CONTEXT: Notifiable disease surveillance is essential to rapidly identify and respond to outbreaks so that further illness can be prevented. Automating reports from clinical laboratories has been proposed to reduce underreporting and delays. OBJECTIVE: To compare the timeliness and completeness of a prototypal electronic reporting system with that of conventional laboratory reporting. DESIGN: Laboratory-based reports for 5 conditions received at a state health department between July 1 and December 31, 1998, were reviewed. Completeness of coverage for each reporting system was estimated using capture-recapture methods. SETTING: Three statewide private clinical laboratories in Hawaii. MAIN OUTCOME MEASURES: The number and date of reports received, by reporting system, laboratory, and pathogen; completeness of data fields. RESULTS: A total of 357 unique reports of illness were identified; 201 (56%) were received solely through the automated electronic system, 32 (9%) through the conventional system only, and 124 (35%) through both. Thus, electronic reporting resulted in a 2.3-fold (95% confidence interval [CI], 2.0-2.6) increase in reports. Electronic reports arrived an average of 3.8 (95% CI, 2.6-5.0) days earlier than conventional reports. Of 21 data fields common to paper and electronic formats, electronic reports were significantly more likely to be complete for 12 and for 1 field with the conventional system. The estimated completeness of coverage for electronic reporting was 80% (95% CI, 75%-85%) [corrected] compared with 38% (95% CI, 36%-41%) [corrected] for the conventional system. CONCLUSIONS: In this evaluation, electronic reporting more than doubled the total number of laboratory-based reports received. On average, the electronic reports were more timely and more complete, suggesting that electronic reporting may ultimately facilitate more rapid and comprehensive institution of disease control measures.
CONTEXT: Notifiable disease surveillance is essential to rapidly identify and respond to outbreaks so that further illness can be prevented. Automating reports from clinical laboratories has been proposed to reduce underreporting and delays. OBJECTIVE: To compare the timeliness and completeness of a prototypal electronic reporting system with that of conventional laboratory reporting. DESIGN: Laboratory-based reports for 5 conditions received at a state health department between July 1 and December 31, 1998, were reviewed. Completeness of coverage for each reporting system was estimated using capture-recapture methods. SETTING: Three statewide private clinical laboratories in Hawaii. MAIN OUTCOME MEASURES: The number and date of reports received, by reporting system, laboratory, and pathogen; completeness of data fields. RESULTS: A total of 357 unique reports of illness were identified; 201 (56%) were received solely through the automated electronic system, 32 (9%) through the conventional system only, and 124 (35%) through both. Thus, electronic reporting resulted in a 2.3-fold (95% confidence interval [CI], 2.0-2.6) increase in reports. Electronic reports arrived an average of 3.8 (95% CI, 2.6-5.0) days earlier than conventional reports. Of 21 data fields common to paper and electronic formats, electronic reports were significantly more likely to be complete for 12 and for 1 field with the conventional system. The estimated completeness of coverage for electronic reporting was 80% (95% CI, 75%-85%) [corrected] compared with 38% (95% CI, 36%-41%) [corrected] for the conventional system. CONCLUSIONS: In this evaluation, electronic reporting more than doubled the total number of laboratory-based reports received. On average, the electronic reports were more timely and more complete, suggesting that electronic reporting may ultimately facilitate more rapid and comprehensive institution of disease control measures.
Authors: Michael M Wagner; J Michael Robinson; Fu-Chiang Tsui; Jeremy U Espino; William R Hogan Journal: J Am Med Inform Assoc Date: 2003-06-04 Impact factor: 4.497
Authors: Deepthi Rajeev; Catherine Staes; R Scott Evans; Andrea Price; Mary Hill; Susan Mottice; Ilene Risk; Robert Rolfs Journal: AMIA Annu Symp Proc Date: 2011-10-22
Authors: Patina Zarcone; Dale Nordenberg; Michelle Meigs; Ulrike Merrick; Daniel Jernigan; Steven H Hinrichs Journal: Public Health Rep Date: 2010 May-Jun Impact factor: 2.792