With such a data-rich industry as healthcare, it’s no wonder that many organizations are leaning in on big data and analytics to transform the way care is delivered. From medical records, medical images, insurance claims, medical devices, administrative data, and more, data-driven healthcare organizations can now harness the power of big data and analytics to make life-altering changes regarding patient treatment, research, and more.
Predictive analytics is one of the ways organizations benefit from gathering and analyzing so much data. This branch of advanced analytics makes predictions about future outcomes using historical data combined with statistical modeling and other tools such as artificial intelligence and machine learning. In fact, a survey by the Society of Actuaries found 89% of healthcare providers either already use the predictive modeling methods or are planning to implement them in the future.
The benefits of predictive analytics in healthcare don’t just mean better patient care, either. With that in mind, let’s take a closer look at the importance of predictive analytics in healthcare.
Identifying Patient Status
A major benefit of predictive analytics in healthcare comes down to staying one step ahead with patient status. Early detection of diseases and health progression has the potential for saving lives, resources, and money.
For instance, Emory University researchers built an artificial intelligence engine to predict sepsis in intensive care patients. The engine was designed on Google Cloud using an integrated set of Google Cloud and open-source tools, like TensorFlow. This provided the Emory University research team with almost instantaneous processing of data input, predictive analysis, and output to the front-end interface. Utilizing the engine, healthcare teams are able to make meaningful decisions to better the lives of patients who may become at-risk in the future for this condition.
As we all know, healthcare for patients can often come down to quick decisions. With predictive analytics, doctors and the like are able to predict and take the guesswork out of decisions that are time-sensitive.
The vast quantity of valuable data gathered by providers and the like can be overwhelming but also showcases incredible potential in using big data to individualize medical treatment better now and in the future.
Optimizing Workflows and Scheduling
Predictive analytics is also beneficial with the more administrative side of healthcare. Poor scheduling and workflow management can lead to extreme workloads and needless downtime. With the benefits of predictive analytics in healthcare, organizations can better predict patient traffic to determine peak times and therefore schedule at optimal capacity.
Analytics seasonality, common patterns, and clinic capacity, providers can also prevent patient leakage. Patient leakage, also known as referral leakage and patient referral leakage, describes when a patient seeks or obtains healthcare services outside the hospital or practice network. This can lead to lost revenue and negative patient health outcomes.
Predictive analytics assists providers to retain patients in-network and also improves the operational efficiency of the organization.
Improving Supply Chain Management
It’s no secret that the healthcare industry relies on supplies and equipment being optimally managed. Items such as pacemakers, patient-monitoring systems, MRI machines, and diagnostic imaging equipment are essential to providing care efficiently. So, managing these supply chains is of the utmost importance.
Another major benefit of predictive analytics in healthcare is the ability to determine spend and allocate budgets, which can save organizations hundreds of thousands of dollars in the long run. Utilizing predictive analytics takes the guesswork out of major decisions such as how to negotiate price, reduce the variation in supplies, and optimize the ordering process.
Equipment Management
Speaking of healthcare equipment, predictive analytics also benefits the management of these tools. Medical and clinical equipment degrade over time through regular use. Predictive analytics can be used to determine when equipment is in need of maintenance or replacement, which then minimizes downtime.
Unplanned downtime of an MRI machine, for example, can lead to suboptimal patient care and even patient leakage. Therefore, staying one step ahead in determining the longevity of equipment is essential for both the patient’s sake and the revenue stream for an organization.
Monitoring Fraud
For healthcare insurance providers, monitoring fraud is an essential part of the business. The National Health Care Anti-Fraud Association (NHCAA) estimates that the financial losses due to health care fraud are between 3% up to 10% of funds spent on healthcare, amounting to a sum of up to $300 billion.
Predictive analytics solutions have the potential to determine deliberate healthcare fraud, but also unintentional errors in data. Algorithms powered by machine learning can be utilized to flag suspicious claims for additional review and determine if there is malicious intent behind the case early on.
Healthcare fraud doesn’t just harm providers, either. In fact, health care fraud inevitably translates into higher premiums and out-of-pocket expenses for consumers, as well as reduced benefits or coverage
Ultimately, the ability to examine all actions on a company’s network in real-time to pinpoint abnormalities that indicate fraud and security breaches can save funds and resources for insurance providers.
Benefits of Predictive Analytics in Healthcare
The healthcare industry continues to grow and adopt cloud-based solutions to better various aspects of the industry as a whole. While predictive analytics is only a small part of an organization’s digital transformation, the solution can severely impact factors such as patient care and revenue.
The potential to change the healthcare industry for the better through big data and predictive analytics is evident. Yet for some organizations, the complexities and data compliance regulations can be a deterrent in adopting data-driven solutions.
Emory University’s successful predictive analysis objective is just one example of the expansive Google Cloud Platform. As Google Cloud Premier Partner, Pandera has proven expertise in advanced technologies, broad competencies, and proven methodology. Pandera’s solutions have enabled healthcare companies to achieve business objectives and ensured sustainable success, all through Google-powered tools and processes.
We help healthcare organizations gain valuable business insight into their clinical, financial and
operational processes by extending intelligence across multiple channels and improving business profitability and compliance by increasing data transparency, detecting market trends, and integrating predictive analytics.
Our Landing Zone Accelerator is designed as a readily available, enterprise-grade healthcare solution to meet your cloud transformation objectives.
To learn more about Pandera and work towards data-driven goals like Emory University, please contact us today!