Analysis of Implementing Best Practices for Co-Prescribing Naloxone in Your Agency Online CME Training Module via Pre- and Post- Knowledge Assessment.

The opioid epidemic poses substantial risk to society. Providers must ensure that their patients understand the uses and risks of both opioids and naloxone. One way to analyze this concept is via metacognition. This refers to a person’s knowledge about cognitive phenomena, and thus it regulates self-awareness abilities in decision making, such as planning and evaluating. It is not only important for providers to have knowledge on best practices, but also to have self-awareness, and confidence in their decision making to ensure optimal patient outcomes. True-false confidence weighted scoring can be utilized, whereby various levels of confidence are assessed from “I am confident this is true,” to “I think, but am unsure, if this is true,” and similarly for false answers. This study analyzed the efficacy of an online training module, “Implementing Best Practices for Co-Prescribing Naloxone in Your Agency” and used a metacognitive analysis approach to determine efficacy. The training module, pre- and post-tests were administered at Inspira Health Network on 9/12/22 and 9/13/22. This analysis finds a significant improvement in pre- and post-intervention scores, as well as significant improvement in provider confidence in their answer choices. Such an analysis provides insight not only to efficacy of an intervention, but also the likelihood of confidence, and continued use of the intervention

Mental Health of Medical Students Regarding the COVID-19 Pandemic

Mental health has taken a global priority as more realize that it is just as important as physical health in the overall health of a person. Medical students have faced mental health issues for decades and have been shown to suffer more than the general population. The recent COVID-19 pandemic has only deteriorated this issue. Many primary databases such as PubMed were used. The results indicate that medical students have extremely high rates of depression, burnout, anxiety, and stress that can affect their education but also carry over into their professions. Although many interventions increase mental wellness in medical school, future studies should focus more on how medical schools can implement these changes into their curriculums

Community Health Worker: High Risk Stabilization Study

Introduction: Hypertension is a common health concern among Americans of all age groups. Unregulated hypertension can lead to complications such as atherosclerosis that contributes to an increased risk of heart attack and stroke. The identification of individuals with hypertensive blood pressures allows for the targeted distribution of health management advice and resources. Methods: Blood Pressure measurements are obtained by Community Health Workers at public events through free health screenings Results: Assessing the efficacy of CHW’s efforts to direct high-risk individuals to proper resources for control of their blood pressure relies on the presence of follow up BP measurements that can be compared over time. This level of consistency proved difficult to obtain since data points were only gathered at community events where attendees are not always consistently the same people. Discussion: Free health screenings by CHWs provide the foundation for change by identifying individuals with blood pressures that put them at a high risk of dangerous health complications. Comparing BP measurements at different time intervals, such as 30 and 60 days, can help assess the degree of improvement in blood pressures

Pain and Sleep are Associated in Fibromyalgia Patients

This poster explores whether a correlation exists between lack of sleep and fibromyalgia pain

The Effects of Sleep Quality, Covariates and a Possible Intervention

Sleep is an essential biological process needed to maintain adequate physiologic function. Research has provided growing evidence that chronic failure to get enough sleep is associated with increased risk for obesity, depression, diabetes, hypertension, stroke, cardiovascular disease, and mortality. Sleep deprivation is threatening the health of up to 45% of the world\u27s population. Furthermore, sleep disorders were found to be associated with significantly higher rates of health care utilization and cost, accounting for $94.9 billion in costs each year in the United States. Earlier data from this project demonstrated a correlation between sleep quality and pain. METHODS: Patients were recruited from Family Medicine and NMI. For a pilot study on intervention, student volunteers were recruited. Data was collected and statistical analyses were carried out with IBM SPSS v29.0 and Prism 12.0. RESULTS: Several covariates exhibited correlations with sleep quality. The sleep quality was surprisingly poor even in patients seen for well visits or annual examinations. It was also poor in student volunteers. CONCLUSIONS: The analyses revealed correlations between covariates (that are associated with the Body Mass Index (BMI), education levels) and sleep and circadian rhythms. Preliminary findings suggest a very short intervention was useful for students

Preferences for Support Resources Among Loved Ones of Adults Prescribed Opioid Medications

The opioid epidemic continues to be a leading cause of overdose and related deaths in America. While most interventions are focused on the individuals with opioid and substance use disorder (OUD/SUD); the impact caregivers and family can have on the treatment of patients with OUD is non-existent. The purpose of this study is to collect pilot data on peers, friends, and family members of patients with SUD/OUD to understand the barriers in psychosocial support and maintaining treatment retention; barriers to accessing medication assisted therapy (MAT) and naloxone; and caregiver fatigue and barriers for caregivers. The collected data will be used to develop a digital health intervention (DHI) in the form of a mobile application/web page. To develop the survey, a review of the current literature on PubMed relating to OUD/SUD and stigma, caregiver fatigue, efficacy of DHIs, readiness to change, and promoting naloxone use was conducted. The results of the review support the fact that caregivers of patients with OUD/SUD experience fatigue and often do not have accurate knowledge of how to help patients. Furthermore, DHIs were found to improve access to treatment and reduce stigma and associated barriers. The next step of the study will be to recruit caregivers, peers, and family members of individuals with OUD to conduct surveys and development of the DHI

Community Healthcare Workers (CHW) High-Risk Stabilization Study: Does the Ability of CHWs at Mobile COVID Clinics to Link patients with Uncontrolled Diabetes to a Physician Improve Short Term Outcomes?

Uncontrolled diabetes may cause preventable but significant effects. One major preventative measure is early screening; there are hopes that community healthcare workers can increase awareness and screening availability, especially in underserved populations. We hosted and recorded logs of patients at mobile COVID health clinics, educating those with uncontrolled diabetes and connecting them to healthcare. We then looked to see if any patients had improvements in blood glucose to non-diabetic levels. 378 patients were logged, but only 138 were in events that had a significant amount of repeat visits. Twenty-five of them had blood sugar indicative of uncontrolled diabetes. Out of those, there were six patients with uncontrolled diabetes and multiple visits. Four of them had improvements in blood glucose on their most recent visit, with two maintaining persistently high levels of blood glucose. While these preliminary studies show promise in the potential efficacy of CHWs in improving screening and outcomes of uncontrolled diabetes, there is a very limited sample size. Future studies should incorporate more patient logs and explore other chronic conditions commonly undiagnosed in underserved populations such as chronic kidney disease and hypertension

A phase field method for tomographic reconstruction from limited data

Classical tomographic reconstruction methods fail for problems in which there is extreme temporal and spatial sparsity in the measured data. Reconstruction of coronal mass ejections (CMEs), a space weather phenomenon with potential negative effects on the Earth, is one such problem. However, the topological complexity of CMEs renders recent limited data reconstruction methods inapplicable. We propose an energy function, based on a phase field level set framework, for the joint segmentation and tomographic reconstruction of CMEs from measurements acquired by coronagraphs, a type of solar telescope. Our phase field model deals easily with complex topologies, and is more robust than classical methods when the data are very sparse. We use a fast variational algorithm that combines the finite element method with a trust region variant of Newton’s method to minimize the energy. We compare the results obtained with our model to classical regularized tomography for synthetic CME-like images

A marked point process model with strong prior shape information for extraction of multiple, arbitrarily-shaped objects

We define a method for incorporating strong prior shape information into a recently extended Markov point process model for the extraction of arbitrarily-shaped objects from images. To estimate the optimal configuration of objects, the process is sampled using a Markov chain based on a stochastic birth-and-death process defined in a space of multiple objects. The single objects considered are defined by both the image data and the prior information in a way that controls the computational complexity of the estimation problem. The method is tested via experiments on a very high resolution aerial image of a scene composed of tree crowns

A multi-layer `gas of circles' Markov random field model for the extraction of overlapping near-circular objects

We propose a multi-layer binary Markov random field (MRF) model that assigns high probability to object configurations in the image domain consisting of an unknown number of possibly touching or overlapping near-circular objects of approximately a given size. Each layer has an associated binary field that specifies a region corresponding to objects. Overlapping objects are represented by regions in different layers. Within each layer, long-range interactions favor connected components of approximately circular shape, while regions in different layers that overlap are penalized. Used as a prior coupled with a suitable data likelihood, the model can be used for object extraction from images, e.g. cells in biological images or densely-packed tree crowns in remote sensing images. We present a theoretical and experimental analysis of the model, and demonstrate its performance on various synthetic and biomedical images