Introduction
Over the past couple of years, there has been a substantial rise in the burden of chronic conditions and treatment costs, along with the growing elderly population, which is transforming the healthcare sector at a rapid pace. As per a study, healthcare spending across the globe is anticipated to reach an unprecedented value to total US$ 18.3 trillion by 2030. In response to these trends, volume-based payment models are being replaced by outcome- or value-based models.
Predictive analytics helps health organizations to get in line with these new models and improve patient care and outcomes. From predicting critical conditions such as heart failure and septic shock to preventing readmissions, the recent advancements in big data analytics are boosting the adoption of new predictive analytics solutions that aid clinicians improve outcomes and cut costs.
Predictive analytics in healthcare is most helpful with clinical care, administrative tasks, and managing operations. More importantly, the technology is already making a difference in a wide range of healthcare settings, from small private doctor’s offices and large academic hospitals to healthcare insurance companies.
How is Growing Healthcare Data Favoring the Penetration of Predictive Analytics?
The growing inclination toward digitalization in the healthcare industry has led to the creation of huge new data sets. These include radiology images, electronic medical record (EMR) systems, lab results, and health claims data. The amount of data is expected to reach new avenues with increasing genomics and cytogenesis research data in the near future.
New data is being generated and collected by the novel medical devices at the edge, such as monitors and patient wearables. In addition, outside the healthcare setting, patients are generating quasi-health data through the use of health monitoring applications, fitness trackers, and personal wearable devices.
By using data from these sources, health care providers can find new ways to use predictive modeling for health risks, predictive analytics for medical diagnosis, and prescriptive analytics for personalized medicine.
Predictive analytics has become a crucial component of any strategy for health analytics. Today, it’s an essential tool for measuring, combining, and making sense of biometric, psychosocial, and behavioral data that wasn’t available or was very hard to get a hold of until recently. Here are some of the applications of predictive analytics for healthcare
- Identifying Patients at Risk
- Clinical Predictions
- Disease Progression and Comorbidities
- Predicting Length of Stay
- Speeding Treatment of Critical Conditions
- Reducing Readmissions
The Future Story
With the growing prominence of innovative technologies across the healthcare industry, a number of health IT providers are focusing on developing their own analytics software and engines to assist healthcare spaces deliver optimal patient care.
For instance, in 2020, Eversana, a U.S.-based provider of innovative solutions to the life sciences industry, announced the introduction of its ACTICS predictive analytics solution, which enables clinical spaces to combine multiple data sources into a single comprehensive system.
Also, some U.S. companies are partnering with healthcare institutions to develop proprietary algorithms designed to enhance organizational performance, improve clinical care, and increase operational efficiency. Such developments are projected to increase the popularity of predictive analytics solutions in the healthcare sector in the coming years.
