Using data intelligence to predict and prevent a second wave of COVID-19

COVID-19 has put a laser focus on how data and analytics are powerful tools for healthcare leaders to predict, prepare and respond in a proactive and coordinated manner to a global health crisis. Throughout the pandemic, data has been used to document the spread of the virus, project the curve and more.

Responses and actions to mitigate viral spread hinge on real-time data collection, governance and analysis for instant decision making and the exchange of reliable, real-time data between the government and local health authorities has been proven vital. Yet, while some data and analytics applications have been highly impactful, the crisis has also highlighted ways that data could have been used more effectively.

The demand for data and analytics to help prevent and respond to future viral outbreaks will be just as unprecedented for the data intelligence community as the level of demand faced by hospitals from COVID-19. How can healthcare leaders improve data and analytics to be accessible, accurate and reliable for future crises?

Using data and analytics to detect, track and respond to COVID-19

There have been many innovative uses of powerful analytics that have already aided our global reaction and response to COVID-19. Advanced analytics has been used to identify outbreaks and predict their movement, as well as identify drugs and vaccines that might be effective. Artificial intelligence (AI), algorithms, data visualisation tools and graph technologies are also helping healthcare organisations to understand the nature and character of the virus.

However, these applications of advanced analytics are not pervasively used or relied upon. This must be one of our primary lessons learned from this pandemic — data and analytics had the potential to fundamentally change the game early on, from detecting the outbreak, to responding to critical shortages of tests, resources and supplies, to helping us be more operationally adaptable.

The global impact of COVID-19 has resulted in an absolute imperative to leverage data and advanced analytics as one of the primary offensive tools against future viral outbreaks. Healthcare leaders must, more than ever before, have an “everything data” mindset. As the curve begins to flatten, healthcare organisations can look to reinvent their data and analytics programme to prepare for the future.

Actions for healthcare data and analytics leaders

Understandably, healthcare organisations are in crisis mode — and most will be operating that way for a while. However, this is the time for data and analytics leaders to consider their organisation’s immediate response to COVID-19. How is your organisation using advanced data and analytics? What are the technological limitations you’ve faced? Where have you encountered data-sharing bottlenecks?

This analysis will help healthcare leaders lay out their strategic needs to improve their data and analytics programmes along key competencies. With this analysis in mind, here are four actions healthcare data and analytics leaders should take to ensure they are positioned to prevent, predict and respond to future pandemics:

1) Master your data integration capabilities

Access to quality, accurate and complete data will have a significant impact on whether AI tools will help or hinder prevention and treatment efforts for future health crises. Organisations will need to be positioned for global data participation as we move to the collection of standardised, health-related datasets that can easily detect when and how a virus might spread. Be agile by making your data accessible, liquid and fluid across your health ecosystem. Execute an enterprise data architecture that connects interoperability, integration and real-time capabilities.

2) Augment data science skills and tools

Now is the time to consider investing in advanced data science skills and tools. Look for gaps in your existing analytics capabilities by asking the heads of departments what questions they are not able to answer with the current solutions. Consider implementing pathogen and disease surveillance, advanced warning signal capabilities and graph database technologies, all of which can complement and augment your traditional analytics and business intelligence solutions.

3) Fill data gaps by leveraging synthetic data for early model development

In an emergency, the lack of data availability early on will always create a lag effect. Synthetic data is data that is derived from previous experiences and input by data science models. It can be used as a substitute until observational data arrives. At that point, the observational data can be used to test the validity of the previously used synthetic data. As this cycle continues, the synthetic data model will continuously improve.

As we’ve seen with COVID-19, many pandemics emerge with unknown data points, and thus waiting on data is a mistake. Work to develop models where synthetically derived data can be inferred based on previous experiences.

4) Create transparency and trust in your stewardship of the public’s protected health information

Advanced analytics solutions used in healthcare are 100% dependent on the ability to leverage patient data. Therefore, healthcare leaders must ensure the public that their data is being used safely and securely with regards to privacy, but without compromising public safety, constraining innovation or hindering scientific advancement.

Create a campaign of transparency and trust in your organisation’s stewardship of patient data, while also demonstrating the benefits of data-sharing. Help cultivate patient and consumer awareness by promoting the positive power of being able to use medical data for preventing widespread disease, improving patient care, solving interoperability challenges and ensuring clinicians, public health officials and others have access to the right patient data when needed.