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Healthcare’s ‘valueless data’ problem

By Brian Robertson

Healthcare has a data problem.

The technological maturity of electronic health records (EHRs), data warehouses, and other sophisticated systems of record have enabled health systems and payers to absorb unthinkably massive quantities of data.

An annual research report by EMC and IDC predicts that the digital universe will contain 44 trillion gigabytes of data next year (roughly one byte of data for every star in the universe, with gigabytes to spare). Nearly a third of that data will be collected and stored by the healthcare industry, according to a Ponemon Institute study.
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Alarmingly, this flood of data will never crest. Dr. John Halamka, chief information officer of Beth Israel Deaconess Medical Center, predicted that every patient will add 4 MB of data to his or her EHR storage every year – and this was before the steady ascension of wearables, apps and other consumer devices.

The problem is that about 80 percent of this healthcare data is unstructured. Because those “dark” data elements are difficult to identify and apply to business or clinical challenges, they have very little inherent value. It’s like standing at the edge of a vast, churning ocean and your task is locating and securing specific drops of water. Clearly, an impossible task for any human.

But not for artificial intelligence (AI).

The argument for AI
The analysis and optimization of administrative and financial transactions, health records, or other complex and repetitive tasks can quickly subsume even the most innovative enterprises. High-performance machines and algorithms can examine complex, continuously growing data elements far faster, and capture insights more comprehensively than traditional or homegrown analytics tools.

AI has carved established inroads in multiple industries, including healthcare. Much of that innovation has been put to work solving clinical challenges, but more health system leaders are considering its value on the financial and consumer experience segments of their enterprise.

There are several reasons for this. First, clinical applicability is heavily regulated and strenuously tested for consistency, reliability, and safety. True, administrative and financial tasks bear similar risks, but those risks are not nearly as amplified. As such, AI can be more readily implemented in this area, and the results are more immediate.

Second, AI is ideally suited to tasks that are both repetitive and complex, a common attribute on the non-clinical side of health systems. When an AI solution completes a task, the outcome is evaluated, and lessons learned are applied to make the next task more efficient. This mimics human learning, only at a speed and scale far beyond what is possible for even the smartest individuals.
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