13 Predictive Analytics in Healthcare Use Cases

Healthcare workers must process a massive amount of data from multiple sources in every case they work on. This includes patients’ electronic health records, medical images, screening results, and various administrative data. Furthermore, based on this information, they often need to make informed decisions as fast as possible.

The use of predictive analytics in healthcare organizations can help the industry in a variety of ways. One of the best benefits of predictive analytics in healthcare is the effectiveness of data processing and analysis. However, there are also other use cases for predictive analytics in healthcare that can transform the industry.

According to statistics from Precedence Research, in 2023, the predictive analytics market amounted to $14.43 billion, and by 2032, it will reach an approximate amount of $98.54 billion with an average annual growth rate of 23.8%. So, let’s take a closer look at the importance of predictive analytics in healthcare and some of the most innovative use cases for it right now.

What about the benefits of predictive analytics in healthcare? Using data from multiple sources with the help of advanced predictive analytics methods, the healthcare industry will be able to:

• Improve chronic disease management
• Avoid hospital readmission
• Get assistance with medical research
• Reduce overhead costs (nowadays, the average medical practice overhead is between 60-70%)

Main Techniques in Predictive Analytics

Data Mining

As a part of the use of predictive analytics in healthcare, data mining can be described as a set of methods that help gather relevant medical data into databases, transform it, and pre-process it for later modeling, analysis, and prediction formulation.

An important thing to mention is that, according to HIPAA, any data related to the patient needs to be handled with extreme care and should be protected. That’s why data mining needs to be used with an established EHR system that provides sufficient levels of privacy and security of information.

Data Modeling

Data modeling is a predictive analytics tool that employs statistical methods to analyze historical data. As a result, it helps healthcare providers create a detailed model of how the same data evolves over time, allowing them to predict future events.

Being a statistics-based tool, data mining outlines the possibilities for potential future outcomes. To achieve the best fidelity, the model has to be regularly updated and recalculated. With help from other tools, this process can be streamlined into a real-time modeling tool.

Artificial Intelligence

Artificial Intelligence is a field of computer science that can be effectively described as a combination of complex machine learning algorithms with data processing methods. At its core, its purpose is to replicate intelligent human behavior.

As one of the examples of predictive analytics in healthcare, AI is used to manage patient data and produce calculated predictions based on it. The potential impact of AI on the industry cannot be overstated, as it might eliminate human error from it as a whole.

Machine Learning

Machine learning is a part of the AI science field, with specific algorithms being developed to achieve autonomous learning by machines. The algorithms developed progress over time, meaning the more data sets they analyze, the better they become.

This makes machine learning a perfect tool for predictive analytics in the healthcare industry. With the large data sets available, machine learning algorithms gain experience faster and provide better predictions based on this data.

Deep Learning

Deep learning is a subcategory of machine learning that deals with artificial neural networks. They are built to mimic the biological neural networks of the human brain. However, with modern multi-layered processing capabilities, artificial neural networks exceed the powers of the human mind and can be effectively used to make precise predictions.

One of the major use cases for predictive analytics in healthcare is its use in medical imaging analysis. This helps detect minor deviations in various medical images, from MRI screenings to microscopy images, thus helping to diagnose an issue at its early stages.

Main Fields of Use for Predictive Analytics

Before we start reviewing predictive analytics in healthcare use cases, let’s determine the main fields of use for predictive analytics:

• Diagnosis – predictive analytics can be used to determine the correct condition the patient has based on the predicted progression of their state
• Prognosis – based on current and historical data, predictive analytics can help foresee how the condition will progress and how it would respond to specific treatments
• Designing treatment course – based on the diagnosis and the prognosis, predictive analytics can help determine the correct course of action and generally improve patient outcomes
• Clinical decision support – clinical decision support health systems based on predictive analytics will help physicians at just the right time to seize the opportunity to help the patient
• Remote monitoring – with the right equipment, predictive analysis can easily be conducted remotely
• Reducing adverse events – using predictive analytics in healthcare can help detect the potential for adverse events, like chronic disease exacerbation, medication side effect manifestation, and others early on, thus offering a possibility to avoid them
• Improving care quality – using predictive analytics increases the efficiency and accuracy of care provided, thus being higher quality than the alternatives
• Reducing healthcare costs – predictive analytics can be used to better manage hospital resources, thus lowering certain expenses connected to an unexpected crisis
• Fraud detection – fraud in healthcare is a common problem, and billions of dollars are lost to it every year. Predictive analytics, enhanced with trained machine learning models, can identify certain abnormalities that mark fraudulent actions, thus helping to catch them early on.

13 Examples of Predictive Analytics in Healthcare

Detecting the Early Signs of the Patients’ Condition Deterioration

The first one in our list of predictive analytics in healthcare use cases is detecting the early signs of the patients’ condition deterioration. This is one healthcare branch that requires quick decision-making and constant attention to the patient’s condition. When ICU units often overflow with critical patients (especially during the height of the COVID-19 pandemic) and there is a lack of intensive care specialists, the quality of care often drops.

As the vital signs of each patient are monitored constantly, this data can be used in predictive analytics. Predictive algorithms can be effectively used to determine which patients have a high risk of condition deterioration within the next 60 minutes. This allows the response team to act early on to prevent the crisis or minimize its effects.

Biosensors for ICU Monitoring

One of the other examples of predictive analytics in healthcare in the ICU concerns its application in remote critical care. Tele-ICUs were only made possible by the biosensors that collect patient data and predictive analytics that analyze said data and help the teams effectively respond to the worsening of a patient’s condition.

The use of predictive analytics reduces the response time, allows the provision of more effective care, increases the capacity of the unit, and, what also needs mentioning—provides a way to ensure the safety of healthcare workers.

Risk Scoring for Chronic Illnesses

Six in ten US adults suffer from incurable or ongoing chronic diseases. Some of them are at constant risk of flare-ups and complications. To correctly determine the possibility of such a complication at any given time requires a continuous analysis of data on the patient’s condition.

Here’s where predictive analytics in healthcare using Big Data comes into play. By analyzing lab results and patient-generated data on their lifestyles and biometrics, the system can assign the person a specific risk score that signifies the possibility of a complication in the near future. It’s also more likely to detect the early signs of deterioration and inform a physician about it.

Predictive Care for At-risk Patients

Besides the chronically ill, there are other at-risk groups of patients that can benefit from predictive healthcare. This especially concerns older adults and patients recently discharged from the hospital after invasive manipulations.

Through the benefits of telecare and predictive analytics, these patients can avoid adverse events or get help in a crisis event as fast as possible. Through historical healthcare data processing, the software can even predict a fall event in the case of an elderly patient, thus rescuing them from potential trauma and hospital readmission.

Preventing Patient Suicide and Self-harm

Mental health issues deserve just as much attention as other chronic conditions. Suicide, self-harm, and other violent tendencies might seem to occur at random and even without any provocation, yet predictive algorithms can detect specific patterns.

Professional help provided at the right time can help prevent the mental health crisis even in the most unstable patients. So, predictive health analytics can be used not only to improve patients’ quality of life but also to save their lives.

Reducing Hospital Readmission Rates

While the Hospital Readmission Reduction Program has implemented measures to limit unplanned 30-day patient readmission, it still happens all across the US. In 2022, the average adult readmission rate reached 17% and referred to one of four conditions: heart failure, tumor or cancer, diabetes, and COPD.

With the help of predictive healthcare analytics, patients with a high risk of readmission can be identified, warned, and provided with better preventive care. As for predictive analytics in healthcare real examples, let’s take a look at this case.

Predictions Based on Genetic Research

Genetic irregularities are present in at least 10% of adults. Catching some of them at the early stages can help manage them and avoid complications later in life. However, analyzing genetic information is a complicated process, as the human genome is a complex system.

Predictive analytics can be used to analyze and compare a person’s genetic data with a database of possible defects and illnesses connected to them. Furthermore, it can be used as early as the neonatal stages, warning the parents of the condition their child might have.

Research into New Treatments and Precision Medicine

Besides patient care, healthcare predictive analytics can be effectively used in the research branch of healthcare. The algorithms can accurately predict the person’s response to the medication or treatment plan based on their data (genetic information, clinical history, etc.) and the responses of the earlier studied patient groups.

This can effectively cut the need for inpatient groups and overall streamline the research process. It also allows one to narrow the focus to a single patient and develop a precise solution for their particular case.

Improvement of Patient Engagement and Satisfaction

Establishing a trusting personal relationship is as important in healthcare as choosing the right treatment plan. It can help incentivize patients to follow their treatment, return for the second examination, and overall form healthier habits, thus avoiding complications and more severe issues.

Using predictive analytics to evaluate the person’s behavioral patterns allows the healthcare system to determine the best approach to them. It can also select the most suitable professional that has the best chance of establishing a personal connection.

Managing the Supply Chain

The supply chain for any hospital is a complicated system, as the supplies needed depend on the patient load and the specifics of each patient case.

Using predictive analysis can help the hospital make utterly data-driven decisions for future purchasing. This makes purchasing more efficient and cost-effective by reducing unnecessary purchases and wasting equipment.

Managing the Staff

By identifying the patterns of inpatient care, a hospital, with the help of predictive modeling, can determine how many people should be in the hospital at any given moment. This can also help correctly identify the needs of certain specialists and allow them to have time off at any other moment.

Optimizing the staff occupation can greatly reduce operational costs, thus helping to reorganize the hospital budget and provide better patient care.

Predetermining the Medical Equipment Maintenance Needs

Physics is merciless—no machine can work forever due to friction and resistance. That’s why, for some time now, healthcare predictive analytics have been used in various industries to predict the potential wear-out and failures of certain components.

Healthcare tools can benefit from such analysis as well. For example, by analyzing the data from sensors in an MRI machine, the system might predict its failure before it occurs. This allows for an issue to be fixed with a partial repair or a separate detail replacement.

Insurance Fraud Detection

The US government expects to recover more than $3 billion USD from healthcare fraud and misspent funds in fiscal year 2023. Insurance companies have spent much time and effort to reduce these sums, but only a few of them employ healthcare predictive analytics to tackle the problem on a large scale.

With enough data on fraudulent insurance claims and insurance fund mismanagement, tailored machine learning algorithms can be developed and trained to determine whether there is any malicious intent behind the case early on. Thus, they will help to reduce the funds lost and dissuade the scammers from future attempts.

NIX’s Experience with Healthcare Predictive Analytics

We’re working together with private heart surgery hospitals on a project which is able to improve the prediction of heart diseases using machine learning. Heart failure is a major and ever growing medical problem worldwide, and the ability to predict readmissions in patients with heart failure is very important.

Our client had a unique system of early heart failure prediction that is based on a complex mathematical model and 2D data (contrast left ventriculography). The main task for our team was to improve heart failure prediction using the MRI 4D data. 4D-flow MRI offers advantages for the recording, visualization, and analysis of blood flow. 4D flow MRI also allows measuring and visualizing of the temporal evolution of complex blood flow patterns within an acquired 3D volume.

Currently, data preparation is a big challenge. We overcame this challenge in two steps:

• DICOM file decoding. The main problem lies in the different dialects of MRI machines that write data in the DICOM files format.
• Complex mathematical transformations. Namely, the conversion of raw DICOM data into values which input to the neural network to determine a diagnosis and check for related complications.

The project is still under development and we use deep learning techniques—particularly CNN, which is applied to analyze visual imagery. Input data is a mathematical model of bloodstream change during a certain period of time. We’ve already passed the stage of improving a mathematical model and now we’re proceeding to train the neural networks.

This new approach helped us to enhance the quality of the current one significantly and we continue to work on the project. Also, we’re considering partners from medical institutes which are able to provide MRI 4D data for testing and launching the system.

You can learn more about our experience in predictive analytics in healthcare here:

Final Thoughts

Big data and predictive analytics in healthcare have an incredible potential to change the industry for the better. Not only can they help improve the quality and effectiveness of the treatment, but also reduce its costs, achieve better patient engagement, and even develop new methods and medication.

If you want to employ predictive modeling methods but don’t have enough knowledge or experience in the area, contact us at NIX. We’ll share our expertise and help you develop unique solutions to your challenges.

FAQ

01/

How is Predictive Analytics Used in Healthcare?

Predictive modeling methods in healthcare are used to detect the early signs of patient deterioration and risk-scoring for chronic patients.

02/

What are the Ways of Using Predictive Analytics in Healthcare?

Predictive analytics in healthcare can be used for diagnosis, prognosis, treatment course design, clinical decision support, remote monitoring, adverse events reduction, care quality improvement, and care cost reduction.

03/

What are the Main Opportunities of Predictive Analytics in Healthcare?

Some opportunities provided by healthcare predictive analytics include genetic-based predictive modeling, improvement of patient engagement, and staff management.