Systematizing and Optimizing Collection of Patient-reported Outcomes after Community-based Care: A Quality Improvement Project

Introduction: Healthcare systems routinely assess patients' perspectives on inpatient and outpatient care. Still, patients cared for outside of these clinical settings have limited opportunities to share experiences and provide feedback. Current data collection strategies may also under-capture feedback from marginalized or hard-to-reach populations, potentially reinforcing existing inequities in care. This project aimed to (1) systematize the collection of feedback from participants in a pediatric hospital-based violence intervention program (HVIP) providing community-based case management services following violence-related injury and (2) optimize collection methodology to achieve and maintain a quarterly response completion rate of 50%. Methods: Collection of the HVIP Client Satisfaction Questionnaire (HVIP-CSQ), a brief survey assessing satisfaction with and perceived short-term outcomes of HVIP participation, began in July 2021. Caregivers and patients self-completed surveys electronically in response to text message or email invitations. In March 2023, a rapid-cycle quality improvement initiative was initiated to optimize response rates. Using an expanded data collection methodology, an outreach coordinator invited nonrespondents to complete surveys via phone and text message. Results: During the initial HVIP-CSQ data collection, quarterly response rates were suboptimal. On average, 29.5% of caregivers and 23.5% of patients completed the HVIP-CSQ. Following improvements to the collection methodology, average quarterly response percentages exceeded the set targets, reaching 78.4% for caregivers and 51.8% for patients. Conclusions: Multimodal outreach strategies improved response rates for patients and caregivers. Although automated and self-directed survey methods offer advantages in reach and efficiency, they may not sufficiently engage respondents. Flexible, multimodal feedback systems can enhance data completeness and support more inclusive quality improvement efforts.