Partnership pledges to improve disease diagnosis and treatment

The companies claim that this project will lead to better outcomes, higher patient satisfaction, and lower costs a so-called ‘Triple Aim’.

Valentina Gburcik, PhD, CVMD Director at GlobalData, commented: “This big new alliance’s pledge will have a very positive impact on healthcare as it will become easier to share medical data among hospitals. Both physicians and patients will have easier access to information, which will lead to faster diagnosis and treatment. In the same time, the six tech giants will eventually profit from speeding up their AI research.

“In particular, this deal is good news for the companies’ AI sectors. If health data is fully standardised and interoperable, the ‘smarter’ AI can progress faster. Breaking down barriers between chunks of big data will create extremely large data sets, allowing extensive machine learning to boost AI effectiveness and revolutionise healthcare systems.”

The Information Technology Industry Council (ITI) issued a letter, signed by all six tech giants, which states that they are jointly committed to removing barriers for the adoption of technologies for healthcare interoperability.

In the letter, the technology giants also claimed that they will base this alliance on four foundational assumptions. The first one is about frictionless and safe exchange of healthcare data; the second covers healthcare data interoperability; the third looks at open standards, open specifications and open source tools; while the fourth is about commitment to actively engage ‘among open source and open standards communities for the development of healthcare standards, and conformity assessment to foster agility to account for the accelerated pace of innovation’.

There are currently over 100 companies that are applying AI algorithms and predictive analytics to healthcare, with the above mentioned six giants leading the way. The highly developed AI programs now have the increasing capacity to delve into big data, identify patterns, and generate algorithms to explain them.

Gburcik added: “These programs can help researchers generate more accurate hypotheses faster, making the drug discovery process less expensive and more effective. In addition, the database of electronic medical records and public health data can be analysed to identify hidden patterns that can lead to the quick identification of potential molecular targets for a disease.”