Artificial Intelligence (AI) and machine learning are fueling productivity across all industries. Jobs are shifting to gain a competitive edge in today’s data-driven world. Are you ready to adapt to these changes? 

Nowadays, even the field of medicine can take advantage of these processes through healthcare predictive analytics. Here are the things you need to know about it. 

What is Predictive Analytics in Healthcare? 

Predictive analytics is a branch of data analytics that focuses on forecasting future outcomes based on gathered historical data and employing techniques such as machine learning and statistical modeling. It is a scientific discipline capable of generating highly precise insights into future events. 

With the help of models and tools in predictive analytics, any organization can now use current and past data to reliably predict behaviors and trends milliseconds, days, or years into the future. 

According to a study conducted by The Insight Partners, the global market size for predictive analytics reached $12.49 billion in 2022. The market is expected to rise to $38 billion by 2028, increasing at a compound annual growth rate (CAGR) of about 20.4% from 2022 to 2028.¹ 

Predictive analytics plays an important role in precision medicine, a healthcare approach that develops personalized treatment plans based on the patient’s lifestyle, environmental data, and genetics. Precision medicine allows medical professionals to know if an individual is at high risk for developing certain diseases or conditions such as cancer before they even show symptoms. 

Data for predictive analytics can be obtained from various sources, including electronic health records (EHRs), wearable devices, and claims data. This will transform how experts process information through highly efficient and rapid data collection. 

Examples of Beneficial Applications in Patient Care 

Here are some cases where predictive analytics can help medical professionals and patients in healthcare. 

1. Preventing Hospital Readmissions  

Between March 13 and April 9, 2020, 4.5 percent of COVID-19 patients who had been hospitalized at New York-Presbyterian Queens experienced a deterioration in their condition within 30 days after their initial discharge. Out of all these patients, 1 out of 5 individuals passed away upon readmission.²

Predictive analytics enables healthcare professionals to identify individuals who are more likely to be readmitted. This can assist providers in developing targeted interventions aimed at reducing the likelihood of patients requiring repeat stays at healthcare facilities. 

While the technological process is still under development, accurate and rapid AI-driven data delivery may significantly benefit providers in assessing peoples’ readiness to be discharged from their care on a larger scale, particularly in emergency situations. 

2. Improving Cybersecurity  

Data breaches in the healthcare industry are on the rise and may cause significant damage to affected facilities. In the United States, the largest recorded incident thus far was at Anthem Inc., a prominent health insurance company. Back in 2015, cybercriminals were able to obtain the personal data of 78.8 million individuals.³ 

Predictive analytics in cybersecurity can play a crucial role in safeguarding patient data. By combining predictive analytics with artificial intelligence, facilities can rapidly assess risk scores for various online transactions and respond accordingly. 

For instance, when users log in, the system can grant access to low-risk processes while blocking high-risk ones. Facilities can also implement multi-factor authentication as an additional layer of security. Furthermore, predictive models can continuously monitor access and sharing patterns to detect any signs of intrusion. 

Cybersecurity predictive analytics have two types: 

  • Vulnerability-based solutions: can search for weaknesses in the facility’s system that were not patched, ranging from misconfigurations to Common Vulnerabilities and Exposures (CVEs). 
  • Threat-focused solutions are platforms that can look for and identify potential threats. 

3. Public Health Management 

Here are two ways predictive analytics may help manage the health care of the US population. 

Treatment Effectivity 

A research team at the University of Michigan Rogel Cancer Center is developing a new blood test that they claim will be able to predict if individuals with metastatic HPV-positive throat cancer will respond to treatment months ahead of taking standard imaging scans. 

If their research proves to be successful, medical experts will then be able to determine if certain treatments can be effective for specific patients. This will provide guidance for doctors to quickly modify treatment plans in cases where positive results are unlikely. Such early intervention can spare patients from enduring months of potential toxic side effects.⁴

Identifying Health Trends and Predicting Disease Outbreaks 

A Canadian software company named BlueDot was able to predict the upcoming COVID-19 pandemic before the World Health Organization even released its statement. Utilizing their outbreak intelligence platform, they employed advanced analytics to forecast health trends in specific regions. 

BlueDot uses natural language processing and machine learning to extract data from a wide range of sources, including statements from official public health organizations, global airline ticketing data, digital media, population demographics, and livestock health reports. BlueDot is able to process information 24 hours a day, in 15-minute intervals. 

In addition to sending out alerts, BlueDot successfully pinpointed the specific cities linked to Wuhan by analyzing global airline ticketing data. This helped identify where the infected people traveled to. 

4. Improvement of Patient Outreach and Engagement 

Medical facilities could use a marketing streak, too. Their team members can use predictive analytics to engage with their patients, which then strengthens their relationships with their appointed physicians. These predictive modeling tools can help create profiles of patients, send personalized messages, and help the team come up with strategies that would create more impact on each patient. 

On the other hand, pharmacies and hospitals can use predictive models to analyze patient data, which enables them to identify dynamic customer personas and predict their preferences and behavioral patterns. Physicians can subsequently align with these insights to devise tailored outreach strategies, such as forecasting drug effectiveness and treatment adherence. 

Furthermore, the marketing team of healthcare facilities can utilize the gathered data to craft impactful email campaigns directed towards patients. 

Related Reading: 11 Must-Have Resources if You’re a Global Nurse 

Limitations and Challenges 

The application of predictive analytics in healthcare holds transformative possibilities, offering professionals valuable insights and predictions to enhance decision-making and elevate patient outcomes. Nevertheless, it is crucial to tackle the obstacles associated with utilizing this technology to ensure the delivery of dependable and credible results. 

These challenges may include data quality, model selection, algorithm bias, and more. Each of these must undergo thorough understanding and careful consideration before being used for analysis. 

To address the limitations of healthcare predictive analytics, facilities are required to take a multifaceted approach involving model selection, performance evaluation, and careful preparation of data. They should also strongly focus on privacy, fairness, and security. 

Most importantly, the dynamic and complex nature of data in healthcare must go through constant adaptation and monitoring to ensure the best results. 

Related Reading: 5 Key Healthcare Staffing Industry Trends in 2022 That Will Transcend to 2023 

Utilize AI and Machine Learning to Improve Disease Management 

Healthcare providers can further improve patient care through advanced technology. Predictive analytics is here to advance healthcare, offering you the potential to enhance your services through the power of data and algorithms.  

By utilizing AI tools and other technologies, providers can proactively identify risks, adjust treatment plans, and improve their resource allocation. The future of healthcare can be found in using predictive analytics to consistently provide personalized, precise, and proactive care for everyone. 


Predictive analytics is beneficial for healthcare, and PRS Global is here to support you. Our direct hiring services connect you with qualified nurses who can effectively leverage this technology. Let us help you meet your staffing needs seamlessly.     

Get in touch with us today to learn more about our services. 


1 “Predictive Analytics Market Forecast to 2028.” The Insight Partners, Accessed 22 Jun. 2023. 

2 “Assessment of Thirty‐Day Readmission Rate, Timing, Causes and Predictors After Hospitalization with COVID‐19.” National Library of Medicine, 5 Feb. 2021, Accessed 22 Jun. 2023.  

3 Petrosyan, Ani. “Largest Healthcare Data Breaches to Date in the United States As of May 2023, by Number of Affected Individuals.” Statista, 15 May 2023, Accessed 22 Jun. 2023. 

4 “Human Papilloma Virus Circulating Tumor DNA Assay Predicts Treatment Response in Recurrent/Metastatic Head and Neck Squamous Cell Carcinoma.” Oncotarget, Accessed 25 Jun. 2023.