Automated screening and assessment tools can identify and target patients who are at the greatest risk for nonadherence (e.g., those with dementia, depression, homelessness, or drug use) (123). Health in- formation technologies increasingly have the ability to collect pooled data on prescription lls as well as to share these data among care providers and across settings. This offers the potential to characterize patient medication adherence in real time and automatically identify prob- lems. Electronic health record-based algorithms to iden- tify and optimize use of GDMT are already in use for these purposes (124 126). Several other mechanisms can help to optimize adherence: 1. Integration of pharmacists, patient navigators, and registered nurses in collaborative practice may help with optimization of GDMT (127 131). 2. Limiting copays has been associated with small in- creases in patient prescription lls (101,132). Value- based insurance designs that tailor cost sharing to value are promising. 3. The CMS Innovation Center s Bene ciary Engagement and Incentives models aim to support patient adher- ence (https://www.cms.gov/newsroom/fact-sheets/ bene ciary-engagement-and-incentives-models-shared- decision-making-model). 4. Monetary incentives or other rewards for adherence to medications may be cost-saving for highly ef cacious and inexpensive drugs, such as beta-blockers, in HFrEF. 5.6. What Is Needed in Speci c Patient Cohorts: African Americans, Older Adults, and the Frail Randomized clinical trials typically enroll only a subset of patients with HFrEF, resulting in limited demographic, economic, and clinical diversity. Consequently, there is uncertainty about the bene ts and risks of HFrEF thera- pies in patients not resembling those studied. As a result, only approximations of risks and bene ts can guide therapy in the least-studied populations (Table 11) (133). Examples of populations that have been relatively understudied in HFrEF trials include African Americans, older adults ($75 years), and frail patients. African Americans. 