Health Care Management Review最新文献

Background: Clinicians in the United States often prefer quick, efficient, and convenient ways to communicate with colleagues. However, using standard text messaging without a secure text messaging system (STMS) constitutes a violation of Health Insurance Portability and Accountability Act regulations.

Purposes: This systematic review aims to investigate why clinicians might choose not to use STMSs when sharing patient health information (PHI) with their peers. This research will explore whether these clinicians are consciously disregarding federal guidelines concerning the texting of PHI and the reasons behind this choice. Deliberate noncompliance could be classified as a form of deviant behavior within the workplace.

Methodology/approach: We conducted a Preferred Reporting Items for Systematic Review and Meta-Analysis-guided systematic literature review across four databases, examining 10 empirical studies from the United States published in English between January 2017 and August 2024.

Conclusion: We identified deviant behaviors associated with the unsecured text messaging of PHI through a deductive approach. These behaviors were classified based on Lawrence and Robinson's workplace deviance framework, which includes the need for autonomy, social identity, and perceived organizational justice. We also found inductive themes, which included training needs and increased workloads.

Practice implications: The findings of this study demonstrate a number of areas hospital leaders might examine relevant to this topic, including reluctance to use STMSs, a choice to adopt it, or a tendency to blame issues on inadequate policies or insufficient training. This situation emphasizes the necessity of offering comprehensive training and clear policies to address both the psychological barriers and practical challenges faced by clinicians.

Background: Organizations worldwide have faced many challenges due to the COVID-19 pandemic. In the health care sector, including breast cancer, multidisciplinary teams have suffered disruptions due to changes in the organization of work and in membership composition. Moreover, the entire health care workforce has been impacted by significant stress.

Purpose: This study aimed to understand how these challenges affected the probability of observing changes in breast unit (BU) patients' perceptions of the quality of care received.

Methodology/approach: We tested three research hypotheses using a sample of 366 patients nested in 68 BUs, performing logistic regression using STATA 14.

Results: The stress experienced by BU team leaders had a positive impact on the perceived stability of quality of care, whereas changes in team composition and social distancing among members had a negative impact on it.

Practice implications: Health care managers should develop new training paths to help professionals adapt to new ways of working using appropriate technologies. Organizations should make teams able to share knowledge and practices through specific instruments, such as repository tools that can support teams even in cases of membership changes. Policymakers should pay greater attention to the mental health of health care workers, especially in emergencies.

Conclusions: This paper highlights the key factors that enable multidisciplinary teams to succeed, even under emergency conditions.

Background: Health care organizations have failed to replicate the performance of high-reliability organizations (HROs), systems that operate nearly error-free despite inherently complex, interdependent, and time-pressured environments.

Purposes: We sought to better understand conditions that facilitate and inhibit HRO in health care by expanding the conceptualization of who contributes to high reliability to include patients. We also examine how cancer care teams and patients describe jointly enacting (or failing to enact) each of the five HRO principles.

Methodology/approach: We conducted a qualitative field study, interviewing 25 oncology clinicians from diverse roles (e.g., surgery, nursing, dentistry) (17 interviews) and eight cancer patients. Interviews were audio-recorded, transcribed, and deductively coded for barriers and facilitators to the five HRO principles.

Findings: We identified conditions that supported and hindered high-reliability cancer care. HRO principles commonly overlapped to support or hinder system resilience. A common barrier was insufficient processes to proactively identify clinical deterioration. Patients and their family caregiver(s) were primarily responsible for monitoring their health and identifying and communicating any signs of deterioration. Cancer patients and caregivers were routinely engaged as team members, and patients developed expertise throughout treatment, thereby supporting system safety.

Conclusions: These findings expand the conceptualization of how high reliability is produced. Specifically, we highlight the key role that patients play in preventing errors and monitoring care.

Practice implications: Our findings expand our understanding of how HRO principles interact to promote cancer care system safety and elucidate how patients and their family caregivers contribute to system resilience. This work can inform the redesign of care processes to optimize patient safety.

Background: Various forms of Physician-Hospital Integration (PHI) have occurred across the U.S. health care delivery system, and research has shown that the structure and characteristics of PHI models may influence various aspects of hospital performance.

Purpose: This study explores the relationship between different PHI types and patient experience (PX), with a specific interest in the influence of these structures on physician communication and overall hospital ratings.

Methodology: Using a Generalized Estimating Equations model, we analyzed data from 2,716 U.S. acute care hospitals (n=2,716) to explore how "tight," "loose," and "multimodal" PHI types influence PX during the COVID-19 pandemic, utilizing data from two time points: 2019 (prepandemic) and 2022 (post-peak pandemic).

Results: Our results reveal that hospitals with "tight" PHI type, characterized by greater control over physicians, were associated with lower physician communication ratings and overall hospital ratings.

Conclusions: Our findings suggest that rigid organizational structures may hinder effective physician-patient interactions and hospital performance during crises. Conversely, hospitals with "loose" PHI type demonstrated better resilience in maintaining PX, likely due to increased physician autonomy and flexibility in care delivery.

Practice implications: These findings highlight the importance of adaptable strategies in health care management, emphasizing the need for balance between physician oversight and autonomy to improve PX.

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.