How Predictive Analytics Aids Decision-Making in Healthcare

ODSC - Open Data Science
4 min readJun 11, 2024

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Predictive analytics is one of the most powerful applications of AI today. While virtually every industry can benefit from these predictions, the healthcare sector stands to gain more than most.

Optimization in most businesses means saving time and money. In medical processes, it can translate into saved lives. In light of that urgency, many medical organizations have jumped at the opportunity to integrate data-driven decision-making into their operations. Here are five powerful examples of these predictive analytics use cases.

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1. Streamlining Patient Prognoses

One of the most exciting applications of predictive analytics in healthcare is automated prognoses. Early case studies have found AI can diagnose some conditions more accurately than human doctors. Predictive analytics can analyze the same data to predict how a condition may progress, informing the best course of treatment.

It’s important to note that machine learning doesn’t replace doctors here. The stakes are too high and hallucinations too prominent an issue to rely on AI insights entirely. However, these predictions can help doctors make an accurate prognosis and treatment plan in less time.

As treatment continues, predictive analytics can give doctors more insight into what to expect, whether that means they should consider another approach or proceed as usual. Whatever the specifics, predictive analytics means doctors can make better decisions faster, leading to improved patient outcomes.

2. Improving Risk Scoring

Similarly, predictive analytics can make patient risk scores more accurate. Hospitals must already predict which patients need care the most urgently to triage them effectively, but doing so manually leaves significant room for error. Machine learning can consider a wider variety of factors and make decisions in less time.

Hospitals can apply these systems to their administrative workflows to manage payments more efficiently. Today’s analytics tools can identify who’s less likely to be able to pay bills and suggest methods that’ll be more effective for different individuals. That way, hospitals can collect what patients owe sooner while maintaining better relationships with them.

3. Streamlining Clinical Trials

Predictive analytics has uses in healthcare decision-making outside of hospitals, too. One of the impactful methods is how it accelerates the testing and approval process for new medicines or medical devices. Later clinical trial stages can take years, but AI can shorten these timelines by removing many common inefficiencies.

Finding ideal populations to test a drug or device in can take a lot of time. Organizations must find areas where enough people will be willing to participate and some trial stages require those with a certain condition. Predictive analytics can find these populations faster.

AI models can start by identifying areas with the appropriate demographics for a trial stage and predict their likelihood to participate. Trials can then get underway sooner, leading to faster testing and approval.

4. Predicting Disease Outbreaks

Another significant way to use predictive analytics is to predict future outbreaks. Events like the COVID-19 pandemic how important but difficult it can be to understand incoming health crises to allocate resources efficiently. Machine learning can help by recognizing early warning signs of an outbreak.

One such tool predicts the likelihood of virus mutations that would escape immune responses, which would worsen existing outbreaks. Others identify trends among recent health reports to highlight when a new condition may be rising. In both cases, AI produces early warnings so health officials can prepare ahead of time and mitigate the event’s impact.

5. Bolstering Medical Supply Chains

Predictive analytics also has significant potential for supply chain resilience. In the medical sphere, this could help prevent shortages of needed active pharmaceutical ingredients (APIs).

API manufacturing is highly concentrated, with 62% of these ingredients coming from India and China alone. Just 10% come from the U.S., meaning American healthcare organizations must rely on far-away sources for 90% of their APIs. That dependency — with the added complications of delays and supply chain transparency issues — makes significant disruptions likely.

AI models can pave the way forward by predicting supply chain disruptions so U.S. drug developers can adapt earlier to prevent shortages. Predictive analytics could also highlight where medical supply chains are too concentrated or reliant on disruption-prone sources, even suggesting more resilient alternatives. That way, supply chains could rethink their sourcing strategies to ensure a more consistent supply of needed medications.

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Predictive Analytics Drives Healthcare Forward

Predictive analytics has huge potential in the healthcare industry. These models can make medical decision-making more accurate and efficient than ever before, leading to saved lives.

Many of these use cases are still in their early stages as far as real-world implementation goes, but they already show great promise. As organizations smooth out the edges of this technology and AI becomes more reliable, these applications could change the medical sector for the better.

Originally posted on OpenDataScience.com

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