Improving Population Health Through Enhanced Targeted Regional Decision Support

项目摘要/摘要 许多医疗保健任务需要来自广泛数据源的全面临床信息， 包括比较有效性研究、改进临床护理流程和全面改进 人口健康。医疗信息交换(HIE)是医疗数据的新兴来源 为这些目的汇总和综合临床数据。众所周知，传统的临床护理 由于各种原因，流程低估了人口水平和疾病负担：记者负担过重或 资源不足，缺乏知识和/或意愿；临床数据分散在不同的 系统采用不同的格式，这使得填写报告变得繁重。不完整的报告可能会导致 对社区疾病负担的评估不准确，这进一步阻碍了人们的健康 干预措施，只向个别患者提供部分的预防性护理。 我们的长期目标是通过创新的信息学战略来改善人口健康 在临床护理中无缝整合和有效使用以实践为基础的人口健康工具。目标是 在这个项目中的目的是通过改善急性和预防性护理过程的有效性 医疗保健提供者和人口健康利益相关者之间的信息共享和数据质量 新颖的决策支持工具。这些工具将使用预先填充的 包含患者人口统计数据和相关病例管理信息的可报告情况表。 此外，本研究将调查部署框架整合HIE数据的过程和效果 从当前和以前的临床接触中捕获，以改进对病情的识别和报告 对人口健康具有重要意义。这一提议的中心假设是自动数据捕获和 信息增强将简化基于提供商的人口健康报告工作流，降低门槛 报告和病例跟踪，提高数据完整性，捕获更大比例的可传播疾病 减轻社区疾病负担，提高人群健康水平。 虽然这个项目的重点是新的人口健康决策支持技术的影响，但 框架适用于各种用例。因此，这个项目的发现将为未来的大规模 不同技术环境下的临床决策支持计划。我们建议同时采用定量和定量 和定性研究方法，以确定对临床医生至关重要的数据元素和数据特征 病例报告、这些报告的公共卫生消费和病例报告双向传播 在人口健康利益攸关方之间提供信息。

Project Summary/Abstract 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.