Health Care Management Review最新文献

Background: Social ties between health care workers may be an important driver of job satisfaction; however, research on this topic is limited.

Purpose: We used social network methods to collect data describing two types of social ties, (a) instrumental ties (i.e., exchange of advice that enables work) and (b) expressive ties (i.e., exchange of social support), and related those ties to workers' job satisfaction.

Methodology: We surveyed 456 clinicians and staff at 23 primary care practices about their social networks and workplace attitudes. We used multivariable linear regression to estimate the relationship between an individual's job satisfaction and two network properties: (a) eigenvector centrality (a measure of the importance of an individual in a network) and (b) ego network density (a measure of the cohesiveness of an individual's network). We examined this relationship for both instrumental and expressive ties.

Results: Individuals who were more central in the expressive network were less satisfied in their job, b = -0.40 (0.19), p < .05, whereas individuals who had denser instrumental networks were more satisfied in their job, b = 0.49 (0.21), p < .05.

Conclusion: Workplace relationships affect worker well-being. Centrality in an expressive network may require greater emotional labor, increasing workers' risk for job dissatisfaction. On the other hand, a dense instrumental network may promote job satisfaction by strengthening workers' access to full information, supporting competence and confidence.

Practice implications: Efforts to increase job satisfaction should consider both the positive and negative effects of social networks on workers' sense of well-being.

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.