As Google entered healthcare, it expended much on its expertise in AI. Health data is being highly digitized and structured, things are being updated from a new electronic record standard to imaging to DNA sequencing. Google is helping both ways i.e it speeds up the process by creating new means of ingesting health data and betting that it can use AI to make sense of the data more efficiently and more accurately than current methods. It has been reported by Google that around 7 percent of its daily searches are health related, and it receives around 1 billion health questions every day, or 70,000 health-related searches each minute.
Did you know? Google Health completed its acquisition of U.K. startup DeepMind, which was initiated for $500 million in 2014. DeepMind focuses on artificial intelligence research and mobile tools to improve patient care and clinical workflows. DeepMind now has access to Google’s app development, data security and cloud-storage expertise.
Google partnered with company named Verily in February for a program which is is working on detecting diabetic retinopathy via a partnership with Nikon’s subsidiary Optos, which makes the machines for retinal imaging tests and eye disease detection. It isn’t only focused on detecting eye diseases, but also on potentially fixing certain diseases as well. After the analytics in the frequency of words in Verily’s granted patents revealed it has consistently filed patents related to contacts and eye implants.
Google is approaching data generation and heart condition monitoring currently in two best ways. One of them is via the Study Watch, produced by Verily and used by researchers to monitor different biomarkers of study participants. This includes both an electrocardiogram (ECG) and heart rate monitor, which researchers can use to help detect anomalies earlier and to better understand what other factors might lead to or be precursors to heart episodes. This could help in identifying better predictors of heart disease earlier in the development of the condition. Other one is for a passive heart monitor using optical sensors and machine vision that seems more catered towards the everyday person.
Keeping in mind that one of the biggest challenges in healthcare is that data is heavily siloed and there’s very little interoperability between systems. Integrating data across differing EMRs is quite challenging even within the same hospital, not to mention data across mobile apps, connected devices, and other health-tracking products. In fact, while 79% of doctors believe that having all available patient data in one place is critical to their jobs, only 14% could access EMR information across different departments, patient care centers, etc., even within the same hospital. Google stated s part of the solution by powering a new data infrastructure layer with below mentioned 3 key efforts:
- Create new data pipes for health giants
- Push Google Cloud
- Build Google’s own healthcare datasets for third parties