A Cognitive Approach to Refine and Enhance Use of a Dental Diagnostic Terminology

描述(申请人提供)：电子健康记录(EHR)的出现为改善国民健康提供了前所未有的临床研究机会。牙科在利用EHR的力量方面处于独特的地位，因为几乎所有美国牙科学校都使用相同的EHR，axiUm。20所使用axiUm的牙科学校已经成立了口腔健康相关信息学联盟(COHRI)，并同意将他们的数据存放在一个共同的注册表中。该注册中心将成为有史以来最大的口腔健康研究数据库。这些数据的一致整合的一个重要障碍是缺乏统一接受的牙科诊断术语。COHRI学校已经同意了一个标准化的诊断术语，EZ代码，它包含并扩展了现有的诊断术语。然而，为了有用，这些术语必须一致和正确地输入到《电子健康记录》中。我们的初步研究表明，目前情况并非如此，axiUm中的诊断术语输入过程效率低下，容易出错。目的：该建议的目的是提高诊断术语的利用率，降低术语输入的错误率，提高提供者对输入过程的满意度，并增加采用axiUm牙科诊断模块的COHRI学校的数量。具体目标：(1)我们将确定在电子病历工作流程中输入诊断的认知和功能障碍以及与牙科诊断术语的接口。(2)我们会改进电子健康记录的工作流程和与诊断术语的接口，以及试点的诊断术语，以减少在电子健康记录中输入诊断的认知和功能障碍。(3)我们将传播和评估最终确定的诊断术语和EHR与诊断术语的接口的采用情况。设计和方法：由哈佛大学牙科医学院领导的四所COHRI学校将参与这项工作，两所测试学校和两所对照学校。在初步评估后，这两个测试学校将经历基于认知的迭代EHR和诊断术语细化过程，直到创建最终诊断模块。我们将在测试和控制学校实施该单元，并将评估改进对实现我们的目标的影响。至少，这项提案中的四所学校将采用该模块，每年有超过8万名患者进行超过25万次的就诊。最后，我们将向使用axiUm的所有50所学校传播诊断模块，使这些学校能够进行有效的基于诊断的评估。 与公共健康相关：20所牙科学校已同意将其电子健康记录(EHR)中的数据合并，这将产生有史以来最大的口腔健康研究数据库。这一努力的范围受到以下事实的限制：尽管牙科临床医生提供了正确的治疗，但他们没有一致或正确地将诊断数据输入到EHR中。在这项建议中，我们会利用人机互动的方法，提高牙医对将牙科诊断输入电子健康记录的满意度、增加输入诊断的次数、减少输入诊断时的错误，以及增加采用将牙科诊断输入其电子健康记录的牙科学校的数目。

DESCRIPTION (provided by applicant): The advent of the electronic health record (EHR) affords unprecedented clinical research opportunities to improve the nation's health. Dentistry is uniquely positioned to leverage the EHR's power because nearly all U.S. dental schools use the same EHR, axiUm. Twenty dental schools that use axiUm have formed the Consortium for Oral-Health- Related Informatics (COHRI) and have agreed to deposit their data in a common registry. This registry will become the largest ever oral health research database. An important impediment to the consistent integration of these data is the lack of uniformly accepted dental diagnosis terms. COHRI schools have agreed upon a standardized diagnosis terminology, EZcodes, which encompass and expand upon existing diagnosis terminologies. However, to be useful, these terms must be consistently and correctly entered into the EHR. Our preliminary studies show that this is not currently the case and that the diagnostic term entry process in axiUm is inefficient and error-conducive. OBJECTIVE: The goals of this proposal are to increase utilization of diagnosis terms, decrease error rates of term entry, increase provider satisfaction with the entry process, and increase the number of COHRI schools that adopt axiUm's dental diagnosis module. SPECIFIC AIMS: (1) We will identify cognitive and functional impediments to diagnosis entry in the EHR workflow and interface to the dental diagnostic terms. (2) We will make refinements to the EHR workflow and interface to the diagnostic terms, as well as the diagnostic terminology at pilot sites to reduce the cognitive and functional impediments to diagnosis entry in the EHR. (3) We will disseminate and evaluate the adoption of the finalized diagnostic terminology and EHR interface to the diagnostic terms. DESIGN AND METHODS: Headed by the Harvard School of Dental Medicine, four COHRI schools will participate in this effort, two test and two control schools. After an initial evaluation, the two test schools will undergo an iterative cognitively-based EHR and diagnostic term refinement process until a final diagnosis module is created. We will implement the module at the test and control schools and will evaluate the impact of the refinements in terms of achieving our objectives. At a minimum, the four schools within this proposal will adopt the module, where over 80,000 patients make over 250,000 visits per year. Finally, we will disseminate the diagnosis module to all 50 schools that use axiUm, empowering these schools to make efficient diagnosis-based evaluations. PUBLIC HEALTH RELEVANCE: Twenty dental schools have agreed to combine data from their electronic health records (EHRs), which will result in the largest oral health research database ever created. The scope of this effort is limited by the fact that, though they provide the correct treatment, dental clinicians do not enter diagnostic data into the EHR consistently or correctly. In this proposal, we will use human-computer interaction methods to increase dentist satisfaction with the dental diagnosis entry into the EHR, increase how often diagnoses are entered, decrease mistakes made while entering the diagnosis, and increase the number of dental schools that adopt entering dental diagnoses in their EHR.