Synthetic Data as a Solution for Improving Healthcare in Africa.
Artificial intelligence, as it becomes more prevalent in clinical decision-making, has the potential to further shape the value of a healthcare system. The use of data-driven models can help to improve patient care and reduce costs.
The successful application of AI in any arena is dependent on high-quality data. Africa faces the challenge of digitizing the data required to make intelligent decisions with AI.
Synthetic data
Synthetic data is a term for computer-generated data that mimics real-world scenarios with high similarity. It allows us to test without surveying, therefore the cost of implementing it and collecting the data is reduced drastically.
The use of synthetic data has been proven to be effective in modeling real-world scenarios and it is a substitute for collecting real-world data in circumstances where it might be unethical, expensive, or otherwise infeasible to do so. Synthetic Data provides an affordable, scalable, fast, and effective way of building ML models.
High-quality synthetic data can help us to learn from a much broader range of patients that are in the public domain. Thus, it allows for larger datasets to be created for training and validation purposes. For the time being, as we collect and digitize the existing records in the background, a lot can still be gleaned from the raw data and inform future work.
Synthetic data can be used for real-world testing prior to production. For example, using synthetic data can offer an opportunity to assess and predict type-2 diabetes risks with wearable health devices, by simulating cases that might be encountered during the development cycle(s).
Now, Why is it important for healthcare providers in Africa?
A common occurrence in healthcare research is that it requires a great deal of data and time to analyze the data due to the complexity of the data sets. Models built from synthetic data have been shown to have comparable performance to those trained on real-world data, which is relevant for applications like medical imaging and diagnosis.
Most healthcare providers still keep data generated and stored through manual processes on paper or excel spreadsheets which poses the risk of storing data that is not always accurate.
For African countries to achieve sustainable growth in addressing key challenges in public health using technology, it is imperative to have data that is reliable, accurate, and timely; that can be used to inform decision-making. To achieve this, it is crucial to consider;
- Engaging skilled staff who’ll organize, label, and check the format and accuracy of data to be included in machine learning models. For example, healthcare providers will need to ensure that the data shared meet defined standards and is free of errors. Additionally, the data must be organized in a way that makes it easy to access and use.
- Healthcare data privacy concerns and the cost of data retention are the main reasons why open data standards and data ownership are necessary. Policies need to be in place for data security and governance to prevent data misuse and breaches
- Incubate more health-tech startups in Africa to help them grow and scale up faster thus providing better healthcare to the region and the world. These startups will have access to the latest data science and machine learning tools and techniques, to help them deliver better healthcare to patients.
As we are yet to see much data being used in healthcare, it is interesting to see how synthetic data will be used to study diseases through machine learning and other methods.
Originally published at https://fourbic.com/
