What Is Predictive Analytics in Healthcare?

An Upclose Look Into Predictive Analytics & Its Role in Healthcare

The global Big Data in Healthcare Market is forecast to be worth USD 78.03 Billion by 2027.

As we move into the next decade, the rapid increase in data collection will significantly enhance the impact of predictive analytics in public healthcare.

What is predictive analytics?

It is the use of high-end technology like AI and ML to gather and analyze data to identify valuable insights for healthcare providers.

By leveraging healthcare data analytics solutions, experts can use these insights to drive improvements and enable better patient outcomes.

Our article is a detailed exploration of predictive analytics and how this data-based approach can revolutionize healthcare.

How Does Predictive Analytics Work For Healthcare?

Basically, predictive analytics is a technological discipline of data analytics. It is an approach driven by high-end technologies like Artificial Intelligence, Machine Learning, Data Mining, and modeling to detect patterns and trends in data.

Predictive analytics is gaining traction in healthcare because it can make valuable predictions based on insights from collated healthcare data. These predictions are enabling healthcare professionals to make informed clinical decisions and even manage the spread of diseases.

How does predictive analytics work?

The technology sifts through vast volumes of datasets related to healthcare. This data may include:

• Patient health records
• Clinical trial site information
• Medical history
• Patient logs from healthcare facilities

Analysts then create a statistical model to validate and apply assumptions to datasets to generate predictions for inputs.

In short, predictive analytics empowers healthcare professionals to make preventional and interventional life-saving decisions based on analytical models. In the field of medicine, data analytics will soon be a significant aspect of patient care and operational management.

Also Read: Predictive Analytics as a Catalyst for Preventive Health Strategies

What Are the Types of Predictive Analytics?

Predictive analytical models are of two types:

1. Regression models

These models are widely preferred for statistical analysis for two reasons: they can decipher patterns in large datasets and are useful when there is a relationship between inputs.

Regression models analyze the formula, which essentially is the relationship of the inputs found in the datasets.

2. Machine Learning

Machine Learning (ML) is an algorithm that predicts outputs based on statistical input analysis. It “learns” and improves its predictive abilities by giving new outputs as fresh inputs become available. Neural networks and multilayer perception are classic examples of ML techniques.

With technological advancements like AI and ML, predictive analytics can help diagnose diseases and benefit the healthcare sector in many ways.

Predictive Analysis & its Amazing Benefits For Healthcare

1. Helps deliver better patient care

Predictive analysis gives healthcare professionals access to valuable data such as:

• Medical history
• Economics
• Comorbidities
• Demographics

This data gives doctors and healthcare professionals valuable insights to guide life-saving decisions. When healthcare providers make smarter, data-driven decisions, they provide better patient care. By analyzing data and outcomes from previous patients using AI and ML, various treatment methods will be possible to work for each patient.

2. Mitigates human errors in medical care

Human errors in the medical field can cause irreversible mistakes that can potentially be fatal. Fortunately, predictive analytics helps identify potential mistakes and take preventive measures in real time. By leveraging the power of data, doctors and other medical experts can access real-time insights to guide actions, make life-saving decisions and prevent fatal mistakes. These approaches can optimize healthcare delivery and create a safer environment for patients.

3. Enables early intervention for acute and chronic diseases

Unlike earlier times, analytical tools are now being used in healthcare to predict disease risks and improve treatment for critical care patients. Predictive analysis interprets large volumes of case histories in progressive diseases. This approach has already demonstrated promising results in assessing risks associated with cardiovascular disease and diabetes much before patients show any sign or symptoms.

Improving treatment for long-term illnesses translates to fewer patients reaching critical conditions and healthcare providers being able to focus on other needs.

4. Organizes hospital stays and readmissions

Hospital resources, such as beds for inpatients, are valuable but limited. Too many patients overstay their time, increasing hospital expenses and patient wait times and also exposing patients to the risk of secondary infections.

At the same time, patients cannot be discharged before they receive adequate medical care.

Predictive analytics determine patients’ ideal hospitalization time in such scenarios based on their health condition, medical history, and age. Using these insights, doctors can adjust treatment plans and allocate resources more efficiently.

5. Provides risk scores to support clinical decisions

Risk scores are numbers assigned to patients based on demographics and diagnoses. These scores analyze risk factors for adverse events and assess how they render patients more vulnerable to these risks. They are then used in risk-scoring models to gather information from diverse sources to determine the extent of risk for a patient or a group of people. Risk scoring also allows doctors and clinical assistants to predict the worsening of a disease and modify the treatment accordingly.

6. Monitor and prevent fraud

From unnecessary billing to creating false claims and upcoding, the medical world is fraught with fraudulence. Here’s a brief definition of what these fraudulent activities mean:

Upcoding - Billing for expensive medical procedures and services that were not provided for the patient.

False claims - Identity theft or unlicensed persons posing as certified medical personnel to perform medical services. Misuse of certified doctors’ signatures is another major fraud.

Fortunately,  predictive analytics identify and flag fraudulent patterns, thereby helping authorities nip them in the bud before they snowball into major issues.

7. Enable health-related population management

Having data about prevalent diseases, chronic conditions, patients' health status, medications, and medical histories enables predictive analysis to identify similar patients within a population. It also helps identify the majority of people exposed to disease outbreaks, giving health professionals a head start on commencing treatments and improving patients’ chances of survival.

Harnessing Predictive Analytics in Healthcare With Health Vectors

Predictive analytics are making great strides in advancing patient care and operational efficiency.

Healthcare providers can use data and machine learning to identify patients at risk of chronic conditions, optimize treatment plans, reduce readmissions, and improve patient outcomes.

As this technology continues to develop, its potential to revolutionize healthcare remains significant and promising. Health Vectors, a comprehensive data analytics solution provider, delivers a competitive edge by harnessing the power of predictive analytics to transform healthcare delivery. Get in touch with our team to optimize personal health with our solutions.