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

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.

Nurses are the principal caregivers in acute care. Evidence links nursing to patient outcome disparities. Conceptual frameworks addressing health inequities, however, overlook nursing factors including staffing, work environment, and structural factors. This paper addresses this gap by presenting a framework postulating nursing system factors as contributors to inequities, distinguishing it from frameworks focusing mainly on individual or social determinants. The literature demonstrates that hospitals with better nurse staffing and work environments have lower mortality and complication rates, particularly among vulnerable populations. Additionally, nursing factors vary by hospital and correlate with patient racial composition and outcomes. Authoritative reports and frameworks on healthcare disparities from the National Academies of Sciences, Engineering, and Medicine and the National Institute on Minority Health and Health Disparities were reviewed. The role of nursing in each was summarized. Kilbourne et al.'s framework was adapted to propose that disparities in patient outcomes are shaped by the organizational context of nursing, for example, nurse staffing, work environment, structural competence, and the patient-nurse clinical encounter. Nursing's impact on equitable care and outcomes should be central to health disparity frameworks. This framework implies that policymakers include nursing elements in equity performance measures and incentivize them through payment systems. Administrators should consider nursing system features as integral to equitable care. Research on the framework assertions is warranted to inform health equity strategies through nursing. By highlighting mechanisms through which nursing factors contribute to disparities, this framework motivates health equity research and policy in acute care settings.

This systematic review and meta-analysis evaluated how exergaming (EXG) compares with various conventional physical therapies in improving balance and reducing fall risk among prefrail and frail older people. We searched 6 databases PubMed, Medline, CINAHL Complete, Scopus, the Cochrane Library, and Web of Science up to April 2025. Study quality and evidence certainty were appraised using PRISMA, TESTEX, Rob 2, and GRADE. For meta-analysis, Hedge's g effect sizes were computed for balance and fall risk outcomes. We chose fixed- or random-effects models and conducted subgroup analyses based on therapy dosage (sessions per week and minutes per session). The protocol is registered in PROSPERO (CRD420251009891). From 2434 records, 10 RCTs (n = 400; mean and standard deviation age 75.7 ± 5.9 years) met inclusion criteria. Overall and subgroup meta-analyses (4 each) showed significant EXG benefits for the Mini-BESTest (P < .01), Timed Up-and-Go (TUG; P < .05) and Fall Efficacy Scale-International (FES-I; P < .05). No statistically significant change was found for the Berg Balance Scale (BBS; P = .05). When stratifying by dosage, EXG outperformed controls in TUG specifically for protocols with fewer than 3 sessions/week and under 50 min/session (P < .01). Dosage did not significantly influence FES-I outcomes. EXG is an alternative therapy that improves balance by reducing the fall risk, as measured by the Mini-BESTest, TUG, and FES-I, compared with conventional physical therapies (ie, physiotherapy, balance training, strength training, aerobic training, multicomponent training). Notably, protocols with <3 weekly sessions of <50 min each yielded the most pronounced TUG improvements.

The rapid digital transformation in the public healthcare sector demands effective digital leadership to improve organizational performance. This study investigates the impact of digital leadership on employee empowerment and its subsequent effects on techno-work engagement and sustainability performance within public healthcare institutions in Pakistan. A survey-cum questionnaire method was employed for 334 respondents of employees of public healthcare institutions in the Punjab Province of Pakistan. It used structured questionnaires to measure digital leadership, sense of empowerment, techno-work engagement, and sustainability performance. Structural Equation Modeling (SEM) was performed on data to examine the proposed relationships among the variables. The findings of SEM showed that digital leadership positively influences employees' sense of empowerment. Empowerment significantly predicted techno-work engagement and sustainability performance. Techno-work engagement also positively affected sustainability performance. Mediation analysis revealed that the sense of empowerment mediates the relationship between digital leadership and both techno-work engagement and sustainability performance. The findings demonstrate that digital leadership enhances employee empowerment, which in turn boosts techno-work engagement and sustainability performance in the public healthcare sector. Organizations should promote digital leadership practices to empower employees and achieve sustainable outcomes.