Tag Archives: Featured
man holding his chest

Advanced Therapies for Massive Pulmonary Embolism

The report by Secemsky et al1 in this issue of The American Journal of Medicine illustrates the current management of pulmonary embolism at the Massachusetts General Hospital (MGH). A Pulmonary Embolism Response Team sees all patients in whom pulmonary embolism is diagnosed by computed tomography. The team manages these patients during their hospitalization and after discharge for up […]

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Computed tomography of the chest demonstrated bilateral pulmonary nodules.

Not All It’s CrAged Up to Be: Disseminated Cryptococcosis

Phenomena known as cognitive biases, when applied to the details of a patient’s medical history, evidently steered the first attempts at diagnosis in the wrong direction. A 60-year-old man presented after 6 weeks of progressively worsening fevers, weight loss, malaise, night sweats, and confusion. Originally, the fevers were intermittent and low grade at 37.7°C (99.9°F). […]

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William H. Frishman, M.D.

Reform in House Staff Working Hours and Clinical Supervision

  When we were medical interns (WHF and JSA), training in 1969, the weekday work schedule on the ward services was 36 hours on and 12 hours off. Every other weekend, there was a shift from Saturday morning to late Monday afternoon, 56 straight hours. We were off 1 Sunday every 2 weeks. There were […]

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woman holding a hot drink

Effects of Coffee Intake on Incident Chronic Kidney Disease

Drinking coffee can raise public health problems, but the association between coffee and kidney disease is unknown. We studied whether coffee intake can affect the development of chronic kidney disease in the general population. Methods We analyzed 8717 subjects with normal renal function recruited from the Korean Genome and Epidemiology Study (KoGES) cohort. Based on […]

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Optimal Systolic Blood Pressure Target in Resistant and Non-Resistant Hypertension

Prior studies suggest benefits of blood pressure lowering on cardiovascular risk may be attenuated in patients with resistant hypertension compared with the general hypertensive population, but prospective data are lacking. Methods We assessed intensive (<120 mm Hg) versus standard (<140 mm Hg) systolic blood pressure targets on adverse outcome risk according to baseline resistant hypertension […]

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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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