Predictive modeling in healthcare – This is Something that Healthcare providers Must Consider
After the global pandemic hit, healthcare has become the need of the hour. It is a safe bet to say that we all have learned the phrase’s true meaning – health is wealth. Fortunately, with the rise in technological innovations, the healthcare industry seems to have been enhanced by leaps and bounds. However, today one cannot deny the significance of technology, especially when transforming the entire approach. Some of the common examples include – electronic health records, electronic medical data, personalized treatment using wearable technology, telehealth, surgical tech stack, the immense use of artificial intelligence and augmented reality, machine learning, predictive modeling, and predictive analytics, data analysis, precise clinical outcomes and what not! The following post emphasizes the significance of using predictive modeling in the healthcare industry and how healthcare providers can benefit from it?
About Predictive Analytics in healthcare
Healthcare providers have always been up for new advanced technologies and innovations; after all, they are responsible for saving the human clan now and then. As a result, accurate and precise software and applications can assist in taking the load off and reduce the unwanted pressure of making mistakes while diagnosing the patient. In the worst cases, one wrong analysis or medication can cost a life. Therefore, these healthcare providers have started incorporating predictive modeling and analytics in healthcare organizations to improve medical care.
Now, what exactly is predictive modeling in healthcare or predictive analytics? It is said that analytics are derived after conducting all the statistical methods, data mining, analyzing the previous and current data, and information/medical data analytics. These data assist healthcare professionals in offering the utmost care and favorable outcomes. What is this data, you may ask? Everything is included here, from the patient’s age to their social characteristics, allergic conditions, anatomy, susceptibility to diabetes, asthma, high blood pressure, and whatnot!
Now, do you think predictive modeling in healthcare is a new concept? Of course not! This one has been around a decade. It’s just that more and more healthcare organizations are found trending towards it these days; why? Simple – to gain a competitive advantage and trust their current and potential patients. Yes, it’s like killing two birds with one arrow.
Further, I would like to mention certain points stating the significance and predictive modeling or predictive analytics and why it must be the top priority for healthcare organizations across the globe.
Importance of predictive models or analytics among healthcare organizations
Now, why would anyone look forward to new advanced technologies? Simple it’s when they help solve either existing problems or make room for new and endless opportunities; this concept of predictive modeling in healthcare is applicable for both scenarios. Below, I would like to mention certain ways predictive models and predictive analytics are beneficial for the healthcare industry.
#1 Detecting fraud
One of the apparent benefits of using predictive models is that fraud or misdiagnosis can be detected earlier. When you combine more than one kind of analysis, you won’t just end up offering precision medicine but also prevent criminal behavior. At present, cybersecurity is something that must be taken into consideration. Healthcare professionals can examine all the actions in real-time and spot issues or abnormalities using high-performance behavioral analytics. Everything can be spotted right from fraud, zero-day vulnerabilities, etc.
#2 Improved operations
The next benefit offered by using predictive modeling is improved operations. It may quite interest you to know that not just healthcare but even the aeronautic industry uses predictive analytics to raise and lower the prices of airline tickets. Predictive analytics is being used everywhere, but it is used to forecast healthcare inventories and manage resources in the healthcare sector.
#3 Low chances of Risk
Another advantage of using predictive models is that risk is reduced significantly. You see here, the buyer’s likelihood is accessed with the help of credit scores. It is a number that incorporates all the relevant information or creditworthiness. Apart from this, it is even used for insurance claims and collections.
Apart from all this, predictive modeling or data analytics can result in the best patient outcomes. Since issues, frauds, and serious medical conditions can be detected early basis which automatically enables healthcare providers to predict clinical outcomes accurately.
Predictive Modeling Examples Worth Considering
#1 Predicting the Patient’s Flow
Most healthcare organizations have started incorporating software offering predictive modeling or predictive analytics. This software is well-integrated with the existing hospital management systems. By doing this, healthcare guys can easily analyze the patient’s behavior, keep tabs on their existing and changing patterns, and then suggest medications and treatment. By using this software, doctors get to know their patients better; they exactly know if the patient is visiting them regularly or skipping appointments
#2 Data Security concerns
Predictive analytics is often used to deal with the emerging security concerns of hospitals. Yes, right from identifying security risks to coming up with appropriate solutions is all you get using predictive tools. Cyber Attackers are getting wealthier and better. So it’s high time even we should be prepared. With the help of AI-enabled software, alerts can be raised if any uncommon patterns such as suspicious access or excessive information shared are detected.
#3 Medical Imaging
Did you know that predictive modeling in healthcare or using predictive tools can automate medical image analysis? By doing this, what happens is ample time, and staff resources can be saved. Also, not to mention that vulnerable patients are treated more proactively. From identifying disease-specific anatomical changes to any signs of COVID, anything can be identified in real-time. It has been proven that predictive modeling or analysis is also used to diagnose breast cancer and lung diseases.
And this is it! I can go on and on when it comes to healthcare predictive analytics. In fact, with the rise of such predictive analytics tools, Medical companies seem a bit relaxed as now they can improve patient outcomes or save lives with minimal risks and errors. So yes, advancement in technologies is the key to success here. You have to seek a reliable tech partner and leave the rest to them.