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Hospitals in North America consistently have employee injury rates ranking among the highest of all industries. Organizations that mandate workplace safety training and emphasize safety compliance tend to have lower injury rates and better employee safety perceptions. However, it is unclear if the work environment in different national health care systems (United States vs. Canada) is associated with different employee safety perceptions or injury rates. This study examines occupational safety and workplace satisfaction in two different countries with employees working for the same organization.

Survey data were collected from environmental services employees (n = 148) at three matched hospitals (two in Canada and one in the United States). The relationships that were examined included: (1) safety leadership and safety training with individual/unit safety perceptions; (2) supervisor and coworker support with individual job satisfaction and turnover intention; and (3) unit turnover, labor usage, and injury rates.

Hierarchical regression analysis and ANOVA found safety leadership and safety training to be positively related to individual safety perceptions, and unit safety grade and effects were similar across all hospitals. Supervisor and coworker support were found to be related to individual and organizational outcomes and significant differences were found across the hospitals. Significant differences were found in injury rates, days missed, and turnover across the hospitals.

This study offers support for occupational safety training as a viable mechanism to reduce employee injury rates and that a codified training program translates across national borders. Significant differences were found between the hospitals with respect to employee and organizational outcomes (e.g., turnover). These findings suggest that work environment differences are reflective of the immediate work group and environment, and may reflect national health care system differences.

This chapter reviews and integrates the empirical literature on the influence of organizational factors on hospital financial performance. Five categories of organizational characteristics that research has addressed are identified and examined as part of the review: ownership, governance, integration, management strategy, and quality. With some exceptions, our review reveals a general lack of consistency and conclusiveness across studies in each area. Exceptions were found in the areas of governance (e.g., physician participation and board processes) and integration (e.g., horizontal system centralization). Despite the lack of conclusive findings across studies, our review suggests substantial opportunities for future work, including opportunities for qualitative and exploratory work. Additional implications for theory and management are discussed.

Community health clinics serving the poor and underserved are geographically expanding due to changes in U.S. health care policy. This paper describes the experience of a collaborative alliance of health care providers in a large metropolitan area who develop a conceptual and mathematical decision model to guide decisions on expanding its network of community health clinics.

Community stakeholders participated in a collaborative process that defined constructs they deemed important in guiding decisions on the location of community health clinics. This collaboration also defined key variables within each construct. Scores for variables within each construct were then totaled and weighted into a community-specific optimal space planning equation. This analysis relied entirely on secondary data available from published sources.

The model built from this collaboration revolved around the constructs of demand, sustainability, and competition. It used publicly available data defining variables within each construct to arrive at an optimal location that maximized demand and sustainability and minimized competition.

This is a model that safety net clinic planners and community stakeholders can use to analyze demographic and utilization data to optimize capacity expansion to serve uninsured and Medicaid populations.

Communities can use this innovative model to develop a locally relevant clinic location-planning framework.