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July 21, 2020 -As the healthcare industry has increasingly aimed to deliver proactive, quality care, predictive analytics models have emerged as viable tools for improving outcomes and cutting costs.

From mapping the spread of infectious diseases, to forecasting the potential impact of certain conditions, predictive analytics tools can help organizations stay one step ahead in an unpredictable industry.

Researchers and provider organizations are applying predictive analytics techniques to improve practices across the care continuum, leading to more informed decision-making and better patient care.

Throughout the COVID-19 pandemic, predictive analytics models have played a major role in helping healthcare leaders track and prepare for the spread of the virus.

Researchers at the University of Texas Health Science Center at Houston (UTHealth) recently developed a new COVID-19 tracking tool that leverages predictive modeling to help mitigate the impact of the virus.

READ MORE: Predictive Analytics Model Examines Droplets to Map COVID-19 Spread

We created this interactive public health dashboard because we wanted to help our fellow Texans. By understanding future trends of this virus, it can help aid in the effective management of local resources, said Jos-Miguel Yamal, PhD, an associate professor of biostatistics and data science at UTHealth School of Public Health in Houston and one of the projects lead developers.

The interactive tool is freely available to the community to inform public health decision-making across the state. In addition to the figures for the daily number of confirmed cases throughout the state, the tool provides charts that show the measure of contagiousness of each COVID-19-positive individual in the region.

The dashboard identifies the current hot spots, predicts future spread both at the state and county level, and houses relevant public health resources. It can effectively inform decision-makers across Texas to help mitigate the spread of COVID-19, said Shreela Sharma, PhD, a member of the research team and professor of epidemiology, human genetics, and environmental sciences at UTHealth School of Public Health in Houston.

A team at CommonSpirit Health has also leveraged predictive analytics tools to help public health officials better prepare for the spread of COVID-19. The organization used de-identified cell phone data, public health information, and data from its own care sites to build predictive models and gain insight into COVID-19 dips and surges.

We started looking at the first peaks of the virus, and there were enough cases for us to do some modeling and prediction. Now, just three months later, we're looking at the same virus as people are getting back together and the contact rate is going up. And we're trying to manage this resurgence in our markets, Joseph Colorafi, MD, System VP of Clinical Data Science for CommonSpirit Health, told HealthITAnalytics.

READ MORE: Predictive Analytics Model Forecasts COVID-19 Risk, Outcomes

Researchers have also applied predictive analytics techniques to manage and stem the onset of chronic conditions.

A study recently published in Diabetes Care showed that building predictive models with patient data, including history of comorbidities and medications, can determine the five- to ten-year life expectancy of older adults with diabetes and help providers develop personalized treatment plans.

The guidelines don't give doctors guidance for how to decide whether or not people fall into these different bins for life expectancy,saidKevin Griffith, research analyst at the Department of Veterans Affairs (VA) Boston Healthcare System.

Furthermore, clinicians are notoriously inaccurate in predicting life expectancy, with studies frequently showing both over- and underestimating. We developed models with high predictive validity of future mortality in a large sample of older veterans with diabetes.

The models could help providers use shared decision-making to establish A1c target ranges that balance treatment and risks. The models could also serve as comprehensive clinical decision support tools for diabetes management and care.

READ MORE: 60% of Healthcare Execs Say They Use Predictive Analytics

Predictive analytics tools can also help improve chronic disease prevention. Researchers from Mount Sinai recently developed new predictive analytics tools and identified environmental risk factors that could lead to a new understanding of what triggers Crohns disease.

Early identification of individuals at high risk for disease development could allow for close monitoring and interventions to delay, attenuate, or even halt disease initiation, said Jean-Frederic Colombel, MD, Professor of Medicine (Gastroenterology) at the Icahn School of Medicine at Mount Sinai and Co-Director ofMount Sinais Susan and Leonard Feinstein Inflammatory Bowel Disease Clinical Center.

This is highly relevant as we seek to predict and prevent IBD, which continues to sharply increase in numbers across the globe. In the absence of a cure, our clinical strategy will center on aggressive and innovative mechanisms to predict and prevent the disease.

Using predictive analytics models, researchers and provider organizations can also prepare for potential future trends and events that impact clinical care delivery.

In a study published in the Lancet Public Health, researchers showed that a big data model projected that without any changes in alcohol consumption or interventions to address high-risk drinking, deaths from alcohol-related liver disease will rise significantly in the US.

The studys results revealed that lawmakers should consider taking measures to curb high-risk drinking across the country.

Our study underscores the need to bring alcohol-related disease to the forefront of policy discussions and identify effective policies to reduce high-risk drinking in the US,saidsenior authorJagpreet Chhatwal, a senior scientist at theMGH Institute for Technology Assessmentand an assistant professor at Harvard Medical School.

On a smaller scale, researchers can use predictive analytics tools to get ahead of adverse healthcare events in individual patients. Recent research funded by the National Institute on Minority Health and Health Disparities showed that a simple risk prediction model helped forecast stroke risk in adult patients who have migraine with aura.

The model could help providers identify patients at high risk and intervene before the stroke occurs.

People who have migraine with aura are at increased risk for anischemic stroke, said Souvik Sen, MD, MPH, study co-author, and professor and chair of the neurology department at the University of South Carolina School of Medicine in Columbia, South Carolina. With our new risk-prediction tool, we could start identifying those at higher risk, treat their risk factors and lower their risk of stroke.

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3 Ways Predictive Analytics is Advancing the Healthcare Industry - HealthITAnalytics.com