A Novel Visual Recognition-based Authentication Model Using a Hybrid Trust Theme to Verify Provider Profiles for Enhancing Information Assurance in Online Healthcare

Authors

  • Ankur Chattopadhyay Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA
  • Michael J. Schulz Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA
  • Katie L. Turkiewicz Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA
  • Eli Hughes Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA

DOI:

https://doi.org/10.13052/2245-1439.733

Keywords:

Antecedents, Authentication, Biometric, Consumer, Cyberchondria, Hard Trust, Hybrid Trust, Online Healthcare Information, Provider, Soft Trust, Visual Recognition and Verification

Abstract

As the number of online healthcare consumers grows day by day, we notice a corresponding rise in the quantity of online healthcare information (OHI), as provided by a large number of different healthcare related web service providers, including several third-party websites, like HealthGrades.com, ZocDoc.com, and ShareCare.com. Given the convenience and ease of having OHI at their disposal through web browsing, today’s healthcare often resort to making “Dr. Google” their first point of contact instead of an actual physician. However, this may lead to cyber psychological issues, like cyberchondria, which are related to uncertainty, mental anxiety, and credibility concerns in regard to OHI. Existing literature shows that there been some research work done on the challenges posed by cyberchondria like cyber psychological issues, especially in non-computing disciplines. However, to our knowledge, the research work, as presented here, is the first of its kind to directly propose an information assurance driven technical computing solution to this interdisciplinary problem. None of the previous research work has proposed to address the reliability issues associated with OHI by handling multi-layered trust antecedents from different domains at both the website and the organizational levels. Additionally, the OHI research in these efforts have not cumulatively accounted for multi-level factors like security, assurance, social presence, verification, reputation, and familiarity, which together contribute towards building trust for countering cyberchondria. Hence, in order to enhance the process of trust formation for OHI, we conceptualize and propose a novel hybrid is driven by biometric authentication of physician image profiles. Our proposed approach uniquely addresses the multi-dimensional and social aspects of OHI related trust building, including interpersonal equations of both the patient and the physician at the website as well as the institutional levels. One of the major contributions of this work is proposition of a hybrid, multilayered analysis model for OHI based trust computing that includes a unique, improvised amalgamation of different trust factors from interdisciplinary and disciplinary research domains, including information assurance and security. The uniqueness of this model lies in its biometrics-inspired basis, along with its hybrid trust focus with a fine blend of soft trust and hard trust approaches.As part of our research investigation with this proposed approach, an experiment has been conducted with a unique set of about close to seventy (70) OHI website based physician visual profiles in order to demonstrate a potential implementation of this trust-computing model. Another significant contribution of this research is the creation of the first of its kind unique dataset of acceptable physician profile images from variousOHIwebsites in relation to testing our proposed OHI trust-computing model based approach.We see this research work as a novel initiative for improving OHI credibility in an effort to set up a prospective benchmark pathway towards a new multi-dimensional OHI trust metric for addressing cyberchondria like cyber psychological issues. The entire collection of our varied experimental results from this research are shared and reported as part of this paper.We believe that this work will drive further innovative research experiments with the novel OHI trust computing model, as proposed in this paper, and shall form the basis of future trust computing research towards finding ways to mitigate cyber psychological issues in the realm of OHI.

 

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Author Biographies

Ankur Chattopadhyay, Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA

Ankur Chattopadhyay is an Assistant Professor in the Information and Computing Sciences (ICS) department at the University of Wisconsin, Green Bay. He has a Ph.D. in computer science from the University of Colorado. His research interests include information assurance and cybersecurity, privacy-enhancing computer vision and pattern recognition, image processing & analysis, and computer science plus security education. He has published and presented in international conferences like IEEE Security & Privacy, ACM SIGCSE, IEEE CVPR and IEEE FIE. Chattopadhyay has more than 16+ years of experience in both academics and industry. As an academician, his passion is innovating computer science plus cybersecurity education, conducting research and applying his research to benefit the society. His industry profile includes multiple roles such as IT analyst, software engineer and embedded systems engineer, having worked with Tata Consultancy Services for several years.

Michael J. Schulz, Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA

Michael J. Schulz is an associate programming analyst at Express Scripts. He received his Bachelor’s degree in Computer Science from the University of Wisconsin Green Bay where his coursework placed an emphasis on big data and data science. Michael continues to explore his passions in data science in the healthcare industry where he seeks to leverage data through machine learning in order to obtain the best outcomes for patients. He is currently pursuing his Master’s degree in Data Science in the University of Wisconsin.

Katie L. Turkiewicz, Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA

Katie L. Turkiewicz is an Assistant Professor in the Information and Computer Science Department at the University of Wisconsin Green Bay. She received her MA and Ph.D. from the University of Wisconsin Milwaukee where her research focus included online health information seeking practices. Dr. Turkiewicz continues to explore the impact of counterproductive online health information seeking on the provider-patient relationship in addition to the impact of new technology on health.

Eli Hughes, Department of Information and Computing Sciences University of Wisconsin – Green Bay Green Bay, USA

Eli Hughes spent three years at the University of Wisconsin Green Bay studying Health and Organizational communication before graduating with a Bachelor’s degree emphasizing both of those areas in 2017. Eli is particularly interested in areas of study focused on Cyberchondria and Health Anxiety. He has recently started his career in the transportation industry and remains highly interested in research, lifelong learning, and the study of communication

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Published

2018-04-19

How to Cite

1.
Chattopadhyay A, Schulz MJ, Turkiewicz KL, Hughes E. A Novel Visual Recognition-based Authentication Model Using a Hybrid Trust Theme to Verify Provider Profiles for Enhancing Information Assurance in Online Healthcare. JCSANDM [Internet]. 2018 Apr. 19 [cited 2024 Oct. 2];7(3):1-46. Available from: https://journals.riverpublishers.com/index.php/JCSANDM/article/view/5307

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