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Fatima Irshaidat

Assistant Professor, Information Management and Business Analytics, Feliciano School of Business

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Feliciano School of Business
Email:
irshaidatf@montclair.edu
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Bio

Dr. Fatima Irshaidat is an Assistant Professor at the Feliciano School of Business in Information Management and Business Analytics (IMBA) department. She has a Ph.D. and Masters' degree in Industrial and Systems Engineering from The State University of New York (SUNY) at Binghamton with Healthcare focus. Prior to teaching at Montclair State, Fatima worked in the Program Management at the Governance and Continuous Improvement division at Underwriters Laboratories and was also an adjunct Professor at Farmingdale State College.

Expertise

Data Analytics, Business Analytics, Data mining, Machine Learning, Healthcare processes and regulations, continuous improvement, Statistical Quality Control

Research

  • Application of machine learning, predictive modeling, and Natural Language Processing (NLP) in healthcare problems. This research focuses on the use of Machine Learning (ML), Predictive Modeling, and Natural Language Processing (NLP) integrated with data analytics to enhance decision-making in healthcare and detect patterns. By leveraging NLP techniques and the creation of decision-aid tools, this research theme aims to drill into healthcare applications and case studies that allows us to use historical data and concurrent data to feed predictive models that allows healthcare practitioners to create a data-driven decisions customized to their own patient’s population (Shickel, James Tighe, Bihorac, & Rashidi, 2018). My research focuses on exploring the integration of machine learning (ML), data analytics, and predictive modeling in addressing critical healthcare challenges, with a specific focus on healthcare problems such as patients with cancer, obesity, hypertension, and diabetes and analyzing real-world patient data. Seeking to develop AI-driven analytical models that enhance clinical decision-making, disease prediction, and personalized treatment strategies. My research will explore several healthcare problems to identify and predict disease progression, enhance early diagnosis and interventions capabilities, optimize treatment plans and possibly improve hospitals resource allocation.
  • Business Intelligence (BI) tools such as Power BI and Tableau evolved over the past five years to integrate AI capabilities. What are the key trends, challenges, and impacts of AI-driven analytics in modern data-driven decision-making? and how have AI-driven features like Copilot and Tableau AI enhanced data analytics, automation processes Those questions allows for a structured review of recent literature (last five years) on BI tools and their AI integrations, focusing on advancements, use cases, limitations, and future directions. The narrow focus will be on advancements and how AI capabilities have evolved in BI tools, AI-Driven features like examining specific AI integrations such as Copilot (Power BI) and Tableau AI, and lastly enhancements which looks into assessing how AI has improved analytics, automation, and decision-making

Professional Experience

  • Program Manager, Underwriters Laboratories (2019 - 2024)
  • Adjunct Associate Professor, Farmingdale State College (2020 - 2024)
  • Adjunct Instructor, Broom Community College (SUNY) (2015 - 2016)
  • Quality Data Analyst, Our Lady of Lourdes Memorial Hospital (2016 - 2018)

Consulting

  • Management Consulting, UL Solutions. (September 2024 - Present). Manage compliance programs to align with regulatory agencies standards including project planning and implementation. Business and Data Analytics tool design to improve processes, monitor compliance and reduce risks.
  • Technical/Professional Work, Our Lady of Lourdes Memorial Hospital. (March 2022 - Present). Collaborate with physicians ‘research group to work on multiple research projects that involve healthcare analytics.

Refereed Published Articles

  • N. Sabounchi, N. Sharareh, F. Irshaidat, S. Atav (2020). Spatial dynamics of access to primary care for the Medicaid population. Health Systems