Digital Pathology Blog

This article is the third in a four-part series highlighting the evolution of digital pathology and its impact on the access to pathology services throughout the world. If you missed it you can find Part 1 here and Part 2 here.

This article is the second in a four part series highlighting the evolution of digital pathology and its impact on the access to pathology services throughout the world. If you missed it, you can find Part 1 here.

Many pathology practices associated with academic medical centers have begun using digital pathology to facilitate the sharing of cases through the use of internet services, private networks, and now, cloud technology. As the use of whole slide imaging (WSI) and digital pathology consultations becomes more widespread, hospitals can access experts in any pathology subspecialty from any location around the globe. Secure, web-based access to expert centers and remote subspecialist colleagues is now a reality.¹

This article is the first in a four part series highlighting the evolution of digital pathology and its impact on the access to pathology services throughout the world.

"In 2012, there were 14 million new cases of cancer reported and 8.2 million cancer-related deaths worldwide. The number of new cancer cases will rise to 22 million within the next two decades.”¹

With cancer among the leading causes of death worldwide, the demand for pathology services is becoming more critical. However, the number of new pathologists entering the field is decreasing while the projected demands of the patient population are expected to grow by more than 150% over the next 20 years.

25+ years ago, as a resident, the hard(er) part was looking at the slides and the easy part was writing the report. Before breast conserving therapies, when mastectomies were standard of care, for example, a report might read: “Breast, right, mastectomy: Invasive mammary carcinoma, grade 3, measuring 3.4 cm; deep margin and nipple negative for tumor.” With the header, all the necessary elements in the report, the logo, gross description and disclaimers you could get this onto 1 page. And have space left over. With large fonts (the initial MUMPS based laboratory information systems did not give you the option of 500+ font styles and types with bold, italics, etc…)

Quality Control (QC) and Quality Assurance (QA) activities in anatomic pathology are embedded into the daily work. These efforts are put into place to assist with error reduction and prevention and for improved patient safety. No one denies their value.

Digital pathology has played a role in laboratory workflows for decades. Early use of static images for teaching and tumor boards has grown to include today’s clinical applications including remote assisted fine needle aspirations and frozen sections and the use of whole slide images (WSI) for expert consultations and quality assurance (QA).

Late last month, the CEOs of Amazon, Buffett and JPMorgan Chase announced they were teaming up to form an independent healthcare company for their employees.

The details were scant and vague but mentioned the new corporation would be “free from profit-making incentives and constraints”. Mr. Buffett is quoted as saying, “The ballooning costs of health care act as a hungry tapeworm on the American economy… Our group does not come to this problem with answers. But we also do not accept it as inevitable.”

Probably not the best analogy considering tapeworms are easily treated with oral generic medications that are very low cost.

There is a lot of talk currently about artificial intelligence. AI- in short, is the ability for computing systems to be programmed to perform tasks AND to learn from those tasks to refine their abilities the next time a problem or question or decision is faced by the system tasked to perform the operation.

Until recently, the practice of pathology has been entirely “human-driven”.  Well-trained pathologists examine all tissues and arrive at diagnoses based on their application of learned criteria and experience.  However, it is well-known that the accuracy of human interpretation can be hampered by subjectivity (inter-observer variability), inconsistency (intra-observer variability), and fatigue. Recently, the rise of digital methods in pathology has led to a growing interest in applying artificial intelligence (AI) to aid or even improve on the analysis of medical specimens.  For the pathologist, AI has the potential to improve accuracy, productivity, and workflow by allowing the computer do what it does well: consume lots of data, recognize patterns, and perform automated analyses.  Objective and reproducible specimen examination, along with certainty  that all material has been “seen,” leads to greater accuracy.  AI can be trained to identify specific features and present them selectively to human observers, leading to prescreening (“guided screening”) of specimens and promising improved workflow and productivity.  And, finally, the “holy grail”: under the right circumstances, AI-driven systems can make novel observations of morphologic patterns in pathology specimens, potentially leading to new knowledge and ultimately computer-generated diagnosis.  In this blog, we describe our general approach to applying AI to digital pathology specimens, detail some of the important steps involved in establishing an AI workflow, and discuss the potential benefits of AI.

The American Recovery and Reinvestment Act (ARRA) 2009 introduced the Health Information Technology for Economical and Clinical Health (HITECH) Act with the aim to incentivize “meaningful” implementation and usage of Electronic Health Records (EHRs) throughout the nation. Under HITECH, two incentive programs for Medicare and Medicaid were introduced which allow eligible professionals (EPs) to claim thousands of dollars in incentives over several years, if they could meet the requirements set out for different stages of Meaningful Use.