Clinton Scott, managing director at TechSoft International.

The pandemic has firmly emphasised healthcare and whether hospitals, service providers, medical aids, and other stakeholders in the sector can adequately deal with a major global crisis. While each country’s response has been unique, a central theme has been that there is room for improvement in investing in data and analytics in healthcare.

Even before the onset of COVID-19, hospitals were producing an average of 50 petabytes of data per year - the equivalent of 50 million gigabytes. But according to the World Economic Forum, 97% of that data is unused and not fulfilling its potential of transforming the quality of medical care. Fast forward to the present day, and data production would only have increased. How can healthcare providers unlock the value of their data through predictive models and unified data platforms to save more lives?

With patient data spanning numerous diagnostic and care systems, greater data alignment and sharing can provide the complete picture of the patient that doctors require. This can accelerate diagnosis and treatment while yielding healthier outcomes. By getting better access to and insights from their data, providers can personalise patient care. This more focused approach saves time and money while putting people first. Furthermore, data analytics can be used to prepare for future healthcare scenarios, an increasingly important option for emerging countries like ourselves.

Predicting healthcare analytics

By combining patient care and historical data, hospitals can leverage technology to develop a predictive model in a real-time environment. This enables faster and accurate decision-making and can become invaluable for the healthcare industry as financial and pandemic related pressures continue to mount.

The need for data-fuelled insights that can arm healthcare workers with better information on what is wrong with their patients and the available treatment for each case extends well beyond the pandemic. As science itself based on patterns and behaviour changes, healthcare is already starting to draw from technologies like AI to map behaviour changes to pre-emptively help diagnose diseases from their early onset.

For example, the University of Chicago Medicine uses streaming analytics and carefully modelled algorithms to predict when a cardiac arrest is about to happen. And at the University of California in Berkley, researchers are using an AI system they developed to predict Alzheimer’s from brain scans. This insight helps with diagnosis and ensures superior medical care that can also alleviate the strain a terminal disease places on the healthcare system when provided early.

Breaking down data silos

Ultimately healthcare is designed with the patient at its core – yet a broken system helps no one. Unfortunately, because of the sensitive nature of patient information, it is often not shared between providers, which has left room for a lackadaisical approach to how it is stored, processed, and kept in silos. If we look at the local landscape and the emergence of private hospital chains as an example and how they are working with medical aid providers to create a more centralised view of a patient – we can ourselves predict the opportunities this will unearth.

Big data analytics only works when there is data, and today data is often still treated like a second-rate citizen. To start, data in any industry needs to move up the value chain, not just healthcare.

A great example of this in action is that throughout the pandemic, public health officials in South Africa have traced the spread of genomic variants both locally and worldwide. Using this data, they were able to ascertain that dozens of distinct coronavirus variants were already circulating in South Africa well before the appearance of B.1.351. This finding spotlights the importance of genomic surveillance, a science that would not exist without data and, more importantly, data in the right place at the right time.

Dr Data mobilising for the greater good

More data and better analytical models can help improve testing efficacy before the next health crisis. Being able to deliver high-quality care will always remain a priority for healthcare providers. When appropriately applied, big data will improve drug utilisation, treatment variability, clinical trial eligibility, billing discrepancies, and self-care programme attribution specific to major chronic conditions.

Investments in more unified patient data and more intelligent analytics can significantly improve patient care every day and in times of crisis. Beyond the technology, there must be a willingness to change. Yes, systems and processes can enable better analysis. But to affect real change, people need to remain front and centre. After all, that is what healthcare is all about.