How Big Data Analytics Models Can Impact Healthcare Decision-Making | 7wData

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How Big Data Analytics Models Can Impact Healthcare Decision-Making
by 7wData
August 22, 2020
- In healthcare, providers and lawmakers are faced with the task of making the best possible decisions for patients and the industry as a whole. From choosing the best treatments, to determining the most effective ways to utilize resources, leaders are making decisions every day that can impact health outcomes and costs.  
With all of this information to consider, it’s no wonder that Big Data analytics tools have played an increasingly significant role in healthcare decision-making.
Researchers, providers, and policymakers are turning to Big Data analytics models to help improve care delivery, allocation of resources, and preventive health measures.
As the industry continues to innovate and refine these tools, data-driven decisions will soon become standard, leading to more proactive, successful healthcare operations.
The current COVID-19 pandemic is perhaps the most notable example of organizations leveraging big data analytics models to inform decision-making.
In the months since the virus has entered and spread throughout the US, entities from all sectors of the healthcare industry have moved quickly to leverage their big data assets and better understand how to respond to COVID-19.
Recently, researchers from the University of Washington received a $33,000 grant to develop a model that uses local data to generate policy recommendations that could reduce the spread of COVID-19 in King County. The model will help decision-makers answer important questions related to coronavirus, including when and how to reopen businesses and schools, and how to distribute a vaccine when one becomes available.
“We will be simulating the impact of various interventions — including social distancing measures, school closure policies, testing capacity, contact-tracing strategies and mask wearing — on population health outcomes,” said lead researcher Shan Liu, a UW associate professor of industrial and systems engineering.
“Once a vaccine becomes available, we plan to expand the model to simulate vaccination rollout and coverage, and optimize for the best delivery configuration, such as vaccination priority if supply is limited.”
Researchers have also utilized big data analytics tools to forecast possible constraints on hospital capacity and resources. A team from Cedars-Sinai recently developed a machine learning model that can predict data points related to the COVID-19 pandemic and predict staffing needs.
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The platform tracks local hospitalization volumes and the rate of confirmed COVID-19 cases, running multiple forecasting models to help anticipate and prepare for increasing COVID-19 patient volumes with 85 to 95 percent degree of accuracy.
“Our goal is to have the capacity and the right care available every day to treat the patients who need us, which fluctuates on a daily basis,” said Michael Thompson, executive director of Enterprise Data Intelligence at Cedars-Sinai, which developed the platform and runs the forecasts. “We need to match that daily demand with the necessary resources: beds, staff, PPE and other supplies.”
In addition to monitoring and preparing for COVID-19 surges, healthcare researchers have increasingly turned to big data analytics tools to improve chronic disease detection and treatment.
In developing these models, investigators seek to evaluate specific clinical and non-clinical factors that may contribute to a person’s risk of developing a chronic disease.
At the College of Information Sciences and Technology at Penn State, researchers have developed an artificial intelligence algorithm to predict susceptibility to substance use disorder among young homeless individuals. The model could help inform officials about personalized rehabilitation programs for highly susceptible young people.