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Objective: To determine the value of a program instituted by an academic medical library to train public library workers across the State of Tennessee, US to deliver quality health information to patrons. Methods: Qualitative analysis including focus groups. Results: The East Tennessee State University (ETSU) Medical Library used four three-hour classes developed by the US National Library of Medicine to train public library workers across the State of Tennessee over the last five years. All four classes were offered multiple times in the nine regions of the state public library system. Approximately 250 library workers received the Consumer Health Information Specialization designation from the US Medical Library Association for participating in 12 hours of training. The motivation behind this effort was reports that the first place people with a new diagnosis go for information is the public library. Conclusion: We wanted to look for evidence to see if there was value in this project in order to know whether to repeat it in the future or whether to promote it as a model to other geographical areas. A qualitative analysis of the results of the project will be presented at the conference.

Objective: Remote Area Medical (RAM) Volunteer Corps is a nonprofit volunteer relief corps dedicated to serving mankind by providing free health care, dental care, eye care, and technical and educational assistance to people in remote areas of the United States and the world. The East Tennessee State University Medical Library participated in two RAM expeditions. Approximately 3,000 patients were seen at the 2 events. Methods: The library obtained funding to purchase laptops and printers and printed consumer health information. The library had a prominent position in the educational section of the events. Librarians from other medical libraries and public libraries were invited to be part of the team. During the event, library staff aggressively sought out opportunities to give consumer health information to patients. Results: The library provided health information to approximately 1,300 of the participants. Conclusions: RAM provided the library with a chance to really help out with the community. It was an interesting eye opening experience. It is the medical library’s wish to have a continued relationship with RAM. The library's presence made an impression on the various health care providers as to what a powerful tool information could be.

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The present study investigated the effectiveness of health related feedback as a positive reinforcer of abstinence from smoking during the 11 week period following cessation. Fifty-three subjects were randomly assigned to one of three groups: (a) maintenance program plus health related feedback (MF), (b) maintenance program only (M) and, (c) discussion control (D). All groups received an aversive treatment for smoking after which the MP group received coping skills raining and several forms of health related feedback, the M group received coping skills training only, and the D group received discussion. Groups did not differ significantly in abstinence at each of the 11 weeks.

The present study takes legibility research and extends it to the medical setting. Internal Medicine Physicians from UCF developed six passages of medical text detailing a History of Present Illness (HPI) Report from an emergency department as well as comprehension questions for the purpose of our study. In our study, we first presented non-medical passages and comprehension questions in six common fonts to identify participants' individual fastest and slowest fonts. We then gave participants medical passages in both their best and worst fonts while measuring reading speed and comprehension. This study was delivered to a population of Amazon Mechanical Turk crowd workers to help us better understand how legibility improvements can be made within specific fields. We hope that with this study we can begin the process of restructuring Electronic Health Records to be more usable and efficient for physicians.

This dissertation comprises three papers introducing strategies, models, and frameworks to guide pandemic response. The first paper uses a novel mathematical model to analyze the coordination between government and humanitarian non-governmental organizations (NGOs) in response to pandemics. This is a vital form of public-private partnership between governments as the primary source for the humanitarian supplies required during a crisis and aid organizations. This coordination involves the equitable distribution of personal protective equipment, including face masks and face shields among health workers, patients, and the public in hospitals. Considering social costs such as deprivation and equity costs in the model, in addition to the other important classic cost terms, enables managers to organize the best possible response when such outbreaks happen. The second paper introduces a decision support framework designed to assist healthcare managers, and clinical informatics specialists in analyzing and selecting the most appropriate consensus algorithm for their organization's blockchain-based health platforms, with a specific focus on managing pandemic-related information. Blockchain technology holds great potential in addressing pandemics by enhancing security and transparency in various aspects of pandemic tracking and mitigation while promoting public engagements by facilitating real-time exchange of electronic health information. By improving information sharing and coordination among healthcare organizations, it offers more effective response efforts and helps reduce the spread of viruses. However, the performance of consensus algorithms, which are a crucial component of blockchain architecture, can vary, posing a challenge in selecting the appropriate algorithm. To address this, the framework incorporates two techniques: data envelopment analysis (DEA) and the ranking distribution technique. DEA enables the analysis of efficiency without relying solely on expert judgment, providing a more objective assessment. The ranking distribution technique enhances differentiation among algorithms, providing decision-makers with a robust basis for selecting the most suitable blockchain architecture and its associated properties. The third paper focuses on the challenges of disseminating guidance-related information to the public during a pandemic, specifically the role of opinion leaders as reliable sources of information. The study determines the practical characteristics of pandemic opinion leaders on public attitudes using surveys and identifies domain-sensitive pandemic opinion leaders on Twitter based on the discovered characteristics using social network analysis and text mining. The framework's results show that pandemic opinion leaders are active in eight different domains on the Twitter platform. Results also demonstrate that trust is the most influential characteristic of pandemic opinion leaders, while expertise, uniqueness, innovation, and reputation also play important roles. / Doctor of Philosophy / This dissertation presents a collection of three research papers that offer insights and practical techniques and strategies to effectively tackle the challenges posed by pandemics through enhanced information sharing, public engagement, and robust public-private partnerships. The first paper introduces a novel mathematical model that thoroughly examines the collaboration between governments and humanitarian non-governmental organizations (NGOs) during crises such as the Covid-19 pandemic. The model's primary focus is on the equitable distribution of vital supplies, including face masks and shields, to healthcare workers as well as the public. By incorporating considerations of social costs, fairness, and other critical factors, this model aids managers in organizing the most efficient response to initial impacts of outbreaks within a short-term planning horizon. Our primary objective is to ensure the prompt and equitable delivery of essential supplies to individuals in need, achieved primarily through establishing strong public-private partnerships. The second paper proposes a decision support framework for healthcare managers, IT analysts, and clinical informatics specialists to help them effectively analyze consensus algorithms, as the most important layer of blockchain architecture. The framework further helps them select the most suitable algorithm for their organization's blockchain-based health platforms, aligning with specific policies, needs, requirements, and goals in managing pandemic-related information. Blockchain technology offers potential in tracking medical supplies, identifying virus hotspots, and verifying protective equipment authenticity to manage pandemics. By enhancing information sharing and coordination among healthcare organizations, blockchain can minimize virus spread and improve overall response efforts. The proposed framework reduces reliance on expert judgment and addresses data uncertainty when selecting proper algorithms for blockchain-based information management systems in mitigating the effects of pandemics. The third paper delves into the intricate challenges associated with effectively disseminating guidance-related information to the public during a pandemic, placing particular emphasis on the pivotal role played by opinion leaders (OLs) as reliable sources. This study thoroughly examines the distinctive characteristics of pandemic OLs and their profound influence on public attitudes. By employing surveys, social network analysis, and text mining techniques on Twitter data, the research successfully identifies OLs within distinct pandemic-related domains. The study's significant findings provide insights into the dynamic role assumed by pandemic OLs on Twitter and their consequential impact on public perception and behavior across various domains. Ultimately, the dissertation findings strive to support decision-makers and public health officials in their efforts to effectively manage pandemics and protect public health. The research emphasizes facilitation of seamless, rapid, and dependable information sharing across various planning horizons.