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Evaluation of the Effectiveness of Advanced Technology Clinical Simulation Manikins in Improving the Capability of Australian Paramedics to Deliver High-Quality Cardiopulmonary Resuscitation: Pre- and Postintervention Study

Evaluation of the Effectiveness of Advanced Technology Clinical Simulation Manikins in Improving the Capability of Australian Paramedics to Deliver High-Quality Cardiopulmonary Resuscitation: Pre- and Postintervention Study

Overall, paramedic participants had high resuscitation skills before the SCOPE intervention, which were not significantly improved by training and did not degrade over time following the deployment of the manikins. However, there was one exception to this finding, namely that paramedic participants had significantly lower odds of achieving compressions with adequate rate approximately 6 to 11 months after the SCOPE intervention compared with immediately following training.

Alison Zucca, Jamie Bryant, Jeffrey Purse, Stuart Szwec, Robert Sanson-Fisher, Lucy Leigh, Mike Richer, Alan Morrison

JMIR Cardio 2024;8:e49895

Personalized Smartphone-Enabled Assessment of Blood Pressure and Its Treatment During the SARS-CoV-2 COVID-19 Pandemic in Patients From the CURE-19 Study: Longitudinal Observational Study

Personalized Smartphone-Enabled Assessment of Blood Pressure and Its Treatment During the SARS-CoV-2 COVID-19 Pandemic in Patients From the CURE-19 Study: Longitudinal Observational Study

This allowed deployment of the app into the community setting within the United States in an observational study facilitated by our study partner Curebase. Additional data collection and integration components and a financial reconciliation program were developed to facilitate the study’s operational aspects.

Leanne Richardson, Nihal Noori, Jack Fantham, Gregor Timlin, James Siddle, Thomas Godec, Mike Taylor, Charles Baum

JMIR Mhealth Uhealth 2024;12:e53430

The Opportunities and Risks of Large Language Models in Mental Health

The Opportunities and Risks of Large Language Models in Mental Health

To maximize the positive impact of LLMs on mental health, LLM development, testing, and deployment must be done ethically and responsibly (see Textbox 1). This requires identification and evaluation of risks, taking preemptive steps to mitigate risks, and establishing plans to monitor for ongoing or new and unexpected risks [45,46].

Hannah R Lawrence, Renee A Schneider, Susan B Rubin, Maja J Matarić, Daniel J McDuff, Megan Jones Bell

JMIR Ment Health 2024;11:e59479

Moral Distress, Mental Health, and Risk and Resilience Factors Among Military Personnel Deployed to Long-Term Care Facilities During the COVID-19 Pandemic: Research Protocol and Participation Metrics

Moral Distress, Mental Health, and Risk and Resilience Factors Among Military Personnel Deployed to Long-Term Care Facilities During the COVID-19 Pandemic: Research Protocol and Participation Metrics

Finally, the deployment was focused on assisting the most sick and vulnerable Canadian older adult patients and residents of LTCFs, a population that even seasoned CAF medical personnel were largely unfamiliar with. This combination of traditional and new stressors meant that the initial Operation LASER deployment was unique in many ways.

Anthony Nazarov, Deniz Fikretoglu, Aihua Liu, Jennifer Born, Kathy Michaud, Tonya Hendriks, Stéphanie AH Bélanger, Minh T Do, Quan Lam, Brenda Brooks, Kristen King, Kerry Sudom, Rakesh Jetly, Bryan Garber, Megan Thompson

JMIR Res Protoc 2023;12:e44299

Providing Self-Led Mental Health Support Through an Artificial Intelligence–Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care

Providing Self-Led Mental Health Support Through an Artificial Intelligence–Powered Chat Bot (Leora) to Meet the Demand of Mental Health Care

To address this issue, it is imperative that the detailed development, testing, and deployment of AI chatbots be transparent to clinicians and consumers. This means clearly explaining the algorithms and processes that the chatbots use to generate responses, as well as the limitations of the technology and any and all risks to users. By providing this information, clinicians and patients may make more informed decisions about the advice provided by an AI chatbot and how to use it effectively.

Emma L van der Schyff, Brad Ridout, Krestina L Amon, Rowena Forsyth, Andrew J Campbell

J Med Internet Res 2023;25:e46448

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

A Standardized Clinical Data Harmonization Pipeline for Scalable AI Application Deployment (FHIR-DHP): Validation and Usability Study

Consequently, FHIR forms an important component for the scalable development and deployment of AI in clinics and hospitals. However, to apply AI, the input data need to be adapted to the AI algorithms. The conventional AI frameworks such as Tensorflow [12] and Pytorch [13] require data to take a tensor form, which is a vector or matrix of n-dimensions that represents various types of data (eg, tabular, time series, image, and text).

Elena Williams, Manuel Kienast, Evelyn Medawar, Janis Reinelt, Alberto Merola, Sophie Anne Ines Klopfenstein, Anne Rike Flint, Patrick Heeren, Akira-Sebastian Poncette, Felix Balzer, Julian Beimes, Paul von Bünau, Jonas Chromik, Bert Arnrich, Nico Scherf, Sebastian Niehaus

JMIR Med Inform 2023;11:e43847

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

Key Technology Considerations in Developing and Deploying Machine Learning Models in Clinical Radiology Practice

This asymmetrical nature of the classification problem can be handled either at the time of deployment or during development. The trained model can be tuned to achieve higher sensitivity or specificity according to the requirements at deployment time. Alternatively, the variation in misclassification penalties can be represented as a cost matrix, where each element C(i,j) represents the penalty of misclassifying an example of class i as class j.

Viraj Kulkarni, Manish Gawali, Amit Kharat

JMIR Med Inform 2021;9(9):e28776

Electronic Paper Displays in Hospital Operations: Proposal for Deployment and Implementation

Electronic Paper Displays in Hospital Operations: Proposal for Deployment and Implementation

For successful deployment of a novel technology, it may be helpful to form a centralized committee of network specialists, hospital administration, and clinical experts who understand outcomes surrounding the use of electronic ink displays and information security, to ensure that all stakeholders required to successfully deploy an electronic ink display can assemble and map key tasks prior to implementation.

Guruprasad D Jambaulikar, Andrew Marshall, Mohammad Adrian Hasdianda, Chenzhe Cao, Paul Chen, Steven Miyawaki, Christopher W Baugh, Haipeng Zhang, Jonathan McCabe, Jennifer Su, Adam B Landman, Peter Ray Chai

JMIR Form Res 2021;5(8):e30862

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment

Medical Specialty Recommendations by an Artificial Intelligence Chatbot on a Smartphone: Development and Deployment

This continuous deployment method eases the burden on developers by eliminating the need for complex configured scripts on a deployment server. In the cloud-native environment, this chatbot framework laid the groundwork to easily extend further features. Architecture of the chatbot.

Hyeonhoon Lee, Jaehyun Kang, Jonghyeon Yeo

J Med Internet Res 2021;23(5):e27460