Creating a Modern Data strategy to drive innovation and improve patient care.

Health data is a growing area

Today, the role of healthcare data is extremely important in delivering personalized medicine. As healthcare data grows and becomes increasingly diversified, physicians and radiologists need easy access to data that is seamlessly integrated, aggregated and visualized in applications and services across modalities and within their existing workflows. 

Enabling this vision requires a comprehensive data strategy that helps unify data in the cloud and use advanced analytics and machine learning tools to gain insights, accelerate decision making and improve patient care. In this note, we will discuss some of the key challenges facing healthcare organizations today and how technology and the cloud are enabling innovation to unlock the potential of scientific and healthcare data.

According to Taha Kass-Hout (CMO and Director of Machine Learning at AWS) in his dissertation at HIMSS 2022, it is necessary to use datasets from different subjects to mix them in the training of algorithms, for example, putting together structured with unstructured information (laboratories, images and genomics), always bearing in mind that Machine Learning studies must be very careful with the treatment of deviations (BIAS).

of the studies. It is necessary to pay special attention that the conclusions and recommendations do not affect the subsequent learning of the system. To generate operational efficiency, it is necessary to stop and think about flows that add value with Machine Learning in conjunction with Artificial Intelligence and, on the other hand, it is necessary to standardize, prepare and clean the information before applying an algorithm (or several) as they can take erroneous results. 

Taha Kass-Hout insists, as does Vignesh Shetty (GM of Edison AI + Platform GE Healthcare), that the technical statistical criteria must be well applied in order to be able to use mathematical conclusions as valid, otherwise they will not be. This "purity" of the mathematical process is very often repeated by the different speakers in different talks.

An equally repeated concept is that Artificial Intelligence and Machine Learning must be trained, used to compare and retrain, it is not a quick process. 

To perform this training , it is necessary to create a Data Lake so that the information it can provide is all together available to the algorithm. If it is in silos, the ideal result will not be obtained.

These are some of the key points to take into account when working with health data. The management of information, its processing and analysis are necessary stages for the strategy to be effective and help to make the best decisions.

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