Modern healthcare remains structurally and conceptually fragmented, with profound clinical and policy implications. At its root lies an ontological fracture: the prevailing biomedical model reduces patients to discrete biological systems (organs, biomarkers, and symptoms) detached from the psychological, social, and ecological contexts in which health and illness are experienced. This is compounded by epistemological fragmentation, where medical knowledge is compartmentalized into increasingly narrow specialties, limiting holistic understanding. These philosophical divisions manifest in downstream operational, informational, financial, and policy dysfunctions duplicative testing, misaligned incentives, disconnected care pathways, and population health failures. To address these multilevel fractures, we propose a unified architecture grounded in three interlocking components. First, the Precision and Personalized Population Health (P3H) framework offers a principle-based realignment toward care that is integrated, personalized, proactive, and population wide. P3H addresses the conceptual shortcomings of fragmented care by focusing on the full human trajectory across time, systems, and determinants. Second, General Purpose Technologies including artificial intelligence, biosensors, mobile diagnostics, and multimodal data systems enable the operationalization of whole-person care at scale, especially in low-resource settings. Third, the AI-WHOLE policy framework (Alignment, Integration, Workflow, Holism, Outcomes, Learning, and Equity) provides governance principles to guide ethical, equitable, and context-specific implementation. We argue that this triadic blueprint is particularly critical for Global South nations, where the lack of legacy infrastructure offers an opportunity for leapfrogging toward integrated, intelligent systems of care. Early models illustrate how policy-aligned, technology-enabled care rooted in whole-person principles can yield improvements in continuity, cost-efficiency, and chronic disease outcomes. This manuscript offers a systems-level strategy to overcome fragmentation and reimagine healthcare delivery, not only by refining clinical tools, but by redefining what it means to care for the human being in full.

Few studies have assessed sleep across all sleep continuity variables (eg, sleep latency [SL], number of awakenings [NWAK], wake after sleep onset [WASO], early morning awakenings [EMA], total sleep time [TST], total wake time [TWT]) concurrently, at the community level, leading to gaps in understanding racial sleep disparities. Moreover, many studies do not examine sleep-related problem endorsement and daytime function. To examine race differences in insomnia symptom severity (eg, sleep continuity disturbance [SCD]), problem endorsement, and daytime dysfunction among Black and White non-Hispanic adults. Participants (N=8172) living in Greater Philadelphia completed an online community survey about their sleep between 2011 and 2021. Chi-square and t-test analyses were used to compare sleep-related differences by race. Multiple regression analyses were conducted to investigate the effect of race on sleep, controlling for age, sex, BMI, and education. Black non-Hispanics had significantly worse SL (Δ>3 min), WASO (Δ>8.4 min), and TWT (Δ >12.1 min) but not NWAK (Δ36.8 min). Appreciable effect sizes ranged from .01 to .44. White non-Hispanics consistently endorsed greater daytime dysfunction with small effects ranging from .07 to .14. Findings suggest that Black non-Hispanics experience less restorative sleep, which can lead to impaired cognitive function, mood disturbances, and increased risk of chronic illness. Observed differences in TST may be due to the unmeasured effects of EMA or to reduced time in bed, underscoring the importance of comprehensive approaches to understanding sleep disturbances like insomnia. Addressing insomnia severity using multidimensional approaches and with targeted interventions, such as Cognitive Behavioral Therapy for Insomnia and/or systematic sleep extension, could help improve sleep health. Moreover, examining differences in sleep across sociodemographic characteristics leads to nuanced understandings that highlight the importance of developing targeted, precision-based behavioral health interventions.

Background/Objectives: This study aims to understand gaps and strategies in Chinese Americans’ utilization of SDOH services in the pediatric primary care context in Sunset Park, Brooklyn, from a patient–provider partnership perspective. Methods: The study was guided by an integrated Patient–Provider Partnership, Engagement, and Collaboration (PEC) framework that influenced patient–provider interaction during the provision of SDOH services. A qualitative study design was applied, and eight quality improvement interviews with healthcare providers were conducted to understand the existing community and health service system context. Six in-depth interviews were conducted with Mandarin-speaking Chinese American caregivers. Interviews were transcribed and coded in Mandarin and then translated into English. Results: Consistent with the PEC framework, we identified cognitive, affective, and communication gaps from both the patient and provider. Caregivers reported unaddressed needs in food, financial security, and mental health. Providers identified gaps in patient workflow, staffing, and the intake form process. Conclusions: Addressing social determinants of health among Chinese American immigrant populations is crucial for mitigating poor health outcomes in children and families. Multi-level community-engaged strategies are needed to alleviate the challenges facing this community. Recommendations for future research should consider the importance of language and cultural affinity, digital intake forms translated into the patient’s language, and regular on-site staffing during SDOH screenings.