September 30, 2021
Imagine if healthcare organizations could solve some of the industry’s most challenging problems before they begin. Or estimate the likelihood of medical events before they happen, so healthcare professionals could make more informed decisions about how to deliver the best patient care. That’s the power of predictive analytics in healthcare.
Predictive analytics use a set of technologies including data mining, predictive modeling, and machine learning to help healthcare organizations learn from their data and make meaningful predictions about the future. From lowering readmission rates to predicting viral outbreaks, predictive analytics are the promising new frontier of healthcare AI. Thanks to advancements in artificial intelligence, algorithms can be fed large historical data sets — as well as real-time data — to make predictions that help healthcare providers enhance workflows and improve patient care.
Healthcare AI and predictive models have already played a big role in clinical medicine and have become invaluable to healthcare physicians and providers alike. And when it comes to patients, big data and predictive analytics have extraordinary potential to improve patient care.
1. Predicting and reducing patient no-shows
It’s no question — patient no-shows can negatively affect patient care and result in substantial reimbursement losses, wasted capacity, and underutilized medical resources. In fact, the healthcare industry loses more than $150 billion a year to no-shows alone.1 Today’s predictive models help identify and reduce patient no-shows and improve clinician workflows by predicting which patients may be likely to cancel so they can staff/schedule accordingly, send appointment reminders, and more.
2. Lowering hospital readmission rates
Preventing readmissions and improving patient outcomes is critical in meeting value-based care goals, leading many healthcare organizations to create a readmissions reduction strategy. By using predictive analytics, healthcare providers can predict which patients are at higher risk of being readmitted, allowing them to intervene early to reduce emergency room visits and hospital readmissions. As healthcare organizations continue to transition to the value-based payment model, these predictive models have become even more important in preventative care and delivering comprehensive care plans that are more personalized to healthcare patients.
3. Offering insights and clinical decision support
Healthcare data is unique and complex, and the amount of data sources providers have to navigate to determine the best treatment plan for a single patient can be exhausting. Through predictive analytics, healthcare providers can identify factors that will impact future health outcomes, develop risk scores for chronic diseases and tap into predictive algorithms to create more tailored patient care plans. By combining predictive analytics with EHR records and various data sets, healthcare providers can detect early warning signs of serious medical events and proactively prevent them from happening. Think actionable insights that actually streamline workflows and improve patient care.
4. Predicting patterns to inform utilization management (UM)
Utilization management, or UM for short, is defined as the evaluation of appropriate medical care according to evidence-based criteria and health payer guidelines. In simpler terms, utilization management helps manage the agility, cost and delivery of services at a hospital, coordinating resources in a way that ensures efficient care delivery. When healthcare utilization management is inefficient, it leads to frustrating care delays on the patient and provider side and increased operating expenses for the payer. Predictive analytics helps healthcare providers predict patterns in utilization management to help optimize staffing capacity, reduce patient wait times, and increase patient satisfaction.
5. Accelerating medical research with AI
Predictive analytics contribute to medical research in many ways, from determining how our bodies react to various chemicals to better understanding how promising new medicines may help combat certain medical conditions. One promising use case is using predictive analytics to anticipate the outcomes of clinical trials, leading to faster drug approvals and ultimately lower costs for new drug development. These predictions can help reduce the uncertainty around potentially life-saving drug development and enable better, faster drug approvals to improve the lives of the patients who may need them most.
As healthcare organizations face mounting pressure to deliver better patient care outcomes, predictive analytics are helping healthcare providers deliver preventive, more comprehensive care while driving efficiency and patient satisfaction. As the processing power of AI increases, the opportunities for predictive analytics to transform patient care are truly endless — and better predictions could save lives and make patient care decision-making more effective.
Want to learn how to apply predictive analytics in healthcare? Learn how other leading healthcare organizations are tapping into Olive for AI-powered clinical analytics to translate clinical data sets into actionable insights that improve care.
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