How Does Predictive Analytics in Healthcare Revolutionize Patient Care?

This blog has been updated on - November 03, 2023

Predictive analytics in healthcare transforms how medical experts use data science services for precise medical diagnosis, patient health monitoring, general medicine and treatment, high-risk disease forecasts, and more.

Not just for patient care, medical specialists and researchers are using predictive AI to predict pandemic-like emergencies.

There is much more to delve into the role of predictive healthcare analytics and its benefits. If you're a healthcare group, a medical health officer, a healthcare researcher, or a chief medical analyst, this blog is a must-read for you.

As per the GlobeNewsWire's report, the worldwide healthcare predictive analytics market will grow at a CAGR of 22.23% to be valued at $30 billion by 2028.

For a long time, the medical community has been trying to organize their tremendous amounts of data and derive value from it for patient care. Now, with predictive analytics, there lies an opportunity to revolutionize the global healthcare industry.

What is Predictive Analytics in Healthcare?

The healthcare and medical industry has vast amounts of data.

From research papers and academic teachings to patient data and lab tests, big data insights and records offer many opportunities for modern computational tools to analyze and derive value.

Predictive analytics in healthcare uses data-driven insights to:

• Identify potential health conditions or underlying risks for early diagnosis.

• Predict patient outcomes and perform precise treatments, improving care quality at reduced costs.

• Offer personalized treatment plans based on Electronic Health Records data (EHR systems) for clinical decision support and real-time risk prediction.

• Encourage operational efficiency of the medical staff during emergency conditions to manage resource allocations and reduce readmissions.

• Enable patients to track their recovery and achieve goals in home settings. It eventually assists doctors in considering evidence-based data for clinical decision-making.

Most medical practitioners stay updated with the latest big data analytics trends while working with technologies like Artificial Intelligence for robotic surgery.

Even central and state healthcare departments plan their population health management strategies based on collective data insights and analytical patterns. It helps them identify and control alarming diseases and reduce hospitalization, mortality, and treatment costs.

What are the Benefits of Predictive Healthcare Analytics?

Internal software for healthcare providers and staff, EHRs, smart diagnosis, and remote patient health tracking and management tools are significant game changers for the medical industry.

They deploy predictive AI and data analytics in healthcare to make essential data available to medical staff and facilities.

Let's look at some of the advantages of inculcating predictive analytics into healthcare management systems:

Early Diagnosis of Various Diseases

Many diseases and medical conditions can be prevented with early intervention.

With predictive data analytics services in healthcare, the system can derive trends and algorithms, enabling doctors to look for possible symptoms. It has been proven as a life-saving method to detect severe medical conditions, including cancer and tumors, at an early stage.

More Efficient Individual Care

General treatment may not be efficient and safe for each and every patient. It doesn't need to consider the specific requirements and conditions of the individual.

Predictive analysis in healthcare will make way for efficient and targeted treatment since the system will have all the medical records of the individual patient.

Increased Accuracy of Diagnosis

The accuracy of the diagnosis increased with predictive healthcare data analytics and services. The primary reason is the vast amount of data available to the system through data visualization analytics.

As a result, medical experts identify uncertain patterns and make precise predictions during diagnosis.

These patterns result from various factors, including individual patients' health records, statistics involving similar designs, academic papers involving similar issues, and more.

Hospitals and Medical Experts Will be Better Prepared

Predictions resulting from patterns will remove the element of surprise, which leaves medical experts and doctors in a dilemma.

Unable to see forthcoming scenarios, doctors and hospitals are often caught off guard by various specific scenarios, which leads to inefficient treatment. By leveraging different types of data analytics, this element of surprise will be reduced as patterns will be visible beforehand.

Predictive Analytics in Healthcare Examples

Let's discuss some real-life examples of predictive healthcare analytics.

'Healthy at Home' Campaign by Health Data Analytics Institute (HDAI)

HDAI launched an advanced predictive analytics platform to empower Accountable Care Organizations ACOs that include a group of doctors, patient care specialists, and other healthcare providers.

HDAI's vision was to keep patients healthy in a home setting by using data engineering services and predictive analytics tools to:

• Assess the risk of unplanned hospitalizations, particularly for pneumonia and fatal cardiac conditions.

• Generate reports to let users create cohorts based on patients in advanced, chronic, and high-risk care groups.

• Focus on cohorts for high-risk patients who may need unplanned hospitalizations, enabling hospitals to manage their healthcare staff accordingly.

Most physicians often stress about helping patients needing special care or support. HDAI's technology counters this limitation with digital twinning and predictive modeling in healthcare.

The impact of predictive maintenance for risk management resulted in stable hospital admissions during public health emergencies in the United States.

Anti-fraud Analytics by the US Department of Justice in Healthcare

The US Department of Justice, DOJ used data analytics to identify physicians prescribing expensive and unnecessary back pain injections. That uncovered physicians who claimed high reimbursement amounts for injections from Medicare and health insurance programs.

During the pandemic, many federal agencies used on-premises and cloud data analytics to track suspicious activities and counter fraud.

Make Informed Decisions: Hire BluEnt as your Partner for Healthcare Data Analytics and Management

Including predictive analysis in our mainstream healthcare management systems will propel us with the benefits of AI and advanced data integration solutions, including Salesforce MuleSoft services.

Big data and predictive analytics transform patient care into a more efficient and effective system.

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Frequently Asked Questions

What is predictive AI in healthcare?

Predictive AI technology uses Machine Learning algorithms to analyze patient data and forecast future health outcomes.

The use cases of predictive AI in healthcare involve medical records, patient health history, diagnostic data, and other valuable insights to:

• Predict risks based on patterns of patient medical history, genetics, lifestyle, and other health metrics.

• Provide personalized treatments or therapies to individual patients.

• Forecast and reduce the risks of readmissions after patient discharge.

• Ensure adequate resource management and minimize staff burnout due to manual techniques.

• Prevent fraud by observing behavioral patterns in patients and hospital data management systems.

How does predictive modeling in healthcare work?

Predictive modeling is a mathematical term for predictive analytics. It uses data mining, data visualization, and predictive AI to predict upcoming events based on past happenings.

The stages of predictive modeling in healthcare are:

• Data gathering from multiple sources.

• Analyzing data on graphs, charts, and other visualization tools.

• Build a predictive model using different approaches and algorithms.

• Deploy the model into internal processes and systems.