5 Applications of Predictive Analytics in Healthcare [2024]

We, humans, have come a long way from using cigarettes to cure asthma and using Heroin as a cough medicine to utilizing historical data and predictive models to accurately predict potential issues and anticipate the future needs of a patient, really impressive if you ask us. So what is the technology behind all this?

The answer to this question is Predictive Analytics; specifically Predictive Analytics in healthcare industry. 

Data is in the frontier of predictive analytics, where no matter the industry if data is employed and implemented correctly, it is meant to bring revolutionary changes to the said sector. According to projections, the worldwide artificial intelligence (AI) market in the healthcare sector is expected to experience a compound annual growth rate (CAGR) of 25.4% during the period from 2019 to 2025, indicating a significant and rapid expansion of AI adoption and revenue generation within the healthcare industry over the specified six-year timeframe. But what is Predictive Analytics in healthcare industry? What are some of the applications of Predictive Analytics in Healthcare? Let us find it out together in this blog by SoftmaxAI, a predictive analytics healthcare company.

What Is Predictive Analytics in Healthcare?

Predictive analytics unlike the traditional analytical approach is branch data analytics using historical and current data to make informed predictions about future events. Predictive analytics relies on data, statistical algorithms, & machine learning techniques. These tools go through vast datasets, identifying patterns, trends, and correlations among the data that might not be obvious to the human eye. 

When applied to healthcare, predictive analytics in the healthcare industry becomes a powerful tool with the potential to change patient care forever, improve operational efficiency, and even save lives. Healthcare data can be any data that is related to health and by leveraging vast amounts of data from sources like electronic health records (EHRs), claims data, health surveys, and medical devices, predictive analytics can find hidden patterns and insights that inform and ease decision-making.

Related Read: Predictive Analysis in Pharmaceutical Industries

Process of Predictive Analytics in the Healthcare Industry

1. Data Collection and Integration

• Gather Data: This is the foundation of the entire process. Healthcare data of public health comes from various sources, including:
  • Electronic Health Records (EHRs): Patient demographics, medical histories, diagnoses, medications, lab results, etc.
  • Claims Data: Information on procedures, diagnoses, and costs.
  • Wearable Devices: Data on activity levels, sleep patterns, and vital signs.
  • Social Determinants of Health (SDOH) Data: Information on socioeconomic status, education, and living conditions.
• Data Integration: The challenge is to bring together these disparate data sources into a unified format that can be analyzed effectively. This often involves cleaning, standardizing, and de-identifying the data to ensure patient privacy.

2. Data Exploration and Analysis

• Exploratory Data Analysis (EDA): This step involves summarizing the main characteristics of the data, identifying patterns, and uncovering potential relationships between variables. This can be done through visualizations like histograms, scatter plots, and heatmaps.
• Feature Engineering: Data analytics in healthcare involves selecting, transforming, and creating new features that can improve the accuracy of predictive models. For example, a new feature could be created to represent the total number of chronic conditions a patient has.

3. Model Building and Validation

• Selecting a Model: There are various predictive models in healthcare industry that are available, such as:
  • Regression Models: Used to predict continuous outcomes (e.g., length of hospital stay).
  • Classification Models: Used to predict categorical outcomes (e.g., disease risk).
  • Time Series Models: Used to predict future values based on past trends (e.g., hospital readmission rates).
• Training the Model: The selected predictive analysis model is trained on a portion of the data to learn the relationships between the features and the outcome of interest.
• Model Validation: The model’s performance is assessed on a separate portion of the data to ensure it can generalize well to new, unseen data.

4. Deployment and Monitoring

• Deployment: The validated model is integrated into the healthcare organization’s existing systems and workflows. This could involve creating a dashboard for clinicians to access predictions, or automating certain decisions based on the model’s output.
• Monitoring: The model’s performance is continuously monitored to ensure it remains accurate and relevant. This might involve retraining the model periodically with new data or adjusting its parameters as needed.

Predictive Analytics Applications and Use Case in Healthcare

1. Early Disease Detection: Catching Illnesses Before They Strike

A few decades ago knowing your health risks before a disease even takes hold would’ve been a dream, but this isn’t science fiction anymore, but the reality of predictive analytics in healthcare today. By meticulously analyzing your unique health data – from your genes and lifestyle to medical history and lab results – sophisticated predictive models can act as your personal health guardian. Predictive Analytics in the healthcare industry can spot subtle patterns & red flags that might indicate an increased risk for chronic conditions like diabetes, heart disease, or even cancer, often years before symptoms manifest.

This early warning system helps you and your healthcare providers to take proactive measures through data analytics in healthcare. Early intervention through lifestyle changes, preventive medications, or targeted screenings can significantly alter the course of a disease, leading to better outcomes, improved quality of life, and potentially avoiding costly and invasive treatments down the line.

A real-life example of this is an AI system developed at Johns Hopkins University that can detect sepsis symptoms several hours earlier than conventional methods, resulting in a 20% lower mortality rate for patients.

2. Personalized Treatment Plans: Tailoring Care to Individual Needs

With the help of predictive analytics in healthcare, patients do not need to worry about getting generic treatment for ailments that are not unique to their biology, such as cancer. Cancer isn’t one disease; it’s a complex spectrum. Each tumor carries a unique genetic signature that influences how it grows and responds to different therapies.

Predictive analytics in the healthcare industry can analyze this genetic blueprint of yours, along with your personal health data, to identify the most effective treatment strategies for your specific type of cancer. This means less guesswork for the oncologist & more targeted therapies that are more likely to succeed. FoundationOne CDx by Foundation Medicine is a genomic profiling test that utilizes artificial intelligence to assist oncologists in pinpointing targeted therapies and clinical trial possibilities for cancer patients according to their genomic profile.

One of the most exciting frontiers in personalized medicine is pharmacogenomics, the study of how genes influence drug response. Predictive analytics can decode your genetic profile to determine which medications are most likely to be effective and least likely to cause adverse reactions. This translates to safer, more targeted therapies, eliminating the potential risks associated with traditional trial-and-error approaches. Personalized medicine isn’t solely about genetics. Predictive analytics takes a holistic view, considering your lifestyle factors like diet, exercise, and environmental exposures, alongside your medical history. 

Also read: Advantages & Limitations of Predictive Analytics

3. Optimizing the Healthcare Supply Chain: Ensuring the Right Resources at the Right Time

Predictive analytics in healthcare supply chain management is a game-changer. Just like in any other industry, where Artificial Intelligence with the help of predictive analytics can optimize the supply chain, predictive analytics in healthcare supply chain can help the hospitals by analyzing data on inventory levels, patient demand, and supplier performance, healthcare providers can optimize their supply chains, reducing waste and ensuring the timely availability of critical supplies.

One such real-life example is of Mayo Clinic which uses predictive analytics to manage its supply chain efficiently. By forecasting demand for medical supplies, they can maintain optimal inventory levels, reducing costs & ensuring that essential items are always available.

4. Enhancing Cybersecurity: Protecting Patient Data

Healthcare organizations are prime targets for cyberattacks due to the sensitive nature of patient data and the critical role of digital systems in care delivery. The consequences of a breach can be devastating, including financial losses, reputational damage, and even threats to patient safety. Predictive analytics, with its data-driven insights, offers a powerful tool in the fight against cyber threats.

Predictive analytics acts as a vigilant watchdog, continuously monitoring systems and data for anomalies that could indicate a potential cyberattack. Here’s how it works:

1. Data Collection and Analysis: Predictive models analyze massive amounts of data analytics in healthcare from various sources, including network traffic, system logs, user behavior, and threat intelligence feeds.
2. Anomaly Detection: Algorithms identify patterns and deviations from normal activity, such as unusual login attempts, data transfers, or changes in system configurations.
3. Risk Assessment: Predictive models assess the severity of potential threats, prioritizing responses based on the level of risk.
4. Proactive Response: Healthcare organizations can then take proactive measures to mitigate threats, such as blocking suspicious traffic, isolating affected systems, or implementing patches and updates.

5. Automated Documentation and Transcription: Streamlining Efficiency and Accuracy

Automated documentation systems use advanced speech recognition technology and natural language processing to convert spoken words into accurate, structured medical records. These systems can transcribe doctor-patient conversations, surgical notes, and even radiology reports in real-time. The transcribed notes are automatically integrated into electronic health records (EHRs), eliminating the need for manual data entry. This seamless integration ensures that patient records are always up-to-date and easily accessible. 

But it doesn’t stop there – predictive analytics takes this a step further by analyzing the transcribed data to identify patterns, flag potential issues, and even suggest diagnoses or treatment plans. Data analytics in healthcare plays an important role in enhancing these automated systems. By continuously learning from vast amounts of medical data, these systems become more accurate & efficient over time. They can predict common phrases or medical terms based on the context, reducing errors & improving the overall quality of documentation.

A successful example of this is  ScribeAmerica, a leading provider of medical scribe services, has integrated AI-powered scribes into their offerings. These AI scribes use predictive analytics models and NLP to transcribe physician-patient interactions in real time. By automating documentation, ScribeAmerica has significantly reduced the administrative burden on healthcare providers, allowing them to spend more time with patients.

Also read: A Beginner’s Guide to Natural Language Processing

Hospital Readmissions

Hospital readmissions are a major concern for healthcare providers. When patients are readmitted shortly after being discharged, it often indicates that their initial treatment was insufficient or that follow-up care was lacking. According to the Agency for Healthcare Research and Quality (AHRQ), the average cost of readmissions was 12.4 percent higher than the average cost of index admissions ($16,300 vs. $14,500). 

Predictive analytics healthcare companies are developing sophisticated predictive analytics models to predict which patients are at risk of being readmitted. By analyzing factors such as previous hospital visits, comorbidities, and social determinants of health, these models help healthcare providers intervene early. This not only improves patient outcomes but also reduces costs associated with readmissions.

Wrapping Up

Even though predictive analytics in healthcare is still a relatively new field, it has been making incredible strides in the healthcare industry. And guess what? As we move forward, this technology is only going to keep upgrading, offering even more powerful tools and high-quality insights. With predictive analytics healthcare companies like SoftmaxAI, you too can be a part of the transformative power of predictive analytics in healthcare. Don’t miss out on the opportunity to change your healthcare business forever — contact us right away to set up a meeting with one of the top AI companies to help you with how predictive analytics can help your healthcare business.