IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v22y2025i2p265-d1589631.html
   My bibliography  Save this article

System Models for Synchronous Strategies in Operational Healthcare Forecasting

Author

Listed:
  • Arnesh Telukdarie

    (Johannesburg Business School, University of Johannesburg, Johannesburg 2006, South Africa)

  • Logistic Makoni

    (Johannesburg Business School, University of Johannesburg, Johannesburg 2006, South Africa)

  • R. Raghunatha Sarma

    (Vidyagiri, 5R77+32M Prasanthi Nilayam, Puttaparthi 515134, Andhra Pradesh, India)

  • Megashnee Munsamy

    (Johannesburg Business School, University of Johannesburg, Johannesburg 2006, South Africa)

  • Sunil Kumar

    (Vidyagiri, 5R77+32M Prasanthi Nilayam, Puttaparthi 515134, Andhra Pradesh, India)

Abstract

The delivery of healthcare in Low-to-Medium-Income Countries (LMICs) has long posed challenges, with established models predominantly found in wealthier nations. These models are found to be either strategic or operational, and very rarely combine these two perspectives. Most importantly, these models lack a comprehensive, holistic and synchronous construct that accompanies a systems thinking approach. This research evaluates international best practices, fundamental global theories and existing systems and tools in healthcare through a systems approach. It collates these data to propose a customized systems-based, comprehensive framework for modeling and optimizing both the management and operational tiers of healthcare in LMICs. The approach is based on the adoption of digital tools, inclusive of AI, to analyze, assimilate, align and develop advanced, holistic and inclusive frameworks. The current gap in global healthcare delivery is characterized by an ongoing lack of ability to provide quality and cost-effective care, especially in the LMICs. Despite the fact that developmental challenges are unique and specific to respective countries, there are commonalities with regard to healthcare processes that present opportunities for optimization. The main challenge lies in the effective collation and synchronization of data and tools with the specific contexts of each country. This situation highlights the need for a cohesive systems approach to enhance healthcare delivery in LMICs, allowing for tailored solutions that can bridge existing gaps. This paper presents a strategic model, with initial data quantification guiding the development of the system model. The practical significance of this research lies in its potential to transform healthcare delivery in LMICs, leading to enhanced access and quality of care through optimized systems.

Suggested Citation

  • Arnesh Telukdarie & Logistic Makoni & R. Raghunatha Sarma & Megashnee Munsamy & Sunil Kumar, 2025. "System Models for Synchronous Strategies in Operational Healthcare Forecasting," IJERPH, MDPI, vol. 22(2), pages 1-30, February.
  • Handle: RePEc:gam:jijerp:v:22:y:2025:i:2:p:265-:d:1589631
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/22/2/265/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/22/2/265/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sophie Witter & Natasha Palmer & Dina Balabanova & Sandra Mounier‐Jack & Tim Martineau & Anna Klicpera & Charity Jensen & Miguel Pugliese‐Garcia & Lucy Gilson, 2019. "Health system strengthening—Reflections on its meaning, assessment, and our state of knowledge," International Journal of Health Planning and Management, Wiley Blackwell, vol. 34(4), pages 1980-1989, October.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    3. Lakshmy Subramanian, 2021. "Effective Demand Forecasting in Health Supply Chains: Emerging Trend, Enablers, and Blockers," Logistics, MDPI, vol. 5(1), pages 1-21, February.
    4. Bloom, David E. & Canning, David & Kotschy, Rainer & Prettner, Klaus & Schünemann, Johannes, 2024. "Health and economic growth: Reconciling the micro and macro evidence," World Development, Elsevier, vol. 178(C).
    5. Sterman, J.D., 2006. "Learning from evidence in a complex world," American Journal of Public Health, American Public Health Association, vol. 96(3), pages 505-514.
    6. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
    7. Said Abasse Kassim & Jean-Baptiste Gartner & Laurence Labbé & Paolo Landa & Catherine Paquet & Frédéric Bergeron & Célia Lemaire & André Côté, 2022. "Benefits and limitations of business process model notation in modelling patient healthcare trajectory: a scoping review protocol," Post-Print hal-04366441, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Majd Oteibi & Adam Tamimi & Kaneez Abbas & Gabriel Tamimi & Danesh Khazaei & Hadi Khazaei, 2024. "Advancing Digital Health using AI and Machine Learning Solutions for Early Ultrasonic Detection of Breast Disorders in Women," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(11), pages 518-527, November.
    2. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Zheng Yan & Wenqian Robertson & Yaosheng Lou & Tom W. Robertson & Sung Yong Park, 2021. "Finding leading scholars in mobile phone behavior: a mixed-method analysis of an emerging interdisciplinary field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9499-9517, December.
    4. Freddy Gabbay & Rotem Lev Aharoni & Ori Schweitzer, 2022. "Deep Neural Network Memory Performance and Throughput Modeling and Simulation Framework," Mathematics, MDPI, vol. 10(21), pages 1-20, November.
    5. Gang Yu & Kai Sun & Chao Xu & Xing-Hua Shi & Chong Wu & Ting Xie & Run-Qi Meng & Xiang-He Meng & Kuan-Song Wang & Hong-Mei Xiao & Hong-Wen Deng, 2021. "Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    6. DonHee Lee & Seong No Yoon, 2021. "Application of Artificial Intelligence-Based Technologies in the Healthcare Industry: Opportunities and Challenges," IJERPH, MDPI, vol. 18(1), pages 1-18, January.
    7. Shang Li & Fei Yu & Shankou Zhang & Huige Yin & Hairong Lin, 2025. "Optimization of Direct Convolution Algorithms on ARM Processors for Deep Learning Inference," Mathematics, MDPI, vol. 13(5), pages 1-19, February.
    8. Dario Sipari & Betsy D. M. Chaparro-Rico & Daniele Cafolla, 2022. "SANE (Easy Gait Analysis System): Towards an AI-Assisted Automatic Gait-Analysis," IJERPH, MDPI, vol. 19(16), pages 1-27, August.
    9. Julian Schiele & Thomas Koperna & Jens O. Brunner, 2021. "Predicting intensive care unit bed occupancy for integrated operating room scheduling via neural networks," Naval Research Logistics (NRL), John Wiley & Sons, vol. 68(1), pages 65-88, February.
    10. Oded Rotem & Tamar Schwartz & Ron Maor & Yishay Tauber & Maya Tsarfati Shapiro & Marcos Meseguer & Daniella Gilboa & Daniel S. Seidman & Assaf Zaritsky, 2024. "Visual interpretability of image-based classification models by generative latent space disentanglement applied to in vitro fertilization," Nature Communications, Nature, vol. 15(1), pages 1-19, December.
    11. Taneja, Anu & Arora, Anuja, 2019. "Modeling user preferences using neural networks and tensor factorization model," International Journal of Information Management, Elsevier, vol. 45(C), pages 132-148.
    12. Hanning Ying & Xiaoqing Liu & Min Zhang & Yiyue Ren & Shihui Zhen & Xiaojie Wang & Bo Liu & Peng Hu & Lian Duan & Mingzhi Cai & Ming Jiang & Xiangdong Cheng & Xiangyang Gong & Haitao Jiang & Jianshuai, 2024. "A multicenter clinical AI system study for detection and diagnosis of focal liver lesions," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    13. Nao Aisu & Masahiro Miyake & Kohei Takeshita & Masato Akiyama & Ryo Kawasaki & Kenji Kashiwagi & Taiji Sakamoto & Tetsuro Oshika & Akitaka Tsujikawa, 2022. "Regulatory-approved deep learning/machine learning-based medical devices in Japan as of 2020: A systematic review," PLOS Digital Health, Public Library of Science, vol. 1(1), pages 1-12, January.
    14. Cristian Simionescu & Adrian Iftene, 2022. "Deep Learning Research Directions in Medical Imaging," Mathematics, MDPI, vol. 10(23), pages 1-25, November.
    15. Aglika Kaneva, 2024. "Digitalisation in the Financial Sector," Innovative Information Technologies for Economy Digitalization (IITED), University of National and World Economy, Sofia, Bulgaria, issue 1, pages 264-274, October.
    16. Jingui Zhang & Chuangji Meng & Cunlu Xu & Jingyong Ma & Wei Su, 2022. "Deep Transfer Learning Method Based on Automatic Domain Alignment and Moment Matching," Mathematics, MDPI, vol. 10(14), pages 1-14, July.
    17. Yuming Jiang & Zhicheng Zhang & Wei Wang & Weicai Huang & Chuanli Chen & Sujuan Xi & M. Usman Ahmad & Yulan Ren & Shengtian Sang & Jingjing Xie & Jen-Yeu Wang & Wenjun Xiong & Tuanjie Li & Zhen Han & , 2023. "Biology-guided deep learning predicts prognosis and cancer immunotherapy response," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    18. Marta Mazur & Artnora Ndokaj & Divyambika Catakapatri Venugopal & Michela Roberto & Cristina Albu & Maciej Jedliński & Silverio Tomao & Iole Vozza & Grzegorz Trybek & Livia Ottolenghi & Fabrizio Guerr, 2021. "In Vivo Imaging-Based Techniques for Early Diagnosis of Oral Potentially Malignant Disorders—Systematic Review and Meta-Analysis," IJERPH, MDPI, vol. 18(22), pages 1-22, November.
    19. Khalid A. Ibrahim & Kristin S. Grußmayer & Nathan Riguet & Lely Feletti & Hilal A. Lashuel & Aleksandra Radenovic, 2023. "Label-free identification of protein aggregates using deep learning," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    20. Songhee Cheon & Jungyoon Kim & Jihye Lim, 2019. "The Use of Deep Learning to Predict Stroke Patient Mortality," IJERPH, MDPI, vol. 16(11), pages 1-12, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:22:y:2025:i:2:p:265-:d:1589631. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.