Routine case investigations are critical for enteric disease control and surveillance. Given limited resources and staffing, public health agencies are exploring more efficient case investigation methods. To identify and describe the advantages and disadvantages of using online surveys to supplement routine enteric disease case investigations. We evaluated routine Campylobacter interview data collected via telephone vs online by interviewers with the Colorado Department of Public Health and Environment. Colorado laboratory-confirmed Campylobacter cases reported from September 1, 2020, through December 31, 2021. We calculated modality preference, response rates, and data quality (missing and unknown answers) and compared demographics (age, gender, and urban vs rural) by modality. Estimated staff time savings and investigation timeliness were compared. Modality preference was split among the 966 contacted Campylobacter cases (46% telephone, 50% online, and 4% refusal). Among online respondents, 57% completed the survey for an overall 63% response rate. Females and those 18 to 44years of age were most likely to select (55%, 60%) and complete (57%, 66%) the online survey, while those under 18 and over 65years of age were least likely to select (47%, 45%) or complete (53%, 46%). Those who identified as non-Hispanic Black were most likely to select online (62%), whereas those who identified as mixed-race non-Hispanic and non-Hispanic White had the highest completion (78%, 60%). Modality preference was comparable by geography; however, rural residents had higher completion rates (61%). Data quality and completeness were comparable between modalities. Completing the 274 online surveys via telephone would have taken an estimated 78hours of additional staff time. Online surveys can increase public health efficiency and capacity while maintaining data quality. However, use should be limited to high-burden, low-resource pathogens due to reduced response rates. Understanding implementation best practices and conducting regular evaluation are critical for optimization.