I earned my Doctor of Philosophy (Ph.D.) in Statistics from the University of Birmingham in 2016, specializing in clustering analysis, multivariate nonparametric statistics and statistical computing. Following my Ph.D., I embarked on a dynamic career in research and academia, with a particular focus on medical statistics and data-driven research.

I have held various research positions, including Research Fellow at the Institute of Applied Health Research and the Institute of Inflammation and Ageing at the University of Birmingham. In addition, I have served as an Assistant Professor of Statistics at Helwan University in Egypt and as a Teaching Assistant and Statistical Consultant at the University of Birmingham.

Research Interests:

Throughout my career, I have demonstrated a prolific output, contributing to numerous research papers spanning areas such as clustering analysis for multivariate and functional data, constrained models for meta-analysis of diagnostic test accuracy, image comparisons based on similarity measures, machine learning with medical applications and numerical nonlinear optimization for bivariate random effects models. These impactful papers have found a home in esteemed journals, including Nature Methods, Statistical Methods in Medical Research, BMC Medical Research Methodology, British Journal of General Practice, and Journal of Clinical Epidemiology.

My motivation for pursuing the project “Clinical Decision Support Tools in Acute Care” lies in the opportunity to leverage my expertise in health data, data science, and statistical modelling to address critical patient safety issues. This project aligns perfectly with my research interests and offers the chance to collaborate with a multidisciplinary team to develop innovative solutions and positively impact patient outcomes.

In summary, my background is rooted in rigorous statistical training, and my research interests encompass a wide spectrum of statistical and data-driven topics, with a strong commitment to addressing real-world challenges in healthcare and patient safety.

Project Information

Research Driver Programme: Medicines in Acute and Chronic Care

Title: Clinical decision support tools in acute care


In this project, we’re using advanced data science and statistical techniques to make healthcare safer and more efficient for patients in acute care settings. Acute care refers to the immediate treatment patients receive in places like emergency rooms or intensive care units.

Imagine a scenario where a patient is prescribed a medication, and there’s a risk of harmful drug interactions. Our project aims to prevent such situations. We’re creating smart tools that help doctors, nurses, and pharmacists make better decisions when treating patients. These tools use electronic health data, which is a digital record of a patient’s medical history and treatment.

We’re investigating how to avoid medical errors and improve patient safety. Our team includes experts in statistics, data science, medicine, and nursing. Together, we’re working to identify and reduce prescription errors, spot dangerous drug interactions, and ensure that healthcare providers follow the best guidelines for treatment.

By doing this, we hope to make healthcare more reliable and secure, ultimately improving the outcomes and experiences of patients in acute care. It’s all about using numbers and data to protect people’s health and well-being.

This project combines cutting-edge data analysis with real-world healthcare needs to create a safer and more effective medical environment.

What is your motivation for undertaking this project and how will this funding impact your research?

My motivation for undertaking the project titled “Clinical Decision Support Tools in Acute Care” is deeply rooted in my passion for using data science, statistical modelling, and artificial intelligence to address critical healthcare challenges. The opportunity to work within Birmingham’s NIHR-funded Patient Safety Research Collaborative presents a unique and exciting platform for me to contribute to the field of patient safety and clinical decision support.

Throughout my academic and professional journey, I have developed a strong interest in research areas such as Clustering Analysis, Constrained Models for Meta-Analysis of Diagnostic Test Accuracy, and Machine Learning Medical Applications. These experiences have equipped me with the skills and knowledge needed to make a significant impact in the development and testing of clinical decision support tools.

The funding provided for this project will have a substantial impact on my research by allowing me to collaborate with a diverse team of experts, including clinicians, pharmacists, nurses, and patients. This multidisciplinary approach is essential for addressing complex healthcare issues and developing innovative solutions.

Working with electronic health data from PIONEER, the HDR UK Hub in acute care, offers a wealth of opportunities to harness the power of data for improving patient outcomes. By reducing errors in prescriptions, identifying drug-drug interactions, and enhancing guideline compliance, we aim to make a tangible difference in patient safety. This project aligns perfectly with my research interests and allows me to apply my expertise to a real-world, patient-centered context.

In summary, I am motivated to undertake this project because it provides a platform to merge my research passions with the pressing need for patient safety improvements. The funding and collaborative environment within the Birmingham Acute Care Research Group will enable me to make a meaningful contribution to the development of clinical decision support tools, ultimately enhancing healthcare outcomes and patient well-being.