Learning Patterns of Collaboration to Optimize the Management of Care Providers
学习协作模式以优化护理提供者的管理
基本信息
- 批准号:9260987
- 负责人:
- 金额:$ 25.06万
- 依托单位:
- 依托单位国家:美国
- 项目类别:
- 财政年份:2015
- 资助国家:美国
- 起止时间:2015-05-01 至 2019-04-30
- 项目状态:已结题
- 来源:
- 关键词:AdoptionAlgorithmsBehaviorBig DataBlood PressureBody mass indexCaringCatheterizationCessation of lifeClinicalCodeCollaborationsCollectionCommunitiesCommunity HealthcareComplexDataDependencyDetectionDiagnosisDiagnosticDiseaseDisease modelElectronic Health RecordEmployeeEnvironmentEvaluationEventGoalsHealth Information SystemHealth PersonnelHealthcareHeart DiseasesHospitalsKnowledgeLeadLearningLengthLength of StayMedical Care TeamMedical ErrorsMethodologyMethodsMiningModelingNaturePathway AnalysisPatient CarePatient-Focused OutcomesPatientsPatternPatterns of CarePrincipal Component AnalysisProceduresProviderResearchResource AllocationSafetySeriesSocial NetworkSourceSurveysSystemTechniquesTimeWorkbasecollaborative environmentcostdata accessdata miningdemographicsdesigndiabetes managementeffective therapyhealth care qualityhealth information technologyimprovedlearning communitymembersocial
项目摘要
DESCRIPTION (provided by applicant): Health information systems (HIS), especially electronic health record (EHR) systems, can significantly improve efficiency and quality of healthcare because it enables the employees of Healthcare Organizations (HCOs) to coordinate and collaborate more effectively and on a large scale. At the same time, healthcare environments are composed of highly diverse and dynamic workflows and, as modern EHR systems increase in size and scope, they may exacerbate the complexity of HCOs. If organizational complexity is not managed appropriately, it could limit the benefits of EHR systems and significantly contribute to negative effects, such as longer waiting times for care, replication of diagnostics, and medical errors. We hypothesize that patterns of collaboration can assist in managing complexity and facilitating patient management and that such patterns can be discovered by data mining on the utilization of EHR systems. This work is timely because EHR adoption has grown significantly over the past several years and HCO employees are increasingly using such systems to document patient status and communicate with other providers. The quantity and detail of such data provides an opportunity for big data mining techniques to learn patterns of care that are not explicitly documented. We propose to develop methods to learn patterns of collaboration through the utilization of EHR systems to determine how the management of care providers can be optimized. This will be accomplished through three specific aims: (1) Discover effective teams of care providers tailored to specific types of disease through the analysis of utilization data. Based on such teams, HCO will be able to manage patients more efficiently by prepping team members in a more timely manner. (2) Learn dependencies between care providers, which will be critical for resource allocation and management of care teams. (3) Model disease-specific treatment workflows to assess which sequences of events lead to the most efficient and effective outcomes for patients. In doing so, this project aims to reduce the length of patients' hospital stay and, ultimately help patients (an HCOs) reduce costs.
描述(由申请人提供):健康信息系统(HIS),特别是电子健康记录(EHR)系统,可以显着提高医疗保健的效率和质量,因为它使医疗保健组织(HCO)的员工能够更有效地协调和协作,并在规模很大。与此同时,医疗保健环境由高度多样化和动态的工作流程组成,随着现代 EHR 系统规模和范围的增加,它们可能会加剧 HCO 的复杂性。如果组织复杂性管理不当,可能会限制 EHR 系统的优势,并显着产生负面影响,例如更长的护理等待时间、重复诊断和医疗错误。我们假设协作模式可以帮助管理复杂性并促进患者管理,并且可以通过对 EHR 系统的利用进行数据挖掘来发现这种模式。这项工作是及时的,因为 EHR 的采用在过去几年中显着增长,并且 HCO 员工越来越多地使用此类系统来记录患者状态并与其他提供者进行沟通。此类数据的数量和细节为大数据挖掘技术提供了学习未明确记录的护理模式的机会。我们建议开发方法,通过利用 EHR 系统来学习协作模式,以确定如何优化护理提供者的管理。这将通过三个具体目标来实现:(1) 通过分析利用数据,发现针对特定类型疾病量身定制的有效护理提供者团队。基于这样的团队,HCO 将能够通过更及时地为团队成员做好准备来更有效地管理患者。 (2)了解护理提供者之间的依赖关系,这对于护理团队的资源分配和管理至关重要。 (3) 对特定疾病的治疗工作流程进行建模,以评估哪些事件序列可为患者带来最有效的结果。在此过程中,该项目旨在缩短患者的住院时间,并最终帮助患者(HCO)降低费用。
项目成果
期刊论文数量(0)
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You Chen其他文献
Association of Cigarette Consumption and Body Mass Index in the Cardiovascular Risk Survey
心血管风险调查中香烟消费与体重指数的关联
- DOI:
- 发表时间:
2014 - 期刊:
- 影响因子:0
- 作者:
Zhen-Ye Gan;Zi-Xiang Yu;You Chen;Dong-Ze Li - 通讯作者:
Dong-Ze Li
You Chen的其他文献
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{{ truncateString('You Chen', 18)}}的其他基金
Machine learning drives translational research from drug interactions to pharmacogenetics
机器学习推动从药物相互作用到药物遗传学的转化研究
- 批准号:
10608598 - 财政年份:2023
- 资助金额:
$ 25.06万 - 项目类别:
Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient Outcomes
发现电子病历中的护理协调实践模式:解释及其对患者结果的影响
- 批准号:
10015335 - 财政年份:2019
- 资助金额:
$ 25.06万 - 项目类别:
Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient Outcomes
发现电子病历中的护理协调实践模式:解释及其对患者结果的影响
- 批准号:
10460162 - 财政年份:2019
- 资助金额:
$ 25.06万 - 项目类别:
Discovering Care Coordination Practice Patterns in the EMR: Interpretation and Impact on Patient Outcomes
发现电子病历中的护理协调实践模式:解释及其对患者结果的影响
- 批准号:
10217257 - 财政年份:2019
- 资助金额:
$ 25.06万 - 项目类别:
Learning Patterns of Collaboration to Optimize the Management of Care Providers
学习协作模式以优化护理提供者的管理
- 批准号:
9265940 - 财政年份:2015
- 资助金额:
$ 25.06万 - 项目类别:
Learning Patterns of Collaboration to Optimize the Management of Care Providers
学习协作模式以优化护理提供者的管理
- 批准号:
8820357 - 财政年份:2015
- 资助金额:
$ 25.06万 - 项目类别:
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