Author
Listed:
- Damilola Oluyemi Merotiwon
- Opeyemi Olamide Akintimehin
- Opeoluwa Oluwanifemi Akomolafe
Abstract
Clinical audits are essential mechanisms for evaluating and enhancing the quality of healthcare service delivery. However, traditional audit processes often suffer from inefficiencies due to fragmented data sources, manual evaluations, and lack of real-time feedback mechanisms. With the increasing availability of electronic health data and analytics tools, there is an urgent need for a model that integrates these assets into a coherent, data-driven clinical audit framework. This paper proposes a comprehensive model that utilizes structured health data, predictive analytics, and feedback loops to enhance clinical audit efficacy across diverse healthcare settings. The model was developed using a design science approach and evaluated through pilot implementations in three tertiary hospitals. Results show measurable improvements in care delivery, compliance with clinical guidelines, and patient outcomes. The study underscores the transformative potential of data-driven audits in advancing evidence-based quality improvement practices and provides a replicable framework for healthcare institutions aiming to institutionalize continuous performance monitoring.
Suggested Citation
Damilola Oluyemi Merotiwon & Opeyemi Olamide Akintimehin & Opeoluwa Oluwanifemi Akomolafe, 2024.
"Designing a Data-Driven Clinical Audit Model for Quality Improvement in Healthcare Service Delivery,"
International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 11(5), pages 718-735, October.
Handle:
RePEc:etm:ijsrst:v11:y2024:i5:id:939
DOI: 10.32628/IJSRST52310377
Download full text from publisher
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:etm:ijsrst:v11:y2024:i5:id:939. 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.
We have no bibliographic references for this item. You can help adding them by using 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsrst.com/home .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.