The technology toolkit employed in healthcare solution development contains many fascinating components. we now depend on new sensors for a myriad of health, wellness, and medical applications, wireless transmission standards, power sources, and algorithms used to transform data into actionable next steps. Analytics company GlobalData recently published a report, ‘The State of the Biopharmaceutical Industry – 2019,” which states that big data and artificial intelligence will have the greatest impact on healthcare overall and the pharmaceutical industry in particular over the coming year.
Big data and AI have a chicken-and-egg symbiosis. The resource costs of AI development find their justification, at least in a fiscal sense, with great volumes of work to accomplish. Big data without meaningful analysis is just research landfill. But when a sophisticated AI’s machine-learning neural network engines get cranking through massive metaphorical mounds of data, magic can happen. Not only does the process accomplish the initial analytical, diagnostic, or predictive tasks, but the depth and speed of analysis afforded by powerful CPUs and graphical processing units (GPUs) can uncover unforeseen relationships and correlations.
GlobalData’s report draws from a survey of global pharmaceutical industry participants. According to Bonnie Bain, GlobaData’s Global Head of Pharma, “Increasingly high volumes of data are required for all decisions and Big Data will not only alter the regulatory process as we know it, but payers will increasingly require this evidence as a prerequisite for reimbursement.”
According to the report, 38% of the pharma industry respondents think big data will have the greatest technology impact on their industry in 2019, followed by 32% who believe AI will be the greatest force.
GlobalData’s report focuses on the changes big data and AI will make on healthcare’s pharmaceutical sector, but it’s not a stretch to see similar effects in other segments of the healthcare system.
Core functions that will change most dramatically with big data and AI are discovery and design, clinical trials, electronic health records used to discover trends, lower treatment costs, and improve quality of life, healthcare intelligence to track patient statistics and vital signs, and facility and systems that will be better empowered to predict healthcare outcomes and design treatment plans.
Bain cautioned that choosing AI solutions that are correct for specific applications isn’t a simple matter; companies must understand how the various technologies work and how they can best help their needs. The best AI applications for big data processing are product innovation, revenue growth (not cost reduction), operational efficiency, and improved customer experience.
The carryover from GlobalData’s report to other healthcare sectors isn’t seamless. For one thing, custom AI applications to churn thru massive datasets aren’t appropriate for small healthcare organizations today due to required computing power and expert personnel. However, if Moore’s Law that processing power doubles and costs are halved every 18 to 24 months still holds, even if roughly, than the day isn’t too many years distant when one-person operations will have massive data analytical capability on a desktop computer, or more likely, on a mobile device.
