When it comes to medical advances, some of them are related to improving the treatment of a certain disease or having more efficient hospital equipment to practice interventions. But there’s another very important focus for healthcare professionals, and that’s prevention. Having the ability to predict health risks or warning signs -before they become a real problem or a serious condition- is key for healthcare services to get sharper in the pursuit of responding faster and better to patients’ needs. In this sense, predictive health analytics is going a long way.
What Are Predictive Analytics and Modeling in Healthcare?
Predictive Analytics is a discipline in the field of data analytics that makes use of digital technologies such as Data Mining, Artificial Intelligence (AI), and Machine Learning (ML) to process and analyze past and present data to predict future events. In the healthcare sector, among other uses, Predictive Analytics are applied to:
- Identify patterns and trends in medical data (from different sources like medical records, Electronic Health Records (EHRs), patients’ surveys, claims databases, etc.).
- Get ahead of a potential disease spread.
- Predict the success of a particular treatment or foresee no-shows in medical appointments.
With Predictive Modeling -an advanced form of analytics- doctors and healthcare organizations can detect correlations to gain valuable insights for intelligent decision-making. Predictive modeling uses specific methods like:
- Data refinement
- Logistics regression
- Decision trees
- Time series analysis
All these methods are used to, for example, decide which treatment to pursue or what patients need attention now to prevent problems in the future. AI modeling goes way beyond estimations and is now applied to supply management, clinical trials, calculation of health insurance costs, and other specific applications.
Benefits of Using Predictive Health Analytics in Healthcare
- Cost reduction by preventing expensive incidents such as no-shows or readmissions, calculating cost savings in treatments and equipment, or increasing the efficiency of staff management.
- Risk prevention not only by detecting upcoming population health trends but also by monitoring cyberattacks.
- Enhanced operational efficiency by speeding up administrative tasks, improving the accuracy of diagnosis, and refining treatment planning.
- Personalized patient care (and engagement), by increasing the effectiveness of prognosis models, allowing faster decisions on prescribed medication or procedures, and launching personalized data-based campaigns.
Predictive Analytics in Healthcare: 7 Use Cases
Now, let’s see how these benefits show in Predictive Analytics specific implementations:
1. Population Health Management
With Predictive Analytics, it is possible to extrapolate data from an individual level to a population cohort with similar characteristics. This way, it’s possible to treat a patient using a cross-patient pool of consolidated data (demographics, existing conditions, meds, personal registers, etc.) to identify people at risk of developing chronic diseases, find population health trends, and predict outbreaks. For example, the University of Texas Health Science Center at Houston developed a tool for Covid-19 tracking to monitor current and future trends of the virus.
2. Reduction of hospital readmissions
Predictive Analytics can also save lives while cutting costs by identifying patients with risks of readmission in hospitals or clinics and the reasons behind this likelihood. With these valuable insights, physicians can prevent turnarounds from happening with personalized follow-ups and discharge protocols.
As healthcare organizations are subject to expensive penalties on readmissions, cutting down on the number of relapsing patients translates into significant savings. A great example is UnityPoint Health, which reduced the readmission rate by 40% with a predictive health analytics system.
3. Predictive analytics in health insurance
Predictive Analytics can help calculate the cost of health insurance for a specific person with higher precision based on cross-analyzed data like age, gender, hereditary and medical history. Moreover, Predictive Analytics can help with insurance claim management – making claims submission more efficient, minimizing human errors – and preventing fraud by detecting malicious activity with Machine Learning algorithms.
4. Suicide attempts prediction
An estimated 700,000 people take their own lives per year in the world. AI and ML can save lives by forecasting suicidal tendencies in patients. At the Vanderbilt University Medical Center, health professionals have at their disposal a predictive model that uses EHRs to identify patients at risk.
As it’s impossible to screen all the institution’s patients for potential suicidal factors, this technology is the first filter that triggers the alert to assist certain patients with mental health care and further screening.
5. Medical imaging
Automating medical image analysis is another impactful use case of Predictive Analytics. Besides saving resources, the increase in efficiency is radical. In the field of radiology, for example, AI models can detect anatomical changes in patients or disease-specific markers through X-ray image data to predict breast or lung diseases early.
6. Appointment no-shows forecast
Predictive modeling tools can help hospitals, clinics, and individual practitioners predict which patients are more likely to skip an appointment without previous notice, improving cost efficiency, time management, and quality of service.
A great example is the Predictive Analytics tool that Duke University used to identify nearly 5000 appointment no-shows using EHR data. After identifying these cases, healthcare organizations can also reach out to patients prone to skipping to ensure they are fine and offer remote consultation.
7. Equipment maintenance
As in other industries such as Retail or Automotive, Predictive Analytics can spot healthcare equipment maintenance needs before machines break down due to wear out or degradation. For instance, hospitals can analyze data from sensors in MRI machines to get ahead of failures and replace a component outside service hours to avoid disrupting workflow for both patients and health professionals.
These case uses of Predictive Analytics in healthcare show this discipline’s huge impact on industrial processes and, more importantly, on people’s lives.
The power of predictive health analytics
It’s amazing how far AI and other data management technologies can go for industries like this, which operate massive amounts of data with high risks and responsibility. At Vanguard X, we know that in today’s world, big data analytics is driving everything we do across all industries.