Page 113 - Building Big Data Applications

Chapter 5 Pharmacy industry applications and usage 109

The next segment of pharmaceutical industry usage of big data applications and
platform is in the integration of social media data, from different communities of pa-
tients who are working with them on several clinical trials and the experiences of the
patients with the therapies at home and nonhospital surroundings The application layer
integrates all the social media data, and it acquires the data with several tags that will
link it to the specific patient, and align their responses in the community portals to their
records. The patient data is aligned with all their sentiments, outcomes, recordings,
experiences, and overall responses. Once this data is loaded, there are several artificial
intelligence algorithms that work on interpreting the analytical outcomes and provide
several prediction and prescription models. The application layer uses the mashup and
leverages the data integration in the underlying infrastructure to create the magical
outcome. The social media integration has accelerated drug clinical trials and reduced
cycles from beyond 20 years in multiple phases to agile cycles with outcomes in less than
10 years at a maximum. It used to take forever to finish clinical trials, before the initial
release, and this has been changed for good.
The focus of pharmaceutical industry has shifted to patient and has transformed to
patient reactions and outcomes. The only way this focus has transformed is due to
several large research teams from IBM, Google, and other industries contributing
to several technologies, algorithms, neural networks, and more.

Google deep mind

Each scan and test result contain crucial information about whether a patient is at risk of
a serious condition, and what needs to be done. Being able to interpret that information
quickly and accurately is essential for hospitals to be able to save lives. AI systems could
be hugely beneficial in helping with this process. Rather than programming systems by
hand to recognize the potential signs of illness, which is often impossible given the
number of different factors at play, AI systems can be trained to learn how to interpret
test results for themselves. In time, they should also be able to learn which types of
treatments are most effective for individual patients.
This could enable a series of benefits across healthcare systems, including the
following:
1. Improved equality of access to care: Demands on these healthcare systems are felt
more acutely in certain areas of the world, and even within certain departments in
hospitals, than others. If we can train and use AI systems to provide world-class
diagnostic support, it should help provide more consistently excellent care.
2. Increased speed of care: We hope that AI technologies will provide quick initial as-
sessments of a patient to help clinicians priorities better, meaning patients go from
test to treatment faster.
3. Potential for new methods of diagnosis: AI has the potential to find new ways to
diagnose conditions, by uncovering and interpreting subtle relationships between