Automated Portal-Enabled Elicitation of Health-Related Patient Values in Solid Tumor Oncology: Feasibility and Patient Characteristics Associated With Patient Response

Abstract

Purpose: Patient portals increase communication, but little is known about response rates or patient characteristics associated with patient responses to portal-enabled questionnaires about health-related values (HRVs).

Methods: We tested feasibility of sending seven HRV questions by portal to medical oncology patients, with responses automatically returned to clinicians. HRV questionnaires were sent 1 week in advance of scheduled follow-up visits. The primary feasibility measure was patient response rate. Secondarily, we assessed patient characteristics associated with responses, and oncology clinicians' impressions.

Results: One thousand five hundred fifty-six HRV questionnaires were sent by portal to as many individual patients of five total clinics between July 2023 and July 2024. Seven hundred thirty questionnaires were returned with at least one of the questions answered (47% response rate). Response rate increased significantly (45%-50%, P = .05) after April 2024 when the portal notification was updated with a personalized message from oncologists and two study clinics were added to the initial 3. Nonresponders either returned a blank questionnaire (133, 9%) or did not return anything (693, 45%). Response rates were significantly different by patient race (P = .05): Asian (51%) and White (48%) versus Black (44%) and Hispanic (41%). Female patient responses were longer than males. Response time was associated with patients' oncology team and shorter after April 2024. Participating clinicians' impressions were consistently favorable.

Conclusion: Automating a patient portal-driven process to broadly scale elicitation of medical oncology outpatients' HRVs and sending aggregated reports of HRVs to oncology teams are feasible and valued by clinicians. Patient characteristics appear to be associated with responses. Ongoing research will investigate technology-supported strategies to optimize response rates across diverse populations (including minoritized patients who may suffer digital health inequity), and measure effects on person-centered communication and care outcomes.