Balancing Clinical Objectives with Patient Centered Care

This poster is a reflection focuses on the challenges of balancing the medical needs of a patient and their preferences in an interdisciplinary health care setting. Students from UNE\u27s Physician Assistant, Osteopathic Medicine, Social Work, Dental Medicine, and Nursing programs collaborated with Allopathic Medicine and Podiatric Medicine students from Rosalind Franklin University Medical School as a virtual health care team to care for a patient with long-covid

Social Determinants of Health Month: A Collaboration of Interprofessional Education on Care for Future Patients

Throughout the month of October, various student groups will host guest lecturers, student and physician panels, and alternative opportunities for the UNE community to learn about factors impacting health. During SDoHM, students and community members will explore conditions in which people live, grow, work, learn, practice religion, and age that impact individual health, while also learning about systems of oppression that perpetuate those conditions and disparities in healthcare. The events will encourage participants to think about how we might affect positive change in our communities and in our future patient populations. Furthermore, throughout SDoHM, students will be provided resources and virtual learning opportunities about the intersections of healthcare and social injustice, environmental racism, and disenfranchisement, underrepresentation, and discrimination in medicine. Lastly, students will be able to participate in fundraisers and drives which will benefit our community and provide essential resources. This project was created in the hopes that the UNE community would engage in discussions that are difficult but necessary. We also hope that through conversations, reflections, learning (and some un-learning), UNE community members can make a commitment to accessible, diverse, inclusive, and equitable health and wellness.https://dune.une.edu/cecefall2022/1014/thumbnail.jp

LGBTQ Health: Effective Training for Interprofessional Emerging Health Professionals

Studies find that cultural competency in healthcare practice with lesbian, gay, bisexual, transgender, and queer (LGBTQ) patients is significantly lacking across health professions’ curricula.1 LGBTQ patients are vulnerable to uninformed care as well as to biases and discrimination that, as a consequence, may result in healthcare avoidance and/or poor outcomes.2,3 Further, lack of knowledge regarding LGBTQ health contributes to practitioner discomfort and low confidence when addressing LGBTQ needs.1 The goals of this event was to raise awareness of distinctive features of LGBTQ health needs, identify biases and mitigate potential discrimination, and increase knowledge surrounding the health needs of our patients. The event presented both didactic content and interactive discussion using multimodal virtual technology to promote interprofessional collaboration among students while gaining increased understanding of culturally competent care for LGBTQ patients.https://dune.une.edu/cecespring2022/1010/thumbnail.jp

Wearable Feet Pressure Sensor for Human Gait and Falling Diagnosis

Human falls pose a serious threat to the person’s health, especially for the elderly and disease-impacted people. Early detection of involuntary human gait change can indicate a forthcoming fall. Therefore, human body fall warning can help avoid falls and their caused injuries for the skeleton and joints. A simple and easy-to-use fall detection system based on gait analysis can be very helpful, especially if sensors of this system are implemented inside the shoes without causing a sensible discomfort for the user. We created a methodology for the fall prediction using three specially designed Velostat®-based wearable feet sensors installed in the shoe lining. Measured pressure distribution of the feet allows the analysis of the gait by evaluating the main parameters: stepping rhythm, size of the step, weight distribution between heel and foot, and timing of the gait phases. The proposed method was evaluated by recording normal gait and simulated abnormal gait of subjects. The obtained results show the efficiency of the proposed method: the accuracy of abnormal gait detection reached up to 94%. In this way, it becomes possible to predict the fall in the early stage or avoid gait discoordination and warn the subject or helping companion person