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Problem statement: Unprecedented consumption of health care resources in the USA coupled with increasing rates of chronic disease has fueled pursuit of improved models of health care delivery. The Chronic Care Model provides an organizational framework for chronic care management and practice improvement. Sea Mar, a community health care organization in Washington state, implemented the Chronic Care Model, but has not evaluated the outcomes related to provider and staff satisfaction. The specific aim of this project was to evaluate the effectiveness of the Chronic Care Model with the addition of the Chronic Care Coordinator role. Approach: A descriptive method was used, which incorporated quantitative, and qualitative data from providers and clinic staff collected through a Web-based survey consisting of Likert-type questions sent via an electronic link. Results: This evaluation identified the strengths of and barriers to the chronic care model with a focus on provider and staff satisfaction regarding patient care since the addition of the Chronic Care Coordinator role. We found a high appreciation (94%) and acceptance of the role; 80% agreed that the Chronic Care Coordinator was well-integrated into clinic operations. Major strengths of the program included more patient education, better follow-up, and improved team communications. Barriers to success included limited provider access, confusion regarding role expectations of the Chronic Care Coordinator, inconsistent communications, and Chronic Care Coordinator turnover. Conclusions/recommendations: Our findings help to validate the importance of community health organizations such as Sea Mar, the utility of the chronic care model, and the potential value for specific roles such as the Chronic Care Coordinator to positively impact quality of care by helping to empower patients to improve self-management and ultimately impact patient outcomes. However, future studies involving larger samples are needed to further explore themes among staff and patients. [ABSTRACT FROM AUTHOR]

In recent years, the techniques for screening transportation networks to identify high crash locations have become more sophisticated with significant data requirements. This paper presents the results of an empirical analysis of screening and ranking for weather related crashes on rural 1.6 km (1 mi) highway sections of Oregon highways. The analysis includes data generated with the extensive use of spatial techniques and incorporates climate data to enhance environmental considerations. The paper compares the results of five ranking methods: Critical rate (by functional class), critical rate (by functional class and climate zone), potential for crash reduction, expected frequency (adjusted by empirical-bayes), and frequency. For the empirical-bayes methods, safety performance functions were generated using negative binomial regression techniques. The 20 top 1.6 km (1 mi) sections were identified for each method and compared. The results reveal that the frequency and expected frequency methods identified the most sites in common, followed by the rate-based methods. The potential for the crash reduction method identified the most unique ranked list. The results highlight the differences in ranking methods. [ABSTRACT FROM AUTHOR]