How Predictive Modeling in Healthcare Boosts Patient Care

What is predictive modeling and why is it critical?

Research firm Deloitte offers a simple definition: "Predictive analytics can be described as a branch of advanced analytics that is used to make predictions about unknown future events or activities that lead to decisions."

In contrast to prescriptive analysis, which uses datasets to streamline existing processes and improve operational performance, predictive frameworks use machine learning and artificial intelligence models to identify correlations between disparate data sources and provide actionable recommendations.

As Phan says, the predictive power of data analysis is particularly important in medicine. "Medical treatment is an art," he says. “While science is behind it, decision making is an art. An AI-driven model can help support decisions for doctors - and not just the model itself, but also the sensors and devices that can be used to collect medical data. "

LEARN MORE: Find out how predictive analytics can improve service delivery.

Potential use cases for predictive analytics in healthcare

In practice, predictive analytics offers advantages for several use cases, e.g.

• Improved patient outcomes. By integrating patient records with other health data, health organizations can identify warning signs of serious medical events and proactively prevent them from occurring.
• Holistic health support. The development of patient-centered models focuses on the person as a whole rather than isolated outcomes. Predictive tools enable the collection and integration of lifestyle, symptom and treatment data to create holistic treatment plans.
• Advanced operations. Predictive tools can also be applied to internal health processes such as the provision of equipment or staffing requirements to reduce overall costs.
• Personalized service delivery. The personalization of care has been at the center of the pandemic pressures. Predictive tools enable the creation of truly personalized treatment plans that are tailored to the individual needs of patients.

Support of predictive analytics in healthcare

To realize the potential of predictive analytics, healthcare providers need a combination of tactics and technology.

For Phan, effective delivery starts with a problem: Where are providers missing critical insights, and what data could help improve patient outcomes? "This should work in a similar way to building a computer model," says Phan. "Develop some theories about how to approach the problem, and then build one or more algorithms that you will implement."

This ties in with the second pillar of predictive analytics: Advanced ML and AI technologies. By providing verified health data to these tools, reliable and responsive models can be developed that can be used to analyze incoming data to identify potential patient problems, improve current operations, and predict emerging trends.

However, Phan notes that this is a complex process. Companies need to "take their best models and run them through clinical trials to eliminate data that isn't strong," he says. In addition, data must be broken down into specific subgroups for training, testing, and validation to ensure that prediction factors are not overweighted or overweighted in the analysis results.

MORE OF HEALTH: How Predictive Analytics Helps Healthcare Organizations Anticipate Needs.

Reducing potential security risks for health care organizations

Companies also need to be aware of potential risks. For example, the Deloitte paper points out that regulatory guidelines are still emerging in relation to predictive analytics in healthcare, particularly in relation to machine-driven interventions in patient-centered care. When predictive models go wrong, who is responsible?

Additionally, Phan points out more practical device control and security considerations, including:

• Calibration. "When dealing with electronic sensors," says Phan, "we have to deal with calibration and embedded software updates." The Food and Drug Administration has strict controls for updating medical device software.
• Access control. "Some of the most important issues concern access control," says Phan. "Because predictive analytics requires the continuous transmission and processing of data, you need to control who can access that data."
• Encryption. Strong encryption is also important, as "at some point, data will have to be transmitted wirelessly or over the Internet". Unencrypted data can compromise both patient privacy and healthcare networks.
• Data storage. Increasing amounts of data make cloud storage a logical choice for healthcare organizations. However, according to Phan, new standards like the General Data Protection Regulation of the European Union and California's Consumer Protection Act may regulate where that data can be stored and how it can be processed.

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