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What Happens When an Unstoppable Force Meets an Immovable Object?
This paradox plays out in many high-stakes global arenas—geopolitics, climate change, and financial markets, to name a few. Artificial intelligence thrives on massive disparate data sets, so it is not surprising that these data-dense megatrends are being shaped by artificial intelligence1 Although most humans do not face election, live near receding glaciers, or trade in cryptocurrencies, we are all learning that when manmade forces and intelligent machines collide, the costs and consequences can be real.

So it is with human health data.

Humans share 1 inherent right and bear 1 attendant risk—good health. In an inexorably older and progressively sicker world,2 another potential cataclysm confronts patients and physicians—the silent explosion of health data. The uses and fates of these health data, which will have grown from an estimated 153 exabytes in 2013 to 2314 exabytes by 2020 in the United States alone, are already being influenced by artificial intelligence.3 Medical students and trainees learn humanistic principles based on the Hippocratic precept, Primum non nocere (‘First do no harm’). Will humans use intelligent machines wisely, using big data responsibly for health care and medical training? Or will their power and immensity paradoxically produce unintended harms?

The expanding digital health data universe is the unstoppable force saturating the Cloud with big data droplets.

Individual personal health information resides in and fluxes through 2 types of data repositories. Administrative health care databases are operated by entities responsible for resourcing care and managing costs in socialized and quasi-market health insurance systems. Administrative health care databases are massive multigenerational payloads of demographic and utilization data (ie, pharmacy, physician, ambulatory, and hospital services)4 linked to multiple electronic medical records (EMRs) and increasingly homed in Cloud platforms compliant with the Health Insurance Portability and Accountability Act. Despite informatician and analyst expertise, their complexity and the density of their data defy standard statistical methods, limiting their applications to health care process and utilization management.

 CLEF = Cross Language Evaluation Forum; CNN = convolutional neural network; LSTM = long short-term memory unit; METEOR = Metric for Evaluation of Translation with Explicit Ordering; MS COCO = Microsoft Coco framework; RNN = recurrent neural network.

The Big Health Data–Intelligent Machine Paradox

What Happens When an Unstoppable Force Meets an Immovable Object? This paradox plays out in many high-stakes global arenas—geopolitics, climate change, and financial markets, to name a few. Artificial intelligence thrives on massive disparate data sets, so it is not surprising that these data-dense megatrends are being shaped by artificial intelligence1 Although most humans do not […]

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Returning to (Electronic) Health Records That Guide and Teach

Over the last 2 decades, medical records in the United States have undergone rapid digitization. Unfortunately, electronic health records have not delivered on their promise of improving clinical workflow, or even information access, given the lack of system interoperability.1 With the implementation of the Health Information Technology for Economic and Clinical Act in 2009, legislative mandates […]

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Teaching Diagnostic Reasoning in the Digital Age: The Role of the Mentor

Each case has its lesson—a lesson that may be, but is not always, learnt, for clinical wisdom is not the equivalent of experience. A man who has seen 500 cases of pneumonia may not have the understanding of the disease which comes with an intelligent study of a score of cases, so different are knowledge […]

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Humanizing the Electronic Medical Record with the Personal Fact

The current model of patient care promotes a system wherein patients are viewed as diseases with treatment plans rather than individuals with a lifetime of experiences. This is further exaggerated by the electronic medical record, wherein streams of impersonal data are presented above the patient narrative. Quantitative data are crucial for medical management, but it […]

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Using the Electronic Medical Record to Identify Patients at High Risk for Frequent Emergency Department Visits and High System Costs

A small proportion of patients account for a high proportion of healthcare use. Accurate preemptive identification may facilitate tailored intervention. We sought to determine whether machine learning techniques using text from a family practice electronic medical record can be used to predict future high emergency department use and total costs by patients who are not […]

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Best Practice Advisories Should Not Replace Good Clinical Acumen

Electronic medical records (EMR) have revolutionized clinical practice. They facilitate documentation, improve communication and coordination of care among medical providers, and afford a manner of communication for patients, as well as provide a platform for the storage of large quantities of clinical data. Several of these qualities, in addition to federal incentives for their use, […]

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