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

The concept of a healthy organisation promotes environments that enhance employee well-being, motivation, and productivity. Despite its benefits, it remains underexplored in the shipping sector. To address this gap, a pilot study was conducted to examine how seafarers and management perceive a healthy shipping company, with a focus on identifying the key determinants. This approach underlines the importance of active employee involvement, which is consistent with the principles of a healthy organisation. An exploratory, convergent, mixed-methods pilot study combined qualitative and quantitative data collection. The questionnaire included demographic and work-related characteristics, an open-ended question on the definition of a healthy shipping company from a personal perspective, and scaled ratings of key determinants of healthy shipping companies identified through a literature review. The sample consisted of 309 participants from the international shipping sector, including 238 seafarers and 71 shipping company managers. The study identified key factors that contribute to a healthy shipping company, with both seafarers and managers recognising the importance of employee well-being, safety, work-life balance, and a supportive organisational culture. These findings were consistently reflected in both the quantitative and qualitative data. The qualitative results also revealed group-specific perspectives: managers highlighted the importance of financial management, strategic innovation, and corporate governance, while seafarers placed greater emphasis on immediate working conditions and mutual respect. Overall, the findings underscore the importance of a collaborative approach, where both seafarers and managers are actively engaged in shaping a healthy organisational environment.

Following the leak of the Dobbs decision in 2022, abortion access in the United States has faced heightened barriers, including legal restrictions, financial constraints, and logistical challenges. In response, individuals seeking abortion care can employ innovative behavioral strategies to overcome these barriers and reshape their abortion experiences (ie, "behavioral innovations"). This paper explores the behavioral innovations to access abortion that people discussed and recommended within a geographically dispersed community of peers on an abortion-supportive Reddit community (r/abortion). Using a hybrid inductive and deductive thematic qualitative analysis approach with a purposive sample of comments in the r/abortion community in 2022 following the Dobbs leak (May-December, n = 131 comments), we identified discussion of abortion access innovations related to getting in-clinic care, self-managed abortion (SMA), funding assistance, privacy, and emotional support. Innovations included sharing online resources for clinic locations, specific travel recommendations to less restrictive states, accessing abortion medications through online services, and navigating the SMA process. Additionally, other less tangible innovations were discussed, including strategies for keeping abortions private and seeking emotional support. Our findings highlight how individuals within the r/abortion community discuss and share creative strategies for navigating the evolving barriers to abortion care. The r/abortion platform serves as a crucial resource for individuals seeking innovative solutions to these barriers, underscoring the need for diverse information-sharing practices to improve access to care as shifting legislation increasingly demands approaches beyond conventional norms.

Social determinants of health (SDOH) knowledge and skills are crucial to improving health outcomes and achieving health equity. Yet, many barriers exist including gaps in training for nurse practitioner (NP) faculty, challenges in curricular integration, and translation of SDOH skills to NP practice. With the pedagogical shift to competency-based education, SDOH can be integrated throughout NP education utilizing a structured approach. To improve NP faculty knowledge of SDOH and enhance curricular inclusion, a team of NP educators developed a faculty enrichment initiative that consisted of a comprehensive toolkit and 3-part webinar series. As a result, the toolkit provides evidence-based strategies for SDOH curricular integration as well as assignment exemplars to streamline the process for NP faculty. The webinar series, structured using evidence-based teaching strategies, faculty engagement, and competency-driven learning objectives, focuses on disseminating knowledge, skills, and the practical application of SDOH principles in NP curricula. In conclusion, with successful faculty enrichment initiatives, existing barriers can be overcome resulting in knowledgeable faculty, robust curricular integration, and practice-ready NPs that improve health outcomes and promote health equity.

People with disabilities (PWD) face persistent health and rehabilitation disparities, including poorer health outcomes often driven by non-inclusive healthcare and technologies that overlook their unique needs and values. Artificial intelligence (AI) holds opportunities to transform health and rehabilitation services; however, without inclusive, participatory, and disability-centered design efforts, AI tools risk perpetuating existing health and rehabilitation disparities and inequalities. This paper introduces an integrated framework for disability-inclusive AI design grounded in Self-Determination Theory (SDT) and Self-Efficacy Theory (SET). The framework aims to guide the design, development, and implementation of inclusive AI tools for PWD. It also outlines implications for public health, workforce, training, and policy, supporting the integration of disability-centered AI in health and rehabilitation.

Communicating health research through local and national print, television, and radio news can amplify the impact of research findings. However, relatively few health researchers work with the media to communicate their findings to a broader audience. In April 2024, we convened a group of specialists with expertise in traditional media, health news, and health advocacy for a webinar sponsored by the Helping to End Addiction Long-term (HEAL) Initiative. We present an overview of the discussion, including opportunities within the context of traditional media, guidance for health researchers on partnering with the media, and themes on translating health research to a general audience. Health researchers can use this article as a guide to working with the media to expand the influence of their research findings.

To evaluate changes in enrollment, average risk scores, and premiums in the Affordable Care Act individual market after states transitioned from the federally facilitated marketplace (Healthcare.gov) to a state-based marketplace (SBM) between 2018 and 2023. This study employed a retrospective, quasi-experimental design of secondary data using a synthetic difference-in-differences analysis methodology. Our primary data source consisted of individual market risk adjustment summaries from 2016 to 2023. We conducted a synthetic difference-in-differences analysis to evaluate changes in enrolled member months, average premium paid, and state average risk scores in the individual health insurance market. Our treatment group comprised 4 states that transitioned from Healthcare.gov to a state-based marketplace between 2018 and 2023. The comparison group was the 33 states that continuously used Healthcare.gov from 2016 to 2023. States that converted from the FFM, Healthcare.gov, to a state-based marketplace did not experience statistically significant changes in enrollment, premiums paid, or state average risk scores. These results were robust to alternative specifications. The transition to state-based marketplaces in 4 states did not lead to significant changes in the ACA individual market risk pool enrollment, or premiums paid while potentially increasing state policy autonomy. Future policy efforts should explore how states can leverage policy autonomy to improve market outcomes and coverage.

In the implementation of diagnosis-related groups (DRGs), hospitals respond to price changes by incorporating more patients into the more profitable DRGs, thereby providing evidence for upcoding. This study proposes a two-stage DRGs grouper (ML-DRG) to alleviate the risk of upcoding. The ML-DRG employs machine learning methods to build a predictive model of patients' clinical resource consumption and assigns the model output as the resource consumption index, which comprehensively considers various patients characteristics and is challenging to modify. We utilize the data from the Chengdu Healthcare Security Administration of China, covering the period from 2011 to 2018, to compare the performance of the proposed method with the 3 mainstream approaches. Our findings indicate that the intracranial hemorrhagic disease (BR1) group and respiratory infection/inflammation disease (ES2) group of ADRG were divided into 4 DRGs, with the coefficient of variation of each group being less than .8. Among the 4 grouping methods, ML-DRG demonstrated the best performance. These findings suggest that the application of ML-DRG may reduce the risk of upcoding by helping hospitals avoid selecting incorrect DRG codes for higher reimbursement rates.

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.