Measuring and improving data quality for clinical quality measure reliability
测量和提高临床质量测量可靠性的数据质量
基本信息
- 批准号:9428949
- 负责人:
- 金额:$ 14.27万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2017
- 资助国家:美国
- 起止时间:2017-09-15 至 2020-08-31
- 项目状态:已结题
- 来源:
- 关键词:AdministratorAffectAreaAwardBehaviorCardiologyCaringClinicalClinical DataClinical ResearchClinical assessmentsDataData QualityData SourcesDocumentationElectronic Health RecordEnsureEnvironmentError SourcesEvaluationFirst Independent Research Support and Transition AwardsFoundationsFunding MechanismsGoalsGoldHealth SciencesHealthcareHeart failureInstitutionInsurance CarriersInterventionInterviewK-Series Research Career ProgramsKnowledgeLeadLogicManualsMeasurementMeasuresMedical InformaticsMentorsMethodologyMethodsOregonPatient CarePatient Self-ReportPatientsPerformancePlanning TheoryPolicy MakerProcessProviderQuality of CareRecordsReportingResearchResearch PersonnelResearch Project GrantsResourcesSamplingScienceStructureSurveysTimeTrainingTreesTrustUnited States National Library of MedicineUniversitiesUpdateWorkbasebiomedical informaticscareercareer developmentclinical careclinical epidemiologyclinical predictorscomputerizeddata integrationelectronic structureexperiencehealth care deliveryhealth care qualityimprovedmemberpoint of careprofessortool
项目摘要
Project Summary/Abstract
Research Project: Reliable electronic health record (EHR)-based clinical quality measures (CQMs) are
necessary for the assessment and measurement of healthcare quality and delivery. They are also a vital tool
for evaluating the impact of interventions meant to improve care. Unfortunately, stakeholders have limited
assurance of CQM reliability, due in part to problems with data quality. In order to ensure the reliability and, by
extension, the usefulness of automated EHR-based CQMs, I propose the following: 1) generate a better
understanding of the causal relationships between EHR data quality and CQM reliability; 2) provide
stakeholders with methods for the assessment of CQM reliability, based upon underlying data quality; and 3)
identify provider-approved interventions for improving EHR data quality in order to improve CQM reliability.
Career Goals: I am seeking a National Library of Medicine K01 Career Development Award in Biomedical
Informatics for two primary reasons. First, this award would provide me with an opportunity to become an
independent researcher through the protected time the award ensures and allow me to develop my expertise in
key subject matter and methodological areas. Second, through the proposed research I will provide
stakeholders with a deeper understanding of CQM reliability, methods to measure CQM reliability, and
approaches for improving CQM reliability through interventions at the point of care, enabling the improvement
of clinical care itself. My long-term goal is to become an expert in mixed-methods approaches for measuring
and improving the quality of EHR data, thereby ensuring reliable and valid reuse of these data to support
patient care, evaluation of patient care, and clinical research.
Career Development and Training: I am an assistant professor with the Department of Medical Informatics &
Clinical Epidemiology at Oregon Health & Science University. Through my department and mentors I have a
unique level of access to EHR data, an existing CQM calculation engine, and various clinical environments.
The career-development goal of this award period is to use these resources and the expertise of my mentors
to solidify my knowledge and methodological abilities in three primary areas: 1) EHR data quality assessment
and improvement, 2) mixed-methods approaches, and 3) healthcare delivery science. The projects, mentors,
and consultants included in this proposal were chosen to provide me with the experience, knowledge, and
expertise that will prepare me for a career as an independent researcher specializing in these areas and give
me the foundation upon which to apply for a traditional funding mechanism. Specifically, the aims of this
proposal have been developed to lead directly to EHR-based and documentation-level interventions intended
to improve EHR data quality, CQM reliability, and the quality of clinical care overall, which will be the subject of
an application building upon the work described here.
项目总结/摘要
研究项目:基于可靠电子健康记录(EHR)的临床质量测量(CQM)
评估和衡量医疗保健质量和提供所必需的。它们也是一个重要的工具,
用于评估旨在改善护理的干预措施的影响。不幸的是,
CQM可靠性的保证,部分原因是数据质量问题。为了确保可靠性,
扩展,实用性的自动EHR为基础的CQM,我提出以下建议:1)生成一个更好的
理解EHR数据质量和CQM可靠性之间的因果关系; 2)提供
利益相关者的方法,用于评估CQM的可靠性,基础上的数据质量;和3)
确定提供商批准的干预措施,以提高EHR数据质量,从而提高CQM的可靠性。
职业目标:我正在寻求国家医学图书馆K 01生物医学职业发展奖
信息学有两个主要原因。首先,这个奖项将为我提供一个机会,
独立研究员通过受保护的时间,该奖项确保,并允许我发展我的专业知识,
关键主题和方法领域。第二,通过研究建议,我将提供
对CQM可靠性有更深入了解的利益相关者,衡量CQM可靠性的方法,以及
通过在护理点进行干预来提高CQM可靠性的方法,
临床护理本身。我的长期目标是成为混合方法测量方法的专家
提高EHR数据的质量,从而确保这些数据的可靠和有效重用,以支持
病人护理、病人护理评估和临床研究。
职业发展和培训:我是医学信息学系的助理教授,
俄勒冈州健康与科学大学的临床流行病学。通过我的部门和导师,
对EHR数据的独特访问级别、现有的CQM计算引擎和各种临床环境。
这个奖励期的职业发展目标是利用这些资源和我的导师的专业知识
巩固我在三个主要领域的知识和方法能力:1)EHR数据质量评估
和改进,2)混合方法的方法,和3)医疗保健提供科学。项目,导师,
和顾问包括在这个建议被选择为我提供经验,知识,
专业知识,将准备我的职业生涯作为一个独立的研究人员专门在这些领域,并给予
我的基础上,申请一个传统的资金机制。具体而言,其目的是
已经提出了一项建议,旨在直接导致以电子健康档案为基础的文件级干预措施
提高EHR数据质量、CQM可靠性和临床护理整体质量,这将是
一个建立在这里描述的工作基础上的应用程序。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Nicole Gray Weiskopf其他文献
Nicole Gray Weiskopf的其他文献
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{{ truncateString('Nicole Gray Weiskopf', 18)}}的其他基金
Health equity and the impacts of EHR data bias associated with social determinants
健康公平以及与社会决定因素相关的电子病历数据偏差的影响
- 批准号:
10584190 - 财政年份:2023
- 资助金额:
$ 14.27万 - 项目类别:
Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data
识别和理解临床 COVID-19 数据中选择偏差和信息偏差的驱动因素
- 批准号:
10192372 - 财政年份:2021
- 资助金额:
$ 14.27万 - 项目类别:
Identifying and understanding drivers of selection bias and information bias in clinical COVID-19 data
识别和理解临床 COVID-19 数据中选择偏差和信息偏差的驱动因素
- 批准号:
10380032 - 财政年份:2021
- 资助金额:
$ 14.27万 - 项目类别:
Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency
运用机器学习和离散事件模拟模型来提高诊所效率
- 批准号:
10460170 - 财政年份:2020
- 资助金额:
$ 14.27万 - 项目类别:
Operationalizing Machine Learning and Discrete Event Simulation Models to Improve Clinic Efficiency
运用机器学习和离散事件模拟模型来提高诊所效率
- 批准号:
10664923 - 财政年份:2020
- 资助金额:
$ 14.27万 - 项目类别:
Measuring and improving data quality for clinical quality measure reliability
测量和提高临床质量测量可靠性的数据质量
- 批准号:
9761576 - 财政年份:2017
- 资助金额:
$ 14.27万 - 项目类别:
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