Diagnostic Error Crisis

Big Data Versus Doctors

The US healthcare system is facing both a physician workforce and diagnostic error crisis, and legacy information technology systems are a major culprit. Legacy silos and interfaces impede physicians’ cognitive processes and communication. Further, these systems place healthcare professionals under increasing stress with the explosion of big data. According to published research, the amount of healthcare big data has exploded at a rate of roughly 10-fold over the last 20 years, while the number of doctors has increased only 2-fold. Diagnostic errors negatively impact the health and safety of patients, but also cost the US economy billions of dollars annually.


Annual Cost To The Usa

  • Physician work burn-out rates up to ~60%
  • 15% of patients experience diagnostic errors
  • 1 in 30 cancers are misdiagnosed
  • 1 in 5 cancers are misclassified
  • 10% of patients deaths are caused by diagnostic errors
  • cancer diagnostic error rates may be increasing
  • 44% of radiology malpractice claims for diagnostic errors are cancer-related

REFERENCE LINKS: (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11)

Led by physicians with extensive direct clinical experience, Cubismi’s team of top experts and designers has deeply examined how legacy systems and interfaces are impacting physician users’ decision-making processes. We have investigated how these pains are being exacerbated by big data. We have identified four major underlying culprits:


Interfaces for healthcare IT remain grossly outdated. According to research, most all leading Electronic Health Record (EHR) interfaces earn an “F” rating from doctors. Legacy PACS interfaces used by Radiologists allow mostly viewing of only 2D slice images with limited 3D modeling, and provide physicians very limited interactivity with patient data.


Doctors today expend vast hours to simply gather the data they need to make each decision. Due to interoperability limitations, physicians are forced to perform “cognitive interoperability.”  Critical patient data is often not available or must be faxed between health systems. Legacy interface chaos also forces doctors to surf through datasets in separate data silos, each with separate interfaces and logins.


Radiology reports and other types of medical reports remain in legacy text formats, often stored as separate PDF files in legacy systems. Legacy text reports often generate confusion and unwarranted anxiety for patients. Worse, legacy text formats are a poor form of physician communication and can be a source of diagnostic errors and slow physician data gathering.


Diagnoses today are still primarily made only by subjective “impressions” based on a doctor’s wealth of expertise and experience. No tool has yet been significantly adopted in clinical practice to allow doctors, such as Radiologists, to quantify their diagnoses using modern algorithmic tools.  Legacy system designs impede adoption of critically-needed advanced algorithmic tools.

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Cubismi is not

In recent years, many groups have attempted to use “artificial intelligence” “black box” algorithms (aka, neural networks) on single medical images to automate diagnostic decisions made by Radiologists today, coined “AI Automation.” Although it has proven powerful in closed-system training, these brittle algorithms perform poorly on complex tasks in the open and less predictable environments of clinical medicine. (AI Automation algorithms perform well on simple tasks where they can be used safely, such as for anatomy segmentations.)  Further, trained algorithms for complex tasks show poor transferability from one healthcare system to another, and are not interpretable by medical experts. In a recent ACR survey, 93% of radiologists say these tools are unreliable in open-system clinical medicine. Top experts believe it will take >10 years to safely implement AI Automation for complex tasks into healthcare, leaving it in the “trough of disillusionment” on the Gartner Hype Cycle.  Further, recent studies support the experience of physicians that AI Automation latched onto legacy system designs actually increase their data harvesting times.

Yet, “Radiomics” research also supports evidence that patterns of big data from medical images, sometimes combined with other data such as genetics, can provide biometric predictions of patient outcomes, such as the likelihood of response to a certain drug treatment. However, these methods use outdated standard segmentations that have been shown repeatedly to lack even the most basic measure reproducibility. Radiomics and Radiogenomics have thus seen limited successful translation into clinical medicine.

We know that powerful new big data technology provides a great opportunity to solve massive problems. Failure is simply not an option.

Reference Links: (14) (15) (16) (17) (18) (19) (20) (21) (22) (23) (24) (25

Everyone deserves better.
Doctors. Hospitals.
And especially patients.

We Can Solve It. Together.