A researcher in Nigeria, Femi Esan, is conducting a study on the application of information systems, predictive analytics, and artificial intelligence in identifying risks and supporting decision-making in organisations. The study focuses on healthcare and public-service environments where decisions are often made with limited resources and incomplete information. Esan's research aims to explore how data from different sources can be combined to provide information about emerging risks. This study has the potential to improve decision-making in various sectors.
Esan's research considers multiple factors that can impact operational risks in healthcare, such as patient numbers, staffing levels, medicine availability, referral patterns, and facility capacity. By examining these factors, the study can provide a more comprehensive understanding of the challenges faced by healthcare facilities. The use of explainable artificial intelligence is also being explored, which allows users to understand the factors behind predictions or risk assessments generated by AI systems. This can be particularly relevant in healthcare, where AI-generated information is used to support decisions involving patients.
The study also examines risk management and the limitations of predictive models. Rather than treating predictions as certain outcomes, the approach considers them as information that can be used alongside other evidence when assessing possible developments. This approach can help organisations make more informed decisions. The issues being examined are not limited to healthcare, as similar applications can arise in areas such as emergency response, education, social services, and infrastructure management.
Esan's academic background in mathematics, strategic management, data analytics, and information systems has informed his research into data-driven decision-making. His study is centred on how organisations can use available information to determine what has happened, identify possible causes, assess what could happen next, and decide what action may be required. The research aims to provide insights into the use of data and AI in supporting decision-making.
The study's findings can have significant implications for various sectors in Nigeria. By exploring the use of data and AI in risk detection, Esan's research can help organisations make more informed decisions. The study's focus on healthcare and public services can also contribute to the development of more effective risk management strategies in these sectors.
Esan's research is being conducted in the context of Nigeria's growing need for effective risk management strategies. The country's healthcare and public services face numerous challenges, including limited resources and incomplete information. The study's findings can provide insights into how data and AI can be used to address these challenges.
The study's results can also contribute to the development of more effective risk management strategies in various sectors. By exploring the use of data and AI in risk detection, Esan's research can help organisations in Nigeria make more informed decisions. The study's findings can also have implications for policy development and implementation.
Key points
- Researcher Femi Esan is examining the use of AI and data systems for risk detection in healthcare and public services.
- The study explores the use of explainable artificial intelligence and predictive analytics in risk management.
- The research aims to provide insights into the use of data and AI in supporting decision-making in various sectors.
