Key strategies for maximising data analytics in healthcare

Data analytics solutions are crucial for providing value-based healthcare. However, there is one kerfuffle to using them: the quality of insight depends on the data analysed.

If the quality of data is poor or biased, then data analytics will yield unreliable results. Therefore, data analytics strategies should clean data, and improve its quality. Better quality data means more accurate readings, which increases the value of healthcare. Fortunately, healthcare organisations can use several strategies to clean the data and maximise value on analytics.

In this blog, we explore different strategies to improve the quality of healthcare data.

Strategies to maximise value from data analytics in healthcare

Here are some strategies we could use to maximise data analytics in healthcare.

Removing bias from healthcare data

With medical organisations relying on data analytics in healthcare to drive decision-making, it is important to ensure that there is no bias in data. However, accurate, bias-free data is incredibly challenging to create because the process is rather complex. Bias covers different areas, like gender, race and location, making it difficult to fix.

One option is to have data analytics and medical professionals work together on the creation of data analytics tools in healthcare. This way, data analytics engineers know what is important for the healthcare provider. Meanwhile, the provider can make more informed decisions because they know how the analytics platform works. This gives them more confidence and agency in decision-making.

However, creating the right data analysis tools is just one step. This is because data collection tools should also be bias-free to ensure results are free of an implicit or explicit slant.

Working around data restrictions

Data analytics in healthcare thrives on a rich dataset. However, accessing a varied dataset is not always possible due to issues, like data privacy. Normally, machine learning models need a centralised database with all required data in a single source.

Due to legal and practical reasons, compiling data into a single source is not possible. For example, privacy concerns prevent healthcare organisations from using data in this way. Furthermore, data might not be useful for addressing certain scenarios, like local health issues.

To work around this problem, data analytics professionals are using federated learning. This method allows algorithms to access data across several devices or servers. Federated learning allows healthcare professionals to access local data without violating privacy laws.

Creating accurate training data for analytics platforms

While it is common knowledge that data analytics platforms benefit from exposure to rich, varied data, it is not always possible for analytics personnel to conduct this practice. This is because obtaining quality training data is challenging. The current crisis has brought this shortcoming into sharp focus. Healthcare researchers need to understand how the Coronavirus works to create treatment programs.

Data analytics in healthcare can get through the vast literature to help optimise the process. However, for that to work, data analytics platforms must be nurtured on a steady diet of raw data to ensure accurate results. For example, healthcare professionals are using AI to refine the quality of training data. Automated systems can scan medical images to get more data than conventional technology could. By using this new technology, healthcare professionals create a large dataset to train data analytics platforms in healthcare.

Automated technology can also optimise the data collection and analysis process. For example, AI technology can scan hundreds of thousands of articles on a medical condition to help healthcare professionals find useful information that aids treatment.

However, AI tools can do more than prep data analytics tools. They can also help healthcare providers. For example, AI can help providers manage workloads to make them more sustainable, like aiding clinical decision-making at the point-of-care.

Creating an efficient healthcare system

The key to creating an efficient healthcare system is to use data analytics in healthcare to its full potential. However, analytics platforms must be trained with the right dataset. Therefore, creating strategies to maximise the quality and value of data is crucial for improving the healthcare process.

While there is much to gain from investing in data analytics platforms, the investment wouldn’t amount to much if datasets are not complete or lack the quality required.

When collection and analysis are properly optimised, it allows organisations to make the most out of their medical data and analytics platforms. To know more about analytics platforms for the healthcare industry, check out the SAS solution on data analytics for healthcare.