Improving Population Health Through Enhanced Targeted Regional Decision Support

描述(由申请人提供)：许多医疗保健任务需要来自广泛数据来源的全面临床信息，包括比较有效性研究、改进临床护理流程和改善总体人口健康。卫生信息交换(HIE)是用于这些目的的卫生保健数据聚合和综合临床数据的新兴来源。众所周知，传统的临床护理流程低估了人口水平的疾病负担，原因有很多：记者负担过重或资源不足，他们缺乏知识和/或意愿；临床数据以不同的格式分散在不同的系统中，这使得完成报告变得繁重。不完整的报告可能会导致对社区疾病负担的不准确评估，从而进一步阻碍人口健康干预措施，并仅部分为向个别患者提供预防性护理提供信息。我们的长期目标是通过创新的信息学战略改善人口健康，这些战略无缝地整合并有效地在临床护理中使用以实践为基础的人口健康工具。该项目的目标是通过使用新的决策支持工具改善医疗保健提供者和人口健康利益攸关方之间的信息共享和数据质量，从而提高急性和预防性护理过程的有效性。这些工具将使用包含患者人口统计数据和相关病例管理信息的预先填写的可报告状况表格向临床提供者发送提醒。此外，这项研究将调查部署一个框架来整合从当前和以前的临床接触中捕获的HIE数据的过程和效果，以改进对人口健康意义重大的状况的识别和报告。这项提议的中心假设是，自动化数据捕获和信息增强将简化基于提供者的人口健康报告工作流程，降低报告和病例跟踪的门槛，提高数据完整性，捕获社区更大比例的传染病负担，并改善人口健康。虽然该项目侧重于新的人口健康决策支持技术的影响，但该框架适用于各种用例。因此，该项目的发现将为未来在不同技术环境中的大规模临床决策支持倡议提供信息。我们建议使用定量和定性研究方法来确定对临床医生病例报告至关重要的数据元素和数据特征、这些报告的公共卫生消费以及在人口健康利益相关者之间双向传播病例报告信息。 公共卫生相关性：这项提案的目标是通过使用新的决策支持和临床信息工具，简化医疗保健提供者和人口健康利益攸关方之间的信息共享，并提高信息质量，从而提高急性和预防性护理流程的有效性。这项提议的中心假设是，自动数据捕获和提供者警报将改善治疗时间，简化基于提供者的人口健康报告工作流程，并导致对基于人口的疾病负担进行更准确的评估。

DESCRIPTION (provided by applicant): Comprehensive clinical information from a broad set of data sources is required for many healthcare tasks, including comparative effectiveness research, improving clinical care processes, and improving overall population health. Health information exchanges (HIEs) are an emerging source of healthcare data aggregation and comprehensive clinical data for these purposes. It is well-known that traditional clinical care processes underreport population level disease burden for a variety of reasons: reporters are overburdened or under resourced and they lack knowledge and/or willingness; and clinical data is scattered across different systems in different formats, which makes completing reports burdensome. Incomplete reporting can lead to inaccurate assessment of the disease burden in a community, which further hinders population health interventions and only partially informs preventive care delivered to individual patients. Our long-term goal is to improve population health through innovative informatics strategies that seamlessly integrate and effectively use practice-based population health tools in clinical care. The objective in this project is to improve the effectiveness of acute and preventative care processes by improving information sharing and data quality among healthcare providers and population health stakeholders using novel decision support tools. These tools will deliver reminders to clinical providers using pre-populated reportable condition forms that contain patient demographics and pertinent case management information. Further, this research will investigate the process and effects of deploying a framework to integrate HIE data captured from present and previous clinical encounters to improve the identification and reporting of conditions of population health significance. The central hypothesis of this proposal is that automated data capture and information enhancements will streamline provider-based population health reporting workflows, lower barriers to reporting and case follow-up, increase data completeness, capture a greater portion of communicable disease burden in the community, and improve population health. While this project focuses on the impact of novel population health decision support technology, the framework is applicable to a variety of use-cases. Thus, findings from this project will inform future large-scale clinical decision support initiatives in heterogeneous technical settings. We propose to employ both quantitative and qualitative research methods to determine the data elements and data characteristics vital for clinician case reporting, public health consumption of these reports and bidirectional transmission of case reporting information among population health stakeholders. PUBLIC HEALTH RELEVANCE: The objective of this proposal is to improve the effectiveness of acute and preventative care processes by streamlining information sharing and enhancing information quality among healthcare providers and population health stakeholders using novel decision support and clinical messaging tools. The central hypothesis of this proposal is that automated data capture and provider alerts will improve time-to-treatment, simplify provider-based population health reporting workflows, and result in a more accurate assessment of population-based disease burden.