Literature Collection

To make good decisions, health leaders need information about their communities and the health workforce available to meet their needs. Raw data and indicators of population needs and workforce capacity must be transformed into usable intelligence that can support decision-making. Using the case study of integrated primary care workforce planning in Toronto, we outline our workforce planning framework, and with a focus on workforce analysis, describe the inputs and outputs that are needed for planning, key steps in the conversion of data to intelligence, and the impact of the approach. Raw data flow from data partners through a planning model into a six-step workforce analysis that renders the results of data analysis, modelling, synthesis and visualization relevant, and accessible to planners and decision-makers. We highlight important challenges and considerations related to data standardization, comprehensiveness, granularity, accessibility, and timeliness, and envision a system that more effectively supports workforce planning and decision-making.

Hazardous alcohol use remains a major contributor to acute and chronic liver disease, while alcohol-associated liver disease (ALD) is a leading indication for liver transplantation. In recent years, embedded, interprofessional ALD clinics have improved access to alcohol use disorder care within hepatology and liver transplantation, but more work is needed to meet this challenge. The literature is lacking regarding scaling procedures to provide services for increasingly large ill patient populations. This article begins to fill this gap by describing "expanded ALD care": broad, innovative, longitudinal, interprofessional care delivery strategies surpassing standalone clinics. Drawing from analogous patient populations served by collaborative models in primary care and comprehensive eating disorder treatment, the expanded ALD care framework proposes practical strategies toward specific innovations: equipoise between biomedical and psychosocial care elements, increased clinician number and reach, long-term patient relationships, harm reduction and palliative care, outreach to external agencies and clinicians, and enhanced support for patients and families. The article also defines attributes of innovative healthcare systems that support expanded ALD care.

Older adults frequently experience depression alongside physical multimorbidity, with mutual reinforcement through biological, behavioural and social mechanisms. Traditional models of care often address these conditions in isolation, resulting in fragmented services poorly suited to the complex realities of ageing. Social prescribing has emerged in the UK as a potential solution, offering structured referrals from healthcare to community-based resources. While its theoretical promise aligns well with the needs of older adults, the evidence base remains limited, and current implementation strategies often fail to match individuals to interventions effectively. This narrative review explores how social prescribing might evolve to bridge the gap between mental health and multimorbidity in later life. Using depression and metabolic syndrome as a case example, we examine shared inflammatory mechanisms, barriers to behavioural change and the risks of individualising structural disadvantage. We propose a future framework in which social prescribing is supported by complexity-sensitive evaluation tools, including patient-reported outcome measures, biomarker-informed stratification and artificial intelligence-driven narrative assessment. We also highlight the critical role of integrated care systems in developing the community infrastructure necessary to sustain these interventions equitably. By reframing social prescribing as part of a broader public health strategy rooted in integration and prevention, we argue it can move beyond symbolic appeal to become a meaningful component of multimorbidity care in older adults.

As healthcare systems navigate increasing complexity, the need to embed quality improvement (QI) into organizational culture has never been greater. In alignment with national trends emphasizing a shift from compliance-driven quality to shared responsibility and excellence, the University of California Los Angeles (UCLA) Health launched its inaugural Quality Symposium, led by the Department of Medicine. The event aimed to cultivate a Learning Health System community by strengthening shared learning, accelerating system improvement, and increasing the visibility and adoption of QI across clinical settings. The symposium featured a keynote presentation on artificial intelligence (AI) and healthcare transformation, followed by nine Technology, Entertainment, Design (TED)-style talks organized across three modules: ambulatory care quality, inpatient care quality, and research and education. Attendees completed pre- and post-event evaluations assessing satisfaction, perceived learning, and actionable takeaways. The symposium drew 218 registrants and approximately 160 attendees (73%), both in person and online, including physicians, nurses, administrators, and trainees. Among post-survey respondents (62 of approximately 160 attendees, 39%), 52 (88%) reported gaining at least one actionable insight applicable to their work. Qualitative analysis identified themes emphasizing the value of interprofessional storytelling, visible leadership engagement in QI initiatives, and the integration of behavioral science and technology to support improvement efforts. This learning report describes the design, implementation, and perceived educational impact of a health system-wide symposium, framed as a descriptive program evaluation. Findings suggest that narrative-driven education, behavioral insight, and cross-disciplinary engagement may support system learning and collaboration within academic health systems.