March 2, 2020
Over the past decade, there have been innumerable discussions about the potential benefits of predictive analytics in healthcare, and how this technology can renovate the entire game for patients and healthcare providers.
From reducing costs to significantly saving time and effort, the use of predictive analytics in the healthcare industry can be boundless. It is rising at a fast pace as an integral pillar of operating a healthcare facility, rather than a luxury. From insurers and hospitals to patients, predictive analytics undoubtedly benefit all stakeholders as it anticipates the future based on valuable data in real-time.
How Predictive Analytics Works in Healthcare
Machine learning and artificial intelligence are manifesting upon most industries, including healthcare. By leveraging these emerging technologies, predictive solutions can make use of old historical data and real-time data, to develop insights and predict the future.
Benefits of Predictive Analytics for the Healthcare Sector
Powered by machine learning, predictive analytics has plenty of benefits depending on their use cases.
While humans cannot compare millions of data sets, predictive analytics solutions can. By analyzing historical data, intelligent solutions have the potential to pinpoint risk at a higher accuracy rate as compared to human diagnosis.
Note that the predictive analytics suite does not intend to replace the doctor but assist them and other healthcare professionals in analyzing the most possible risks and diagnoses. This is especially true for diseases that are challenging to identify and anticipate, such as heart disease.
According to the American Heart Association, cardiovascular disease costs amounted to $555 billion in 2016, and are predicted to skyrocket to $1.1 trillion in 2035. Heart failure, an ambiguous disease that can be masked as any other illness, is one of the costliest health operations and can be identified in the early stages with predictive analytics. The tricky part about the heart-disease is if we could predict the risk of a heart attack. The branch of statistics that has conventionally dealt with this kind of events is called the “Survival Analysis” where the probability of an “event of interest” is estimated using data both across the population (or a cohort) and that of an individual. However, just like any other conventional statistical methods, the above method may overlook certain patterns and correlations in the data unless explicitly taken care of in the models created. Here predictive analytics along with machine learning models could be of great value. While ML models tend to be more of a black box, predictive analytics attempts to look at explaining the reasons as well.
Suffice it to say, healthcare providers can significantly reduce operational costs by leveraging predictive analytics.
Predictive solutions have the ability to anticipate the future, thus decreasing the need for searching through heaps of appointments. It also enables healthcare providers to decrease costs used for preventable operations by identifying diseases at their early stages.
By identifying trends and busy operational timings, a predictive analytics model can optimize the number of employees needed during specific operational hours. This can also be applied for maintaining resources, in order to prevent any shortages in beds or medical equipment, enabling healthcare facilities to run more smoothly, and at an optimal level.
A machine learning solution can predict whether a patient will show for an appointment or not. This is made possible by recording a patient’s ‘show’ history or a hospital’s average show and no-show analytics.
Overbooking occurs in hospitals by the hospital operators in case a patient is expected not to show up for an appointment. Traditionally, due to overbooking, patients have to wait for long periods of time at hospitals. It is further complicated by the non-availability of doctors in a certain time or in other cases the overflow of patients from their original booked slots into successive slots thereby making doctors work overtime. However, with predictive analytics, a healthcare facility can predict with some confidence whether a patient will show up for an appointment or not thereby advising the operator whether overbooking could be done. This can offer innumerable advantages not only for the patient but for the facility as well.
As predictive analytics is used in decreasing the appointment wait time for patients as well as predicting potential diseases during the early stages, this can guarantee an increased level of satisfaction for patients.
If a patient were to choose from a hospital that does not have predictive analytics and a hospital that does utilize this technology, it’s highly likely that the patient would choose the latter. This is due to the fact that predictive analytics in healthcare can offer reduced wait time for patient’s appointments.
While patients may not be aware of whether a healthcare facility uses predictive analytics or not, they will notice the difference through experience.
Does Your Healthcare Facility Utilize Predictive Analytics?
There are ample benefits of predictive analytics including, but not limited to, boosting your facility’s operations while enhancing both customer and employee satisfaction.
At Saal, we offer a no-show predictor that utilizes the power of artificial intelligence to forecast which patient would show for an appointment and which patient would not. Our solution also provides data-backed recommendations for your employees not only to help convert these no-show patients into show-patients but also to handle their bookings in the most effective way possible.