Advanced computing techniques are making their way into clinics, hospitals and life sciences companies. The availability of machine learning and artificial intelligence technology as part of cloud services has helped make sense of large amounts of patient data, with greater accuracy.
In a survey conducted by Amazon Web Services (AWS), 94% of healthcare providers said that AI could help address the industry’s existing business needs, such as reducing unnecessary spending, improving quality, and creating a better patient experience.
Predictive models at a large scale are already in existence. For example, how a pandemic can grow, etc. Hyper personalized models can help diagnose more accurately as well as prescribe precisely. Moreover, healthcare data, whether structured or unstructured, is an asset. All the data collected from multiple devices, wearables, or trackers can be aggregated and analyzed using cloud solutions. The collected data is fed into machine learning models, helping in training and improving accuracy over time. This increased accuracy, in return, helps care providers provide more personalized recommendations to their patients.
Organizations leverage the AI and ML models to say, predict an event in a patient’s life – (for example relapsing) – lot of models have to be built, tested and then tweaked. This takes a lot of computing power that is not necessarily available to every organization. While years of data is available, it is available as un-structured data. The conversion of un-structured data to structured data so that systems can use them to run predictive models needs modern technology and computational power. This is where cloud computing supplies organizations with both tools and the horsepower needed.
Though healthcare data has been collected for a very long period of time crunching it to extract usable information takes a lot of power. The cloud can perform that heavy lifting. We can safely and securely send data over the cloud and develop the ML models. We use the power of cloud computing to automatically read documents and create data sets that can be used by data scientists to train and build models. The tools needed to do these are available on the cloud.
Have a look at some useful use cases of AI in healthcare:
Using computer vision, time saved by physicians in analyzing reports and images can now be spent with patients to provide them personalized and fruitful advice. On the other hand, natural language processing is helping make sense of unstructured data, which had gone unexplored until now. Physicians record patient information in the notes section of the EMR. This unstructured information, if not mapped backed to EMR, is lost. With NLP algorithms, we can extract risk factors from these notes. Furthermore, predictive analytics can help calculate risk scores, predict appointment no-shows and patient utilization patterns (for optimal staffing and reducing wait times); even predicting medicine side effects based on a patient’s EHR data.
The list of use cases goes on but I believe, as is with any other technology implementation, it is of utmost importance to create a strong foundation for AI. Using cloud-computing services can ensure a future proof approach to AI innovation and increase the likelihood of succeeding across the healthcare field.
If you are actively looking for technology partners, data science experts, or planning a move towards value based care, we are here to help you extract as much value as possible from your existing data sets.