Managers of pathology groups and clinical laboratories can learn from the challenges confronting the radiology profession
Members of the Intersociety Committee of the American Society of Radiology (ACR) recently met in Coronado, Calif., to discuss the “most pressing” challenges to their profession and investigate possible solutions, according to Radiology Business. Many of these challenges mimic similar challenges faced by anatomic pathology professionals.
The radiology leaders identified seven of the “most important challenges facing radiology today.” They include: declining reimbursement, corporatization and consolidation, inadequate labor force, imaging appropriateness, burnout, turf wars with nonphysicians, and workflow efficiency, according to a report on the meeting published in the Journal of the American College of Radiology (JACR).
“Solving these issues will not be easy,” said Bettina Siewert, MD, diagnostic radiologist at Beth Israel Deaconess Medical Center (BIDMC) in Boston, Mass., professor of radiology at Harvard, and lead author of the JACR report, in the JACR. “This is a collection of ‘wicked’ problems defined as having (1) no stoppable rule, (2) no enumerable set of solutions or well-described set of permissible operations, and (3) stakeholders with very different worldviews and frameworks for understanding the problem,” she added.
“The Intersociety Committee is a freestanding committee of the ACR established to promote collegiality and improve communication among national radiology organizations,” JACR noted.
“Taken together, a ‘perfect storm’ of pressures on radiologists and their institutions is brewing,” said Bettina Siewert, MD (above), diagnostic radiologist at Beth Israel Deaconess Medical Center in Boston, Mass., professor of radiology at Harvard, and lead author of the JACR report. Wise pathology and clinical laboratory leaders will see the similarities between their industry’s challenges and those facing radiology. (Photo copyright: Beth Israel Deaconess Medical Center.)
How Radiology Challenges Correlate to Pathology Practices
Here are the seven biggest challenges facing radiology practices today as identified by the Intersociety Committee of the ACR.
Declining Reimbursement: According to the ACR report, radiologists in 2021 performed 13% more relative value units (RVUs) per Medicare beneficiary compared to 2005. However, the inflation-adjusted conversion factor fell by almost 34%––this led to a 25% decline in reimbursements.
This issue has plagued the pathology industry as well. According to an article published in the American Journal of Clinical Pathology (AJCP), prior to adjusting for inflation, the average physician reimbursement increased by 9.7% from 2004 to 2024 for all included anatomic pathology CPT codes. After adjusting for inflation, the average physician reimbursement decreased by 34.2% for included CPT codes. The greatest decrease in reimbursement observed from 2004 to 2024 was for outside slide consultation at 60.5% ($330.12 to $130.49), followed by pathology consultation during surgery at 59.0% ($83.54 to $34.29). The average CAGR was -2.19%,” the authors wrote.
“Our study demonstrates that Medicare physician reimbursement for common anatomic pathology procedures is declining annually at an unsustainable rate,” the AJCP authors added.
The radiologists who identified this trend in their own field suggest that medical societies could lead the push to minimize the reimbursement cuts. Pathologists could also adopt this ‘strength in numbers’ mentality to advocate for one another.
Corporatization Consolidation: The authors of the ACR report identified this issue as limiting job opportunities for radiologists particularly in private practice. Pathology professionals have seen the same trend in their field as well. Increasingly, small pathology groups have been consolidated into larger regional groups. Some of those larger regional pathology groups will then be acquired by public laboratory corporations.
The authors of the ACR report suggest radiologists should be educated on the pros and cons of consolidation. They also suggest pursuing unionization.
Inadequate Labor Force: In both radiology and pathology there is a supply-and-demand issue when it comes to labor. Staffing shortages have been felt across all of healthcare, but particularly among pathology groups and clinical laboratories. Siewert and her co-authors suggest a three-pronged approach to address this issue:
Creating residency positions in private practice.
Recruiting international medical graduates.
Increasing job flexibility.
Pathology professionals could apply these same ideas to help close the gap between the open positions in the field and the number of professionals to fill them.
Imaging Appropriateness: A gap between service capacity and service demand for radiology imaging has created a frustrating mismatch between radiologists and clinicians. Radiology experts point to overutilization of the service causing the supply-and-demand crisis. Comparatively, pathologists see a similar issue in complex cases requiring more pathologist time to come to an appropriate diagnosis and identify a care plan.
“To facilitate this reduction, better data on imaging outcomes for specific clinical questions are urgently needed,” the authors of the ACR report wrote as a possible solution. “Considering the magnitude of the mismatch crisis, radiologists may also need to consider expanding their consultative role to include that of a gatekeeper, as is done in other more resource-controlled countries.”
Burnout: Perhaps one of the most talked about subjects in the medical field has been burnout. The issue has been thrust to the forefront with the COVID-19 pandemic; however, the burnout crisis began before the pandemic. About 78% of radiologists surveyed for this report claimed to be exceeding their personal work capacity.
The authors of the ACR report suggest a structured approach to air grievances without descending into despair. “Using a team approach based on the concept of listen-sort-empower, burnout can be combatted by fostering free discussion between frontline workers and radiologists,” they said. “Facilitators unaffiliated with the radiology department can help to maintain focus on gratitude for positive attributes of the work and the institution as well as to keep the sessions on task and prevent them from devolving into complaint sessions with a subsequent loss of hope.”
A similar approach could be applied to pathology groups and clinical laboratory to combat worker burnout as well.
Turf Wars with Nonphysicians: Over the last five years the number of imaging exams being interpreted by nonphysician providers has increased by 30%, according to the ACR report. The writers emphasized the need for increased understanding and awareness about the importance of physician-led care. They suggest solidarity among hospital medical staff to provide a united front in addressing this issue in hospital bylaws.
In pathology, the counterpart is how large physician groups are bringing anatomic pathology in-house. This has been an ongoing trend for the past 20 years. It means that the pathologist is now an employee of the physician group (or a partner/shareholder in some cases).
Increase Workflow Efficiency: Image interpretation accounts for only 36% of the work radiologists perform, the ACR report noted. This issue has a direct counterpart in pathology where compliance requirements and various tasks take time away from pathologist diagnosis. These issues could be solved by working AI into tasks, delegating non-interpretive tasks to other workers, and improving the design of reading rooms. All of these possible solutions could also be applied to clinical pathologists.
These issues being faced by radiologists compare directly to similar issues in the clinical pathology world. Pathologists and pathology group managers would be wise to learn from the experience of their imaging colleagues and possibly adopt some of the ACR’s suggested solutions.
Findings could lead to new therapies and clinical laboratory biomarkers for detecting and defeating antibiotic-resistant bacteria
Once again, new research shows that human gut bacteria (microbiota) may be useful in fighting antibiotic-resistant bacterial infections. The study findings could provide new therapeutics and clinical laboratory biomarkers for diagnosing and treating severe gastrointestinal disorders.
Antibiotic-resistant bacterial infections often appear in patients with chronic intestinal conditions and in those with long-term antibiotic use. Enterobacteriaceae is a large family of gram-negative bacteria that includes more than 30 genera and over 100 species.
“Despite two decades of microbiome research, we are just beginning to understand how to define health-promoting features of the gut microbiome,” said Marie-Madlen Pust, PhD, a computational postdoctoral researcher at the Broad Institute and co-first author of the paper, in the news release.
“Part of the challenge is that each person’s microbiome is unique. This collaborative effort allowed us to functionally characterize the different mechanisms of action these bacteria use to reduce pathogen load and gut inflammation,” she added.
The researchers identified a way to treat patients infected by antibiotic-resistant strains of bacteria that does not involve antibiotics. Should further research validate these early findings, this could be a viable approach to treating patients with this condition.
“Microbiome studies can often consist of analyzing collections of genetic sequences, without understanding what each gene does or why certain microbes are beneficial,” said Ramnik Xavier, MD (above), director of Broad Institute’s immunology program, co-director of the infectious disease and microbiome program, and co-senior author on the study, in a news release. “Trying to uncover that function is the next frontier, and this is a nice first step towards figuring out how microbial metabolites influence health and inflammation.” Clinical laboratories that test for intestinal conditions caused by antibiotic resistance will want to follow the Broad Institute’s research. (Photo copyright: Broad Institute.)
Suppressing Growth of Antibiotic-resistant Bacteria
To perform their research, the scientists isolated about 40 strains of bacteria from the stools of five healthy fecal donors. They then used those stool samples in fecal microbiota transplants to treat mice that had been infected with either Escherichia coli (E. coli) or Klebsiella, both forms of Enterobacteriaceae. The scientists tested different combinations of the 40 strains and identified 18 that suppressed the growth of Enterobacteriaceae.
“Antibiotic-resistant Enterobacteriaceae such as E. coli and Klebsiella bacteria are common in hospitals, where they can proliferate in the gut of patients and cause dangerous systemic infections that are difficult to treat. Some research suggests that Enterobacteriaceae also perpetuates inflammation in the intestine and infection by other microbes,” the Broad Institute news release notes.
The researchers discovered that Klebsiella changed the gene expression in carbohydrate uptake and metabolism in the Klebsiella-infected mice that were treated with the 18 beneficial strains. The gene expression included the downregulating of gluconate kinase and transporter genes, which revealed there is increased competition among gut bacteria for nutrients.
When combined, these 18 strains alleviated inflammation in the guts of the treated mice by depriving the harmful gut bacteria of carbohydrates. This non-antibiotic approach also prevented harmful bacteria from colonizing in the gut.
“In partnership with the Broad’s Metabolomics Platform, led by senior director and study co-author Clary Clish, PhD, they analyzed samples from pediatric patients with ulcerative colitis, looking for the presence of alternate gluconate pathway genes of gut microbes and fecal gluconate levels. They found higher levels of gluconate linked to more gluconate-consuming Enterobacteriaceae in samples from pediatric patients with ongoing inflammation, indicated by high levels of the protein calprotectin,” the study authors wrote in Nature.
“Together, the findings suggest that Enterobacteriaceae processes gluconate as a key nutrient and contributes to inflammation in patients. But when a gut microbiome includes the 18 helpful strains, they likely compete with Enterobacteriaceae for gluconate and other nutrient sources, limiting the proliferation of the harmful bacteria,” the scientists concluded.
Promising New Bacterial Therapies
This research could ultimately lead to the development of fecal microbiota transplants for individuals to eradicate antibiotic-resistant bacteria in a more objective and specific manner, with fewer side effects than current treatments.
“Harnessing these activities in the form of live bacterial therapies may represent a promising solution to combat the growing threat of proinflammatory, antimicrobial-resistant Enterobacteriaceae infection,” the scientists wrote in Nature.
According to the news release, they plan to continue research to “uncover the identity and function of unknown metabolites that contribute to gut health and inflammation.” The team hopes to discover how different bacteria compete with each other, and to develop microbial therapeutics that improve gut microbiome and curb bacterial infections.
More studies are needed to prove the efficacy of this type of fecal bacterial treatment. However, this research demonstrates how using nano processes enabled by new technologies to identify the actual work of proteins, RNA, and DNA in the body cheaply, faster, and with greater precision, will open doors to both therapeutic and diagnostic clinical laboratory biomarkers.
Proof-of-concept study ‘highlights that using AI to integrate different types of clinically informed data to predict disease outcomes is feasible’ researchers say
Artificial intelligence (AI) and machine learning are—in stepwise fashion—making progress in demonstrating value in the world of pathology diagnostics. But human anatomic pathologists are generally required for a prognosis. Now, in a proof-of-concept study, researchers at Brigham and Women’s Hospital in Boston have developed a method that uses AI models to integrate multiple types of data from disparate sources to accurately predict patient outcomes for 14 different types of cancer.
The process also uncovered “the predictive bases of features used to predict patient risk—a property that could be used to uncover new biomarkers,” according to Genetic Engineering and Biotechnology News (GEN).
Should these research findings become clinically viable, anatomic pathologists may gain powerful new AI tools specifically designed to help them predict what type of outcome a cancer patient can expect.
“Experts analyze many pieces of evidence to predict how well a patient may do. These early examinations become the basis of making decisions about enrolling in a clinical trial or specific treatment regimens,” said Faisal Mahmood, PhD (above) in a Brigham press release. “But that means that this multimodal prediction happens at the level of the expert. We’re trying to address the problem computationally,” he added. Should they be proven clinically-viable through additional studies, these findings could lead to useful tools that help anatomic pathologists and clinical laboratory scientists more accurately predict what type of outcomes cancer patient may experience. (Photo copyright: Harvard.)
AI-based Prognostics in Pathology and Clinical Laboratory Medicine
The team at Brigham constructed their AI model using The Cancer Genome Atlas (TCGA), a publicly available resource which contains data on many types of cancer. They then created a deep learning-based algorithm that examines information from different data sources.
Pathologists traditionally depend on several distinct sources of data, such as pathology images, genomic sequencing, and patient history to diagnose various cancers and help develop prognoses.
For their research, Mahmood and his colleagues trained and validated their AI algorithm on 6,592 H/E (hematoxylin and eosin) whole slide images (WSIs) from 5,720 cancer patients. Molecular profile features, which included mutation status, copy-number variation, and RNA sequencing expression, were also inputted into the model to measure and explain relative risk of cancer death.
The scientists “evaluated the model’s efficacy by feeding it data sets from 14 cancer types as well as patient histology and genomic data. Results demonstrated that the models yielded more accurate patient outcome predictions than those incorporating only single sources of information,” states a Brigham press release.
“This work sets the stage for larger healthcare AI studies that combine data from multiple sources,” said Faisal Mahmood, PhD, Associate Professor, Division of Computational Pathology, Brigham and Women’s Hospital; and Associate Member, Cancer Program, Broad Institute of MIT and Harvard, in the press release. “In a broader sense, our findings emphasize a need for building computational pathology prognostic models with much larger datasets and downstream clinical trials to establish utility.”
Future Prognostics Based on Multiple Data Sources
The Brigham researchers also generated a research tool they dubbed the Pathology-omics Research Platform for Integrative Survival Estimation (PORPOISE). This tool serves as an interactive platform that can yield prognostic markers detected by the algorithm for thousands of patients across various cancer types.
The researchers believe their algorithm reveals another role for AI technology in medical care, but that more research is needed before their model can be implemented clinically. Larger data sets will have to be examined and the researchers plan to use more types of patient information, such as radiology scans, family histories, and electronic medical records in future tests of their AI technology.
“Future work will focus on developing more focused prognostic models by curating larger multimodal datasets for individual disease models, adapting models to large independent multimodal test cohorts, and using multimodal deep learning for predicting response and resistance to treatment,” the Cancer Cell paper states.
“As research advances in sequencing technologies, such as single-cell RNA-seq, mass cytometry, and spatial transcriptomics, these technologies continue to mature and gain clinical penetrance, in combination with whole-slide imaging, and our approach to understanding molecular biology will become increasingly spatially resolved and multimodal,” the researchers concluded.
Anatomic pathologists may find the Brigham and Women’s Hospital research team’s findings intriguing. An AI tool that integrates data from disparate sources, analyzes that information, and provides useful insights, could one day help them provide more accurate cancer prognoses and improve the care of their patients.
Researchers say their method can trace ancestry back 100,000 years and could lay groundwork for identifying new genetic markers for diseases that could be used in clinical laboratory tests
Cheaper, faster, and more accurate genomic sequencing technologies are deepening scientific knowledge of the human genome. Now, UK researchers at the University of Oxford have used this genomic data to create the largest-ever human family tree, enabling individuals to trace their ancestry back 100,000 years. And, they say, it could lead to new methods for predicting disease.
This new database also will enable genealogists and medical laboratory scientists to track when, where, and in what populations specific genetic mutations emerged that may be involved in different diseases and health conditions.
New Genetic Markers That Could Be Used for Clinical Laboratory Testing
As this happens, it may be possible to identify new diagnostic biomarkers and genetic indicators associated with specific health conditions that could be incorporated into clinical laboratory tests and precision medicine treatments for chronic diseases.
“We have basically built a huge family tree—a genealogy for all of humanity—that models as exactly as we can the history that generated all the genetic variation we find in humans today,” said Yan Wong, DPhil, an evolutionary geneticist at the Big Data Institute (BDI) at the University of Oxford, in a news release. “This genealogy allows us to see how every person’s genetic sequence relates to every other, along all the points of the genome.”
Researchers from University of Oxford’s BDI in London, in collaboration with scientists from the Broad Institute of MIT and Harvard; Harvard University, and University of Vienna, Austria, developed algorithms for combining different databases and scaling to accommodate millions of gene sequences from both ancient and modern genomes.
“Essentially, we are reconstructing the genomes of our ancestors and using them to form a series of linked evolutionary trees that we call a ‘tree sequence,’” said geneticist Anthony Wilder Wohns, PhD (above), in the Oxford news release. Wohns, a postdoctoral researcher in statistical and population genetics at the Broad Institute, led the study. “We can then estimate when and where these ancestors lived. The power of our approach is that it makes very few assumptions about the underlying data and can also include both modern and ancient DNA samples.” The study may result in new genetic biomarkers that lead to advances in clinical laboratory diagnostics for today’s diseases. (Photo copyright: Harvard School of Engineering and Applied Sciences.)
Tracking Genetic Markers of Disease
The BDI team overcame the major obstacle to tracing the origins of human genetic diversity when they developed algorithms to handle the massive amount of data created when combining genome sequences from many different databases. In total, they compiled the genomic sequences of 3,601 modern and eight high-coverage ancient people from 215 populations in eight datasets.
The ancient genomes included three Neanderthal genomes, a Denisovan genome, and a family of four people who lived in Siberia around 4,600 years ago.
The University of Oxford researchers noted in their news release that their method could be scaled to “accommodate millions of genome sequences.”
“This structure is a lossless and compact representation of 27 million ancestral haplotype fragments and 231 million ancestral lineages linking genomes from these datasets back in time. The tree sequence also benefits from the use of an additional 3,589 ancient samples compiled from more than 100 publications to constrain and date relationships,” the researchers wrote in their published study.
Wong believes his research team has laid the groundwork for the next generation of DNA sequencing.
“As the quality of genome sequences from modern and ancient DNA samples improves, the tree will become even more accurate and we will eventually be able to generate a single, unified map that explains the descent of all the human genetic variation we see today,” he said in the news release.
Developing New Clinical Laboratory Biomarkers for Modern Diagnostics
In a video illustrating the study’s findings, evolutionary geneticist Yan Wong, DPhil, a member of the BDI team, said, “If you wanted to know why some people have some sort of medical conditions, or are more predisposed to heart attacks or, for example, are more susceptible to coronavirus, then there’s a huge amount of that described by their ancestry because they’ve inherited their DNA from other people.”
Wohns agrees that the significance of their tree-recording methods extends beyond simply a better understanding of human evolution.
“[This study] could be particularly beneficial in medical genetics, in separating out true associations between genetic regions and diseases from spurious connections arising from our shared ancestral history,” he said.
The underlying methods developed by Wohns’ team could have widespread applications in medical research and lay the groundwork for identifying genetic predictors of disease risk, including future pandemics.
Clinical laboratory scientists will also note that those genetic indicators may become new biomarkers for clinical laboratory diagnostics for all sorts of diseases currently plaguing mankind.
Gene sequencing is enabling disease tracking in new ways that include retesting laboratory specimens from before the SARS-CoV-2 outbreak to determine when it arrived in the US
On February 26 of this year, nearly 200 executives and employees of neuroscience-biotechnology company Biogen gathered at the Boston Marriott Long Wharf hotel for their annual leadership conference. Unbeknownst to the attendees, by the end of the following day, dozens of them had been exposed to and become infected by SARS-CoV-2, the coronavirus that causes the COVID-19 illness.
Researchers now have hard evidence that attendees at this meeting returned to their communities and spread the infection. The findings of this study will be relevant to pathologists and clinical laboratory managers who are cooperating with health authorities in their communities to identify infected individuals and track the spread of the novel coronavirus.
This “superspreader” event has been closely investigated and has led to intriguing conclusions concerning the use of genetic sequencing to revealed vital information about the COVID-19 pandemic. Recent improvements in gene sequencing technology is giving scientists new ways to trace the spread of COVID-19 and other diseases, as well as a method for monitoring mutations and speeding research into various treatments and vaccines.
Genetic Sequencing Traces an Outbreak
“With genetic data, a record of our poor decisions is being captured in a whole new way,” Bronwyn MacInnis, PhD, Director of Pathogen Genomic Surveillance at the Broad Institute of MIT and Harvard, told The Washington Post (WaPo) during its analysis of the COVID-19 superspreading event. MacInnis is one of many Broad Institute, Harvard, MIT, and state of Massachusetts scientists who co-authored a study that detailed the coronavirus’ spread across Boston, including from the Biogen conference.
What they discovered is both surprising and enlightening. According to WaPo’s report, at least 35 new cases of the virus were linked directly to the Biogen conference, and the same strain was discovered in outbreaks in two homeless shelters in Boston, where 122 people were infected. The variant tracked by the Boston researchers was found in roughly 30% of the cases that have been sequenced in the state, as well as in Alaska, Senegal, and Luxembourg.
“The data reveal over 80 introductions into the Boston area, predominantly from elsewhere in the United States and Europe. We studied two superspreading events covered by the data, events that led to very different outcomes because of the timing and populations involved. One produced rapid spread in a vulnerable population but little onward transmission, while the other was a major contributor to sustained community transmission,” the researchers noted in their study abstract.
“The same two events differed significantly in the number of new mutations seen, raising the possibility that SARS-CoV-2 superspreading might encompass disparate transmission dynamics. Our results highlight the failure of measures to prevent importation into [Massachusetts] early in the outbreak, underscore the role of superspreading in amplifying an outbreak in a major urban area, and lay a foundation for contact tracing informed by genetic data,” they concluded.
The use of genetic sequencing to trace the virus could inform measures to control the spread in new ways, but currently, only about 0.33% of cases in the United States are being sequenced, MacInnis told WaPo, and that not sequencing samples is “throwing away the crown jewels of what you really want to know.”
Another role that genetic sequencing is playing in this pandemic is in tracking viral mutations. One of the ways that pandemics worsen is when viruses mutate to become deadlier or more easily spread. Scientists are using genetic sequencing to monitor SARS-CoV-2 for such mutations.
A group of scientists at Texas A&M University led by Yue Xing, PhD, published a paper titled, “MicroGMT: A Mutation Tracker for SARS-CoV-2 and Other Microbial Genome Sequences,” which explains that “Although most mutations are expected to be selectively neural, it is important to monitor if SARS-CoV-2 will eventually evolve to be a stronger or weaker infectious agent as time goes on. Therefore, it is vital to track mutations from newly sequenced SARS-CoV-2 genome.”
Korber’s findings are important because the mutation the scientists identified appears to have a fitness advantage. “Our data show that, over the course of one month, the variant carrying the D614G Spike mutation became the globally dominant form of SARS-CoV-2,” they wrote. Additionally, the study noted, people infected with the mutated variant appear to have a higher viral load in their upper respiratory tracts.
Genetic Sequencing, the Race for Treatments, Vaccines, and Managing Future Pandemics
If, as Fauci and Morens predict, future pandemics are likely, improvements in gene sequencing and analysis will become even more important for tracing, monitoring, and suppressing outbreaks. Clinical laboratory managers will want to watch this closely, as medical labs that process genetic sequencing will, no doubt, be part of that operation.
