It’s difficult to achieve your objectives when the goal posts are always in motion. Yet that’s often reality for the healthcare industry. Ongoing changes in competition, innovation, regulation, and care standards demand real-time insight. Otherwise, it’s all too easy to miss watershed moments to change, evolve, and thrive.
Advanced analytics are often presented as the answer to reveal these hidden patterns, trends, or predictive insights. Yet when spoken about in an abstract or technical way, it’s hard to imagine the tangible impact that unspecified data can have on your organization. Here are some of the real world use cases of big data analytics in healthcare, showing the valuable and actionable intelligence within your reach.
Improve Preventative Care
It’s been reported that six in ten Americans suffer from chronic diseases that impact their quality of life – many of which are preventable. Early identification and mediation reduce risk of long-term health problems, but only if organizations can accurately identify vulnerable patients or members. The success of risk scoring depends on a tightrope walk exploring populace overviews and individual specifics – a feat that depends on a holistic view of each patient or member.
A wide range of data contributes to risk scoring (patient/member records, social health determinants, etc.) and implementation (service utilization, outreach results, etc.). With data contained in an accessible, centralized infrastructure, organizations can pinpoint at-risk individuals and determine how best to motivate their participation in their preventive care. This can reduce instances of diabetes, heart disease, and other preventable ailments.
Encouraging healthy choices and self-care is just one potential example. Big data analytics has also proven an effective solution for preventing expensive 30-day hospital readmissions. Researchers at the University of Washington Tacoma used a predictive analytics model on clinical data and demographics metrics to predict the return of congestive heart failure patients with accurate results.
From there, other organizations have repurposed the same algorithmic framework to identify other preventable health issues and reduce readmission related costs. One Chicago-based health system implemented a data-driven nutrition risk assessment that identified those patients at risk for readmissions. With that insight, they employed programs that combated patient malnutrition, cut readmissions, and saved $4.8 million. Those are huge results from one data set.
Boost Operational Efficiency
It’s well known that healthcare administrative costs in the United States are excessive. But it’s hard to keep your jaw from hitting the floor when you learn Canadian practices spend 27% of what U.S. organization do for the same claims processing. That’s a clear sign of operational waste, yet one that doesn’t automatically illuminate the worst offenders. Organizations can shine a light on wastage with proper healthcare analytics and data visualizations.
For instance, the right analytics and BI platform is capable of accelerating improvements. It can cross-reference patient intake data, record keeping habits, billing and insurance related costs, supply chain expenses, employee schedules, and other data points to extract often hidden insight. With BI visualization tools, you can obtain actionable insight and make adjustments in a range of different functions and practices.
Additionally, predictive analytics solutions can help to improve the forecasting of both provider organizations. For healthcare providers, a predictive model can help anticipate fluctuations in patient flow, enabling an appropriate workforce response to patient volume. Superior forecasting at this level manages to reduce two types of waste: in labor dollars from overscheduling and diminished productivity from under-scheduling.
Enhance Insurance Plan Designs
There is a distinct analytics opportunity for payers, third-party administrators, and brokers: enhancing their insurance plan designs. Most businesses expect their healthcare costs to rise by 6% this year if they do nothing to reduce costs. Whether you want to retain or acquire customers, your organization’s ability to provide a more competitive and customized plan than the competition will be a game-changer.
All of the complicated factors that contribute to the design of an effective insurance plan can be streamlined. Though most organizations have lots of data, it can be difficult to discern the big picture. But machine learning programs have the ability to take integrated data sources such as demographics, existing benefit plans, medical and prescription claims, risk scoring, and other attributes to build an ideal individualized program. The end result? Organizations are better at catering to your members and controlling costs.
Plenty of Other Use Cases Exist
And these are just a sample of what’s possible. Though there are still new and exciting ways you can analyze your data, there are also plenty of pre-existing roadmaps to elicit incredible results for your business. To get the greatest ROI, your organization needs guidance through the full potential of these groundbreaking capabilities.
Want to explore the possibilities of data analytics in healthcare situations? Learn more about our healthcare data analytics services and schedule a no-cost strategy session.