Nine Examples of Predictive Analytics in Healthcare

7 min read ·

Mar 29

The term “quadruple aim” entered the lexicon about 10 years ago to describe four criteria that US healthcare organizations could use to understand, evaluate, and improve their performance. They include:

  • Enhancing the patient experience
  • Improving population health
  • Reducing cost
  • Enhancing the provider (clinicians, staff, etc.) experience

Predictive analytics uses artificial intelligence (AI) and machine learning (ML) to examine immense data sets—far larger than any human could manage—in near-real time, then make predictions about future outcomes. The enabling technology is becoming accessible to more and more users almost daily, and does not require lengthy lead times or use of sophisticated IT resources. 

Both the creation of predictive analytics in healthcare and the democratization of access will play an enormous role in helping healthcare organizations leverage data in the same way that businesses in other fields have done for decades.

When it comes to transforming healthcare with AI, the following use cases will be critical to organizations and society moving forward.

1. Preventing Hospital Readmissions

Leveraging predictive health analytics enables providers to reduce or prevent readmissions by identifying patients at elevated risk of readmission, then identify or suggest measures to reduce that risk. This in turn can help lower costs for hospitals and providers. It also reduces the number of patients that providers must treat, improving the experience for providers and patients who can get more personalized care. Finally, predictive health analytics can help reduce disparities in readmissions, so patients receive the same care for the same conditions.

2. Identify and Predict Public Health Trends

In minutes, predictive analytics technologies can reveal trends in data sets that humans could neither examine nor comprehend in a lifetime. When coupled with generative AI, some of these technologies can even make recommendations about how to address the trends they reveal. The data amassed on population health trends are ideal for use with this technology, because it enables authorities to identify trends quickly, allocate resources, then develop and tailor proactive interventions.

3. Fortify Cybersecurity

Businesses in heavily regulated industries have long used AI-enabled tools to secure their people, data and infrastructure. An important example of predictive analytics in healthcare, cybersecurity, uses these tools to detect anomalies and potential healthcare system security breaches. 

Their AI-enabled models have been trained on billions of webpage visits, emails, device updates and other authentications across industries. This makes them ideal for safeguarding patient and provider data, protecting systems and maintaining regulatory compliance. These tools also help organizations prevent reputational damage, financial losses and other damage that results from security breaches.

The creation of predictive analytics in healthcare and the democratization of access will help healthcare organizations leverage data as other fields have done for decades.

4. Forecast Diseases

By comparing historical data on individual patients to data gleaned from large numbers of other patients, predictive health analytics can enable providers to predict the onset of diabetes, cardiovascular diseases, cancer, and other diseases. This data can also be used to help authorities examine large amounts of data from entire populations and predict the onset of more specific instances of disease. Pharmaceutical manufacturers have begun to use these tools and data to predict the onset and severity of flu, colds, and other seasonal illnesses in specific regions, then optimize their product and supply chains for the upcoming season.

5. Expedite Insurance Claims Processing

Streamlining and accelerating the revenue cycle is a key function for many payors. Predictive algorithms enable accounting departments to assess claims data quickly and accurately, regardless of which vendor provided the EHR system in use. This enables administrators to detect fraudulent claims and more accurately predict their likelihood. 

Similar algorithms enable accounting teams to identify and correct errors in coding and other documentation, all of which can help to eliminate time-consuming manual review processes. This greater efficiency and lower costs help free up funds and people for more important work.

6. Optimizing Medical Equipment Maintenance

Most equipment in commercial use is maintained on an assumptive basis, such as when an operator assumes the need to change the oil in an engine every 5,000 miles. Maintenance in the healthcare sector operates much the same way, though a bit more complex. 

Predictive analytics in healthcare allow organizations to combine Internet of Things (IoT), data, hyper-automation and cloud computing to compare data gathered in real-time from one device to historical data from thousands of similar devices. With this IoT data modernization, the system predicts when and how that device might fail. Then, maintenance crews can service or replace a device just before failure rather than replacing it sooner than necessary. Owners see longer service lives and lower down time, which enhances patient safety as well as operational efficiency.

7. Forecast and Minimize No-Shows

Healthcare providers incur huge costs when they miss the opportunity to see and treat patients. They also lose a key opportunity to meet the quadruple aim. Healthcare organizations can use predictive models to analyze patients’ previous behavior and external factors like foul weather forecasts, distance traveled, public transportation schedules, etc. to predict when a patient might miss an appointment or arrive late. This enables staffers to optimize scheduling and allocate resources more efficiently.

Predictive analytics must be combined with healthcare AI solutions and other emerging technologies to meet the quadruple aim.

8. Create Tailored Treatment Plans

Each patient deserves a specific tailored treatment plan to optimize health, but providers must leverage their knowledge of all disease and illness to find the most effective and efficient path forward. Utilizing the analytics data enables providers to tailor treatment plans to each patient’s individual situation, making treatments more effective and minimizing adverse effects. Providers can also more accurately predict the onset of specific issues and help the patient identify lifestyle changes that might delay or prevent them.

9. Detect Infectious Disease Outbreaks Earlier

Predictive health analytics empower providers to examine vast sets of data in disparate formats in near real time. This is an ideal solution for helping authorities understand the evolving nature of infectious disease outbreaks, as we saw time and again during the height of the COVID-19 pandemic, and as we continue to see when COVID cases see sudden spikes. Analysts use a combination of data to help them determine an appropriate response to pandemics, as well as predict and contain future pandemics.

The Future of Predictive Health Analytics is Now

The astonishing evolution of technology that we’ve seen in the last few years has immense potential to improve the health of the entire human race. The field of predictive analytics in healthcare is uniquely promising, but it must be combined with healthcare AI solutions and other emerging technologies like IoT, edge computing, data analytics, hyper-automation and cloud computing in order to meet the quadruple aim. 

The nine use cases above are the tip of the iceberg. The future of predictive health analytics has already arrived, and healthcare AI solutions are within your grasp.

If you’re uncertain about how to get your team up to speed on AI skills, Sand Academy can help close the IT skills gap. We offer a variety of initiatives to help organizations upskill their teams to stay competitive in the ever-changing AI landscape.


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