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Clinical Laboratories and Pathology Groups

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Clinical Laboratories and Pathology Groups

Hosted by Robert Michel
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Laboratory Leaders at 2024 Annual Executive War College Discuss Critical Challenges Facing Clinical Laboratory and Pathology Managers for 2024 and Beyond

Trifecta of forces at work that will affect the clinical laboratory and pathology industries have been described as a ‘perfect storm’ requiring lab and practice managers to be well informed

Digital pathology, artificial intelligence (AI) in healthcare, and the perfect storm of changing federal regulations, took centerstage at the 29th Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management in New Orleans this week, where more than 1,000 clinical laboratory and pathology leaders convened over three days.

This was the largest number of people ever onsite for what has become the world’s largest event focused exclusively on lab management topics and solutions. Perhaps the highlight of the week was the federal Food and Drug Administration’s (FDA’s) announcement of its final rule on Laboratory Developed Tests (LDTs). Overall, the conference featured more than 120 speakers, many of them national thought leaders on the topic of clinical lab and pathology management. More than 65% of the audience onsite were executive level lab managers.

 “The level of interest in the annual Executive War College is testimony to the ongoing need for dynamic, engaging, and highly relevant conference events,” said Robert Michel (above), Editor-in-Chief of Dark Daily and its sister publication The Dark Report, and founder of the Executive War College. “These in-person gatherings present great opportunities for clinical laboratory and pathology managers and leaders to network and speak with people they otherwise might not meet.” (Photo copyright: Dark Intelligence Group.)

Demonstrating Clinical Value

For those who missed the action onsite, the following is a synopsis of the highlights this week.

Lâle White, Executive Chair and CEO of XiFin, spoke about the future of clinical laboratory testing and the factors reshaping the industry. There are multiple dynamics impacting healthcare economics and outcomes—namely rising costs, decreasing reimbursements, and the move to a more consumer-focused healthcare. But it is up to labs, she said, to ensure their services are not simply viewed as a commodity.

“Laboratory diagnostics have the potential to change the economics of healthcare by really gaining efficiencies,” she noted. “And it’s up to labs to demonstrate clinical value by helping physicians manage two key diagnostic decision points—what tests to order, and what to do with the results.”

But even as labs find ways to increase the value offered to clinicians, there are other disruptive factors in play. Consumer-oriented tech companies such as Google, Apple, and Amazon are democratizing access to patient data in unforeseen ways, and Medicare Advantage plans are changing the way claims are processed and paid.

Redefining Human Data

Reynolds Salerno, PhD, Director of the Division of Laboratory Services for the federal Centers for Disease Control and Prevention (CDC) provided an update on the agency’s top priorities for 2024.

Clinical labs are fundamental components of the public health infrastructure. So, the CDC plans on focusing on delivering high-quality laboratory science, supported by reliable diagnostics and informatics for disease outbreaks and exposures, and engaging with public and private sector partners.

Salerno is an active member of the Clinical Laboratory Improvement Act Committee (CLIAC), which has been working on a number of initiatives, including revisions to the Clinical Laboratory Improvement Act (CLIA) that would change the definition of “materials derived from the human body” to include data derived from human specimens such as medical imaging, genetic sequences, etc.

New Molecular Testing Codes

The history of MolDX and Z-Codes were the topics discussed by Gabriel Bien-Willner, MD, PhD, Chief Medical Officer for healthcare claims and transaction processing company Palmetto GBA. Molecular testing is highly complex, and the lack of well-defined billing codes and standardization makes it difficult to know if a given test is reasonable and necessary.

Z-Codes were established to clarify what molecular testing was performed—and why—prompting payers to require both Z-Codes and Current Procedural Terminology (CPT) codes when processing molecular test claims. Medicare’s MolDX program further streamlines the claims process by utilizing expertise in the molecular diagnostics space to help payers develop coverage policies and reimbursement for these tests.

FDA Final Rule on LDT Regulation

Timothy Stenzel, MD, PhD, CEO of Grey Haven Consulting and former director of the FDA’s Office of In Vitro Diagnostics reviewed the latest updates from the FDA’s Final Rule on LDT (laboratory developed test) regulation. Prior to the FDA releasing its final rule, some experts suggested that the new regulations could result in up to 90% of labs discontinuing their LDT programs, impacting innovation, and patient care.

However, the final rule on LDTs is very different from the original proposed rule which created controversy. The final rule actually lowers the regulatory burden to the point that some labs may not have to submit their LDTs at all. The FDA is reviewing dozens of multi-cancer detection assays, some of which have launched clinically as LDTs. The agency is likely to approve those that accurately detect cancers for which there is no formal screening program.

Stenzel explained the FDA’s plan to down-classify most in vitro diagnostic tests, changing them from Class III to Class II, and exempting more than 1,000 assays from FDA review. He also discussed the highlights of the Quality Management System Regulation (QMSR). Launched in January, the QMSR bought FDA requirements in line with ISO 13485, making compliance easier for medical device manufacturers and test developers working internationally.

Looming Perfect Storm of Regulatory Changes

To close out Day 1, Michel took to the stage again with a warning to clinical laboratories about the looming “Perfect Storm” trifecta—the final FDA ruling on LDTs, Z-Code requirements for genetic testing, and updates to CLIA ’92 that could result in patient data being considered a specimen.

Laboratory leaders must think strategically if their labs are to survive the fallout, because the financial stress felt by labs in recent years will only be exacerbated by macroeconomic trends such as:

  • Staff shortages,
  • Rising costs,
  • Decreasing and delayed reimbursements, and
  • Tightening supply chains.

Lab administrators looking for ways to remain profitable and prosperous should look beyond the transactional Clinical Lab 1.0 fee-for-service model and adopt Clinical Lab 2.0, which embraces HEDIS (Healthcare Effectiveness Data and Information Set) scores and STAR ratings to offer more value to Medicare Advantage and other payers.

Wednesday’s General Session agenda was packed with information about the rise of artificial intelligence, big data, and precision medicine in healthcare. Taking centerstage on the program’s final day was Michael Simpson, President and CEO of Clinisys. Simpson gave a global perspective on healthcare data as the new driver of innovation in diagnostics and patient care.

Michel closed the conference on Wednesday by recapping many of these highlights, and then inviting his audience to the 30th annual Executive War College Diagnostics, Clinical Laboratory, and Pathology Management conference to be held on April 29-30, 2025, here at the Hyatt Regency New Orleans. Register now to attend this critical gathering.

—Leslie Williams

Related Information:

Executive War College: The Ultimate Event for Helping Solve Your Diagnostics, Clinical Lab and Pathology Management Challenges

Labs Should Prepare for Arrival of ‘Perfect Storm’

Executive War College 2025 Registration

India’s Neuberg Diagnostics Embraces AI and Digital Pathology While Opening Its First Clinical Laboratory in the US

One of the world’s fastest growing medical laboratory companies in India is using digital pathology systems and AI to replace older diagnostic technologies

Artificial intelligence (AI) is gaining acceptance around the world and use of AI to analyze digital pathology images is expected to be a major disruptor to the profession of anatomic pathology. Internationally, several pathology companies already use AI-powered solutions to diagnose cancer.

One such example is Neuberg Diagnostics, a fast-growing clinical laboratory company in Chennai, India. Neuberg has been using AI to review digital pathology images for several years, according to Chairman and Managing Director GSK Velu, PhD, BPharm.

“We already use AI in our laboratories,” Velu said in an exclusive interview with Dark Daily. “Our main reference laboratories currently use digital pathology systems to support the pathologists and many of them are using AI with these digital pathology systems.

“AI and data analytics tools are being used in other departments too, such as in our wellness department where we use AI for predictive analytics,” he added. “We also use AI in our genomics division, and we are introducing AI into other divisions slowly and steadily.”

Neuberg operates 120 laboratories in an extensive network in India, South Africa, and the United Arab Emirates (UAE), and now in the US as well.

Neuberg Diagnostics Opens First Lab in US

In “India’s Neuberg Diagnostics Expands into US Market,” Dark Daily’s sister publication, The Dark Report, reported on Neuberg opening its first laboratory in the United States in Raleigh, NC. The Neuberg Centre for Genomic Medicine (NCGM) opened in May and will focus on genomic and molecular testing based on next-generation sequencing (NGS) techniques.

GSK Velu, PhD

“Our idea is to enhance the access and affordability for next-generation techniques, meaning molecular diagnostics, genomics, pathology, digital pathology, proteomics, metabolomics, and all that. This is the spirit behind Neuberg Diagnostics,” said GSK Velu, PhD, BPharm (above), Chairman and Managing Director of Neuberg Diagnostics, in an exclusive interview with The Dark Report. Clinical laboratories that are considering investing in digital pathology technologies may want to follow its development at Neuberg’s Centre for Genomic Medicine in Raleigh, NC, which opened in May. (Photo copyright: Neuberg Diagnostics.)

Replacing Older Pathology Technologies

As has been happening at other anatomic pathology centers around the world, Neuberg has been using digital pathology systems to replace older technologies. “One of our largest labs is our Bangalore Reference Lab,” Velu said. “There, we do not use microscopes for histopathology, and that lab has used digital pathology for routine review of specimens for several years now.

“But because artificial intelligence is still emerging, we can’t rely on AI with all of our digital pathology systems,” he added. “Although, of course, AI is certainly an aid to everything we do with digital pathology.

“For a variety of reasons, the adaptation of artificial intelligence in anatomic pathology is not happening as effectively nor as fast as we would like,” he noted. “So, for now, we need to wait and watch a bit longer, either because adaptation by pathologists is slow, or because AI tools are still a bit of a worry for some pathologists.

Younger Pathologists Adapt Faster to Digital Pathology

One reason could be that conventional pathologists worry about relying completely on AI for any diagnosis, Velu noted. “I’m certain that the more recent generation of pathologists who are now in their 30s, and the new people coming into pathology, will start adapting more quickly to digital pathology and to AI faster than the older generation of pathologists have done.

“The younger pathologists have a greater appreciation for the potential of digital pathology, while the older pathologists don’t want to let go of conventional diagnosis methods,” he added.

“For example, we have not yet seen where pathologists are reviewing breast image scans,” he commented. “But, at the same time, AI has been well-accepted among radiologists who are reviewing breast mammography scans.”

In India and in other markets worldwide, radiologists have adapted AI tools for breast mammography scans to diagnose breast cancer, he noted. “But that’s not happening even among pathologists who are doing cancer screening,” he said.

Velu suggested that another reason for the slow adoption of AI tools in pathology is that these systems are relatively new to the market. “Maybe the AI tools that are used with digital pathology are not as reliable as we hoped they would be, or they are not fully robust at the moment,” he speculated. “That’s why I say it will take some time before the use of AI for diagnosis becomes more widespread among pathologists. So, for now, we must wait until digital pathology and AI tools work together more seamlessly.

Replacing Conventional Pathology Technologies and Methods

“When those two technologies—AI and digital pathology systems—are linked more closely, their use will take hold in a substantial way,” Velu predicted. “When that happens, they are likely to replace conventional pathology methods completely.

“Currently, we are in the early stages of a transformation,” he added. “In our labs, you can see that the transformation is ongoing. We are using digital pathology systems even in our smaller labs. Then, the staff in our smaller labs do the processing of slides to convert them to digital images and send them to our labs in the larger cities. There, the professional staff uses AI to review those digital images and issue reports based on those images.

“Using our digital pathology systems and AI in that way means that we can make that technology available even in smaller towns and villages that have access only to our smaller labs,” he commented.

Velu added that wider use of digital pathology systems could improve the quality of care that pathologists deliver to patients in a significant way, particularly in rural areas. “Here in India, we are not seeing a huge shortage of pathologists, except in rural areas and villages,” he explained. “In those places, we could run short of pathologists.

“That is the reason we are trying to adapt the use of telepathology more widely,” he noted. “To do that, we might have technicians and histologists who will do just processing of slides so that they can send the digital images to our pathologists located in larger cities. Then, those surgical pathologists will review the cases and send the reports out. That’s the model that we are trying to slowly follow here.”

As use of digital pathology images increased, many predicted that specimens would flow from the US to India. This would happen because of the belief that the lower cost of surgical pathology in India would successfully draw business away from pathology groups here in the United States.

However, Neuberg turned the tables on that belief when it announced the opening of its Neuberg Centre for Genomic Medicine (NCGM), a state-of-the-art esoteric and genetic testing laboratory in Raleigh, NC. The NCGM lab is CLIA-certified and Neuberg says it is ready to compete with labs in this country on their home turf.

These are reasons why pathologists and pathology practice administrators in the United States may want to watch how Neuberg Diagnostics continues to develop its use of digital pathology platforms and AI-powered digital image analysis tools throughout its international network of laboratories.

Joe Burns

Related Information

India’s Neuberg Diagnostics Expands into U.S. Market

Neuberg Diagnostics Launches Clinical Laboratory in the US

Neuberg Diagnostics Launches NCGM, Its First Laboratory in the USA

Neuberg Diagnostics Commences Clinical Operations in US

Neuberg Diagnostics to Expand in Africa, ME and India, invest Rs 150cr

Proof of Concept Study Demonstrates Machine Learning and AI Can Identify Cancer Cells Based on pH Levels; May Have Applications in Surgical Pathology

The new method employs a pH sensitive dye and AI algorithms to ‘distinguish between cells originating from normal and cancerous tissue, as well as among different types of cancer’ the researchers said

Might a pH-sensitive dye in tandem with an image analysis solution soon be used to identify cancerous cells within blood samples as well within tissue? Recent research indicates that could be a possibility. If further studies and clinical trials confirm this capability, then anatomic pathologists could gain another valuable tool to use in diagnosing cancers and other types of disease.

Currently, surgical pathologists use a variety of hematoxylin and eosin stains (H/E) to bring out useful features in cells and cell structures. So, staining tissue on glass slides is a common practice. Now, thanks to machine learning and artificial intelligence, anatomic pathologists may soon have a similar tool for spotting cancer cells within both tissue and blood samples.

Researchers at the National University of Singapore (NUS) have developed a method for identifying cancer that uses a pH sensitive dye called bromothymol blue. The dye reacts to various levels of acidity in cancer cells by turning colors. “The pH inside cancer cells tends to be higher than that of healthy cells. This phenomenon occurs at the very early phases of cancer development and becomes amplified as it progresses,” Labroots reported.

In “Machine Learning Based Approach to pH Imaging and Classification of Single Cancer Cells,” published in the journal APL Bioengineering, the NUS researchers wrote, “Here, we leverage a recently developed pH imaging modality and machine learning-based single-cell segmentation and classification to identify different cancer cell lines based on their characteristic intracellular pH. This simple method opens up the potential to perform rapid noninvasive identification of living cancer cells for early cancer diagnosis and further downstream analyses.”

According to an NUS news release, the bromothymol blue dye is “applied onto patients’ cells” being held ex vivo in cell culture dishes. The dye’s color changes depending on the acidity level of the cancer cells it encounters. Microscopic images of the now-visible cancers cells are taken, and a machine-learning algorithm analyzes the images before generating a report for the anatomic pathologist.

The NUS researchers claim the test can provide answers in about half an hour with 95% accuracy, Labroots reported.

“The ability to analyze single cells is one of the holy grails of health innovation for precision medicine or personalized therapy. Our proof-of-concept study demonstrates the potential of our technique to be used as a fast, inexpensive and accurate tool for cancer diagnosis,” said Lim Chwee Teck, PhD, NUS Society Professor and Director of NUS’ Institute for Health Innovation and Technology, in the NUS news release.

Lim Chwee Teck, PhD

The novel technique for differentiating cancer cells from non-cancerous cells being developed at the National University of Singapore (NUS) could eventually become useful in detecting cancer cells in tissue samples, either obtained from tumor biopsies or blood samples. “As the number of cells in these samples can be in millions or even billions, the ability to detect the very few cancer cells among the others will be useful for clinicians,” NUS Society Professor and Director of NUS’ Institute for Health Innovation and Technology, Lim Chwee Teck, PhD (above) told The Straits Times. (Photo copyright: The Straits Times.)

AI Cell Analysis versus Laborious Medical Laboratory Steps

By developing an AI-driven method, Professor Lim and the NUS team sought to improve upon time-consuming techniques for identifying cells that traditionally involve using florescent probes, nanoparticles, and labeling steps, or for cells to be fixed or terminated.

“Unlike other cell analysis techniques, our approach uses simple, inexpensive equipment, and does not require lengthy preparation and sophisticated devices. Using AI, we are able to screen cells faster and accurately,” Professor Lim told Labroots. “Furthermore, we can monitor and analyze living cells without causing any toxicity to the cells or the need to kill them.”

The new technique may have implications for cancer detection in tumor tissue as well as in liquid biopsies.

“We are also exploring the possibility of performing the real-time analysis on circulating cancer cells suspended in blood,” Professor Lim said in the NUS news release. “One potential application for this would be in liquid biopsy where tumor cells that escaped from a primary tumor can be isolated in a minimally-invasive fashion from bodily fluids such as blood.”

Diagnosing Cancer in Real Time

The NUS’ method requires more research and clinical studies before it could become an actual tool for anatomic pathologists and other cancer diagnosticians. Additionally, the NUS researchers acknowledged that the focus on only four cell lines (normal cells, benign breast tumor cells, breast cancer cells, and pancreatic cancer cells) limited their study, as did lack of comparison with conventional florescent pH indicators.

Still, the NUS scientists are already planning more studies to advance their concept to different stages of cell malignancy. They envision a “real-time” version of the technique to enable recognition of cells and fast separation of those that need to be referred to clinical laboratories for molecular testing and/or genetic sequencing.

Medical laboratory leaders may want to follow the NUS study. An inexpensive AI-driven method that can accurately detect and classify cancer cells based on pH within the cells is provocative and may be eventually become integrated with other cancer diagnostics.

Donna Marie Pocius

Related Information

Machine Learning-Based Approach to pH Imaging and Classification of Single Cancer Cells

Machine Learning Can Identify Cancerous Cells by Their Acidity

NUS Researchers Harness AI to Identify Cancer Cells by Their Acidity: Novel Technique Paves Way for Faster, Inexpensive, and Accurate Cancer Diagnosis

AI Test Distinguishes Cancer Cells from Healthy Ones Based on Acidity Levels

Researchers Use AI to Identify the pH of Cancer Cells

How Smart Clinical Laboratories and Genetic Testing Labs Are Collecting More Revenue by Pricing Tests to Meet the Expectations of Patients

By rethinking how their medical labs relate to health insurers, physicians, and patients, a handful of progressive lab companies are enjoying increased revenue while also lifting patient and payer satisfaction

There is widespread agreement across the clinical laboratory industry that it is becoming ever more difficult to have health plans reimburse claims for common tests, molecular assays, and genetic tests in a reliable and consistent manner. Many lab companies report that they are experiencing high rates of denied claims. Moreover—even for claims reimbursed by payers—the amount paid will vary on claims for the same type of lab test.

“Essentially, on this point, the consistent theme we hear from many lab companies—particularly those labs with a menu of proprietary, specialized molecular and genetic tests—is that it is now almost a crap-shoot to submit lab test claims to many payers and see timely and predictable reimbursement for those claims,” stated Robert L. Michel, Editor-In-Chief of Dark Daily’s sister publication, The Dark Report. “One could say that, today, the function of billing patients and payers for clinical lab testing has become financial quicksand for most labs. By following traditional coding, billing, and collection practices, in today’s healthcare market, they find themselves sinking steadily deeper in this financial quicksand.” (more…)

Failure to Pay for New Molecular CPT Codes Created Money Crisis for Clinical Laboratories and Pathology Groups

Confusion, unhappiness, and many unresolved issues remain about the way government and private payers are handling claims for molecular diagnostic tests covered by the 114 new CPT codes
Dust is settling from the fiasco triggered by the Medicare program’s failure to be ready on January 1, 2013, to settle molecular diagnostic test claims filed under the 114 new Tier 1 and Tier 2 molecular CPT codes. The damage is not just limited to Medicare test claims, but also involves private health plans that were waiting to let the Medicare program set precedents on coverage and prices for the new molecular test codes.

Many Clinical Laboratories Must Cope With an Unsatisfactory Situation

Although federal Medicare officials and Medicare contractors have scrambled to rectify the situation, even today there is much unhappiness across the clinical laboratory industry about the current state of things. That unhappiness extends to state Medicaid and private payers because many of these payers have been slow to publish coverage guidelines and prices for these new molecular test CPT codes.