How predictive analytics in healthcare could spot the untraceable
Do you believe that the application of new technologies in healthcare is not ad hoc and is necessary for the advancements in the many applications the sector offers? Well, you are not wrong. There is so much that these new findings could contribute to analyse models and unstructured data in finding answers that have long been hidden within the walls of healthcare. Difficulties that could simply be answered by using the right algorithms together with predictive analytics.
In an era where data has become the new oil, it is paramount to have the right techniques and tools for processing what is collected. Mainly because information extracted by correlation of data comes with a lot of valuable insights that could help make powerful, life-changing decisions. Imagine how it would be if two data sets having no straightforward connections were analysed together to give an absolutely miraculous finding? That’s right, this has become possible today thanks to the innovative technologies that have bolstered the many different industries across the world. And healthcare is one such industry that has immensely benefited.
How the world of healthcare is changing with predictive analytics
Healthcare is known to be one of the richest data pools that collect its data from many different sources. But what’s important to understand here is what we need all this data for, and what challenges, opportunities and threats healthcare is posed with.
Ageing, for instance, is one of the biggest challenges the industry is facing right now. It has created the need to understand the different conditions the elderly population might face with the biological changes they go through. In a study on healthy ageing by a group of researchers from the Italian National Research Council, they too point out that many of these health complications in the elderly are comorbid, which has made it difficult to understand their causes without proper processing of data. And as for their solution, they explain how “a multidisciplinary ICT-based approach” could help them find out ways to prevent such chronic conditions using “unobtrusive and pervasive sensors, interactive activities, and predictive analytics”. Elements that have the potential of collecting and processing data from both structured and unstructured sources to determine patterns that are impossible to have done in a biomedical laboratory.
On the other hand is the growing population, which has become prominent in many of the developed countries, thanks to their relatively good healthcare systems. However, the challenge thus faced by the sector is the proliferation of population health data sets. Especially knowing what to include, where to source them from and how to capitalise on them while being compliant with the different data protection policies imposed. The reason why we need so much data directs us to the boundless value data could add to predictive analytics. Because gone are the days when technology was simply used to present data. Now, with predictive analytics, doctors and surgeons could easily determine the probabilities of certain chronic conditions by looking at the predictive insights presented by these new data analytic systems. But that’s not all it can do. In addition to preventing diseases, Jennifer Bresnick in her blog on 10 High-Value Use Cases for Predictive Analytics in Healthcare points out nine other different ways that predictive analytics can be used in healthcare. For example, creating a good customer experience, financial savings, and even the security of data with the help of AI.
A different but common problem that doctors around the world have long been facing was genetically-adaptive antibiotic-resistant bacteria. These drug-resistant pathogens have caused people to suffer for many years and to lose millions of dollars in ineffective medication. However, the world has come to a point where we can now determine the presence of these bacteria in a sample. Thanks to a group of researchers from Washington State University who found out the possibility with the use of machine learning and game theory. Laying the bricks to a healthier world.
How SAS could help
When the world is spinning its way into predictive analysis, SAS integrates the use of embedded artificial intelligence (AI), image analytics and machine learning to help drive resourceful predictive information that doctors could now easily access. It has the ability to read data streamed from many health and non-health related sources, break them and sort into different sections, identify patterns in unrelated data, and present them in human-readable formats.
Data, as said above, are streamed from different sources such as electronic health records, wearables, social determinants, social media, genetics and diagnostics including Internet of Things (IoT) and Internet of Medical Things (IoMT). All that helps – largely due to their capability of real-time data streaming into the system. And to support this, SAS enables cloud computing as it increases agility and interoperability of data, which has also proved to be an effective way of cutting down data processing time by more than half of what it used to be.
SAS has, therefore, been able to help accelerate data analysis with the use of advanced technologies in a number of researches including Project Data Sphere led by Dr Howard Scher, an oncologist at Memorial Sloan Kettering. In that, they found unique patterns in blood that confirmed a treatment given for prostate cancer to be affecting the condition positively. Yet another breakthrough in this field thanks to data-driven image processing.
If you’d like to know more about how predictive analytics could benefit your organisation, speak to us.
