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Assessing the Cost-effectiveness of Alternative Measures Aimed at Reducing the Prevalence of Foodborne Microbiological HazardsSchmidt, Claudia 13 September 2011 (has links)
Foodborne illnesses place a burden on the entire society. One strategy to lower the costs of foodborne illnesses is to reduce the prevalence of foodborne pathogens through interventions along the food supply chain. There is an ongoing trend that food safety systems are moving towards performance-based regimes, which rely on the implementation of food safety standards. However, the implementation of food safety standards has not garnered much interest in the Canadian policy environment. The assessment of food safety interventions to achieve a standard is challenging as the underlying biological processes are complex, the costs of administering such interventions are not abundantly clear and the set of available interventions is changing.
This thesis investigates the cost-effectiveness of food safety interventions and specifically the applicability of a food safety standard. First, a theoretical model is developed to investigate how; in theory cost-minimization can be used to identify the most cost-effective way to reduce foodborne pathogens with the utilization of a food safety standard. Then, a specific framework is developed for Campylobacter in chicken that consists of interrelated simulation models that represent the level and flow of pathogens through a commodity supply chain, the impact of alternative interventions on pathogen load and their costs. The case study focus is Ontario, Canada. Different interventions are compared and evaluated based on their compliance with a food safety standard. The applicability of different cost-effectiveness measures is assessed.
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Staff Educational Program to Prevent Medication ErrorsHawthorne-Kanife, Rita Chinyere 01 January 2018 (has links)
Medication administration errors (MAEs) may lead to adverse drug events, patient morbidity, prolonged hospital stays, and increased readmission rates, and may contribute to major financial losses for the health system. MAEs are the most common type of error occurring within the health care setting leading to an estimated 7,000 patient deaths every year. Interventions have been designed to prevent MAEs including education for nurses who administer medications; however, little effort has been made to design systematic educational programs that are based on local needs and contexts. The purpose of this project was to identify internal and external factors related to MAEs at the practice site, develop an education program tailored to the factors contributing to MAEs, and implement the program using a pretest posttest design. The Iowa model was used to guide the project. The 26 nurse participants who responded to an initial survey indicated that nurses felt distractions and interruptions during medication administration, and hesitancy to ask for help or to report medication errors increased MAE risks. After the education program, the pretest and posttest results were analyzed and revealed improvement in knowledge and confidence of medication administration (M = 3.2 pre, M = 3.7 post, p < .05). Open-ended question responses suggested a need for dedicated time for preparation and administration of medications without interruptions. Positive social change is possible as nurses become knowledgeable and confident about medication administration safety and as patients are protected from injury secondary to MAEs.
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Analytical Methods to Support Risk Identification and Analysis in Healthcare SystemsCure Vellojin, Laila Nadime 01 January 2011 (has links)
Healthcare systems require continuous monitoring of risk to prevent adverse events. Risk analysis is a time consuming activity that depends on the background of analysts and available data. Patient safety data is often incomplete and biased. This research proposes systematic approaches to monitor risk in healthcare using available patient safety data. The methodologies combine traditional healthcare risk analysis methods with safety theory concepts, in an innovative manner, to allocate available evidence to potential risk sources throughout the system. We propose the use of data mining to analyze near-miss reports and guide the identification of risk sources. In addition, we propose a Maximum-Entropy based approach to monitor risk sources and prioritize investigation efforts accordingly.
The products of this research are intended to facilitate risk analysis and allow
for timely identification of risks to prevent harm to patients.
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