Project Details
Abstract
Using an operations management approach, this project aims at providing a holistic analysis and understanding of the whole hematological cancer patient pathway and its distinct components to promote a better-quality cancer care and patient experiences. In particular we aim at: (1) conducting an analytical epidemiological cohort investigation on patients diagnosed with hematological cancer over the past ten years at the National Center for Cancer Care and Research (NCCCR). This study will help providing a better understanding of the symptomology and pattern of homological malignancies in Qatar. (2) Using artificial intelligence and deep machine learning-based approaches to develop risk prediction models for blood cancers in Qatar and developing related i-health platform to validate these models and eventually run an associated pilot study. (3) Carrying out a structural pathway analysis from symptoms to diagnosis and treatment for patient with hematological cancers. This will allow characterizing the roots of delays and identifying the critical points along the care pathway at the patient, doctor and system levels and propose solutions and recommendations to overcome the causes of the delay. (4) Performing a feasibility study to evaluate the implementation and effectiveness of Home Cancer Care Delivery (HCCD) as an innovative care service alternative with the twin benefits of reducing hospitals congestion and promoting high quality care services. This research is multi-disciplinary and draws on expertise form engineering management and decision science, computer science as well as clinical hematology and oncology. We believe that this research will highly contribute to the body of knowledge related to cancer care delivery and optimization. For instance, to our knowledge, this work is among the very first attempts to develop holistic models to address hematological cancer patient pathway’s main related problems. Moreover, this project is strongly aligned with Qatar National Vision 2030 and Qatar National Health Strategy and is strongly supporting its main objectives on health education, early detection and high-quality cancer treatment and services. Although this project aims at analyzing and optimizing the pathways for patient with hematological malignancies, we expect much of our analysis and recommendations to apply in other cancer malignancies.
Submitting Institute Name
Hamad Bin Khalifa University (HBKU)
| Sponsor's Award Number | NPRP12S-0219-190108 |
|---|---|
| Proposal ID | EX-QNRF-NPRPS-4 |
| Status | Finished |
| Effective start/end date | 5/04/20 → 24/05/24 |
Collaborative partners
- Hamad Bin Khalifa University (lead)
- Ecole centrale de Paris
- Hamad Medical Corporation
- Ministry of Public Health Qatar
Primary Theme
- Precision Health
Primary Subtheme
- PH - Preventative health
Secondary Theme
- Artificial Intelligence
Secondary Subtheme
- AI - Healthcare
Keywords
- Cancer Care,Data analysis,Simulation & modeling,Healthcare operations management,Healthtech
- None
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Dissecting bloodstream infections in febrile neutropenic patients with hematological malignancies, a decade-long single center retrospective observational study (2009-2019)
El Omri, H., Padmanabhan, R., Taha, R. Y., Kassem, N., Elsabah, H., Ellahie, A. Y., Santimano, A. J. J., Al-Maslamani, M. A., Omrani, A. S., Elomri, A. & El Omri, A., Jan 2024, In: Journal of Infection and Public Health. 17, 1, p. 152-162 11 p.Research output: Contribution to journal › Article › peer-review
12 Link opens in a new tab Citations (Scopus) -
An Artificial Intelligence-Based Diagnostic System for Acute Detection Lymphoblastic Leukemia
El Alaoui, Y., Padmanabhan, R., Elomri, A., Qaraqe, M. K., El Omri, H. & Yasin Taha, R., 29 Jun 2023, Healthcare Transformation with Informatics and Artificial Intelligence. Mantas, J., Gallos, P., Zoulias, E., Hasman, A., Househ, M. S., Charalampidou, M. & Magdalinou, A. (eds.). IOS Press BV, p. 265-268 4 p. (Studies in Health Technology and Informatics; vol. 305).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access5 Link opens in a new tab Citations (Scopus) -
Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results
Padmanabhan, R., El Alaoui, Y., Elomri, A., Qaraqe, M. K., El Omri, H. & Yasin Taha, R., 29 Jun 2023, Healthcare Transformation with Informatics and Artificial Intelligence. Mantas, J., Gallos, P., Zoulias, E., Hasman, A., Househ, M. S., Charalampidou, M. & Magdalinou, A. (eds.). IOS Press BV, p. 279-282 4 p. (Studies in Health Technology and Informatics; vol. 305).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Open Access4 Link opens in a new tab Citations (Scopus)