A Conceptual Model for Precision Feedback on Screening and Referral for Health-Related Social Needs in US Primary Care Settings

Unmet health-related social needs (HRSNs) can significantly limit healthcare teams’ abilities to address the root causes of patients’ poor health. Detecting and addressing HRSNs early in primary care offers an opportunity for improving health outcomes and advancing equity across a healthcare system’s patient population. In the US, effective screening and referral for HRSNs remains low in many primary care settings, and primary care teams lack feedback about their screening and referral practices to improve care. Precision feedback, an informatics-enabled approach to generating coaching and appreciation messages, has the potential to enhance the value of feedback interventions on HRSNs screening and referrals. We iteratively developed a conceptual model of precision feedback to primary care professionals and teams in the US about HRSNs screening and referral practices, through a series of literature review, modeling, discussions, and reflection. Our proposed conceptual model contains six essential information domains: 1) performance data, 2) feedback recipient, 3) resources for referrals, 4) message influence, 5) message templates, and 6) algorithms. This model aligns with leading theories of feedback and behavior change interventions. We plan to evaluate this model in US pediatric primary care settings.