What Are the Benefits of Predictive Analytics in Healthcare?

August 02, 2021 - Through the implementation of artificial intelligence, predictive analytics in healthcare has served as a critical component of advancing care and improving outcomes.

With the assistance of predictive analytics, physicians have used the system to aid in the medical decision-making process and to evaluate big data efficiently. By integrating predictive analytics into the healthcare system, providers have seen benefits for both themselves and patients.

Decision-Making Process

Predictive analytics has proven to be a significant asset in the medical decision-making process. Patients respond differently to all types of treatment, especially chronic diseases. University of Michigan Rogel Cancer Center researchers are creating a blood test that can predict if a certain treatment method for HPV-positive throat cancer is working months earlier than standard imaging scans.

"Currently, the only way doctors know if a treatment is working is for the patient to get an imaging scan every few months to see whether their tumors are shrinking," oncologist Paul Swiecicki, MD said in a press release.

"And this isn't fully accurate since some cancers show what we call pseudoprogression, where a successful treatment actually makes the tumors bigger before it shrinks them. Our goal was to develop a test that could tell us whether a treatment is likely to work after a single cycle," Swiecicki said.

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Using this method of predictive analytics, the blood test will allow medical professionals to assess how a patient is responding to treatment months earlier than previously available. This will allow providers to switch their course of treatment sooner if the current one is not working, saving patients months of unnecessary and painful treatment. Overall, this will improve the quality of patient care. 

However, for predictive analytics to be effective, researchers from New York University’s School of Global Public Health and Tandon School of Engineering advocate for social determinants of health to be factored into machine learning models.

In a study using machine learning, researchers used predictive analytics to determine the likelihood of cardiovascular disease and make treatment decisions. To get a full scope of someone's risk factors for chronic disease, providers should be looking at patients’ health as a whole. This includes socioeconomic and environmental factors.

By incorporating social determinants of health into predictive analytic machine learning models, providers can better understand how to manage chronic conditions and recommend treatment options that consider a patient’s socioeconomic and environmental factors.

Improving Patient Outcomes

Predictive analytics is an advancing method of improving patient outcomes. By looking at data and outcomes of past patients, machine learning algorithms can be programmed to provide insight into methods of treatment that will work best for the current patients.

READ MORE: Strategies for Using Predictive Analytics, AI to Improve Care

Additionally, predictive analytics can be used to identify warning signs before conditions become severe. With the COVID-19 pandemic being at the forefront of healthcare, researchers are putting resources into developing predictive analytic methods for combating the virus.

COVID-19 has been a unique struggle for the healthcare system since the severity of the virus can vary from person to person. In a recent study posted on JAMA, researchers highlighted risk factors associated with severe cases of COVID-19 by using machine learning algorithms and predictive analytics.

The study concluded that demographic characteristics and comorbidities were among the highest risk factors.

Predictive analytic methods are also being used to determine the severity of COVID-19 from person to person. American Chemical Society researchers recently developed a blood test that uses predictive analytics to project whether an individual will experience severe COVID-19 symptoms or not.

By using predictive analytics and a process called attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR), researchers determined that the best indication for if a patient would experience severe COVID-19 symptoms is if the patient has diabetes.

READ MORE: Predictive Analytics Tool Accurately Detects Patient Deterioration

Predictive analytic methods allow providers to determine individuals at risk for developing severe infections or chronic diseases.

By identifying those at risk, it provides medical professionals an opportunity for early intervention and chronic disease prevention. With predictive analytics, providers can identify patients who are potentially high-risk for certain chronic conditions such as cancer, cardiovascular disease, diabetes, obesity, and kidney disease.

Relief for Healthcare Workers

While big data analytics has advanced patient care and efficiency, healthcare workers can face information fatigue when navigating through expanding electronic data. According to a recent study, physicians devote 62 percent of their time per patient reviewing electronic health records (EHRs), with clinical data review occupying most of the time.

The study advocates for an artificial intelligence system to assist with EHR data organization, allowing physicians to work effectively and provide a better patient experience. Through the study, 12 gastroenterology physicians used the AI-powered EHR system and compared it to their experience using the current standard method of data review.

Eleven of the 12 physicians said they preferred the AI method to the standard. The AI approach provided physicians with quick and accurate information regarding patients as well as predictive analytic recommendations regarding treatment.

Additionally, predictive analytics lightens the load for healthcare works by assisting in the diagnosis process. Michigan State University researchers are developing an artificial intelligence app that uses predictive analytics to determine if an individual is showing early signs of Alzheimer’s disease.

"Alzheimer's is tough to deal with and it's very easy to confuse its early stage, mild cognitive impairment, with normal cognitive decline as we're getting older," associate professor at MSU’s College of Engineering Jiayu Zhou said in a press release. "It's only when it gets worse that we realize what's going on and, by that time, it's too late."

The AI algorithm records speech patterns of individuals, analyses the data, and notifies the patient of early signs of the disease. While the app cannot make an official diagnosis, users can bring the information to the doctor for review.

"You cannot replace that human interaction," Zhou said. "The final assessment will be done by a patient's physician. But if you have doubts and the app says you're at a higher risk, you don't have to wait. You can visit a clinician and take the next steps."

Predictive analytics allows for healthcare workers to quickly analyze data and plan a course of treatment that will work best for their patients, saving time and producing better outcomes.