How Predictive Analytics Can Help the Healthcare Sector Battle...

How Predictive Analytics Can Help the Healthcare Sector Battle COVID-19

Healthcare Tech Outlook | Thursday, July 22, 2021

Predictive analytics solutions are assisting healthcare organizations in avoiding adverse outcomes, resource constraints, and other COVID-19-related consequences.

FREMONT, CA: The world initially failed to stop the Novel Coronavirus from spreading, resulting in a catastrophic pandemic. As a result, rapid approaches became critical for controlling the virus and its effects. The global healthcare business has taken a stride toward big data and predictive analytics tools to better understand disease patterns, risk factors, and diagnostics.

Predictive analytics tries to warn clinicians and caregivers about the possibility of events and outcomes before they happen, allowing them to prevent rather than treat health problems. The healthcare sector now has algorithms that can be loaded with historical and real-time data to generate meaningful predictions due to the emergence of Artificial Intelligence (AI) and the Internet of Things (IoT). Predictive algorithms can help clinicians make better decisions for individual patients and notify interventions at the cohort or population level. They can also be used to solve operational and administrative problems in hospitals.

Predictive analytics has also aided medical organizations in prioritizing care management accessibility to people at a higher risk of infection. As a result, demand for analytics increased. The data scientists are constantly working to improve the analytical models to improve the accuracy of the predictive analytics.

Let's take a closer look at how data-driven methods might help people worldwide to fight against the pandemic.

Identifying early signs of patient deterioration in the ICU and the general ward

In the ICU, predictive insights can be highly effective because it is a place where a patient's life relies on timely intervention, and their health can deteriorate anytime.

As patients' vital signs are closely monitored and evaluated, predictive algorithms can assist in identifying patients who are most likely to require care within the next 60 minutes. This enables caretakers to intervene at a preliminary phase in the patient's condition, depending on minor symptoms of deterioration.

Providing predictive care for at-risk patients in their homes

Predictive analytics, with its potential to keep healthcare practitioners one step ahead, demonstrates its worth in (virtual) hospital settings and even at home by preventing patients from receiving acute treatment.

Predictive analytics can leverage data from different sources, such as hospital-based electronic medical records, fall detecting pendants, and previous usage of medical alert systems to detect seniors who are at risk of needing emergency transportation in the next 30 days. It enables healthcare providers to contact senior citizens before getting into an accident or developing another medical complication.

Evaluating future demands

One of the essential advantages of using data analytics is the ability to predict future demands. The information supplied can assist hospitals in implementing preventative measures. ICU beds, ventilators, and personal protective equipment (PPE) are all included in the preventive care.

Governments in several nations rely on big data-driven solutions to balance the production and distribution of N95 masks and other medical supplies. For example, if there is a surge of COVID-19 cases, healthcare sectors can devise a plan to organize more beds or other medical resources with the help of predictive analytics.

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