Improving Population Health Through Enhanced Targeted Regional Decision Support

项目总结/摘要 许多医疗保健任务都需要来自广泛数据源的综合临床信息， 包括比较有效性研究，改善临床护理流程， 人口健康。健康信息交换（HIE）是一种新兴的医疗保健数据源 用于这些目的的聚合和综合临床数据。众所周知，传统的临床护理 由于各种原因，流程低估了人群水平的疾病负担：报告者负担过重， 资源不足，缺乏知识和/或意愿;临床数据分散在不同的地方 不同格式的系统，这使得完成报告的负担。不完整的报告可能导致 对社区疾病负担的评估不准确，这进一步阻碍了人口健康 干预措施，仅部分告知向个别患者提供的预防性护理。 我们的长期目标是通过创新的信息学战略改善人口健康， 在临床护理中无缝集成并有效使用基于实践的人口健康工具。客观 在这个项目是提高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， 新的决策支持工具。这些工具将使用预填充的 包含患者人口统计学和相关病例管理信息的可报告状况表。 此外，本研究将探讨部署一个框架来整合HIE数据的过程和效果 从当前和以前的临床遭遇中捕获，以改善疾病的识别和报告 人口健康的重要性。该提案的核心假设是，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， 报告和病例随访，提高数据完整性，捕获更大比例的传染性 减轻社区疾病负担，改善人口健康。 虽然该项目的重点是新的人口健康决策支持技术的影响， 框架适用于各种用例。因此，该项目的研究结果将为未来的大规模 临床决策支持计划在异构技术设置。我们建议采用定量 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的双向传播 人口健康利益攸关方之间的信息。

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.