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.
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.
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|Dixon, Brian E; Gamache, Roland E; Grannis, Shaun J (2013) Towards public health decision support: a systematic review of bidirectional communication approaches. J Am Med Inform Assoc 20:577-83|
|Dixon, Brian E; Lai, Patrick T S; Grannis, Shaun J (2013) Variation in information needs and quality: implications for public health surveillance and biomedical informatics. AMIA Annu Symp Proc 2013:670-9|
|Gichoya, Judy; Gamache, Roland E; Vreeman, Daniel J et al. (2012) An evaluation of the rates of repeat notifiable disease reporting and patient crossover using a health information exchange-based automated electronic laboratory reporting system. AMIA Annu Symp Proc 2012:1229-36|
|Gamache, Roland E; Dixon, Brian E; Grannis, Shaun et al. (2012) Impact of selective mapping strategies on automated laboratory result notification to public health authorities. AMIA Annu Symp Proc 2012:228-36|