Knowledge-based patient screening for rare and emerging infectious/parasitic diseases: a case study of brucellosis and murine typhus.

Details

• Alternative Title:
  Emerg Infect Dis
• Personal Author:
  Carter, C. N. ; Ronald, N. C. ; Steele, J. H. ; Young, E. ; Taylor, J. P. ; Russell, L. H. ; Eugster, A. K. ; West, J. E.
• Description:
  Many infectious and parasitic diseases, especially those newly emerging or reemerging, present a difficult diagnostic challenge because of their obscurity and low incidence. Important clues that could lead to an initial diagnosis are often overlooked, misinterpreted, not linked to a disease, or disregarded. We constructed a computer-based decision support system containing 223 infectious and parasitic diseases and used it to conduct a historical intervention study based on field investigation records of 200 cases of human brucellosis and 96 cases of murine typhus that occurred in Texas from 1980 through 1989. Knowledge-based screening showed that the average number of days from the initial patient visit to the time of correct diagnosis was significantly reduced (brucellosis-from 17.9 to 4.5 days, p = 0.0001, murine typhus-from 11.5 to 8.6 days, p = 0.001). This study demonstrates the potential value of knowledge-based patient screening for rare infectious and parasitic diseases.
• Subjects:
  Artificial Intelligence Brucellosis Decision Support Techniques Diagnosis, Computer-Assisted Diagnosis, Differential Humans Retrospective Studies Time Factors Typhus, Endemic Flea-Borne
• Source:
  Emerg Infect Dis. 3(1):73-76.
• Document Type:
  Journal Article
• Volume:
  3
• Issue:
  1
• Collection(s):
  Emerging Infectious Diseases
• Main Document Checksum:
  urn:sha256:215b9c54a5de21ac7d96c9c2dd7cebd8adcc51bf054253c1bb487a9bca831bfb
• Download URL:
  https://stacks.cdc.gov/view/cdc/15378/cdc_15378_DS1.pdf
• File Type:
  [PDF - 27.13 KB ]