Inquiry-The Journal of Health Care Organization Provision and Financing最新文献

This convergent parallel mixed-methods study examined how a primary care value-based payment (VBP) model affected patient health care use and captured implementation experiences from select clinics. Focusing on outpatient care as a key step to improving outcomes, we used a difference-in-differences model to compare outpatient utilization between PCPM (the VBP model) and non-PCPM clinics, and semi-structured interviews with a subset of participating clinics to explore implementation efforts on the ground. We identified our quantitative study population (cases N = 68 807; control N = 71 695) and outcomes from Oregon's All Payer All Claims (APAC) data system and, qualitatively, we conducted 12 interviews with operational/administrative staff at 7 PCPM clinics. Our findings indicated that PCPM patients experienced greater connection to primary and specialty care-both the proportion who used care and the average amount of care used per member-relative to the control group. Primary care use rose by 4.2 percentage points (95% CI: 3.3%, 5.1%; P < .001), and specialty care by 1.1 points (95% CI: 0.4%, 1.8%; P = .002). Among users, primary care visits increased by 136.9 per 1000 member months (95% CI: 107.2, 166.6; P < .001), and specialty care by 32.1 (95% CI: 10.5, 53.7; P = .004). Qualitative findings added further context: (1) staff communication about PCPM efforts connects directly to improvements in care delivery and patient outcomes; (2) success depends on care team staff being involved in the creation of new workflows and processes; and (3) access to program data helps to identify care gaps and improve patient care delivery. We concluded that care team staff engagement in VBP models is strengthened by making the connection between VBP and direct improvements to patient care. Models that motivate staff can lead to increased connection to primary and specialty care among the clinic's patient population.

Literatures shows that poor air quality index (AQI) is associated with several adverse health impacts, such as asthma, chronic obstructive pulmonary disease, diabetes mellitus, tuberculosis, cancer, pneumonia, cataracts, heart diseases, and mental health disorders. Nepal, one of the lower-middle-income countries, has become vulnerable to adverse health impacts due to poor AQI over time. The capital city of Nepal, Kathmandu, is frequently placed as one of the most polluted cities by IQAir. As such, this statement marks a caution for policymakers and the public to be aware of the future effects of prolonged exposure to air pollution and highlights its causative factors. To solve this burgeoning issue, because of the significant rise in urbanization and population growth, mitigation strategies such as creating awareness, reducing vehicular emission, reducing forest fires, creating and identifying vulnerability maps risk zones for forest fires, switching to electronic vehicles and personal interventions such as staying indoors and reducing physical outdoor activity, wearing face masks must be focused and given priority. Although our study is limited in its methodology and findings, it helps establish a policy base to underscore the need for longitudinal cohort studies to generate further evidences pertaining to not up to par air quality levels and their subsequent health impacts in the Nepalese population.

The discourse on community violence has expanded over the years, shifting from a focus on interpersonal physical harm to a broader understanding that includes systemic and structural harm. Structural violence, characterized by institutionalized inequities in health, education, and generational wealth, disproportionately impacts marginalized communities and reflects deliberate systems of oppression designed to maintain power imbalances. In community-based violence intervention and prevention (CVIP), identifying how harm can be systematically perpetuated is critical for developing and advancing structurally grounded evaluative measures and training strategies for practitioners. This qualitative study involved interviews and focus groups with community violence intervention (CVI) practitioners (N = 45) from Sacramento, Milwaukee, and Baltimore. We analyzed participants' narratives to explore their understandings of the root causes of community-based firearm violence, with particular attention to the core tenets of structural violence: power, marginalization, oppression, adversity, and trauma. Findings revealed that CVI practitioners hold varying levels of structural violence expertise, ranging from individual-level explanations of violence to critical accounts of how systemic forces cultivate and reproduce structural harm. Participants discussed how government divestment, institutional neglect, and collective and vicarious trauma shape the conditions contributing to community violence. Their reflections underscore the need for standardized training and professional development that embeds structural frameworks into CVIP operations and program evaluations. As key actors in CVIP, CVI practitioners must be equipped with the knowledge and skills to address the structural drivers of community violence. Investing in their capacity for research and advocacy will strengthen the field's effectiveness, scale, and legitimacy in preventing community-based firearm violence through structurally informed practice and evaluation.

Drug safety is a critical aspect of public health, yet traditional detection methods may miss rare or long-term side effects. Recently, machine learning (ML) techniques have shown promise in predicting drug-related side effects earlier in the development pipeline. The objective of this policy brief was to propose evidence-based policy options for using ML techniques to predict drug-related side effects. This policy brief was developed upon a previously published scoping review of relevant studies. A secondary analysis synthesized key barriers and opportunities relevant to policy development. Key findings revealed some challenges in data standardization, interpretability, and regulatory alignment. Moreover, the results highlighted the potential of explainable ML and cross-sector collaboration to improve prediction accuracy and fairness. Five policy recommendations were proposed: (1) establishing standardized data collection and secure protocol sharing; (2) funding ML model development and rigorous validation; (3) integrating ML into drug development pipelines; (4) increasing public awareness through targeted education; and (5) implementing fairness regulations to address bias. These recommendations require joint efforts from governments, regulatory bodies, pharmaceutical firms, and academia to be implemented in practice. While ML offers transformative potential for drug safety, its real-world implementation faces ethical, regulatory, and technical hurdles. Policies must ensure model transparency, promote equity, and support infrastructure for ML adoption. Through interdisciplinary coordination and evidence-based policymaking, stakeholders can responsibly advance ML use in drug development to enhance patient outcomes.

Palliative care (PC) services are essential for cancer patients, particularly in low- and middle-income countries (LMICs), where cancer-related deaths are disproportionately high. Despite their significance, access to effective PC remains limited in many LMIC settings. This systematic review aims to identify strategies for implementing PC services for cancer patients in these regions, focusing on the challenges faced. A comprehensive search was conducted for peer-reviewed articles published between January 2004 and July 2024, utilizing the databases Web of Science, Scopus, PubMed, and Google Scholar. The Critical Appraisal Skills Program (CASP) assessment tool was employed to evaluate the quality of the studies following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for transparency. Out of approximately 966 818 articles retrieved, only 17 studies met the defined inclusion criteria. The findings highlighted effective strategies for delivering PC services in LMICs, including patient navigator-led programs, telemedicine, and home health care services. The review highlighted several interventions for PC services, including massage, Cancer and Living Meaningfully (CALM), and light therapies. However, it also identified significant challenges, such as the educational levels of caregivers, patient acceptance of PC services, logistical issues, medication side effects, and a preference for traditional healing practices. This systematic review highlights the critical need for effective PC services for cancer patients in LMICs, where cancer-related mortality rates remain alarmingly high. By synthesizing data from various studies, this analysis offers a comprehensive framework for developing successful palliative care initiatives in these regions. It emphasizes the importance of training caregivers of cancer patients to enhance their confidence in delivering palliative care services and counseling patients about the benefits of these services. Utilizing this information can help practitioners and policymakers improve palliative care services, ultimately enhancing the quality of life for cancer patients in LMICs.

Community Violence Intervention (CVI) programs show promising results in reducing health disparities such as firearm injury and violence. However, the process by which these programs bring about positive change is less well due to program variations and the focus of existing studies. Hence, program components and strategies used in day-to-day community violence intervention work are less clear. To address this gap, this study used in-depth interview data focused on understanding the early engagement of participants in an east coast United States community violence intervention program (n = 32). Questions focused on the process by which credible messengers as outreach workers motivate at-risk individuals to join the program, obtaining descriptions of the personal mentoring and cognitive change efforts driving desistance. Three key themes emerged: outreach workers use their own "lived experience" or self-narratives to build trust and motivate at-risk individuals to join and stick with programing; outreach workers and participants form a unique relationship through which participants are buoyed by belonging to a new "family"; and participants acquire new skills and prosocial peer networks that help them navigate away from the streets. Together, these processes support at-risk individuals through what might be best understood as a social movement as opposed to an individualistic process of "corrections" or reform.

This paper aims to establish an intelligent delivery system integrated with track logistics and explores its impact on the clinical transportation of goods. The study analyzed hospital delivery items before and after the implementation of an intelligent delivery system combined with automated track logistics in 2023. Delivery conditions prior to the system's activation served as the control group, while those post-implementation formed the observation group. This research assesses the effects on transportation efficiency, resource allocation, transport safety, and satisfaction among medical staff. The average delivery time of goods significantly decreased following the adoption of the intelligent delivery system combined with automated track logistics (P < .01). The total number of adverse delivery-related events was reduced from 25 to 4 (P < .01). Departmental medical staff reported substantial increases in scores for delivery efficiency, error incidence, and overall satisfaction with the delivery process (P < .01). The integration of the intelligent delivery system with automated track logistics significantly enhances the efficiency of clinical goods transportation, optimizes resource allocation, ensures the safe transport of items, and improves the patient experience and clinical staff satisfaction with delivery operations. Regular system maintenance and adjustments, supported by professional technical personnel, are essential to fully leverage the system's advantages.

The 4Ms Framework is the foundation of the Age-Friendly Health System (AFHS) movement. While the framework is based on standalone evidence for each M, there is limited evidence about the impact on outcomes when practiced as a set. A composite measure capturing adherence to the many care processes that comprise the 4Ms is a necessary but complex component of closing the evidence gap. We offer a navigation guide that addresses key considerations for developing a hospital-based composite measure of 4Ms care. The Institute for Healthcare Improvement operationalizes the 4Ms Framework as a minimum set of Assessment and Act On care processes. In developing a composite measure of inpatient 4Ms adherence, we offer a 4 step framework with associated discussion of considerations related to composite measure type (eg, continuous, dichotomous), and synchrony within and across the Ms containing these care processes. Using real-world electronic health record data capturing care process adherence in the 4Ms implementation at a large academic hospital, we illustrate the considerations, and report the implications for sample size and composite measure scoring. We also present our selected composite measure-a dichotomous measure delineating 4Ms care when all encounter-level processes (those needing to be done only once during the hospital encounter) are followed and all day- and shift-level processes are followed for at least 50% of hospital days. While there is no single, standard approach to create a 4Ms composite at this early stage of the AFHS movement, as organizations develop their measure(s), our guide and the considerations we suggest should serve to inform this process and support progress toward meaningful measurement.

The Tailored Activity Program (TAP), an intervention for people living with dementia (PLWD) and their caregivers, has been shown to reduce behavioral symptoms for PLWD and caregiver burden. While TAP is proven as an evidence-based practice (EBP), it has yet to be implemented at scale. The Department of Veterans Affairs (VA) has prioritized the Age-Friendly Health System (AFHS) initiative, providing an opportunity to test implementation of TAP in a complex healthcare system. We conducted semi-structured pre-implementation interviews with leaders and clinicians at 6 VA Medical Centers (VAMCs) to engage key implementation partners and understand their unique implementation contexts. We utilized team-based rapid qualitative analysis to identify themes related to implementation determinants. We interviewed 65 unique informants in 58 interviews (5 VAMC leaders, 36 department leaders, and 17 frontline clinical staff). Informants identified 4 key factors critical to consider prior to implementing TAP: (1) alignment with organizational priorities; (2) perceived value and fit with existing clinical workflows; (3) competition with existing organizational and clinical priorities; and (4) considerations about the effect of caregiver burden on participation. We identified key factors to consider for successful implementation of a multicomponent intervention for PLWD and their caregivers within a complex healthcare system. As the AFHS initiative expands, there is a growing need for EBPs focused on the care of PLWD and their caregivers. These factors can guide clinicians, leaders, and implementation scientists in planning for implementation and sustainment of EBPs to bolster AFHS initiatives.Trial RegistrationRegistered 05 May 2021, at ISRCTN #60,657,985.Reporting GuidelinesThe COnsolidated criteria for REporting Qualitative research (COREQ) checklist was used to ensure proper standards for reporting qualitative studies (see attached).

We examined regression models predicting health services standardized costs (HSSC) during the years preceding death using varied temporal parameters related to the dependent and independent variables. The regression models sought to elucidate how costs before the final year of life, temporal factors, and demographics are associated with costs in the final year. Anonymized data were derived from the records of Israel's largest health maintenance organization for 71,855 people aged 65+ in 2006, who died between 2008 and 2011. In the regression models, the Independent Variables of age, sex, and comorbidity (as measured by the Charlson Comorbidity Index) were significant predictors of the dependent variable of HSSC during the final year of life. However, the strongest predictor (independent variable) of the dependent variable, HSSC in the final year of life was the independent variable, HSSC in the years preceding the final year of life. Prediction was more accurate when the predicting period was closer to the predicted period. Accuracy declined as the predicted period approached death. The results provide insights into methodological considerations in the process of prediction of end-of-life expenditures, which may assist in setting methodological standards that may facilitate arriving at consistent findings in this field. While end-of-life is associated with aberrant increases in costs, that is, increases that deviate from prior predictions, significant predictions can still be made.