as managing chest pain in the emergency department (ED). The goal of this project is to implement and evaluate CF as a method to improve information retrieval from EHRs and reduce cognitive overload. The central hypothesis of our proposal is that CF will (1) help clinicians retrieve and review the right patient information more efficiently and effectively than current methods; and (2) score higher on usefulness and ease-of-use than current EHRs. We will implement our CF algorithms in CareWeb Plus, a SMART-on-FHIR app we are currently building to integrate relevant information from the Indiana Network for Patient Care (INPC), Indiana's major health information exchange, with the ED workflow in Cerner/Epic. Our aims are to (1) extend CareWeb Plus to support collaborative filtering; (2) design and implement collaborative filtering algorithms; (3) and implement and evaluate CareWeb Plus in two adult emergency departments. Over 190 clinicians will use and evaluate CareWeb Plus in the two busiest emergency departments in Indianapolis (> 200,000 patient visits/year collectively), with more than 13,000 of them related to chest pain. We will evaluate (1) process measures, such as CareWeb Plus use, information retrieval and viewing patterns, and time to key decisions (first order, admission, discharge); (2) outcomes variables, such as lab/procedure utilization, ED length of stay and admission rate; and (3) user perceptions and attitudes regarding usefulness and usability. Our project is significant because it addresses two current, major limitations of EHRs in clinical practice. (1) Clinicians have difficulty reviewing voluminous patient-specific information, especially from multiple sources, efficiently to find relevant facts, especially in time-sensitive situations. (2) EHR users have little to no ability to change the static and inflexible nature of EHR interfaces for information retrieval. Our proposal is innovative because it uses CF, a method for tailoring information retrieval well-established in many fields except healthcare, to help solve these two problems. Collaborative filtering will provide a continually adapting, dynamic paradigm of informational retrieval and presentation that naturally follows the evolution of clinical practice. In addition, our recommendations are generated from both physicians and patients, and thus go beyond the traditional scheme of CF algorithms that only look at user or item relations to generate recommendations.

项目摘要 当我们考虑在亚马逊网站上购买一本书时，我们经常会从一个名为 ”顾客也看了。“这些推荐是由一种叫做协同过滤（CF）的方法产生的， 根据其他客户浏览和购买的内容建议可能感兴趣的项目。然而，在这方面， 当临床医生搜索关于特定患者问题的电子健康记录（EHR）时， 不会对可能有用的信息提出建议。相反，它要求临床医生通过 同样的手动，繁琐和费力的过程中搜索和检索信息，为类似的 患者/问题每一次。这种局限性在高风险的情况下被放大，例如管理胸部 急诊室疼痛（艾德）。这个项目的目标是实施和评估CF作为一种方法 提高从电子病历中检索信息的能力，减少认知超载。我们的核心假设是 CF将（1）帮助临床医生更有效地检索和审查正确的患者信息， 有效地比目前的方法;和（2）得分较高的有用性和易用性比目前的EHR。我们 将在CareWeb Plus中实施我们的CF算法，CareWeb Plus是我们目前正在构建的SMART on FHIR应用程序， 整合来自印第安纳州患者护理网络（INPC）、印第安纳州的主要健康 通过Cerner/Epic中的艾德工作流程进行信息交换。我们的目标是（1）将CareWeb Plus扩展到 支持协同过滤;（2）设计和实现协同过滤算法;（3）实现 并在两个成人急诊科评估CareWeb Plus。超过190名临床医生将使用和评估 CareWeb Plus在印第安纳波利斯两个最繁忙的急诊科（> 200，000患者就诊/年 总的来说，其中超过13，000例与胸痛有关。我们将评估（1）过程措施，例如 作为CareWebPlus的使用，信息检索和查看模式，以及关键决策的时间（第一顺序， 入院、出院）;（2）结局变量，如实验室/程序利用率、艾德住院时间和 接纳率;（3）用户对有用性和可用性的看法和态度。我们的项目是 重要的是，它解决了目前EHR在临床实践中的两个主要局限性。(1)临床医生 难以有效审查大量患者特定信息，尤其是来自多个来源的信息， 相关的事实，特别是在时间敏感的情况下。(2)EHR用户几乎没有能力改变静态 以及用于信息检索的EHR接口的不灵活性。我们的建议是创新的，因为它使用CF， 一种在除医疗保健以外的许多领域建立的定制信息检索方法， 这两个问题。协同过滤将提供一个不断适应的，动态的范例， 信息检索和呈现，自然遵循临床实践的演变。另外我们 医生和患者都可以提出建议，因此超越了传统的方案 CF算法只考虑用户或项目关系来生成推荐。