By Devin Partida, Editor-in-Chief at

Predictive analytics brought substantial, measurable benefits to the health care field before COVID-19 disrupted the world.

The technology continued to prove its worth as the novel coronavirus spread globally. Here are five ways medical professionals depend on it during this public health crisis:

1. Pinpointing the Locations of Future Case Surges

Unexpected case surges can prove disastrous if the affected locations do not have the resources to cope with them. CommonSpirit Health, a system with more than 1,000 facilities across 21 states, uses de-identified data from various sources to track likely future case surges.

For example, the tool often confirms that those sudden increases happen 2-3 weeks after officials choose to reduce restrictions in a given area. In that case, public health officials could stay on top of authorities’ decisions about reopening parts of the economy, ensuring hospitals, testing facilities and similar facilities are as well-equipped as possible to deal with an impending increase in patient needs.

2. Determining Someone’s Risk of Testing Positive

The novel coronavirus is a global health threat, but it does not affect everyone equally. For example, someone with a work-at-home job who can stay in their house most of the time is probably at a much lower risk of contracting it than someone with a full-time public-facing job. Having a more accurate understanding of risk factors helps health providers give more tailored information to help patients stay safe.

Researchers applied predictive analytics to figure out which factors made someone more or less likely to have a test-confirmed case of COVID-19. They took data from 11,672 patients before assessing the results. Their models showed that African-Americans, older people and individuals with known exposure to a COVID-19 case were at higher risk. Conversely, Asians, people taking particular drugs and those who received the influenza vaccine had a reduced risk.

3. Assessing the Risk of Adverse Outcomes for Emergency Department Patients

Emergency room teams faced the challenge of deciding which COVID-19 patients had the best survival chances, especially as resource scarcity occurred. Researchers examined emergency department cases at 15 community hospitals during March and April 2020. That work led to a system that allowed physicians to calculate risk scores for a person’s likelihood of suffering the most severe consequences of suspected COVID-19 cases.

A possible downside of predictive analytics tools is that they could give biased results by excluding underserved populations. Now that more company representatives are aware of that possibility, some of them clarify how their algorithms work to prevent those outcomes.

For example, some health care professionals avoid cost-based algorithm models and take more holistic approaches. Health professionals who choose to use predictive analytics must always strive to remove bias, especially when skewed results could negatively impact patient care decisions.

4. Monitoring Hospital Availability Levels

An ongoing challenge of COVID-19 is the differing hospital capacities in various locations. Health officials in Louisiana sought to address this issue with predictive analytics. They began developing models that could suggest how many intensive care unit beds a particular area of the state might need at a specific future time. They also built models to predict fatalities and those that made real-time adjustments as patients got admitted or discharged.

The simulations did not provide exact calculations, but they gave ballpark estimates that allowed administrators to make more accurate decisions about resource requirements. Moreover, the algorithms facilitated decisions about when to schedule non-COVID-related care, such as surgeries.

5. Keeping Stored Vaccinations Safe for Use

With COVID-19 vaccines already available in some parts of the world, people are increasingly concerned about the logistical specifics of getting doses to everyone who wants or needs them in the most efficient ways possible. The earliest vaccines on the market — like many medical products before them — have strict temperature requirements. If people store vaccine doses outside the required range for too long, they’ll ruin them.

A new predictive analytics product takes a two-phase approach to keeping vaccines stored correctly and ready for use. The connected, cloud-based solution first analyzes storage conditions and alerts people if the environment falls outside a preset range. Moreover, the product analyzes temperature variations in cold storage compartments.

It can reportedly tell whether a refrigerator will go outside of its parameters days before it happens. Then, technicians get notifications of possible equipment failures before a fridge breaks down and threatens the stability and safety of crucial vaccinations or other delicate medical supplies.

Predictive Analytics Are Assisting in the COVID-19 Fight

Predictive analytics are not perfect, and people should never blindly depend on them. However, when health professionals apply their critical-thinking skills and judgment, use cases like these can help them gain insights that may have otherwise remained hidden.

Devin Partida is a FinTech writer and blogger, as well as the Editor-in-Chief of the website

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