Health Care Management Review最新文献

Issue: Despite rapid innovation, health care systems face a persistent 17-year gap between evidence discovery and implementation, undermining efforts to deliver value-based care. Bridging this "know-do gap" is essential to improving outcomes and reducing waste. Existing Learning Health System (LHS) frameworks often lack mechanisms to institutionalize learning at speed and scale.

Critical theoretical analysis: We propose an AI-enabled LHS framework that leverages artificial intelligence (AI) to connect micro-level clinical learning with macro-level organizational decision-making. Grounded in organizational learning theory, our model illustrates how AI accelerates knowledge capture, conversion, and institutionalization via continuous, bidirectional feedback loops. AI enables real-time learning cycles, linking patient-provider data ("micro") to system-wide insights and policy adjustments ("macro"), and back to point-of-care decision support.

Insight/advance: Our framework advances the LHS paradigm by adding speed, scale, and micro↔macro integration. Unlike earlier models, it centers AI not as an adjunct but as a foundational learning engine. Case examples from UCHealth and Mass General Brigham show how AI can drive real-time operational learning and institutional memory through structured governance and data infrastructure.

Practice implications: To implement an AI-LHS, organizations should (1) assess readiness and align on value-based goals; (2) invest in data infrastructure and interoperability; (3) cultivate a learning culture by engaging clinicians and staff; (4) embed AI into continuous improvement cycles with interdisciplinary governance; (5) adopt a sociotechnical approach integrating people, processes, and technology; and (6) ensure safeguards for equity, privacy, and security. These steps allow systems to reduce lag between insight and impact, accelerating value-based care transformation.

Background: Hospitals' utilization of social determinants of health data represents an important strategy for improving patient outcomes and reducing 30-day readmissions.

Purpose: Using a contingency theory lens, this study investigated the relationship between hospitals' use of social needs data (SND) and 30-day readmission rates for conditions targeted by the Centers for Medicare and Medicaid Services' Hospital Readmission Reduction Program.

Methodology/approach: A cross-sectional retrospective study was conducted using data from general and surgical acute care hospitals (n=1,350). Regression models were used to examine associations between SND usage and 30-day readmission rates.

Findings: Hospitals using SND for referrals to social service organizations experienced a 0.46-point decrease in heart failure readmissions (p<.001). Population health analytics use was associated with a 0.13-point decrease in hospital-wide readmissions (p<.05). Complete utilization of SND use was associated with a 0.30-point decrease in COPD (p<.05) and a 0.33-point decrease in pneumonia readmissions (p<.05).

Conclusions: The effectiveness of SND strategies varies significantly based on hospital characteristics, regional factors, and clinical conditions, supporting contingency theory's premise that performance depends on alignment between strategies and contextual factors.

Practice implications: Rather than advocating for uniform activities, our results suggest that hospital administrators should seek to achieve an optimal fit between the social needs identified in their communities and their specific organizational and environmental circumstances.

Background: The trust that health care employees place in their supervisor is critical to creating positive employee outcomes. However, questions remain regarding the development and outcomes of employee-supervisor trust in the health care context.

Purpose: Recent studies challenge the often-used ability, benevolence, and integrity (ABI) model of trust, particularly that all three dimensions are needed to build trust. This study contests this assumption of the model, assessing ABI-based trust in a supervisor on health care employee person-job (P-J) fit, job satisfaction, and turnover intentions.

Methodology/approach: Using a survey of health care employees, we apply qualitative comparative analysis (QCA), linking ABI configurations to P-J fit, job satisfaction, and turnover intentions.

Results: The results indicate that ABI components of trust, indeed, are not all needed for most employees. For instance, if supervisors exhibit high levels of only benevolence or integrity, many employees experience high P-J fit. Similarly, employees experience high job satisfaction when supervisors possess either high levels of ability or benevolence. Finally, employees hold low turnover intentions when supervisors have high levels of just ability or integrity.

Conclusion: The results call for refinement of the ABI model in the health care context.

Practice implication: Health organizations can train supervisors on specific ABI components based on employee needs, offering strategic use of resources to satisfy and retain employees.

Background: Health care is increasingly organized in networks in which organizations collaborate to address health care's grand challenges. While leadership is key to network effectiveness but can be disrupted by turnovers, little is known about how leadership transitions unfold.

Purpose: This study aimed to understand how such leadership transitions unfold in purpose-oriented networks.

Methodology/approach: We explored this through an in-depth case study of a network prior and during a leadership transition (5 years), performing 34 interviews, 46 hours of observations, and collecting documents.

Results: Our results show that an unexpected leadership transition can take time and affect dynamics such as trust and the network's composition. A lead organization can be established based on either its organizational characteristics or the characteristics of its individuals. In the latter case, the network's governance structure may be less stable. Covert conversations and decision-making occurred outside formal network meetings, showing that the influence of formal leaders, though lacking formal hierarchy, may be larger than previous research suggests.

Findings: Leadership transitions can affect dynamics in networks negatively and the influence of formal leaders, though they lack formal hierarchy, may be larger than previously suggested. Lead organization can emerge through different ways, including personal characteristics, organizational resources, and organizational-network goal alignment.

Practice implications: Networks benefit from consciously designing their governance and leadership structure in a way that diminishes the effects of an unexpected transition, such as dual leadership from two organizations. Furthermore, networks should consider the three pathways for lead organizations to emerge in choosing their lead organization during each life-cycle phase.

Issue: Caring is inextricably intertwined with resilience: the ability of individuals and collectives to deal with setbacks. To respond resiliently, actors draw on resources that may lie beyond their direct reach, requiring the engagement of others within the care system. The complex, hierarchical, and fragmented nature of care systems both necessitates and complicates cooperation and coordination between actors as they pursue or support resilient responses.

Critical theoretical analysis: Relational perspectives on resilience provide a starting point for understanding the relational processes determining whether and how resources are exchanged. However, they do not provide insight into the behavioral dynamics that help explain how interdependent actors negotiate their relationships as they pursue or support resilience.

Advance: Drawing on the integrative negotiation theory, we extend resource-based and relational perspectives on resilience by elucidating the interactional specifics of how actors can collaborate to effectively deal with setbacks-even when they pursue seemingly conflicting outcomes. Highlighting the importance of voice, epistemic motivation, and prosocial motivation, we capture the interpersonal dynamics that shape (a) awareness of resource needs and others' ability to provide these; (b) interactions informing decisions on whether and how resources are exchanged; and (c) the ability to find integrative solutions that go beyond zero-sum exchanges, serving all actors' interests and needs.

Practice implications: Our framework informs the collective development of resilient solutions. We highlight the behavioral repertoires (i.e., the interactions through which resources are pursued, negotiated, and/or shared), enabling actors within care systems to build on their interdependencies as they pursue or support resilient responses to setbacks.

Background: Work burnout and job turnover intentions pose challenges for health care workers. An understudied area of impact is COVID-19 occupational stigma consciousness or the awareness that one's occupation has been stigmatized because of proximity and exposure to COVID-19.

Purposes: Using job demands-resources theory, we examine the impact of COVID-19 occupational stigma consciousness, a novel job demand, on work burnout and job turnover intentions and the effectiveness of common job resources to buffer these effects among nurses in the United States.

Methodology/approach: We surveyed frontline nurses working in the United States during the COVID-19 pandemic ( N = 282). We used structural equation modeling to examine the impact of COVID-19 occupational stigma consciousness on job turnover intentions and the mediating role of work burnout. Perceived supervisor support and supportive work climate were examined as job resources.

Results: COVID-19 occupational stigma consciousness was significantly related to work burnout, and work burnout fully mediated the relationship between COVID-19 occupational stigma consciousness and job turnover intentions. Perceived supervisor support was related to reduced job turnover intentions and reduced work burnout. However, supportive work climate and perceived supervisor support did not moderate the impact of COVID-19 occupational stigma consciousness on work burnout.

Conclusions: This study found support for the negative impact of a novel job demand, COVID-19 occupational stigma consciousness, on nurses in the workplace. Social support resources that are effective in other contexts for reducing the harmful effects of job demands were not effective at reducing occupational stigma among nurses during COVID-19.

Practice implications: As pandemics increase in frequency, organizations and managers should understand the effects of disease-related stigma consciousness on employees and focus on implementing job resources that can mitigate the effects of such stigma.

Background: In the advance of the digital health technology, health care organizations (HCOs) are tasked with balancing technological advances with rising incidence of cyber threats. Despite the importance of robust Information Technology (IT) infrastructure, HCOs may be underinvesting in cybersecurity, prioritizing system integration and other operational needs.

Purposes: This study examines the threat of health information breaches and ransomware attacks via resource-based view of the firm by examining the role of HCO resources in breaches.

Methodology/approach: A multivariate logistic regression analysis of a nationally representative sample of HCOs ( N = 2,262) was executed on data provided by the Office for Civil Rights (2019-2024) and the American Hospital Association (2019).

Results: The study finds mixed evidence that resource availability influences the likelihood of a breach and ransomware attack. HCO centralization and teaching status were more likely to report both breach and ransomware attack, whereas HCO size relates to a significant reduction. The study finds no support for the effectiveness of IT risk mitigation capacity.

Conclusion: The findings show that the incidence of breaches and ransomware attacks relates to HCO resource availability. Although the study finds no evidence that IT risk-mitigation capacity (IT staffing and expenditures) reduced the likelihood of breach or ransomware, this may be driven by the infrequency of these events.

Practical implications: HCOs' understanding of their risk profile is limited, and there is a need for greater transparency in the incidence of ransomware attacks, in particular. There is a need for further examination of IT strategy and operations in an increasingly digital health care environment.

Issue: Two of the most important healthcare management topics studied over the past 37 years are hospital consolidation and quality. The specifics of consolidation's impact on quality are buried in a "black box" of organizational changes that typically follow consolidation. Academic researchers, both inside and outside health care, question the quality benefits. The paper discusses why quality benefits are not often observed.

Critical theoretical analysis: Strategy and management theory are ambivalent about consolidation's impact on quality (i.e., suggest both positive and negative effects). Such ambivalence is evident in a two-stage conceptual model of how consolidation might impact quality. Consolidation is accompanied by strategic initiatives and organizational changes that can involve quality-promoting investments but may also harm quality.

Insight/advance: The paper documents the ambivalent effects of consolidation on quality, which repeatedly manifest themselves in theory, a conceptual model, and several literature reviews. One explanation for the mixed results are methodological issues that hamper model estimation; another explanation is the two-stage conceptual model. In the first stage, hospitals consolidate to pursue organizational changes and strategic goals, some of which may target quality. In the second stage, such changes can exert quality impacts, but not necessarily. Most research focuses on the second stage rather than the first; research on the first is more indirect.

Practice implications: Hospital executives and researchers should not assume that consolidation will yield quality improvements. Executives often espouse quality improvement as a goal of consolidation but may or may not invest in quality improvement initiatives. Even when they do, such initiatives may not lead to higher quality.