Name
Abstract | ||
|---|---|---|
Accurate, complete, and timely disease surveillance data are vital for disease control. We report a national scale effort to automatically extract information from electronic medical records as well as electronic laboratory systems. The extracted information is then transferred to the centers of disease control after a proper confirmation process. The coverage rates of the automated reporting systems are over 50%. Not only is the workload of surveillance greatly reduced, but also reporting is completed in near real-time. From our experiences, a system sustainable strategy, well-defined working plan, and multifaceted team coordination work effectively. Knowledge management reduces the cost to maintain the system. Training courses with hands-on practice and reference documents are useful for LOINC adoption. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.3233/978-1-61499-830-3-808 | Studies in Health Technology and Informatics |
Keywords | DocType | Volume |
Public Health Surveillance,Electronic Health Records,Logical Observation Identifiers Names and Codes | Conference | 245 |
ISSN | Citations | PageRank |
0926-9630 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsiao-Mei Tsao | 1 | 0 | 0.34 |
| Chi-Ming Chang | 2 | 2 | 1.14 |
| Jen-Hsiang Chuang | 3 | 0 | 0.34 |
| Ding-Ping Liu | 4 | 0 | 0.34 |
| Mei-Lien Pan | 5 | 0 | 1.69 |
| Da-Wei Wang | 6 | 0 | 3.38 |