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Toward Automatic Reporting of Infectious Diseases.
Hsiao-Mei Tsao1, Chi-Ming Chang2, Jen-Hsiang Chuang2
1Institute of Information Science, Academia Sinica, Taipei, Taiwan, China.
Studies in Health Technology and Informatics
|January 4, 2018
Summary
Automated disease surveillance systems extract data from electronic health records, significantly reducing workload and enabling near real-time public health reporting. This national-scale effort enhances disease control through efficient data collection and confirmation.
Area of Science:
- Public Health
- Health Informatics
- Epidemiology
Background:
- Accurate, complete, and timely disease surveillance data are crucial for effective disease control.
- Traditional surveillance methods can be labor-intensive and time-consuming.
- The need for rapid data acquisition and analysis in public health is paramount.
Purpose of the Study:
- To report on a national-scale initiative for automated information extraction from electronic health and laboratory systems.
- To evaluate the impact of automated systems on disease surveillance workload and timeliness.
- To identify key factors for the sustainability and adoption of such systems.
Main Methods:
- Automated extraction of information from electronic medical records and electronic laboratory systems.
- Implementation of a confirmation process for extracted data.
- Transfer of confirmed data to centers for disease control.
- Assessment of system coverage rates and impact on workload.
Main Results:
- Automated reporting systems achieved over 50% coverage rates nationally.
- Significant reduction in the workload associated with disease surveillance.
- Reporting is completed in near real-time, improving data timeliness.
- Key strategies for sustainability include system strategy, planning, and team coordination.
Conclusions:
- Automated data extraction from electronic systems is effective for disease surveillance.
- The implemented system significantly improves efficiency and timeliness of public health reporting.
- Knowledge management and targeted training are essential for system maintenance and adoption, such as for LOINC (Logical Observation Identifiers Names and Codes).
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