Using segmentation to address clinical and social needs for Medicaid patients with complex needs and costly utilization can improve the effectiveness of complex care programs.
Dementia care program delivered by an occupational therapist and tailored to the needs of patients and their caregivers shows improved patient quality of life and caregiver well-being.
A community-based care transition and management intervention showed improved outcomes for patients transitioning to outpatient community care following a psychiatric hospitalization.
A cross-sector partnership to enroll older adults experiencing homelessness in permanent supportive housing led to meaningful reductions in health care costs.
Evidence-based behavioral intervention in which positive behaviors are reinforced with material incentives may address clinical problems among people receiving medications for opioid use disorder.
Trust, flexible funding, cross-sector support, sustainability, and an explicit focus on structural racism are identified as key components of effective community engagement to advance health equity.
Offers practical recommendations to improve telemedicine interventions to be more equitable for diverse populations, particularly those with low incomes.
A patient intervention that supported outpatient addiction treatment with destigmatized conversations about substance use between patients and primary care providers showed long-term benefits.
Peer providers with lived experiences of substance use and mental health disorders can help improve patient outcomes and play a unique role in the behavioral health workforce.
Personalized patient navigation supports for people with comorbid substance use disorders reduced rates of hospital readmissions and emergency department use.
Over 14 years, individuals experiencing chronic homelessness enrolled in a permanent supportive housing program had low housing retention and high mortality.
Primary care and alternative payment models that reduce emergency department use and increase access to care for high-need populations share core components for success.
Use of machine learning clustering algorithms revealed 30 distinct subgroups of patients among high-risk veterans, indicating a need for tailored approaches to health care.