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Fatima Irshaidat

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

Office:
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

  • 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
  • Association between change in BMI and uncontrolled Diabetes mellitus type 2 in an outpatient setting.<br>Diabetes mellitus (DM) is a leading noncommunicable disease, with being the 7th most common cause of death worldwide (Liu, Liu, Ma, Xue, & Sorel, 2012). DM is prevalent among 11.6% of U.S. residents and type 2 diabetes mellitus (T2DM) accounts for approximately 95% of diabetes cases (Stierman & Afful, 2020). A critical phase preceding T2DM is prediabetes, characterized by an HbA1c of 5.7–6.4%. Individuals in this stage are advised to implement lifestyle modifications and regularly monitor their HbA1c levels to prevent progression to T2DM (Care, 2019). Over the years, individuals with prediabetes could develop normal HbA1c (6.5) or uncontrolled DM (UDM) (A1c >8) (Care, 2019) (Mahmood, Daud, & Ismail, 2016). Study aims to investigate the relationship between changes in BMI and the progression from prediabetes to uncontrolled diabetes over a six-year period (2017–2023). This longitudinal approach will allow us to monitor the dynamics of BMI and HbA1c in patients transitioning through different stages of glycemic control i.e., prediabetes to UDM. By focusing on a well-defined population, we hope to clarify the impact of weight gain on glycemic outcomes and provide insights into effective management strategies for individuals at risk of progressing to UDM. This study aims to contribute to the understanding of how increase in BMI effect glycemic control in individuals progressing from prediabetes to uncontrolled diabetes, ultimately reducing the burden of UDM and its complications. The main objective is to establish the relation if any between BMI and the progression to uncontrolled T2DM in prediabetic patients by analyzing historical patient data. Once relationships are identified, a logistics regression model will be developed to help understand that relation better. Other machine learning methods will be used as well. We are also looking to identify risk factors in patients progressing from pre-diabetes to uncontrolled T2DM by utilizing AI-driven insights to identify high-risk patients early and recommend targeted interventions.

Professional Experience

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

Consulting

  • 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.
  • 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.

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