Which Healthcare Quality Improvement Frameworks Utilize Big Data Analytics?
Data analytics, particularly big data analytics, holds a great deal of promise where healthcare and clinical implications are concerned. While many frameworks exist to help healthcare practitioners to improve the quality of care, several quality improvement frameworks rely primarily on big data. While the following is by no means a comprehensive list or thorough analysis of modern healthcare data analytics projects, it should help explain why utilizing big data analytics is a benefit to the healthcare system at large. Though challenges still exist and must be overcome in order to know the full potential this technology holds, the following examples demonstrate in no uncertain terms that vast benefits to the healthcare system are already available for early adopters.
Big Data Analytics and Improved Staffing
Ask any shift manager at any hospital and they’ll agree that staffing is a notoriously challenging task. It’s more than just an administrative headache, though. Staffing is one of the most important tasks any healthcare facility undertakes. Knowing how many people to put on staff at any given time is absolutely critical. If there are too many people on shift, it results in wasted resources and unnecessary expenditures. If hospitals are overstaffed repeatedly, that can lead to expenses significant enough to warrant increased healthcare costs. What’s worse, if there are too few people on staff, it can be fatal. Without the support and human resources available to care for patients, people who are sick or in need of immediate medical attention can end up lost in a line or waiting in a room for too long. This can lead to serious medical consequences up to and including injury or death. Staffing a hospital is not like staffing a retail store. If it’s done incorrectly, people die.
That’s why many hospitals are taking on the challenge with big data analytics. A Forbes article from 2016 looked at the case of four Paris hospitals affiliated with Assistance Publique-Hopitaux de Paris. They are each implementing time series analysis techniques utilizing a decade of hospital admissions records to populate hourly predictions of how many patients will be at each hospital. That data is then displayed on an internet-based app allowing staff at the hospitals to plan for 15 days of admissions traffic.
The app is simple enough for anyone to use, even if they don’t have technical knowledge, and so far it has resulted in significant cost savings and improved customer service.
Real-Time Alerting Using Big Data Analytics and Cloud Technology
Several healthcare technology solutions have been developed recently that utilize a combination of cloud computing and big data analytics. These solutions are all aimed at providing real-time alerting to physicians and most are linked to wearable patient devices known as personal analytics devices. These devices range from blood pressure meters that alert doctors when a patient’s blood pressure is too low to products like Asthmapolis, which uses GPS tracking-enabled inhalers to populate individual, population, and even national usage trends. Combined with data from the CDC, this technology is paving the way for better patient care for those with asthma.
Predictive Analytics in Healthcare IT
The entire goal of healthcare business analytics and IT is to empower physicians and providers by enabling them to make data-driven decisions that improve patient care. Predictive analytics helps achieve this goal by providing cumulative information pulled from big data sources. An example of this is Optum Labs, a research collaborative that has been focusing on utilizing a collection of over 30 million Electronic Health Records (EHR) to create predictive analytics tools that can vastly improve healthcare delivery.
Social Media as a Source of Big Data
One area of data that’s often overlooked but very important to the overall picture of how the healthcare system is doing is patient sentiment data. In other words, what do current patients think about the level of care they’re receiving? Do they even seek treatment? Do they trust the system? This kind of data can be pivotal in healthcare data analytics because it tells researches what their audience cares about, and, fortunately, people tend to openly express their opinions on social media. While it’s important to take into consideration that these opinions are not always reliable or representative of the whole, when taken collectively, researchers can tease out what is accurate and which sentiments are universally held. That means they can make better-informed and well-targeted products and services to help address the needs their audience expresses. Utilizing social media is nothing new. Businesses do it all the time to figure out which TV you’re looking to buy or which movies would most appeal to you. However, utilizing that same data to find out how to better serve patients and improve customer service standards in healthcare can lead beyond profit to actual progress and public health improvement.
Benefits of Data Analytics and Predictive Technology
There’s not enough room in a single article to explain every framework in which big data analytics is currently being used to improve the quality of healthcare. However, the benefits of utilizing this technology are becoming obvious. From reducing health care costs to equipping physicians with real-time data, the future of big data analytics in a healthcare context is promising and exciting. If it’s true that knowledge is power, big data analytics just might be the most powerful healthcare quality improvement tool we as a society have ever had access to. While further testing, responsible analysis, and extensive problem-solving are still required to fully realize the potential of big data, it’s still an exciting opportunity for growth and next-level clinical care.
Great strides towards overall quality and performance improvement are being taken every day in the healthcare industry by utilizing big data analytics, collective information, teamwork, and predictive analysis. While this level of data analysis is still relatively new and its full implications won’t be quantifiable for many years, it’s not hard to see that even utilizing what we do know has already resulted in excellent results for patients in many subdisciplines of medicine. The next several years will be pivotal in understanding just how far this technology can go, which challenges will be the most formidable, and how ready the general public will be to accept new ways of monitoring and responding to healthcare concerns. However, it’s an exciting time that should benefit many people and help the healthcare industry reach new levels of quality care, research capabilities, and developmental abilities.