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HI Program Research Priorities​

Priority 1: Enhance data quality at the point of capture.

Accurate and precise data, along with important metadata, are crucial for research use. Strategies to achieve this include coordinating efforts to identify high-priority metadata elements, developing metadata standards, and adopting and using data and metadata standards.

Priority 2: Facilitate data harmonization to enable data analysis and use.

Efforts are needed to reconcile existing data models and promote collaboration between developers and stewards of data models, to allow for analysis of different datasets.

Priority 3: Improve access to interoperable electronic health data.

Access to interoperable electronic health data is essential for advancing research. It is crucial to improve interoperability and provide the necessary recommendations for healthcare leaders and organizations.

Priority 4: Integration of Emerging Health and Health-Related Data Sources

As scientific advancements uncover causal factors in health, previously unexplored data elements such as omics data, social media data, imaging. data, patient-generated health data, and social determinants of health are being identified. Moreover, environmental and location data can significantly contribute to our comprehension of the impact of surroundings on health. It is imperative for health IT systems to support. infrastructure and standardized protocols to integrate and connect with these novel data elements.

Priority 5: Utilization of Health IT Systems to Foster Education and Engagement

Prevailing hindrances, such as lack of awareness of available studies, effort required to participate, and absence of trust in the research community, deter potential participants from engaging in research. While emerging approaches supporting augmented education, engagement, and participation in research are being adopted, further emphasis is needed to pursue infrastructure enhancements that enable and incentivize participation from a diverse patient population.

Priority 6: Acceleration of Knowledge Integration at the Point of Care

Widespread gaps persist in the integration of new knowledge into clinical practice. As the rate of new knowledge generation continues to outpace its integration and utilization, the proper harnessing of infrastructure and capabilities is critical. The ongoing digitization of evidence necessitates complementation through the integration and implementation of this information into clinical care. Establishment of efficient integration strategies, for instance through the usage of CDS or other API tools, is crucial in ensuring the effective utilization of new knowledge in patient care.

Examples of previous research topics include Intelligent Decision Support System, Data Mining in Health care Management, Mobile Health, Consumer Health Informatics, Social media impact on public health, Telemedicine and Telehealth, Personal Health Record, Systems Analysis and evaluation, IT and Patient Safety, Image Processing, Infodemiology and Infodemic,Document Image Analysis, and others.