Franco, Z., Baker, N., R. Okusanya, T., Haque, M. R., Gresser, J., Rubya, S., et al. (2023). Customizing the BattlePeer App: Connecting First Responders with Peer Support to Manage Mental Health Crises. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 272–283). Omaha, USA: University of Nebraska at Omaha.
Abstract: The prevalence and severity of mental health disorders are high among first responders. Routine exposure to trauma, unique work patterns and the social stigma of seeking care exacerbate their challenges. While there are many mHealth applications for effective interventions, they primarily focus on support, education, and symptom identification and management. Our research uses empirical data to inform the customization of the BattlePeer application, previously tested among US veterans. Through focus groups with first responders, we identify specific barriers to help in this population. Our work highlights the potential benefits of adapting an app to create effective peer support strategies. We suggest the modification of BattlePeer to help first responders meet their mental health needs through peer support with tailored feedback and notifications. This will help negotiate the pervasive social isolation and hesitance in articulating emotions described in focus groups that lend to negative mental health outcomes.
|
Sterl, S., Almalla, N., & Gerhold, L. (2023). Conceptualizing a Pandemic Early Warning System Using Various Data: An Integrative Approach. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 284–294). Omaha, USA: University of Nebraska at Omaha.
Abstract: Covid-19 demonstrated the vulnerability of various systems and showed, however, that digital tools and data can serve not only to stop infections but also to detect viruses before or immediately after a zoonosis has occurred, thus preventing a potential pandemic. Although several pandemic early warning systems (P-EWS) and German pandemic-related projects (G-PRP) exist, they often use a limited data range or rely on third-party data. Here, we present a concept of an integrative pandemic early warning system (IS-PAN) applied to Germany using various data such as health data (e.g., clinical/syndromic) or internet data (e.g., social media/apps). Based on a systematic literature research of P-EWS and G-PRP on scientific and public health platforms, we derived indicators that help to detect virus threats with a system consisting of modules monitored in parallel. By integrating various pre collected digital data, this approach can help to identify a potential health threat efficiently and effectively.
|
Sterl, S., Billig, A., Taffo, F. W., & Gerhold, L. (2023). Visualizing the Psychosocial Situation in Crises and Disasters: Conceptualizing a Multi-Functional Crisis Information Platform (CIP-PS). In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 252–262). Omaha, USA: University of Nebraska at Omaha.
Abstract: Crises and disasters are becoming more frequent, long-lasting, complex, and interdependent. This can lead to negative psychosocial consequences in vulnerable population groups, increasing the need to (1) monitor psychosocial indicators and (2) make information on psychosocial topics available to decision-makers, the scientific community, and the public. In this WiPe paper, we present a way to systematically visualize, research, and document different types of psychosocial data in crises and disasters by developing a “Multi-Functional Crisis Information Platform for Psychosocial Situations”, called CIP-PS. The CIP-PS has three components, i.e., an information dashboard (CIP-DAB), a research platform (CIP-REP), and a documentation (CIP-DOC) component which together help visualize, research and document psychosocial topics, such as the psychosocial situation picture in Germany. The platform is a valuable tool for presenting relevant psychosocial information in the context of disaster public health. Its strength lies in an extensive connection between the three components related to healthcare informatics.
|
Tsai, C. - H., Kadire, S., Sreeramdas, T., VanOrmer, M., Thoene, M., Hanson, C., et al. (2023). Generating Personalized Pregnancy Nutrition Recommendations with GPT-Powered AI Chatbot. In Jaziar Radianti, Ioannis Dokas, Nicolas Lalone, & Deepak Khazanchi (Eds.), Proceedings of the 20th International ISCRAM Conference (pp. 263–271). Omaha, USA: University of Nebraska at Omaha.
Abstract: Low socioeconomic status (SES) and inadequate nutrition during pregnancy are linked to health disparities and adverse outcomes, including an increased risk of preterm birth, low birth weight, and intrauterine growth restriction. AI-powered computational agents have enormous potential to address this challenge by providing nutrition guidelines or advice to patients with different health literacy and demographics. This paper presents our preliminary exploration of creating a GPT-powered AI chatbot called NutritionBot and investigates the implications for pregnancy nutrition recommendations. We used a user-centered design approach to define the target user persona and collaborated with medical professionals to co-design the chatbot. We integrated our proposed chatbot with ChatGPT to generate pregnancy nutrition recommendations tailored to patients’ lifestyles. Our contributions include introducing a design persona of a pregnant woman from an underserved population, co-designing a nutrition advice chatbot with healthcare experts, and sharing design implications for future GPT-based nutrition chatbots based on our preliminary findings.
|
