OBJECTIVES: The Electronic Patient Reported Outcome (ePRO) data collection mode selected for trials is often based on efforts to minimize timelines, budgets, and patient burden.  However, are sponsors inadvertently introducing bias into trial results in this selection process?  This conceptual paper reviews common ePRO modes and explores patient groups that may be excluded. METHODS: Common modes for ePRO data collection are reviewed.  An assessment of potential patient groups that may be excluded is performed based on ePRO mode.   RESULTS: Common ePRO modes include telephone, web, and handheld device.  As sponsors look to reduce costs, improve data quality, and reduce patient burden, industry has continued its shift towards patients using their own telephone, computer, tablet, or smartphone and away from sponsors provisioning these devices to patients.  Choice of patient-provisioned device:  Patients from certain geographic areas may be excluded where internet connections and cellular/mobile telephone reception is limited. Requiring patients to use their personal web/mobile device may exclude patient groups with certain economic, cultural, or demographic characteristics who live in rural or underdeveloped areas.  Choice of sponsor-provisioned device:  Logistics issues, i.e. shipment of devices including customs considerations, reliability of data transmission, storage and replacement of devices and cords, training, etc. CONCLUSIONS: The objective of clinical trials is to establish treatment effectiveness, generalizable to the overall patient population.  ePRO mode selection may impact inclusion of individuals from certain economic, cultural, demographic, and geographic areas.  Exclusion of these groups could impact results; therefore, it is important to understand the potential bias that can be introduced when selecting an ePRO mode. Proper planning should include assessment of patient population and inclusion of regions that would render generalizability. ePRO mode selection should be based on which method works best for the required regions to optimize inclusion, as well as the patient population’s characteristics to minimize burden.