Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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- All Subjects: Health Sciences
- Creators: Kaufman, David
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
Perioperative care has a direct and crucial impact on patient safety and patient outcomes, as well as the financial viability of the healthcare facility. The time pressure and workload of caring patients facing surgery are heavier than caring inpatients of other departments. This workload raises requirements for PreOp nurses, the primary PreOp caregiver, to complete information gathering, screening, and verification tasks accurately and efficiently. EHRs (Electronic Health Record System) have evolved continuously with increasing features to meet newly raised needs and expectations. Many healthcare institutions have undergone EHR conversion since the introduction of first-generation EHRs. Thus, the need for a systematic evaluation of changed information system workflow following conversion is becoming more and more manifest. There are a growing number of methods for analyzing health information technology use. However, few studies provide and apply a standard method to understand the impact of EHR transition and inspire opportunities for improvement.
This dissertation focuses on PreOp nurse’s EHR use in PreOp settings. The goals of this dissertation are to: (a) introduce a systematic framework to evaluate EHR-mediated workflow and the impact of the EHR transition; (b) understand the impact of different EHR systems on PreOp nurse’s workflow and preoperative care efficiency; (c) transform the evaluation results into practical user-centered EHR designs. This research draws on computational ethnography, cognitive engineering process and user-centered design concepts to build a practical approach for EHR transition-related workflow evaluation and optimization.
Observational data were collected before and after a large-scale EHR conversion throughout Mayo Clinic’s different regional health systems. For a structured computational evaluation framework, the time-efficiency of PreOp nurses’ work were compared quantitatively by means of coding and segmenting nurses’ tasks. Interview data provided contextual information, reflecting practical challenges and opportunities before and after the EHR transition.
The total case time, the time spent on EHR, and the task fragmentation were improved after converting to the new EHR system. A trend of standardization of information-related workflow and EHR transition was observed. Notably, the approach helped to identify current new system challenges and pointed out potential optimization solutions.
ContributorsZheng, Lu (Author) / Doebbeling, Bradley (Thesis advisor) / Kaufman, David (Committee member) / Wang, Dongwen (Committee member) / Patel, Vimla (Committee member) / Chiou, Erin (Committee member) / Arizona State University (Publisher)
Created2021
Description
Usability problems associated with electronic health records can adversely impact clinical workflow, leading to inefficiencies, error, and even clinician burnout. The work presented in this dissertation is concerned with understanding and improving clinical workflow. Towards that end, it is necessary to model physical and cognitive aspects of task performance in clinical settings. Task completion can be significantly impacted by the navigational efficiency of the electronic health record (EHR) interface. Workflow modeling of the EHR-mediated workflow could help identify, diagnose and eliminate problems to reduce navigational complexity. The research goal is to introduce and validate a new biomedical informatics methodological workflow analysis framework that combines expert-based and user-based techniques to guide effective EHR design and reduce navigational complexity. These techniques are combined into a modified walkthrough that aligns user goals and subgoals with estimated task completion time and characterization of cognitive demands. A two-phased validation of the framework is utilized. The first is applied to single EHR-mediated workflow tasks, medication reconciliation (MedRec), and medication administration records (MAR) to refine individual aspects of the framework. The second phase applied the framework to a pre/post EHR implementation comparative analysis of multiple workflows tasks. This validation provides evidence of the framework's applicability and feasibility across several sites, systems, and settings. Analysis of the steps executed within the interfaces involved to complete the medication administration and medication reconciliation and patient order management tasks have provided a basis for characterizing the complexities in EHR navigation. An implication of the work presented here is that small tractable changes in interface design may substantially improve EHR navigation, overall usability, and workflow. The navigational complexity framework enables scrutinizing the impact of different EHR interfaces on task performance and usability barriers across different sites, systems, and settings.
ContributorsDuncan, Benjamin (Author) / Grando, Adela (Thesis advisor) / Doebbeling, Bradley (Thesis advisor) / Kaufman, David (Committee member) / Greenes, Robert (Committee member) / Arizona State University (Publisher)
Created2021