Matching Items (22)
Description
Medical students acquire and enhance their clinical skills using various available techniques and resources. As the health care profession has move towards team-based practice, students and trainees need to practice team-based procedures that involve timely management of clinical tasks and adequate communication with other members of the team. Such team-based procedures include surgical and clinical procedures, some of which are protocol-driven. Cost and time required for individual team-based training sessions, along with other factors, contribute to making the training complex and challenging. A great deal of research has been done on medically-focused collaborative virtual reality (VR)-based training for protocol-driven procedures as a cost-effective as well as time-efficient solution. Most VR-based simulators focus on training of individual personnel. The ones which focus on providing team training provide an interactive simulation for only a few scenarios in a collaborative virtual environment (CVE). These simulators are suited for didactic training for cognitive skills development. The training sessions in the simulators require the physical presence of mentors. The problem with this kind of system is that the mentor must be present at the training location (either physically or virtually) to evaluate the performance of the team (or an individual). Another issue is that there is no efficient methodology that exists to provide feedback to the trainees during the training session itself (formative feedback). Furthermore, they lack the ability to provide training in acquisition or improvement of psychomotor skills for the tasks that require force or touch feedback such as cardiopulmonary resuscitation (CPR). To find a potential solution to overcome some of these concerns, a novel training system was designed and developed that utilizes the integration of sensors into a CVE for time-critical medical procedures. The system allows the participants to simultaneously access the CVE and receive training from geographically diverse locations. The system is also able to provide real-time feedback and is also able to store important data during each training/testing session. Finally, this study also presents a generalizable collaborative team-training system that can be used across various team-based procedures in medical as well as non-medical domains.
ContributorsKhanal, Prabal (Author) / Greenes, Robert (Thesis advisor) / Patel, Vimla (Thesis advisor) / Smith, Marshall (Committee member) / Gupta, Ashish (Committee member) / Kaufman, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
The rate of progress in improving survival of patients with solid tumors is slow due to late stage diagnosis and poor tumor characterization processes that fail to effectively reflect the nature of tumor before treatment or the subsequent change in its dynamics because of treatment. Further advancement of targeted therapies relies on advancements in biomarker research. In the context of solid tumors, bio-specimen samples such as biopsies serve as the main source of biomarkers used in the treatment and monitoring of cancer, even though biopsy samples are susceptible to sampling error and more importantly, are local and offer a narrow temporal scope.
Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers.
This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.
Because of its established role in cancer care and its non-invasive nature imaging offers the potential to complement the findings of cancer biology. Over the past decade, a compelling body of literature has emerged suggesting a more pivotal role for imaging in the diagnosis, prognosis, and monitoring of diseases. These advances have facilitated the rise of an emerging practice known as Radiomics: the extraction and analysis of large numbers of quantitative features from medical images to improve disease characterization and prediction of outcome. It has been suggested that radiomics can contribute to biomarker discovery by detecting imaging traits that are complementary or interchangeable with other markers.
This thesis seeks further advancement of imaging biomarker discovery. This research unfolds over two aims: I) developing a comprehensive methodological pipeline for converting diagnostic imaging data into mineable sources of information, and II) investigating the utility of imaging data in clinical diagnostic applications. Four validation studies were conducted using the radiomics pipeline developed in aim I. These studies had the following goals: (1 distinguishing between benign and malignant head and neck lesions (2) differentiating benign and malignant breast cancers, (3) predicting the status of Human Papillomavirus in head and neck cancers, and (4) predicting neuropsychological performances as they relate to Alzheimer’s disease progression. The long-term objective of this thesis is to improve patient outcome and survival by facilitating incorporation of routine care imaging data into decision making processes.
ContributorsRanjbar, Sara (Author) / Kaufman, David (Thesis advisor) / Mitchell, Joseph R. (Thesis advisor) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2017
Description
According to the ADA (American Diabetes Association), diabetes mellitus is one of the chronic diseases with the highest mortality rate. In the US, 25 million are known diabetics, which may double in the next decade, and another seven million are undiagnosed. Among these patients, older adults are a very special group with varying physical capabilities, cognitive functions and life expectancies. Because they run an increased risk for geriatric conditions, Type 2 diabetes treatments for them must be both realistic and systematic. In fact, some researchers have explored older adults’ experiences of diabetes, and how they manage their diabetes with new technological devices. However, little research has focused on their emotional experiences of medical treatment technology, such as mobile applications, tablets, and websites for geriatric diabetes. This study will address both elderly people's experiences and reactions to devices and their children's awareness of diabetes. It aims to find out how to improve the diabetes treatment and create a systematic diabetes mobile application that combines self-initiated and assisted care together.
ContributorsLu, Chenyang (Author) / Takamura, John (Thesis advisor) / Herring, Donald (Committee member) / Doebbeling, Bradley (Committee member) / Arizona State University (Publisher)
Created2017
Description
Diabetes is becoming a serious problem in China. At the same time, China’s medical
system has faced a difficult situation because of the lack of medical resources and the unequal medical resource distribution between the BHs and BLHs. BH doctors are tremendously busy with both serious and minor illnesses while BLH medical providers are worried about a sufficient source of patients. This study aims to find the potential feasibility of a new service model in managing diabetes which will solve these medical problems. The study was conducted using an extensive literature review in addition to employing an interview and survey method to explore the perception and current situation in workload and income of medical providers from one BH and one BLH in China. Furthermore, this study tried to understand the acceptance of online medical technology in these medical provider groups. The results showed that doctors in the BH do not have the time needed to engage in extra work. This population is not satisfied with their work responsibilities and income structure. They want to engage in diagnosing and prescribing tasks, with respect to diabetes management. They would like to distribute the management work to BLH. On the other hand, medical providers in BLH have extra time and enthusiasm in doing extra work to improve their income. They are not satisfied with their workload and income, and want to change it. BLHs are willing to do the management work assisting the BH doctors. Additionally, the study showed that online medical technology requires a broader user education for medical providers from both big and BLHs. The conclusion can be summarized as design research advice for future service design in healthcare management. The proposed online medical service should meet different level medical providers' position and requirements regarding time, payment, and value. BH doctors are more suitable for diagnosing and prescribing and BLH medical providers are more suitable for follow-up service. This service should reflect the value of the BH doctors' professional service and the value of BLH medical providers' health management service. (discuss how design can improve this situation through app development)
system has faced a difficult situation because of the lack of medical resources and the unequal medical resource distribution between the BHs and BLHs. BH doctors are tremendously busy with both serious and minor illnesses while BLH medical providers are worried about a sufficient source of patients. This study aims to find the potential feasibility of a new service model in managing diabetes which will solve these medical problems. The study was conducted using an extensive literature review in addition to employing an interview and survey method to explore the perception and current situation in workload and income of medical providers from one BH and one BLH in China. Furthermore, this study tried to understand the acceptance of online medical technology in these medical provider groups. The results showed that doctors in the BH do not have the time needed to engage in extra work. This population is not satisfied with their work responsibilities and income structure. They want to engage in diagnosing and prescribing tasks, with respect to diabetes management. They would like to distribute the management work to BLH. On the other hand, medical providers in BLH have extra time and enthusiasm in doing extra work to improve their income. They are not satisfied with their workload and income, and want to change it. BLHs are willing to do the management work assisting the BH doctors. Additionally, the study showed that online medical technology requires a broader user education for medical providers from both big and BLHs. The conclusion can be summarized as design research advice for future service design in healthcare management. The proposed online medical service should meet different level medical providers' position and requirements regarding time, payment, and value. BH doctors are more suitable for diagnosing and prescribing and BLH medical providers are more suitable for follow-up service. This service should reflect the value of the BH doctors' professional service and the value of BLH medical providers' health management service. (discuss how design can improve this situation through app development)
ContributorsLiu, Maozhen (Author) / Takamura, John (Thesis advisor) / Doebbeling, Bradley (Committee member) / Herring, Donald (Committee member) / Arizona State University (Publisher)
Created2018
Description
Title: A Mobile Health Application for Tracking Patients' Health Record Abstract Background: Mobile Health (mHealth) has recently been adopted and used in rural communities in developing countries to improve the quality of healthcare in those areas. Some organizations use mHealth application to track pregnancy and provide routine checkups for pregnant women. Other organizations use mHelath application to provide treatment and counseling services to HIV/AIDs patients, and others are using it to provide treatment and other health care services to the general populations in rural communities. One organization that is using mobile health to bring primary care for the first time in some of the rural communities of Liberia is Last Mile Health. Since 2015, the organization has trained community health assistants (CHAs) to use a mobile health platform called Data Collection Tools (DCTs). The CHAs use the DCT to collect health data, diagnose and treat patients, provide counseling and educational services to their communities, and for referring patients for further care. While it is true that the DCT has many great features, it currently has many limitations such as data storage, data processing, and many others. Objectives: The goals of this study was to 1. Explore some of the mobile health initiatives in developing countries and outline some of the important features of those initiatives. 2. Design a mobile health application (a new version of the Last Mile Health's DCT) that incorporates some of those features that were outlined in objective 1. Method: A comprehensive literature search in PubMed and Arizona State University (ASU) Library databases was conducted to retrieve publications between 2014 and 2017 that contained phrases like "mHealth design", "mHealth implementation" or "mHealth validation". For a publication to refer to mHealth, the publication had to contain the term "mHealth," or contains both the term "health" and one of the following terms: mobile phone, cellular phone, mobile device, text message device, mobile technology, mobile telemedicine, mobile monitoring device, interactive voice response device, or disease management device. Results: The search yielded a total of 1407 publications. Of those, 11 publications met the inclusion criteria and were therefore included in the study. All of the features described in the selected articles were important to the Last Mile Health, but due to issues such as internet accessibility and cellular coverage, only five of those features were selected to be incorporated in the new version of the Last Mile's mobile health system. Using a software called Configure.it, the new version of the Last Mile's mobile health system was built. This new system incorporated features such as user logs, QR code, reminder, simple API, and other features that were identified in the study. The new system also helps to address problems such as data storage and processing that are currently faced by the Last Mile Health organization.
ContributorsKarway, George K. (Author) / Scotch, Matthew (Thesis director) / Kaufman, David (Committee member) / Biomedical Informatics Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Accurate quantitative information of tumor/lesion volume plays a critical role
in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
in diagnosis and treatment assessment. The current clinical practice emphasizes on efficiency, but sacrifices accuracy (bias and precision). In the other hand, many computational algorithms focus on improving the accuracy, but are often time consuming and cumbersome to use. Not to mention that most of them lack validation studies on real clinical data. All of these hinder the translation of these advanced methods from benchside to bedside.
In this dissertation, I present a user interactive image application to rapidly extract accurate quantitative information of abnormalities (tumor/lesion) from multi-spectral medical images, such as measuring brain tumor volume from MRI. This is enabled by a GPU level set method, an intelligent algorithm to learn image features from user inputs, and a simple and intuitive graphical user interface with 2D/3D visualization. In addition, a comprehensive workflow is presented to validate image quantitative methods for clinical studies.
This application has been evaluated and validated in multiple cases, including quantifying healthy brain white matter volume from MRI and brain lesion volume from CT or MRI. The evaluation studies show that this application has been able to achieve comparable results to the state-of-the-art computer algorithms. More importantly, the retrospective validation study on measuring intracerebral hemorrhage volume from CT scans demonstrates that not only the measurement attributes are superior to the current practice method in terms of bias and precision but also it is achieved without a significant delay in acquisition time. In other words, it could be useful to the clinical trials and clinical practice, especially when intervention and prognostication rely upon accurate baseline lesion volume or upon detecting change in serial lesion volumetric measurements. Obviously, this application is useful to biomedical research areas which desire an accurate quantitative information of anatomies from medical images. In addition, the morphological information is retained also. This is useful to researches which require an accurate delineation of anatomic structures, such as surgery simulation and planning.
ContributorsXue, Wenzhe (Author) / Kaufman, David (Thesis advisor) / Mitchell, J. Ross (Thesis advisor) / Johnson, William (Committee member) / Scotch, Matthew (Committee member) / Arizona State University (Publisher)
Created2016
Description
Type 1 diabetes (T1D) is a chronic disease that affects 1.25 million people in the United States. There is no known cure and patients must self-manage the disease to avoid complications resulting from blood glucose (BG) excursions. Patients are more likely to adhere to treatments when they incorporate lifestyle preferences. Current technologies that assist patients fail to consider two factors that are known to affect BG: exercise and alcohol. The hypothesis is postprandial blood glucose levels of adult patients with T1D can be improved by providing insulin bolus or carbohydrate recommendations that account for meal and alcohol carbohydrates, glycemic excursion, and planned exercise. I propose an evidence-based decision support tool, iDECIDE, to make recommendations to improve glucose control by taking into account meal and alcohol carbohydrates, glycemic excursion and planned exercise. iDECIDE is deployed as a low-cost and easy to disseminate smartphone application.
A literature review was conducted on T1D and the state-of-the-art in diabetes technology. To better understand self-management behaviors and guide the development of iDECIDE, several data sources were collected and analyzed: surveys, insulin pump paired with glucose monitoring, and self-tracking of exercise and alcohol. The analysis showed variability in compensation techniques for exercise and alcohol and that patients made unaided decisions, suggesting a need for better decision support.
The iDECIDE algorithm can make insulin and carbohydrate recommendations. Since there were no existing in-silico methods for assessing bolus calculators, like iDECIDE, I proposed a novel methodology to retrospectively compare insulin pump bolus calculators. Application of the methodology shows that iDECIDE outperformed the Medtronic insulin pump bolus calculator and could have improved glucose control.
This work makes contributions to diabetes technology researchers, clinicians and patients. The iDECIDE app provides patients easy access to a decision support tool that can improve glucose control. The study of behaviors from diabetes technology and self-report patient data can inform clinicians and the design of future technologies and bedside tools that integrate patient’s behaviors and perceptions. The comparison methodology provides a means for clinical informatics researchers to identify and retrospectively test promising insulin blousing algorithms using real-life data.
A literature review was conducted on T1D and the state-of-the-art in diabetes technology. To better understand self-management behaviors and guide the development of iDECIDE, several data sources were collected and analyzed: surveys, insulin pump paired with glucose monitoring, and self-tracking of exercise and alcohol. The analysis showed variability in compensation techniques for exercise and alcohol and that patients made unaided decisions, suggesting a need for better decision support.
The iDECIDE algorithm can make insulin and carbohydrate recommendations. Since there were no existing in-silico methods for assessing bolus calculators, like iDECIDE, I proposed a novel methodology to retrospectively compare insulin pump bolus calculators. Application of the methodology shows that iDECIDE outperformed the Medtronic insulin pump bolus calculator and could have improved glucose control.
This work makes contributions to diabetes technology researchers, clinicians and patients. The iDECIDE app provides patients easy access to a decision support tool that can improve glucose control. The study of behaviors from diabetes technology and self-report patient data can inform clinicians and the design of future technologies and bedside tools that integrate patient’s behaviors and perceptions. The comparison methodology provides a means for clinical informatics researchers to identify and retrospectively test promising insulin blousing algorithms using real-life data.
ContributorsGroat, Danielle (Author) / Grando, Maria Adela (Thesis advisor) / Kaufman, David (Committee member) / Thompson, Bithika (Committee member) / Arizona State University (Publisher)
Created2017
Description
It is well known that the lack of care coordination in the healthcare system causes numerous problems including cost inefficiency and inconsistent care, specifically for complex pediatric and adult patients. Many pediatric patients have complex medical and social service needs which can be expensive for both the patient’s parents and the general healthcare system. Therefore, it is difficult for the healthcare system to deliver the highest quality care possible, due to the number of appointments that have to be scheduled (with some being out of state), the large volume of physical health records, and overall lack of time parents have to coordinate this care while also caring for themselves and other family members. It is integral to find a more efficient way to coordinate care for these patients, in order to improve overall care, cost efficiency, and outcomes. <br/>A number of stakeholders in Arizona came together to work on this problem over several years. They were funded through a PCORI Eugene Washington Engagement grant to investigators at ASU. This project, Take Action for Arizona's Children through Care Coordination: A Bridge to Action was developed in order to further develop a research agenda and build the network (PCOR). Regional conferences were conducted in Flagstaff, Yuma, Phoenix, and Tucson, as well as a final capstone conference held in Phoenix. At these conferences, frustrations, suggestions, and opinions regarding Children with Special Health Care Needs (CSHCN) and navigating the healthcare system were shared and testimonials were transcribed.<br/>This study focused on the capstone conference. The study design was a strategic design workshop; results of the design analysis were analyzed qualitatively using descriptive content analysis. Themes described parent’s common experiences navigating the system, impacts resulting from such experiences, and desires for the care coordination system. Quotes were then grouped into major themes and subthemes for the capstone conference. After these themes were determined, the overarching goals of stakeholders could be assessed, and implementation projects could be described.
ContributorsBrennan, Bayley (Author) / Doebbeling, Bradley (Thesis director) / Lamb, Gerri (Committee member) / College of Health Solutions (Contributor, Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
COVID-19 has been challenging for nearly everyone in different ways. Healthcare organizations have had to quickly change policy, modify operations, reorganize facilities, hire, and train staff to overcome COVID-19 related challenges to be able to still provide care for patients, all while being mindful of the protection of their staff. Some healthcare organizations have responded particularly well, perhaps due to preparedness, planning, or exceptional leadership in times of crisis. To explore this, we invited seven healthcare system leaders from three different organizations in Arizona to talk about how they overcame challenges at the beginning of this pandemic with effective strategies and any leadership tips they had for the future. After the interviews were conducted, the interviews were transcribed, coded qualitatively, and separated into themes and categories to analyze their answers to the questions asked. The results and conclusions included strategies such as having open and honest communication, teamwork, rapidly developing communicating policies, and widely adopting new work practices like Telemedicine, Zoom, and working at home as crucial. This report is designed to assist in aiding and inspiring future or other leaders to be better prepared for solving various challenges with other emergencies that arise in the future.
ContributorsDarira, Saigayatri (Author) / Doebbeling, Bradley (Thesis director) / Don, Rachael (Committee member) / Franczak, Michael (Committee member) / College of Health Solutions (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The unprecedented amount and sources of information during the COVID-19 pandemic resulted in an indiscriminate level of misinformation that was confusing and compromised healthcare access and delivery. The World Health Organization (WHO) called this an ‘infodemic’, and conspiracy theories and fake news about COVID-19, plagued public health efforts to contain the COVID-19 pandemic. National and international public health priorities expanded to counter misinformation. As a multi-disciplinary study encompassing expertise from public health, informatics, and communication, this research focused on eliciting strategies to better understand and combat misinformation on COVID-19. The study hypotheses is that 1) factors influencing vaccine-acceptance like socio-demographic factors, COVID-19 knowledge, trust in institutions, and media related factors could be leveraged for public health education and intervention; and 2) individuals with a high level of knowledge regarding COVID-19 prevention and control have unique behaviors and practices, like nuanced media literacy and validation skills that could be promoted to improve vaccine acceptance and preventative health behaviors. In this biphasic study an initial survey of 1,498 individuals sampled from Amazon Mechanical Turk (MTurk) assessed socio-demographic factors, an 18-item test of COVID-19 knowledge, trust in healthcare stakeholders, and measures of media literacy and consumption. Subsequently, using the Positive Deviance Framework, a diverse subset of 25 individuals with high COVID-19 knowledge scores were interviewed to identify these deviants’ information and media practices that helped avoid COVID-19 misinformation. Access to primary care, higher educational attainment and living in urban communities were positive socio-demographic predictors of COVID-19 vaccine acceptance emphasizing the need to invest in education and rural health. High COVID-19 knowledge and trust in government and health providers were also critical factors and associated with a higher level of trust in science and credible information sources like the Centers for Disease Control (CDC) and health experts. Positive deviants practiced media literacy skills that emphasized checking sources for scientific basis as well as hidden bias; cross-checking information across multiple sources and verifying health information with scientific experts. These identified information validation and confirmation practices may be useful in educating the public and designing strategies to better protect communities against harmful health misinformation.
ContributorsSivanandam, Shalini (Author) / Doebbeling, Bradley (Thesis advisor) / Koskan, Alexis (Committee member) / Roschke, Kristy (Committee member) / Chung, Yunro (Committee member) / Arizona State University (Publisher)
Created2023