Health Care Management Review最新文献

Background: Health care professionals' work motivation is assumed to be crucial for the quality of hospital care, but it is unclear which type of motivation ought to be stimulated to improve quality. Motivation and similar concepts are aligned along a motivational continuum that ranges from (intrinsic) autonomous motivation to (extrinsic) controlled motivation to provide a framework for this mixed-methods systematic review.

Purpose: This mixed-methods systematic review aims to link various types of health care professionals' motivation directly and through their work-related behaviors to quality of care.

Methods: Six databases were searched from January 1990 to August 2016. Qualitative and quantitative studies were included if they reported on work motivation in relationship to work behavior and/or quality, and study participants were health care professionals working in hospitals in high-income countries. Study bias was evaluated using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields. The review protocol was registered in the PROSPERO database (CRD42016043284).

Results: A total of 84 out of 6,525 unique records met the inclusion criteria. Results show that health care professionals' autonomous motivation improves their quality perceptions and work-related behaviors. Controlled motivation inhibits voicing behavior, but when balanced with autonomous motivation, it stimulates core task and proactive behavior. Proactivity is associated with increased quality of care perceptions.

Practice implications: To improve quality of care, policy makers and managers need to support health care professionals' autonomous motivation and recognize and facilitate proactivity as an essential part of health care professionals' jobs. Incentive-based quality improvements need to be complemented with aspects that stimulate autonomous motivation.

Background: Health care organizations are integrating a variety of machine learning (ML)-based clinical decision support (CDS) tools into their operations, but practitioners lack clear guidance regarding how to implement these tools so that they assist end users in their work.

Purpose: We designed this study to identify how health care organizations can facilitate collaborative development of ML-based CDS tools to enhance their value for health care delivery in real-world settings.

Methodology/approach: We utilized qualitative methods, including 37 interviews in a large, multispecialty health system that developed and implemented two operational ML-based CDS tools in two of its hospital sites. We performed thematic analyses to inform presentation of an explanatory framework and recommendations.

Results: We found that ML-based CDS tool development and implementation into clinical workflows proceeded in four phases: iterative solution coidentification, iterative coengagement, iterative coapplication, and iterative corefinement. Each phase is characterized by a collaborative back-and-forth process between the technology's developers and users, through which both users' activities and the technology itself are transformed.

Conclusion: Health care organizations that anticipate iterative collaboration to be an integral aspect of their ML-based CDS tools' development and implementation process may have more success in deploying ML-based CDS tools that assist end users in their work than organizations that expect a traditional technology innovation process.

Practice implications: Managers developing and implementing ML-based CDS tools should frame the work as a collaborative learning opportunity for both users and the technology itself and should solicit constructive feedback from users on potential changes to the technology, in addition to potential changes to user workflows, in an ongoing, iterative manner.

Background: Ensuring safe transitions of care around hospital discharge requires effective relationships and communication between health care teams. Relational coordination (RC) is a process of communicating and relating for the purpose of task integration that predicts desirable outcomes for patients and providers. RC can be measured using a validated survey.

Purpose: The aim of the study was to demonstrate the application of RC practices within the rural Transitions Nurse Program (TNP), a nationwide transitions of care intervention for Veterans, and assess relationships and mechanisms for developing RC in teams.

Methodology/approach: TNP implemented practices expected to support RC. These included creation of a transition nurse role, preimplementation site visits, process mapping to understand workflow, creation of standardized communication templates and protocols, and inclusion of teamwork and shared accountability in job descriptions and annual reviews. We used the RC Survey to measure RC for TNP health care teams. Associations between the months each site participated in TNP, number of Veterans enrolled, and adherence to the TNP intervention were assessed as possible mechanisms for developing high RC using Spearman (rs) correlations.

Results: The RC Survey was completed by 44 providers from 11 Veterans Health Administration medical centers. RC scores were high across sites (mean = 4.19; 1-5 Likert scale) and were positively correlated with months participating in TNP (rs = .66) and number of enrollees (rs = .63), but not with adherence to the TNP intervention (rs = .12).

Practice implications: The impact of practices to support RC can be assessed using the RC Survey. Our findings suggest scale-up time is a likely mechanism to the development of high-quality relationships and communication within teams.

Background: Disruptive behavior can harm high-quality care and is prevalent in many Western public health systems despite increasing spotlight on it. Comparatively less knowledge about it is available in Asia, a region commonly associated with high-power distance, which may limit its effectiveness in addressing disruptive behavior.

Purpose: The aim of this study was to develop a comprehensive framework for tackling disruptive behavior among health care professionals in a public health system.

Methodology: A nationwide cross-sectional study relying on the Nurse-Physician Relationship Survey was conducted in Singapore. Four hundred eighty-six public health care professionals responded.

Results: Two hundred ninety-eight doctors (95.5%) and 163 nurses (93.7%) had witnessed a form of disruptive behavior. Doctors observed disruptive behavior committed by other doctors and nurses much more frequently than did nurses. Doctors made stronger associations between disruptive behavior and negative employee outcomes and between disruptive behavior and negative patient outcomes. Qualitative analyses of participants' open-ended answers produced a multipronged three-dimensional approach for tackling disruptive behavior: (a) deterrent measures, (b) development of knowledge and skills, and (c) demonstration of organizational commitment through proper norms, empathizing with staff, and structural reforms.

Practice implications: Disruptive behavior is a multifaceted problem requiring a multipronged approach. Our three-dimensional framework is a comprehensive approach for giving health care professionals the capability, opportunity, and motivation to address disruptive behavior effectively.

Background: Federally qualified health centers (FQHCs) are pivotal safety net primary care providers for the medically underserved. FQHCs have complex organizational designs, with many FQHCs providing care at multiple physical locations ("sites"). The number of sites, however, varies considerably between FQHCs, which can have important implications for differential access that may perpetuate disparities in quality of care.

Purpose: The objective of this study is to explore the organizational and environmental antecedents of the number of sites operated by each FQHC. The findings of this study contribute to a better understanding of FQHCs' expansion that has vital implications for cost and access outcomes.

Methodology/approach: The study is based on data between the years 2012 and 2018. Using multivariate growth curve modeling, we analyzed the final sample, consisting of 5,482 FQHC-years.

Results: The level of competition, measured as the number of FQHC sites in the Primary Care Service Area (PCSA) and the number of primary care physicians per 1,000 PCSA residents, was positively associated with the number of FQHC sites. The number of patients, the level of federal grant, and the year were also positively associated with the number of FQHC sites, whereas percentage of Medicaid patients; workforce supply, measured as primary care physician assistants per 1,000 PCSA residents; Medicaid expansion; and state/local funding available for FQHCs were not.

Conclusion: Findings of this study indicate that competition, especially between peer FQHCs, is significantly associated with FQHC expansion.

Practice implications: This result suggests that FQHC managers and policymakers may closely monitor cost, access, and quality implications of competition and FQHC expansion.

Background: Effectiveness of end-of-shift patient handover between nurses may be impacted by poor communication. This can be improved with the use of information tools, either electronic or paper-based. Few studies have investigated the activities that support patient handover, and fewer have explored how several of these tools used together affects the handover process.

Purpose: The aim of this study was to understand coordination challenges in end-of-shift patient handover between nurses and the influence of multiple information tools used in that context.

Methodology: A qualitative methodology to investigate phenomena in an acute care hospital in the United States was used in this study. Semistructured interviews were used to elicit insights from 16 nurses. Data were analyzed by coding three types of task dependencies (prerequisite, simultaneous, and shared) and three information tools (electronic medical records [EMRs], Kardex, and printouts of EMR data).

Results: In preparation for a handover, nurses were burdened by ensuring that information in the EMR was correct and complete. A one-sheet Kardex was the tool nurses in the study preferred, because the essential information was at hand and it provided structure to the communication. Printouts of EMR data were often physically cumbersome and not useful in their current form, although they may be useful for communicating anomalous data.

Conclusion: This study provides insights regarding the challenges of care coordination in end-of-shift patient handover between nurses and the usages of a variety of information tools in preparation for handover, as well as the actual handover process.

Practice implications: Multiple interrelated information tools may be used to support patient handover. Health leaders should focus efforts on further advancing protocols for end-of-shift nurse handovers. Health system designers should design information tools to align them with their defined purpose in the handover process. Future work should consider both the information needs of nurses and the goal of improving nurse workflows.

Background: There is growing recognition that health care providers are embedded in networks formed by the movement of patients between providers. However, the structure of such networks and its impact on health care are poorly understood.

Purpose: We examined the level of dispersion of patient-sharing networks across U.S. hospitals and its association with three measures of care delivered by hospitals that were likely to relate to coordination.

Methodology/approach: We used data derived from 2016 Medicare Fee-for-Service claims to measure the volume of patients that hospitals treated in common. We then calculated a measure of dispersion for each hospital based on how those patients were concentrated in outside hospitals. Using this measure, we created multivariate regression models to estimate the relationship between network dispersion, Medicare spending per beneficiary, readmission rates, and emergency department (ED) throughput rates.

Results: In multivariate analysis, we found that hospitals with more dispersed networks (those with many low-volume patient-sharing relationships) had higher spending but not greater readmission rates or slower ED throughput. Among hospitals with fewer resources, greater dispersion related to greater readmission rates and slower ED throughput. Holding an individual hospital's dispersion constant, the level of dispersion of other hospitals in the hospital's network was also related to these outcomes.

Conclusion: Dispersed interhospital networks pose a challenge to coordination for patients who are treated at multiple hospitals. These findings indicate that the patient-sharing network structure may be an overlooked factor that shapes how health care organizations deliver care.

Practice implications: Hospital leaders and hospital-based clinicians should consider how the structure of relationships with other hospitals influences the coordination of patient care. Effective management of this broad network may lead to important strategic partnerships.

Background: New graduate nurses experience difficulty in adapting to a new environment, which affects intent to leave. However, data on the factors contributing to difficulty in adapting and intent to leave among new graduate nurses are insufficient.

Purpose: The aim of the study was to explore and compare factors associated with difficulty in adapting and the intent to leave among new graduate nurses in South Korea.

Methodology: This cross-sectional study used secondary data analysis. Primary data were obtained from the 2015-2016 Korean National Graduates Occupational Mobility Survey. Descriptive statistics, independent t tests, and chi-square statistics with weighted samples besides multivariate logistic regression analyses were conducted (N = 467).

Results: Roughly 46% of nurses reported difficulty adapting, and 16% expressed their intent to leave. The factors linked to difficulty in adapting were working at large hospitals with rotating shifts, low person-job fit, and low satisfaction with personal competency; intent to leave was associated with high monthly salary and low satisfaction with the workplace (all ps < .05). Difficulty in adapting did not equate to their intent to leave.

Conclusions: There were high rates of difficulty in adapting and intent to leave among new graduate nurses. Although different factors were associated with difficulty adapting and intent to leave, workplace condition is a common factor.

Practice implications: Different strategies are needed to improve adaptation and intention of leaving among new graduate nurses. For better adaptation, developing training programs enhancing professional competency with a sufficient training period is required. In addition, providing staff and resources to reduce the intent to leave is crucial.