Chinedu Nzekwe, a data science guru has revealed that the country’s healthcare distribution is flawed because there is too much concentration of resources in wealthier urban areas while rural and low-income regions remain underserved.
This disparity, according to him, results in under-utilised healthcare resources in some areas and shortages in others.
To address these issues, experts recommend leveraging machine learning (ML) algorithms to optimize healthcare delivery and resource allocation, potentially transforming the healthcare landscape in Nigeria and Africa.
Machine learning, a subset of artificial intelligence (AI), involves algorithms that can learn from and make predictions based on data. In healthcare, ML can enhance decision-making and service delivery across various disciplines. ML algorithms can predict patient outcomes, optimize resource allocation, and improve overall healthcare efficiency by recognizing patterns and insights from large datasets.
Healthcare systems are integral to national productivity and human capital formation. Thus, implementing innovative approaches like ML can minimize costs and enhance the quality of healthcare services. By integrating ML algorithms, stakeholders can address critical issues such as the geographical distribution of resources, resource misallocation, and healthcare access inequities.
Chinedu Nzekwe, a final-year PhD candidate in Applied Science and Technology with a concentration in Data Science and Analytics, underscores the importance of this integration.
“Healthcare systems must evolve to leverage the power of data science and machine learning. These technologies can provide significant insights that lead to better resource allocation and improved patient outcomes,” Nzekwe explained.
He noted that the current applications and benefits ML applications in healthcare are diverse and impactful.
They include predictive models for patient readmissions, disease outbreaks, and resource needs. These models can forecast the number of required tests, predict test result delivery times, and estimate future healthcare demands. For instance, during the COVID-19 pandemic, ML models helped forecast fatalities and optimize testing strategies, demonstrating their practical value in managing public health emergencies.
In Nigeria, the use of ML can address healthcare access inequities caused by the uneven distribution of healthcare professionals and facilities. With a patient-to-healthcare professional ratio as high as 10,000 to 1 in some regions, ML can optimize the placement of healthcare facilities and resource allocation to better serve underserved populations.
This optimization, according to him can lead to improved healthcare delivery and higher patient satisfaction.
Nzekwe notes, “By analyzing demographic, social, and health service data, ML can predict healthcare needs more accurately. This ensures that resources are allocated where they are most needed, enhancing the overall effectiveness of the healthcare system.”
Case Studies and Best Practices: Successful implementations of ML in healthcare highlight its potential to enhance service delivery and outcomes. For example, ML models have been used in dermatology to diagnose skin lesions with high accuracy, surpassing human experts in some cases. In medical imaging, ML has improved the accuracy of diagnosing retinal diseases and bone density measurements, showcasing its ability to handle complex diagnostic tasks.
He urged that the focus should rather be on developing practical ML Models: Simulating outputs for experts to evaluate and using real-life data to validate models.
Training and Capacity Building: Investing in training healthcare professionals in ML techniques and fostering interdisciplinary collaboration.
Creating Population-Centric Datasets: Developing comprehensive datasets that include demographic, clinical, and imaging data to support robust ML models.
Addressing Ethical and Privacy Concerns: Ensuring data privacy and ethical considerations in the development and deployment of ML algorithms.
He said these challenges require a multi-faceted approach part of which includes; Infrastructure Development: Investing in data storage and processing capabilities to handle large healthcare datasets.
Policy and Regulatory Support: Establishing policies that promote data sharing and collaboration while ensuring data privacy and security.
Encouraging partnerships between government agencies, healthcare providers, and tech companies to foster innovation and scale ML applications.
“One of the main challenges is the lack of adequate infrastructure to support the extensive data requirements of ML algorithms,” Nzekwe points out.
“Investing in robust data storage and processing systems is crucial for the successful implementation of these technologies.”
The integration of machine learning algorithms in Nigeria’s healthcare system holds great promise for optimizing healthcare delivery and resource allocation. By leveraging ML, Nigeria can address existing challenges, improve the efficiency of healthcare services, and ensure equitable access to quality healthcare for all its citizens. As experts recommend, embracing these technological advancements will pave the way for a healthier, more prosperous future for Nigeria and Africa.
Nzekwe emphasised, “The future of healthcare lies in our ability to harness the power of machine learning. By doing so, we can transform healthcare delivery and improve the lives of millions of Nigerians.”
ALSO READ THESE TOP STORIES FROM NIGERIAN TRIBUNE
CBN’s economic reforms yielding significant results — Cardoso