NLM's Personal Health Record and Consumer Service R&D

NLM 的个人健康记录和消费者服务 R

• 批准号: 7969229
• 负责人: Clement McDonald
• 金额: $ 129.6万
• 依托单位: NATIONAL LIBRARY OF MEDICINE
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/7969229
• 关键词: Access to Information; Acute; Adopted; Advisory Committees; Attention; Beds; Blood Pressure; Body mass index; Caring; Centers for Disease Control and Prevention (U.S.); Child; Clinical; Clinical Data; Code; Collaborations; Colonoscopy; Computer software; Computerized Medical Record; Computers; Country; Cues; Data; Data Collection; Data Sources; Databases; Development; Diagnosis; Equation; Family Caregiver; Family member; Fathers; Genetic; Glucose; Goals; Government; Health; Health Status; Healthcare; Height; Home environment; Hospitals; Human; Hypersensitivity; Immunization; Individual; Industry; Informatics; Internet; Intervention; Java; Laboratories; Link; Logic; Logical Observation Identifiers Names and Codes; Mammography; Measurement; Medical; Medical Informatics Applications; MedlinePlus; Methods; Mothers; Names; Operative Surgical Procedures; Patients; Pattern; Personal Health Records; Persons; Pharmaceutical Preparations; Pregnancy Histories; Preventive; Preventive Health Services; Reaction Time; Recommendation; Records; Research; Research Project Grants; Serum; Services; Solutions; Source; Specific qualifier value; Strategic Planning; Structure; Surveys; Symptoms; Systematized Nomenclature of Medicine; Techniques; Testing; Translating; Trust; United States Centers for Medicare and Medicaid Services; United States Indian Health Service; United States National Library of Medicine; Vaccines; Visual; Vocabulary; Weight; Work; Writing; base; data structure; design; fly; improved; interest; male; open source; patient oriented; research and development; suburb; tool; usability; web site; Access to Information; Acute; Adopted; Advisory Committees; Attention; Beds; Blood Pressure; Body mass index; Caring; Centers for Disease Control and Prevention (U.S.); Child; Clinical; Clinical Data; Code; Collaborations; Colonoscopy; Computer software; Computerized Medical Record; Computers; Country; Cues; Data; Data Collection; Data Sources; Databases; Development; Diagnosis; Equation; Family Caregiver; Family member; Fathers; Genetic; Glucose; Goals; Government; Health; Health Status; Healthcare; Height; Home environment; Hospitals; Human; Hypersensitivity; Immunization; Individual; Industry; Informatics; Internet; Intervention; Java; Laboratories; Link; Logic; Logical Observation Identifiers Names and Codes; Mammography; Measurement; Medical; Medical Informatics Applications; MedlinePlus; Methods; Mothers; Names; Operative Surgical Procedures; Patients; Pattern; Personal Health Records; Persons; Pharmaceutical Preparations; Pregnancy Histories; Preventive; Preventive Health Services; Reaction Time; Recommendation; Records; Research; Research Project Grants; Serum; Services; Solutions; Source; Specific qualifier value; Strategic Planning; Structure; Surveys; Symptoms; Systematized Nomenclature of Medicine; Techniques; Testing; Translating; Trust; United States Centers for Medicare and Medicaid Services; United States Indian Health Service; United States National Library of Medicine; Vaccines; Visual; Vocabulary; Weight; Work; Writing; base; data structure; design; fly; improved; interest; male; open source; patient oriented; research and development; suburb; tool; usability; web site

Personal health records (PHRs) are electronic medical records whose contents are controlled and managed by the consumers whose data they carry. They have attracted much press attention in recent years, and both industry and governments in many countries see them as potential solutions to many IT problems in health care. The National Library of Medicine (NLM) has embarked on the development and deployment of a Personal health record (PHR) in order to study and improve their utility, reduce the barriers to their use, identify best practices, and provide a platform and test bed for advanced applications. The development is based on a set of existing messages and vocabulary standards that are supported by the Health and Human Services and NLM and on both existing (Ruby on Rails, Scriptaculous, and Dojo) and NLM-developed open source software building blocks, some of which will have broad medical informatics applications. NLM's PHR is a pure web application. It is based on a forms generator (developed by our group) that produces web input forms on the fly. These forms include built-in skip logic (e.g. if the person is male, it does not ask about pregnancy history), edit checks and auto complete input. It uses AJAX techniques, so the response time is very fast. The NLM PHR provides tools for managing clinical information from many different family members, so a mother can maintain immunization records for herself and each of her children, and/or keep track of her ailing father's medications. It provides places for recording medications, medical problems, surgeries, immunizations (vaccines), important measurements such as blood pressure and laboratory results (e.g. serum glucose). It is designed to encode the names of the major facts -- e.g. drugs, problems, surgeries, immunizations -- it carries. The encoding translates names into federally supported coding systems, e.g. Rx.terms (a Subset of Rx.Norm that was developed for this project and adopted by The Centers for Medicare and Medicaid Services (CMS) for their post-acute care project) for drugs, Logical Observation Identifiers Names and Codes (LOINC) for measurements, the Centers for Disease Control's (CDC) vaccine codes for immunizations, Systematized Nomenclature of Medicine (SNOMED) for diagnoses. It adopts Health Level 7 (HL7) for much of its data structure and all of its data types. Both of these capabilities facilitate the importation of clinical data from widely available HL7 messages. The orientation toward automatic coding enables NLM's PHR to provide two special capabilities: 1) decision support rules and 2) one-click access to information about concepts -- e.g. symptoms, medications and immunizations -- recorded in the record. Decision support in the NLM PHR is based on predefined rules that tie patient conditions to care recommendations. The NLM PHR compares the patient's data against rules for preventive care and reminds the individual about interventions -- such as mammograms or colonoscopy -- which are due. A button appears next to every coded concept on the screen. These buttons provide one-click access to consumer-tailored information about the concept. For medical problems and drugs the information source is NLM's MedlinePlus, for immunizations the CDC's vaccine information, for preventive care reminders it is US Preventive Task Force web site, etc. When you click on a button next to a concept, the computer immediately pops up 2-4 pages of information. Links to trusted sources -- including MedlinePlus, Genetics Home Reference, the CDC, and the US Preventive Health Service Task Force -- populate the decision support reminders and the consumer-tailored information. We envision that the NLM's PHR will be used by individuals and family caregivers. This is a very young project to produce something that has been a longstanding NLM interest and is part of NLM's strategic plan. It will use and tune the message and vocabulary standards that NLM has supported and also provide another consumer entry point to NLM's rich trove of patient-oriented data. Early research projects will be focused on users' needs, usability, and usage patterns to guide the next round of development and research. During FY 2009, we have made progress on many fronts. New fetch rules methods generate reminders more clearly and efficiently, and define equations that generate calculated variables, such as body mass index from entered height and weight, or scores in survey instruments such as the PHQ-9. Inserting a thin Java Script database between the PHR form and server enables easy construction of these fetch rules, decouples the rule logic from any specific data source, and enables fast and simple backups of user-entered data to the server at specified intervals. Clearly distinguishing previously-entered data from new data with visual cues and menu selection options helps the user avoid accidentally over-writing old data. In addition to storing standard content in the PHR form, such as problems, medications, allergies and preventive care tests, the PHR form no allows the user to add panels for and record arbitrary laboratory test panels, survey instruments, or special research data collections in the form of structured data. The PHR problems list also links to ClinicalTrials.gov. We also improved the help, info buttons and error messages. We are also working toward collaborations to implement the NLM PHR software in two different user settings: Bethesdas Suburban Hospital and the Indian Health Service.

个人健康记录（PHR）是电子医疗记录，其内容受其数据携带的消费者控制和管理。 近年来，他们引起了很多新闻界的关注，许多国家的行业和政府都将它们视为解决医疗保健中许多IT问题的潜在解决方案。 国家医学图书馆（NLM）已着手开发和部署个人健康记录（PHR），以研究和改善其实用性，减少其使用的障碍，确定最佳实践，并为高级应用程序提供平台和测试床。该开发是基于一组现有的消息和词汇标准，这些消息和词汇标准得到了健康和公共服务以及NLM的支持以及现有的（Ruby on Rails，Scriptauly和Dojo）以及NLM开发的开源软件构建块，其中一些将具有广泛的医学信息学应用程序。 NLM的PHR是一个纯净的Web应用程序。它基于一种形式的生成器（由我们的小组开发），该生成器即时生成Web输入表单。这些表格包括内置的跳过逻辑（例如，如果该人是男性，则不询问怀孕历史），编辑检查和自动完成输入。它使用AJAX技术，因此响应时间非常快。 NLM PHR提供了用于管理许多不同家庭成员的临床信息的工具，因此母亲可以为自己和她的每个孩子维持免疫记录，并//或跟踪她生病的父亲的药物。 它为记录药物，医疗问题，手术，免疫接种（疫苗），重要测量（例如血压和实验室结果（例如血清葡萄糖））提供了场所。 它旨在编码主要事实的名称 - 例如药物，问题，手术，免疫接种 - 它具有。编码将名称转换为联邦支持的编码系统，例如RX.TERMS（为该项目开发的RX.NORM子集，并由医疗保险和医疗补助中心（CMS）用于药物，用于药物后的急性护理项目），逻辑观察标识符的名称和代码（LOIINC）用于测量，疾病控制中心（CDC）的疫苗（CDC）疫苗代码，以进行免疫原化的诊断（临床）诊断（SNOMENAT INDEMIND）。 它采用健康级别7（HL7）的大部分数据结构及其所有数据类型。 这两种功能都有助于从广泛可用的HL7消息中进口临床数据。 自动编码的方向使NLM的PHR能够提供两个特殊功能：1）决策支持规则和2）一键式访问有关概念的信息 - 例如症状，药物和免疫接种 - 记录在记录中。 NLM PHR中的决策支持是基于将患者条件与护理建议联系起来的预定义规则。 NLM PHR将患者的数据与预防保健规则进行比较，并提醒个人有关的干预措施（例如乳房X线照片或结肠镜检查）。 一个按钮显示在屏幕上的每个编码概念旁边。这些按钮可单击访问有关该概念的消费者守则信息。对于医疗问题和药物，信息来源是NLM的MEDLINEPLUS，用于免疫疾病预防疾护症中的疫苗信息，为了预防性护理提醒，它是我们的预防工作组网站等。 链接到可信赖的来源 - 包括Medlineplus，遗传学家用参考，CDC和美国预防卫生服务工作组 - 填充决策支持提醒和消费者泰式信息。 我们设想个人和家庭护理人员将使用NLM的PHR。 这是一个非常年轻的项目，旨在产生长期以来一直具有NLM兴趣的东西，并且是NLM战略计划的一部分。 它将使用并调整NLM支持的消息和词汇标准，并为NLM丰富的以患者为导向的数据提供另一个消费者的入口点。 早期的研究项目将集中在用户的需求，可用性和使用模式上，以指导下一轮开发和研究。 在2009财年期间，我们在许多方面取得了进步。 新的提取规则方法会更清晰有效地产生提醒，并定义产生计算变量的方程式，例如来自输入的身高和体重的体重指数，或者在调查工具中的分数（例如PHQ-9）。在PHR表单和服务器之间插入一个薄的Java脚本数据库，可以轻松构建这些获取规则，将规则逻辑与任何特定数据源分解，并以指定的间隔启用对用户输入数据的快速而简单的备份。清楚地将先前输入的数据与具有视觉提示和菜单选择选项的新数据区分开来，有助于用户避免意外地过度编写旧数据。除了以PHR形式存储标准内容（例如问题，药物，过敏和预防性护理测试），PHR形式NO还允许用户以结构化数据的形式添加面板并记录任意实验室测试板，调查工具或特殊研究数据收集。 PHR问题列表还链接到ClinicalTrials.gov。 我们还改进了帮助，信息按钮和错误消息。 我们还努力在两个不同的用户设置中实施NLM PHR软件：贝塞斯达斯郊区医院和印度卫生服务部。