Healthcare is at a turning point, which is being powered by tech advances and a shift toward personalized medicine. At the core of this change is predictive analytics which is the use of data, statistical algorithms, and machine learning to predict future results.
As health care becomes a richer data environment predictive analytics is coming forth as a key tool for very personal, proactive, and effective care.
What Is Predictive Analytics in Healthcare?
Predict in the health care field we see the use of what is past and present patient data to identify trends which in turn predict future health issues. We use data from electronic health records (EHRs), wearables, genetic info, and also social determinants of health to determine risk, put forth interventions, and improve clinical decision making.
Instead of reacting to health issues as they come up which is what we usually do — we now have predictive analytics that take a pro active role. It allows health care providers to identify illness before they happen, to prevent complications, and to tailor treatments for each person.
How Predictive Analytics Powers Personalized Healthcare
- Early Disease Detection:
Predict in what is to come that which patients are at great risk for chronic diseases like diabetes, heart disease or cancer which we may see before symptoms present at all. Through this early recognition of warning signs, we health care providers are able to put into practice lifestyle changes for our patients, order up screenings, and bring in preventive therapies which in turn see great improvement in health outcomes.
- Tailored Treatment Plans:
No two patients present the same. We see that predictive analytics which looks at genetic material, medical background, and how they respond to treatment may put together the best care plans for each individual. This in turn leads to better success rates and reduced side effects of treatments.
- Hospital Readmission Reduction:
Hospitals are using predictive analytics to determine which patients are at greatest risk for readmission. We identify and flag high risk individuals which in turn care teams target with their post discharge support like home visits, telehealth follow up, and medication management which in turn reduces the incidence of unnecessary readmissions and health care costs.
Optimized Resource Allocation:
Hospitals and clinics that use predictive analytics are able to better predict patient admission rates, equipment use, and staff requirements. What we see is that facilities which implement this technology are better able to put resources where they are needed, which in turn improves patient care and operational efficiency.
- Remote Monitoring and Continuous Care:
Wearable technologies and remote monitoring tools produce a constant flow of health data. As it comes in, predictive analytics is applied in real time, which in turn alerts health care providers to health issues as they arise. In terms of chronic conditions, we see which patients benefit from the around-the-clock personal care, thus reducing the need for frequent hospital visits.
Challenges to Overcome
While in the predictive analytics field we see great promise there is also what is to be managed. We have large issues with data privacy and security, which health care info is a part of.
Also, we see that bias in what we put into the models which in turn may cause care to be dispensed in a disparate way if we do not pay close attention to it. Also we put in that predictive analytics into the clinical practices’ routine is a large ask in terms of tech integration and also we had to train staff very well.
Healthcare entities also have a responsibility to be open patients and providers should see how predictions are made and what data is used.
