Healthcare Data Analytics: Post-COVID Amends

Updated on October 16, 2021
Data Analytics

In March 2020, when the pandemic went global, healthcare data analytics has become a highly efficient assistant to doctors. Health IT developers teamed up with providers and researchers to ensure swift decision-making with the help of actionable insights. However, the pandemic has begun scaling down. So what to expect from the tools? Are they just another set of expensive short-lived solutions? 

Not really. 

As we know, the pandemic has uncovered a number of deficiencies in healthcare systems across the globe. Now as the pandemic has loosened its grip, it’s time to set these issues right. That’s where analytical tools may be of help.

Big data analytics for strategic planning and more

During the pandemic, data has become a focal point of researchers and clinicians, and given the scope of the turmoil, the amounts of information to analyze were enormous. Logically, big data analytics tools came into action. Their significance for the industry is hard to overestimate — streamlining clinical processes and the quality of care, they often helped save lives. 

With the pandemic subsiding, there’s a range of industry issues that big data analytics could help resolve at least in part. First of all, it’s strategic planning. For instance, during the virus outbreak, Definitive Healthcare teamed up with Esri and launched a nationwide bed capacity tracking platform that could inform users about available ICU beds and more across a given region of the US. While those data may have lost its relevance after the pandemic, with certain adaptation, such a solution may be beneficial to post-pandemic healthcare, too. 

With the help of big data analytics solutions, clinicians may gain insights into the motivations that drive patients to particular decisions. Thus, the University of Florida combined the data from Google Maps and openly available data on public health in the region to develop the so-called “heat maps”. The maps showcased population growth rate, people with chronic conditions living in the area, and more. The insights the team obtained helped them understand that certain regions lacked care units. As a result, they reconsidered their care delivery strategy accordingly.

As we can see, a different focus may help providers tailor COVID-centered solutions to their practice to solve the standing challenges. 

Predictive analytics for population health

The pandemic left bare yet another healthcare industry issue — insufficient population health management efforts. While the talks about population health have been going on for a while, not so many providers took up the challenge of ensuring it fully. The main benefit of population health is its ability to identify risk groups and predict the likelihood of adverse events in them. This allows providers to inform high-risk patients about a hazard and take some preventive measures in time. This is possible with the help of predictive analytics.

Driven by the algorithms of machine learning and statistics, this field of data analytics studies historical data and makes predictions regarding the most possible outcomes. Amidst the pandemic health crisis, it helped providers identify weak links in the community that may complicate the recovery process.

For example, the UC San Francisco (UCSF) School of Medicine developed an online map covering some critical pieces of the COVID-related statistics in the region. Presenting the virus instances, health outcomes, mortality, and social determinants, the interactive website helped key stakeholders identify the factors that led to poor health outcomes. In that case, such negative factors were food and housing insecurity. When informed, the stakeholders managed to address them efficiently.

In post-pandemic times, the region’s researchers and clinicians plan to retarget the tool. Now they are looking to employ the website to detect health inequities as well as coronavirus recurrence.  

Real-time analytics for continuous surveillance

Well-suited for epidemiology, real-time analytics drives swift data-based decision-making and helps tailor the response to each situation in particular. During the outbreak, real-time analytical tools were widely used to assist emergency-response clinicians. They provided informative dashboards about hospital capacity, equipment availability, and more and enabled them to choose the most suitable location for a patient to save time. 

With the pandemic out of sight, this application of real-time analytics might lose its critical significance. However, there’s yet another poignant healthcare issue it may help solve. It’s preventable premature death in patients younger than 70. The top “culprits” there are heart diseases that take about 18 million lives yearly, according to the WHO. Real-time analytics can assist here by powering continuous surveillance that helps avert premature death timely. 

Unlike patient monitoring that centers on measuring a particular health parameter, continuous surveillance tools extract data from diverse sources, from multiple monitoring devices to EHRs. Hence, continuous surveillance presupposes teamwork, which helps obtain a panoramic view of a patient’s health. Besides, such tools employ predictive analytics to detect alarming trends in real-time data and stop a negative health event at its onset. Such a tool helped Carilion Clinic from Virginia identify high-risk patients before their conditions deteriorated. Thanks to the tool, the provider succeeded in reducing ICU readmissions.

Wrapping up

Along with the long-term turmoil and uncertainty, the pandemic brought an unexpected benefit. It helped industry stakeholders, clinicians, and researchers understand the critical importance of data analytics for care delivery and facility management. 

Luckily, with the pandemic gradually subsiding, the developed analytical solutions aren’t losing their significance. With some adjustments, they may assist providers with gaining insights needed for solving other burning industry issues, such as premature death caused by heart diseases or other preventable health conditions.

Inga Shugalo is a Healthcare Industry Analyst at Itransition, a custom software development company headquartered in Denver, Colorado. She focuses on Healthcare IT, highlighting the industry challenges and technology solutions that tackle them. Inga’s articles explore diagnostic potential of healthcare IoT, opportunities of precision medicine, robotics and VR in healthcare and more.

Inga Shugalo is a Healthcare Industry Analyst at Itransition, a custom software development company headquartered in Denver, Colorado. She focuses on Healthcare IT, highlighting the industry challenges and technology solutions that tackle them.