CONTEXT: Chronic kidney disease (CKD) disproportionately impacts lower socioeconomic groups and is associated with many symptoms and complex decisions. Integration of Kidney Supportive Care (KSC) with CKD care can address these needs. To our knowledge, this approach has not been described in an underserved population. OBJECTIVES: We describe our adaptation of an ambulatory integrated KSC and CKD clinic for implementation in a safety net hospital. We report our utilization metrics
characteristics of the population served
and visit activities. METHODS: We considered modifications from the perspectives of people with CKD, their providers, and the health system. Modifications were informed by meeting notes with key participants (hospital administrators [n = 5], funders [n = 1], and content experts [n = 2]), as well as literature on palliative care program building, safety net hospitals, and KSC. We extracted utilization data for the first 15 months of the clinic's operations, demographics, clinical characteristics, unmet health related social needs, and symptom burden, measured by the Integrated Palliative Outcome Scale-Renal (total Score, and sub-scores of physical, psychological, and practical impact of CKD) from the electronic health record. Results are reported using descriptive statistics. RESULTS: Adaptions were proactive and done by clinical and administrative leaders. Meetings identified challenges of the safety net setting including people presenting with advanced disease and having several social needs. Modifications to our base model were made in staffing, data collection, and work flow. Show rate was approximately 68%, with a majority of people identifying as Black or Hispanic, and uninsured or on Medicaid. Symptom burden was lower than previous reports, driven by a better psychological sub-score. CONCLUSIONS: We describe a feasible ambulatory care model of KSC in a safety net setting that can serve as a framework for the development of other noncancer palliative care ambulatory clinics. Future work will optimize our model.