Doctor of Philosophy (PhD), Ohio University, 2018, Electrical Engineering & Computer Science (Engineering and Technology)

Changes in the morphology of a skin lesion is indicative of melanoma, a deadly type of skin cancer. This dissertation proposes a temporal analysis method to monitor the vascularity, pigmentation, size and other critical morphological attributes of the lesion. Digital images of a skin lesion acquired during follow-up imaging sessions are input to the proposed system. The images are pre-processed to normalize variations introduced over time. The vascularity is modelled as the skin images' red channel information and its changes by the Kullback-Leibler (KL) divergence of the probability density function approximation of histograms. The pigmentation is quantified as textural energy, changes in the energy and pigment coverage in the lesion. An optical flow field and divergence measure indicates the magnitude and direction of global changes in the lesion. Sub-surface change is predicted based on the surface skin lesion image with a novel approach. Changes in key morphological features such as lesions' shape, color, texture, size, and border regularity are computed. Future trends of the skin lesions features are estimated by an auto-regressive predictor. Finally, the features extracted using deep convolutional neural networks and the hand-crafted lesion features are compared with classification metrics. An accuracy of 80.5%, specificity of 98.14%, sensitivity of 76.9% with a deep learning neural network is achieved. Experimental results show the potential of the proposed method to monitor a skin lesion in real-time during routine skin exams.

MS, University of Cincinnati, 2007, Allied Health Sciences : Genetic Counseling

Advances in prenatal screening and testing techniques are identifying fetal anomalies before delivery, therefore changing the course in pregnancy management. High-risk pregnancies with fetal anomalies may benefit from comprehensive evaluation from multiple disciplines promoting appropriate psychosocial assessments and patient decision-making. Therefore, fetal therapy centers aim for a multidisciplinary approach to treatment through the collaboration of different specialties. There is currently no evidence that patients prefer this evaluation strategy in fetal therapy settings. This qualitative study identifies patient impressions of the multidisciplinary approach, including aspects of individual and team meetings with physicians and staff, family support and follow-up management. A survey was mailed to previous patients, who received a full evaluation, from the Fetal Care Center of Cincinnati. The results indicate the majority of respondents endorse the multidisciplinary approach to treatment. Clear areas of recommendation for modification are follow-up management and emotional support from staff following the patient's evaluation.

MS, University of Cincinnati, 2002, Engineering : Computer Engineering

With improvements in VLSI technology, more and more components are fabricated onto a single chip. The importance of system on chip (SoC) is growing rapidly in this era. It is estimated that the percentage of chip area occupied by embedded memory arrays on a SoC will rise to as high as 94% in the next decade. Even worse, memory arrays are more vulnerable to fabrication defects due to the higher packing density of transistors. If some cells of the embedded memory arrays on a SoC are defective, it is not economical to throw the chip away. The solution to this problem lies in designing an intelligent piece of built-in hardware which tests, diagnoses, and repairs the faulty cells of embedded memory arrays. In this thesis, we propose a built-in self-diagnostic march-based algorithm which identifies memory cells as faulty based on a recently introduced non-traditional fault model. This algorithm is developed based on the DiagRSMarch algorithm which is a diagnosis algorithm for embedded memory arrays for identifying traditional faults in memories. A minimal set of additional operations is added to DiagRSMarch for identifying the non-traditional faults without affecting the diagnostic coverage of the traditional faults. The embedded memory arrays are accessed using the bi-directional serial interfacing architecture which minimizes the routing overhead introduced by the diagnosis hardware. Using the concepts of serial interfacing technique, parallel testing and redundant-tolerant operations, the diagnosis process is accomplished efficiently at-speed with minimal hardware overhead. An implementation of the diagnosis algorithm is achieved in the form of a built-in self-diagnosis (BISD) controller with the memory arrays and their associated interfaces. The BISD Controller interacts closely with the built-in self-repair logic via suitable control signals. Ideally, we expect to have a single controller performing built-in self-test, built-in self-diagnosis and built-in self-repair (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Medicine: Biomedical Informatics

Antibodies are secreted proteins forms of B cell receptors (BCR) that can detect, bind and neutralize antigens. A person's BCR repertoire contains immune information of the antigens they have been exposed to. A substantial amount of modern high-throughput sequencing technologies can be applied to sequencing, monitoring and characterization of antibodies, thereby improving our understanding of how antibodies respond to disease antigens and the antibody compartment responsible for pathogen neutralization. With the vast amount of antibody sequence data created via high-throughput technologies and the advancement in computational methods, there is increasing interest in using machine learning to identify patterns within the BCR repertoire, aiming to leverage these insights for disease classification and predictive diagnostics. However, there exists complexities hindering the success of these goals, including the presence of multiple immune states per individual and the fact that deciphering the relationship between the BCR sequence and its antigen is hard to uncover. Convergent antibodies are highly similar antibodies elicited in multiple individuals in response to the same antigen. Convergent antibodies provide insight into the shared immunological responses and show great promise as diagnostic biomarkers. They have typically been identified using amino acid sequence similarity and used in machine learning models for HIV infection status prediction with high accuracy. However, antibodies with similar sequences can have low structural similarity and with structure linked to specificity, the sequence similarity approach at identifying convergent antibodies has limitations. In this thesis, I extend the definition of convergent antibodies to use isotype and structural information and benchmarked their performance by their ability to predict disease status. Additionally, I obtained a reduced set of highly predictive convergent antibody groups and explored the feature (open full item for complete abstract)

Doctor of Philosophy, Case Western Reserve University, 0, Nursing

One in six children in the United States have developmental disabilities, and infants and toddlers may exhibit telling symptoms of delay in their earliest days with a failure to master milestone benchmarks. Despite modest gains in developmental screening rates, less than one in five children with a known delay will receive developmental enrichment services via the federal Early Intervention service line or special education preschool programs, and those remaining are left to struggle with their undiagnosed and untreated delays until they are identified upon entering kindergarten. This research aimed to uncover quantitative data patterns in the diagnosis and treatment of young children with developmental delays. Using predictive modeling techniques, the findings add to related research evidence for future interventions to increase timely conversions of delay diagnosis to treatment utilization. This work is underpinned by a developmental adaptation of Ryan and Sawin's Individual and Family Self-Management Theory (2014), exploring contextual and family management processes leading to service utilization outcomes. Sourcing data from the National Survey of Children's Health (NSCH 2018-2021), three research questions aided in identifying the demographic and social factor clusters of children with undiagnosed and/or untreated delays within the developmental screening, diagnosis of delay, and developmental service utilization trajectory. Indicators of medical care access, including sick care sources and recent preventive visits, and children's special health care needs, including the impact of conditions on the child's daily life, were key indicators in resulting models. However, all created classification and regression tree (CART) and random forest models had varying levels of predictive ability from low-moderate (developmental screening) to high (developmentally related diagnosis and service utilization) predictive ability, with areas under the curve ranging between 0.56 (open full item for complete abstract)

Master of Science, The Ohio State University, 2007, Graduate School

Master of Science, The Ohio State University, 2024, Mechanical Engineering

In the progression towards SAE Level 4 automation, the functional safety of automated driving systems is deemed essential, especially in the event of faults. The ISO 26262 functional safety standard is utilized to evaluate the risks associated with malfunctions in electrical/electronic (E/E) systems, based on a subjective assessment by safety experts. Yet, this standard primarily relies on qualitative measures and lacks provisions for real-time risk estimation. In this thesis, a risk estimation methodology has been developed to fill this gap, offering a quantitative method suitable for real-time risk analysis. A diagnostic system has been created to supplement the existing onboard diagnostic modules provided by the OEM. This integration creates a dual-layer safety net, ensuring secure operation in autonomous mode and providing a reliable fallback to the human operator when required. The quantitative risk estimation model that calculates the probability of collision, accounts for sensor and actuator faults amid measurement uncertainties. Based on the estimated probability, fault behavior is dynamically classified into distinct risk regions. The system is designed to respond appropriately to the situation by tailoring mitigating actions from minor adjustments to fallback protocols based on the level of risk and the type of fault. The proposed framework is illustrated through scenario-based testing via multiple simulations and closed-course evaluation using the test vehicle. This research has been conducted to contribute towards OSU's team, Buckeye AutoDrive, participating in Year 3 of the SAE AutoDrive Challenge II.

PhD, University of Cincinnati, 2024, Arts and Sciences: Psychology

Introduction: Black caregivers report dissatisfaction with the diagnostic process for autism spectrum disorder (ASD), describing delayed referrals, inadequate treatment by service providers, and insufficient information from medical professionals. Families' experiences with the diagnostic process are important to understand, as the diagnostic process is a pivotal time in a child's life that can impact their developmental trajectory and facilitate or hinder access to needed services. The goal of the current study is to examine racial differences in caregivers' perspectives of the diagnostic process and how families' experiences relate to service use. Participants: We recruited 124 (71%) White/Caucasian and 50 (29%) Black/African American caregivers of children diagnosed with ASD through the Cincinnati Children's Division of Developmental and Behavioral Pediatrics (DDBP). Measures: We extracted demographic and evaluation characteristics from the medical record. We used participants' addresses to identify neighborhood-level social vulnerability. Caregivers completed surveys, assessing demographic information, which services their child received since the ASD diagnosis, and their experiences with the diagnostic process (e.g., how providers treated them, the amount of information provided). Caregivers could provide comments to expand upon their Likert responses. Data Analysis: We used generalized linear models to examine the relation between race and satisfaction and the association between race and service utilization, first in unadjusted models. We conducted partially adjusted models, controlling for demographic variables, and fully adjusted models controlling for proxy variables of institutionalized racism such as income, neighborhood vulnerability, and caregiver education level. We examined whether race moderated the association between satisfaction with the diagnostic process and service utilization. We identified qualitative themes from open-text bo (open full item for complete abstract)

Doctor of Philosophy (Ph.D.), Bowling Green State University, 2024, Data Science

The use of statistical and machine learning methods in collection, evaluation and presentation of biological data is very extensive. This reflects a need for precise quantitative assessment of different types of challenges encountered in the field of healthcare. But the sparse nature of medical data makes it hard to find the hidden patterns and as a result makes the prediction a complex task. This dissertation research discusses several biostatistical methods including sample size determination in a balanced clinical trial, finding cohort risk from case control information, odds ratio, Cochran-Mantel-Haenszel odds ratio etc. along with examples and analysis of a real life dataset to further solidify the concepts. Moreover, different classification models: Random Forest, Gradient Boosting, Support vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), Decision Tree (DT), Logistic Regression, Artificial Neural Network (ANN) are applied in the analysis of Wisconsin Breast Cancer (diagnostic and original) dataset and their performance comparison is presented. Later, these classification models are also used in conjunction with ensemble learning methods; since ensemble methods significantly improves the predictive outcomes of the classification models. The evaluation of the classification models is measured using accuracy, AUC score, precision and recall metrics. In tree-based classification models, Random Forest (solely and in conjunction with the ensemble learning) gives the highest accuracy; whereas in the later chapter Artificial Neural Network gives the highest accuracy measure.

Master of Science, The Ohio State University, 2024, Animal Sciences

Doppler ultrasonography allows for the assessment of corpus luteum (CL) blood flow which can be utilized as an indirect method of pregnancy diagnosis at ~21 days after artificial insemination (AI) in cattle. The research presented herein spans four experiments designed to evaluate factors with the potential to affect the diagnostic performance of Doppler ultrasonography for pregnancy diagnosis in cattle. In the first experiment, the objective was to evaluate the effect of pulse repetition frequency (PRF) setting on the diagnostic performance of Color flow Doppler ultrasonography (CFDU) in beef cattle. Suckled beef cows were evaluated by CFDU utilizing three PRF settings (720 Hz, 9600 Hz, 1500 Hz) at 21 days after AI. All three PRF settings had robust sensitivity (> 93%), irrespective of whether subjective luteal blood flow scores (LBFs) or objective LBF percentage (LBF%) was employed for diagnosis. Notably, specificity and accuracy were significantly greater for LBFs than LBF%; however, diagnostic performance was not significantly different between PRF settings. In the second experiment, the objective was to evaluate the effect of Doppler mode, CFDU and power Doppler ultrasonography (PWDU), on the diagnostic performance of Doppler ultrasonography in beef cattle. Pregnancy diagnosis was determined in suckled beef cows and heifers at 21 days after AI using CFDU, PWDU, CL volume, and circulating progesterone (P4). Sensitivity was > 96% for each assessment method, and specificity was not significantly different between iii the four methods. Conversely, accuracy tended to be greater for CFDU than for CL volume, while accuracy for PWDU and P4 was intermediate. In the third experiment, the objective was to determine the effect of P4 administration and day of examination on the accuracy of CFDU for pregnancy diagnosis in beef cattle. Beef heifers were randomly assigned to receive an intravaginal P4 device (CIDR) on day 15 after AI or remain untreated (Control). Pregnancy (open full item for complete abstract)

MS, University of Cincinnati, 2024, Medicine: Genetic Counseling

Current literature has highlighted a diagnostic delay for Duchenne muscular dystrophy (DMD), clinical and familial needs for an early diagnosis, and increasing support for the inclusion of DMD on newborn screening (NBS) panels. Our aim was to investigate whether early diagnosis is associated with a delay in disease progression outcomes in male siblings with DMD. We conducted a retrospective chart review of 42 siblings sets and compared their disease courses. The primary predictor was age of diagnosis. Primary clinical disease outcomes included ages at loss of ambulation (LoA), first abnormal cardiac finding, first abnormal pulmonary finding, and North Star Ambulatory Assessment (NSAA) scores at age 8 years. The median age at last visit was 14.4 years and 10.5 years for the older and younger sibling cohorts, respectively. Median age of diagnosis in the younger sibling cohort was 2.04 years and was significantly earlier than the median age for the older sibling cohort, 4.96 years (p < 0.001). Corticosteroid treatment was initiated at a median age of 6.40 years for the older siblings and 4.45 years for the younger siblings (p <0.001). Age of diagnosis was not a predictor for the four primary outcomes. Although, advanced diagnostic age may be associated with increased time to perform the Gowers sign at age 8 years (p = 0.059). Though, we were unable to conclude that age of diagnosis is a predictor for disease progression outcomes in this cohort, further studies with longer follow-up times are warranted to provide a more accurate understanding of the impact of early diagnosis on disease course in this population.

MS, University of Cincinnati, 2024, Medicine: Genetic Counseling

Objectives Significant gaps exist in the understanding of the presentation of genetic conditions across the lifespan, particularly during the prenatal period. This study aimed to describe the limitations of prenatal phenotyping by detailing the differences between prenatal and postnatal evaluations of neonates with genetic conditions. Methods We conducted a retrospective chart review of neonates with genetic diagnoses who previously received a detailed prenatal phenotype evaluation by fetal ultrasound, MRI, and echocardiogram at the Cincinnati Children's Fetal Care Center (CCFCC) between July 2018 and October 2022. Details of the prenatal and postnatal phenotypes were collected using Human Phenotype Ontology (HPO) terms to compare findings between the time points. Results Between July 2018 and October 2022, there were 85 neonates with genetic diagnoses who were prenatally evaluated in the CCFCC; these patients either received diagnoses prenatally (n=38), postnatally (n=45), or differing diagnoses before and after birth (n=2). The number of HPO terms significantly increased after postnatal evaluation (mean: 8.45) compared to what was identified prenatally at time of referral (mean: 3.45) (p<0.001) and during CCFCC evaluation (mean: 4.41) (p<0.001). There was a significant increase in the number of anomalies noted postnatally in most body systems compared to what was observed prenatally, including the musculoskeletal, nervous, genitourinary, head and neck, and respiratory systems. Conclusions There is a significant increase in phenotypic information in most body systems that becomes available as a fetus grows and after a child is born. Thus, fetuses with anomalies should be evaluated at multiple time points during prenatal life and after birth to ensure comprehensive phenotype information is available, particularly when a genetic etiology is suspected since most genetic testing and interpretation is phenotype driven. Awareness of bod (open full item for complete abstract)

DNP, Kent State University, 2024, College of Nursing

Abstract Background and Review of Literature: The databases utilized were CINAHL, PubMed, Cochrane Database of Systematic Reviews, Cochrane Library, and Cochrane Central Register of Controlled Trials. The number of articles reviewed was over 50 and approximately 30 were used, including practice guidelines, randomized control trials, systematic reviews and meta-analyses, case studies, interventional studies with a single group, and cohort studies. Due to increasing rates of obesity and sedentary lifestyles the incidence of gestational diabetes mellitus (GDM) is increasing. Current literature and practice guidelines recommend screening early in pregnancy for diagnosis and management. Methods: This was a non-experimental quality improvement project based on a practice guideline. Measurements included a pair t-test that measured staff education with a pre and posttest. Results: The sample included 103 women between the ages of 17 and 44. Approval was obtained from Kent State University's Institutional Review Board (IRB) before the project began. Participants were selected from one private practice with inclusion criteria for this project being pregnant in the first trimester of pregnancy with no age restriction, exclusion criteria being already diagnosed with pre-gestational diabetes, or patient refusal. This project initiated a practice guideline in a private practice. Objectives included increasing staff and patient knowledge regarding gestational diabetes mellitus, screening patients for risk factors of gestational diabetes, and testing if positive risk factors are present. The long-term goal was to adopt the project long term. Results included patient demographics identifying the patient population and risk factors, most patients were 27-year-old white women in their first pregnancy with a BMI of 29. This represents a low-risk population with risk factors including increased BMI, gravidity, and minority race. The knowledge of staff was measured (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Plant Pathology

Plants exist in a constantly evolving microbial environment that significantly influences their growth, development, and overall well-being. Within this microbial milieu, certain bacteria play a pivotal role in enhancing plant health and growth. These beneficial bacteria are collectively referred to as plant growth-promoting bacteria (PGPB). They offer valuable services to plants, including improved nutrient absorption, heightened growth stimulation, and increased resilience against pathogens and the other environmental adversities. PGPB engage with plants through diverse modes of interaction, such as root colonization, endophytic association, or rhizosphere competence. An in-depth comprehension of the molecular mechanisms and ecological dynamics governing these interactions is essential for unlocking the potential of PGPB in promoting sustainable agriculture and environmental remediation. In Chapter 1, I provide an overview of current methods used to detect and diagnose Pseudomonas syringae. This encompasses traditional approaches like culture isolation and microscopy, as well as modern techniques such as PCR and ELISA. Furthermore, I explore the upcoming advancements in this domain, emphasizing the necessity for highly sensitive and specific methods to detect pathogens even at low concentrations. Additionally, I delve into approaches for diagnosing P. syringae infections when they coexist with other pathogens. Chapter 1 Figures can be found in Appendix A. In Chapter 2, I present a significant protocol for monitoring the progression of gray mold fungal infection at various developmental stages of strawberries. I detail three distinct in vivo inoculation methods for Botrytis cinerea on strawberry plants, focusing on early, middle, and late stages of strawberry growth. Chapter 2 Figures can be found in Appendix B. In Chapter 3, I introduce Bacillus proteolyticus OSUB18 as a novel inducer of ISR (Induced Systemic Resistance). This bacterium enhances plants' r (open full item for complete abstract)

Doctor of Philosophy, University of Toledo, 2023, Counselor Education

With health information readily available on the Internet and social media, mental health counselors (MHCs) must stay informed of the current virtual trends and how clients are utilizing them. Evidence shows that the Internet and social media aid in the self-diagnosis of mental disorders. Despite this, researchers have yet to explore how MHCs are working with clients who utilize the Internet and social media to research and diagnose themselves with mental disorders. The author of the present study adopted a constructivist grounded theory to examine the experiences of MHCs and how they are counseling clients who self-diagnose via the Internet and social media. Twenty participants who have experience working with clients across the lifespan that have rendered a self-diagnosis either from an Internet search or via social media were interviewed. The data collected from their interviews informed a theoretical model showcasing six themes: (a) Origins of Self-Diagnosis, (b) Treatment-Seeking Behaviors, (c) Counselor Identity, (d) Therapeutic Collaboration, (e) Therapeutic Interventions, and (f) Outcomes of Therapeutic Exploration. The results of this study inform implications for future research and practice on the topic of client self-diagnosis via the Internet and social media.

Master of Science, The Ohio State University, 2023, Mechanical Engineering

The National Aeronautics and Space Administration announcement indicating intent to return to the moon as a part of the Artemis plan has sparked research supporting the development of lunar surface technologies. Among these technologies are lunar power systems to provide energy for life support, lunar science experiments, and in-situ resource utilization. A candidate technology in this respect are direct current microgrids which are capable of grid reconfiguration and the integration of distributed energy resources and loads. There properties provide enhanced grid reliability in the face of system faults. However, existing power systems protections and diagnostics lack key detection and isolation capabilities when considering the wide range of faults that can occur in the direct current microgrid environment. This work seeks to develop a model-based fault diagnosis scheme for direct current microgrids using structural analysis methodologies. The proposed diagnostic concept includes three stages. First, traditional overcurrent protection for the rapid mitigation of low impedance short circuit faults. Second, sensor validation algorithms to detect sensor failure. Third, a model-based diagnostics concept leveraging analytical redundancy concepts to detect and isolate the remaining system faults. A structural microgrid model is created and applied for sensor placement analysis, detectability and isolability analysis, and residual generation. These residuals are used to design diagnostic tests to evaluate the presence of faults in the system. Microgrid models are developed in Simulink to generate calibration data for these diagnostic tests. A statistical microgrid model is developed to enable Monte Carlo analysis for the generation of validation data. The proposed diagnostic scheme is shown to provide fast fault detection while maintaining low error rates. It is demonstrated that model-based diagnostics offer the capability to detect and isolate a wider range of faults c (open full item for complete abstract)

MS, University of Cincinnati, 2023, Engineering and Applied Science: Mechanical Engineering

Data-driven health assessment approaches for anomaly detection and fault diagnosis rely on domain knowledge, algorithm performance, and computational power. Different techniques have been proposed to alleviate the requirements given the variety of problems in the industry. Thus, given its adaptability to identify patterns from data in several applications, deep learning is one of the most feasible approaches that exhibits great prediction capability in a variety of fields. However, deep learning algorithms require high training time, complex hyperparameter optimization, and high computational power. Furthermore, classical machine learning techniques show high performance but depend on domain knowledge and feature engineering. However, domain knowledge is not always available in the industry, and it can bias the algorithm due to human preferences, experiences, etc. Thus, in this thesis, due to the challenges of the current state-of-the-art algorithms, an alternative method is proposed to perform multivariate time series classification. By using deep forest algorithm which performs a layer-by-layer learning style inspired by deep neural networks and utilizing only raw data, the proposed methodology approaches the mentioned challenges of deep learning and machine learning. Three main steps are required for this method: data preparation, deep forest modeling, and prediction. Furthermore, the technique works with sequential raw data in the time, frequency, and time & frequency domain without any feature engineering. Moreover, two cases of study are presented to validate the proposed method: fault diagnosis of rock drills and anomaly detection of traumatic brain injury data. The proposed method is compared with deep learning algorithms in terms of accuracy, training time, hyperparameter sensitivity, and robustness. Deep learning is used as a benchmark in this thesis since works with raw data as the input of the algorithm like the proposed method does. The results s (open full item for complete abstract)

MFA, Kent State University, 0, College of Communication and Information / School of Visual Communication Design

This thesis investigates the practices of female designers affected by Attention Deficit Hyperactivity Disorder (ADHD). The inadequacy of research on female designers with ADHD in academia propels the study. Women with ADHD are often left undiagnosed until later in life due to their distinct presentation, while men tend to be diagnosed during childhood. Significant life events, such as pursuing higher education or conducting thesis research, may lead a woman to pursue a diagnosis. This thesis seeks to employ design research methodologies to examine the intersection between female designers and the late diagnosis of ADHD. Historically, ADHD research has been largely focused on hyperactive boys, leading to gender inequality in the discourse on ADHD. However, women and girls tend to exhibit different ADHD symptoms. This research aims to foster dialogue on the combination of female designers and ADHD, with a view to appreciating their unique perspectives and impact on design and, at the same time, advocating for their recognition as an asset to any team. Additionally, this research contributes to developing AI and virtual assistants that provide essential external structures for female designers with ADHD by proposing a conceptual application that utilizes research results and AI to create a virtual assistant to aid female designers with ADHD in reaching their full potential.

Doctor of Nursing Practice, Mount St. Joseph University , 2023, Department of Nursing

Historically, a visit to the medical director was not consistently provided to each new patient at the TriHealth Outpatient Alcohol Drug Treatment Program (TOADTP). This resulted in patients being dispossessed of access to care such as evaluation and comprehensive treatment of co-occurring mental health diagnoses, the initiation of pharmacotherapy, and initiation of medication-assisted treatment for persons living with substance use disorders. This project centered on development and implementation of a standardized intake process for TOADTP patients with the goal of increasing referrals to the medical director to expand access to these lifesaving interventions. Over an eight-week period, pre-intervention data were collected. During this pre-intervention period, only one of the 12 new patients was referred to the medical director. Development, education, and mobilization of the new standardized intake process included: analyzing the old intake process for variances, educating the direct care team about the benefits of practicing from an evidence-based platform, developing a new intake process that included a hard stop in the electronic health record, and educating the TOADTP team about it. The process then was mobilized. Post-intervention outcome measures were collected over an 8-week period. These measures revealed that 17 out the 24 new patients were referred to the medical director. In sum, the implementation of a standardized referral process to the medical director increased referrals from 8.4% to 70.8%, thus expanding access to life-saving evidence-based care for people living with substance use disorders.

Master of Science (MS), Bowling Green State University, 1953, Communication Disorders