ABSTRACT
The COVID-19 pandemic and social justice movement have highlighted the impact of social determinants of health (SDOH) and structural racism in the United States on both access to care and patient outcomes. With the evaluation for liver transplantation being a highly subjective process, there are multiple ways for SDOH to place vulnerable patients at a disadvantage. This policy corner focuses on three different methods to reverse the deleterious effects of SDOH-identify and reduce implicit bias, expand and optimize telemedicine, and improve community outreach.
Subject(s)
COVID-19 , Health Equity/organization & administration , Liver Transplantation , Racism/prevention & control , Social Determinants of Health/ethnology , Telemedicine/methods , COVID-19/epidemiology , COVID-19/prevention & control , Health Services Accessibility/standards , Healthcare Disparities/ethnology , Humans , Liver Diseases/ethnology , Liver Diseases/surgery , Liver Transplantation/methods , Liver Transplantation/standards , Policy Making , Public Health/standards , Quality Improvement , SARS-CoV-2 , Socioeconomic Factors , United States/epidemiologyABSTRACT
During the coronavirus 2019 pandemic we converted our liver transplant waitlist candidate education and support program to a virtual format and expanded it to include ongoing engagement sessions aimed to educate and empower patients to maximize opportunity for live donor liver transplantation. Over a period of 6 months from April 2020 to Sept 2020 we included 21 patients in this pilot quality improvement program. We collected data regarding patient response and potential donor referral activity. Overall, patient response was positive, and some patients saw progress toward live donor liver transplantation by fostering inquiry of potential live liver donors. Optimization of logistical aspects of the program including program flow, technology access, and utilization is required to enhance patient experience. Long-term follow-up is needed to assess impact on the outcome of transplantation rates. Future data collection and analysis should focus on assessment of any potential disparity that may result from utilization of virtual programming. Herein we provide a framework for this type of virtual program and describe our experience.