| Title |
Design and Implementation of Data Exchange Formats for Molecular Detection of Drug-Resistant Tuberculosis.
|
|---|---|
| Published in |
AMIA Summits on Translational Science Proceedings, May 2019
|
| Pubmed ID | |
| Authors |
Wilfred Bonney, Sandy F Price, Roque Miramontes |
| Abstract |
Drug-resistant tuberculosis (TB) remains a public health threat to the United States and worldwide control of TB. Rapid and reliable drug susceptibility testing (DST) is essential for aiding clinicians in selecting an optimal treatment regimen for TB patients and to prevent ongoing transmission. Growth-based DST results for culture-confirmed cases are routinely reported to the U.S. Centers for Disease Control and Prevention through the National TB Surveillance System (NTSS). However, the NTSS currently lacks the capacity and functionality to accept laboratory results from advanced molecular methods that detect mutations associated with drug resistance. The objective of this study is to design and implement novel comprehensive data exchange formats that utilize the Health Level Seven (HL7) version 2.5.1 messaging hierarchy to capture, store, and monitor molecular DST data, thereby, improving the quality of data, specifications and exchange formats within the NTSS as well as ensuring full reporting of drug-resistant TB. |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 13 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 15% |
| Student > Doctoral Student | 1 | 8% |
| Other | 1 | 8% |
| Student > Ph. D. Student | 1 | 8% |
| Researcher | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 7 | 54% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 2 | 15% |
| Computer Science | 1 | 8% |
| Nursing and Health Professions | 1 | 8% |
| Social Sciences | 1 | 8% |
| Immunology and Microbiology | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 7 | 54% |