Current Search:  Healthcare (x)

View All Items

  • CSV Spreadsheet
(1 - 4 of 4)
ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE
Title
ASSESSING METHODS AND TOOLS TO IMPROVE REPORTING, INCREASE TRANSPARENCY, AND REDUCE FAILURES IN MACHINE LEARNING APPLICATIONS IN HEALTHCARE.
Creator
Garbin, Christian, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Artificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The...
Show moreArtificial intelligence (AI) had a few false starts – the AI winters of the 1970s and 1980s. We are now in what looks like an AI summer. There are many useful applications of AI in the field. But there are still unfulfilled promises and outright failures. From self-driving cars that work only in constrained cases, to medical image analysis products that would replace radiologists but never did, we still struggle to translate successful research into successful real-world applications. The software engineering community has accumulated a large body of knowledge over the decades on how to develop, release, and maintain products. AI products, being software products, benefit from some of that accumulated knowledge, but not all of it. AI products diverge from traditional software products in fundamental ways: their main component is not a specific piece of code, written for a specific purpose, but a generic piece of code, a model, customized by a training process driven by hyperparameters and a dataset. Datasets are usually large and models are opaque. We cannot directly inspect them as we can inspect the code of traditional software products. We need other methods to detect failures in AI products.
Show less
Date Issued
2020
PURL
http://purl.flvc.org/fau/fd/FA00013580
Subject Headings
Machine learning, Artificial intelligence, Healthcare
Format
Document (PDF)
THERE IS NO “I” IN TEAM: IMPACTS OF SURGICAL TEAM DYNAMICS ON OPERATIONAL PERFORMANCE AND CLINICAL OUTCOMES
Title
THERE IS NO “I” IN TEAM: IMPACTS OF SURGICAL TEAM DYNAMICS ON OPERATIONAL PERFORMANCE AND CLINICAL OUTCOMES.
Creator
Hasse, Christopher H., Behara, Ravi S., Florida Atlantic University, Department of Information Technology and Operations Management, College of Business
Abstract/Description
While the complexities and challenges facing healthcare continue to grow, the focus on improving surgical practices remains constant. Possessing a strong influence over patient referral patterns, public reputation/prominence, and financial performance, surgical practices command heightened attention on operational performance and clinical outcomes. Executive leadership cannot support (nor improve) a surgical practice without comprehending the importance of team dynamics in the operating room ...
Show moreWhile the complexities and challenges facing healthcare continue to grow, the focus on improving surgical practices remains constant. Possessing a strong influence over patient referral patterns, public reputation/prominence, and financial performance, surgical practices command heightened attention on operational performance and clinical outcomes. Executive leadership cannot support (nor improve) a surgical practice without comprehending the importance of team dynamics in the operating room (OR) environment. Previous literature offers mixed and incomplete results on themes of team familiarity and OR efficiency, frequently citing handoffs, late starts, and task disruptions as catalysts for negative performance. Studies routinely use historical interaction counts to measure team familiarity, which often neglect the degree of participation (engagement) across prior experiences. Similarly, counts of handoffs or individuals entering an OR do not offer an accurate assessment of team performance. Guided by historical studies, four hypotheses are presented and argue that enhancing surgical team dynamics yield favorable improvements for operational performance and clinical outcomes. Utilizing data from 9,049 neurologic surgery cases performed at two separate campuses (belonging to the same organization) over a three-year timeframe (March 2019 to November 2021), this study measures surgical team dynamics in a highly complex setting through the lens of case continuity and surgeon familiarity to assess key outputs: case scheduling errors (proxy for operational performance) and post-operative complications within 30-days of surgery (proxy for clinical outcomes).
Show less
Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014137
Subject Headings
Surgery, Operating room personnel, Healthcare management
Format
Document (PDF)
DIGITAL TRANSFORMATION OF HEALTHCARE USING ARTIFICIAL INTELLIGENCE
Title
DIGITAL TRANSFORMATION OF HEALTHCARE USING ARTIFICIAL INTELLIGENCE.
Creator
Gogova, Jennifer, Marques, Oge, Florida Atlantic University, Department of Computer and Electrical Engineering and Computer Science, College of Engineering and Computer Science
Abstract/Description
Digital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare...
Show moreDigital transformation is rapidly changing the healthcare industry, and artificial intelligence (AI) is a critical component in this evolution. This thesis investigates three selected challenges that might delay the adoption of AI in healthcare and proposes ways to address them successfully. Challenge #1 states that healthcare professionals may not feel sufficiently knowledgeable about AI. This is addressed by Contribution #1 which is a guide for self-actualization in AI for healthcare professionals. Challenge #2 explores the concept of transdisciplinary teams needing a work protocol to deliver successful results. This is addressed by Contribution #2 which is a step-by-step protocol for medical and AI researchers working on data-intensive projects. Challenge #3 states that the NIH All of Us Research Hub has a steep learning curve, and this is addressed by Contribution #3 which is a pilot project involving transdisciplinary teams using All of Us datasets.
Show less
Date Issued
2023
PURL
http://purl.flvc.org/fau/fd/FA00014179
Subject Headings
Healthcare, Medical care, Artificial intelligence—Medical applications
Format
Document (PDF)
Microservices-based approach for Healthcare Cybersecurity
Title
Microservices-based approach for Healthcare Cybersecurity.
Creator
Trivedi, Ohm H., Shankar, Ravi, Florida Atlantic University, College of Engineering and Computer Science, Department of Computer and Electrical Engineering and Computer Science
Abstract/Description
Healthcare organizations, realizing the potential of the Internet of Things (IoT) technology, are rapidly adopting the technology to bring signi cant improvements in the quality and e ectiveness of the service. However, these smart and interconnected devices can act as a potential \back door" into a hospital's IT network, giving attack- ers access to sensitive information. As a result, cyber-attacks on medical IoT devices have been increasing since the last few years. It is a growing concern...
Show moreHealthcare organizations, realizing the potential of the Internet of Things (IoT) technology, are rapidly adopting the technology to bring signi cant improvements in the quality and e ectiveness of the service. However, these smart and interconnected devices can act as a potential \back door" into a hospital's IT network, giving attack- ers access to sensitive information. As a result, cyber-attacks on medical IoT devices have been increasing since the last few years. It is a growing concern for all the stakeholders involved, as the impact of such attacks is not just monetary or privacy loss, but the lives of many patients are also at risk. Considering the various kinds of IoT devices one may nd connected to a hospital's network, traditional host-centric security solutions (e.g. antivirus, software patches) are at odds with realistic IoT infrastructure (e.g. constrained hardware, lack of proper built-in security measures). There is a need for security solutions which consider the challenges of IoT devices like heterogeneity of technology and protocols used, limited resources in terms of battery and computation power, etc. Accordingly, the goals of this thesis have been: (1) to provide an in-depth understanding of vulnerabilities of medical IoT devices; (2) to in- troduce a novel approach which uses a microservices-based framework as an adaptive and agile security solution to address the issue. The thesis focuses on OS Fingerprint- ing attacks because of its signi cance for attackers to understand a target's network. In this thesis, we developed three microservices, each one designed to serve a speci c functionality. Each of these microservices has a small footprint with RAM usage of approximately 50 MB. We also suggest how microservices can be used in a real-life scenario as a software-based security solution to secure a hospital's network consisting of di erent IoT devices.
Show less
Date Issued
2018
PURL
http://purl.flvc.org/fau/fd/FA00013140
Subject Headings
Cybersecurity, Healthcare, Internet of things--Security measures, Medical care--Information technology--Security measures
Format
Document (PDF)