Promoting interprofessional collaboration through development of an organizational strategy to improve intra-organizational referrals at Scenic Bluffs Community Health Center

This graduate project details the development of a foundation for organizational strategy aimed at improving the intra-organizational referrals and interprofessional collaboration at Scenic Bluffs Community Health Center in Cashton, WI evolving out of the author's preceptorship-related strategic planning responsibilities. Guided by the identification and prioritization of the need which was identified in alignment with the organization's health care quality improvement and Patient-Centered Medical Home (PCMH) goals, a needs and capacity assessment was completed to identify already-established areas of strength and weakness related to the goal of improving intra-organizational referrals. Based on qualitative focus groups and key informant interviews, pre-pilot and pilot protocols were developed to test specific language and situations and their potential for prompting intra-organizational referral opportunities. Development of a web-based toolkit for defining and improving intra-organizational referrals at SBCHC was guided by the findings from the pilot protocol. It was the intent of the author and of the organization that the toolkit would be available for use by other health centers with the strategic goals of improving health care quality, attaining, and maintaining PCMH status by improving intra-organizational referrals

Laser Surface Treatment of Sintered Alumina

Sintered alumina ceramics are used as refractory materials for industrial aluminum furnaces. In this environment the ceramic surface is in permanent contact with molten aluminum resulting in deposition of oxidic material on its surface. Consequently, a lower volume capacity as well as thermal efficiency of the furnaces follows. To reduce oxidic adherence of the ceramic material, two laser-based surface treatment processes were investigated: a powder- based single-step laser cladding and a laser surface remelting. Main objective is to achieve an improved surface quality of the ceramic material considering the industrial requirements as a high process speed

Predictive modeling of infant mortality

The Infant Mortality Rate (IMR) is defined as the number of infants for every thousand infants that do not survive until their first birthday. IMR is an important metric not only because it provides information about infant births in an area, but it also measures the general societal health status. In the United States of America, the IMR is higher than many other developed countries, despite the high level of prosperity. It is important to note here that the U.S.A. exhibits strong and persistent inequalities in the IMR across different racial and ethnic groups (Kochanek et al. in Natl Vital Stat Rep 65(4):1–122, 2006). In this paper, we study predictive models in the problem of infant mortality. We implement traditional machine learning models and state-of-the-art neural network models with various combinations of features extracted from birth certificates. Those combinations include features that can be summed as socio-economic and ethical features related to the mother and the father of the infant and medical measurements during the pregnancy and the delivery. We approach the classification problem of infant mortality, whether an infant will survive until her first birthday or not, both as binary and multi-class based on the time of death. We focus on understanding and exploring the importance of features extracted from the birth certificates. For example, we test the performance of models trained on the general population to models trained in subsets of the population, e.g., for individual races. We show in our experimental evaluation comparisons between different predictive models (including those used by epidemiology researchers), various combinations of features, different distributions in the training set and features’ importance. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature