Knowledge-based patient screening for rare and emerging infectious/parasitic diseases: a case study of brucellosis and murine typhus.
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.
Carter
C N
CN
Texas A&M University, College Station, USA.
Ronald
N C
NC
Steele
J H
JH
Young
E
E
Taylor
J P
JP
Russell
L H
LH
Eugster
A K
AK
West
J E
JE
D001185:Artificial Intelligence; D002006:Brucellosis / Q000175:diagnosis*; D003661:Decision Support Techniques; D003936:Diagnosis, Computer-Assisted; D003937:Diagnosis, Differential; D006801:Humans; D012189:Retrospective Studies; D013997:Time Factors; D014437:Typhus, Endemic Flea-Borne / Q000175:diagnosis*