Pathologists and clinical laboratories have an opportunity to help create newborn rWGS programs in their parent hospitals and health systems
Diagnosing disease in infants is particularly difficult using typical clinical laboratory testing and modalities. Thus, the use of rapid Whole Genome Sequencing (rWGS) is gaining acceptance when such a procedure is deemed “medically appropriate” based on the child’s symptoms.
In “Whole Genome Sequencing for Newborns Gains Favor,” Robert Michel, Editor-in-Chief of Dark Daily’s sister publication The Dark Report wrote, “Evidence is swiftly accumulating that use of rapid Whole Genome Sequencing for certain children in NICUs can enable diagnostic insights that guide effective interventions. Further, these pilot rWGS programs in children’s hospitals are showing a solid return on investment because of improved care. It is predicted that more hospitals may soon offer rWGS.”
Conducted at Tufts Medical Center in Boston, the researchers found that “Whole genome tests are nearly twice as good as narrower tests at unearthing genetic abnormalities that can cause disease in infants—the study found 49% of abnormalities, compared to 27% with more commonly used tests targeting particular types of genetic diseases,” the Associate Press reported.
The AP story follows the medical journey of a now 4-year-old who was diagnosed with a rare bleeding disorder. The nearly fatal condition was only caught because broad genetic testing found she suffered from factor XIII deficiency, a blood disorder characterized by the inability to clot properly.
“I’ve been doing clinical trials of babies for over 40 years,” neonatologist Jonathan Davis, MD (above), Chief, Division of Newborn Medicine at Tufts Children’s Hospital at Tufts Medical Center and Professor of Pediatrics, Tufts University School of Medicine, told the AP. “It’s not often that you can do something that you feel is going to really change the world and change clinical practice for everyone.” Clinical laboratories that work with oncologists to treat children suffering from cancer will understand Davis’ enthusiasm. (Photo copyright: Tufts Medicine.)
Incorporating Rapid Whole Genome Sequencing into Infant Care
Genetic diseases are responsible for 41% of infant deaths, according to a Rady Children’s Institute press release, which goes on to say the usage of rWGS may significantly improve the odds for infants born with genetic disorders.
“Broad use of genomic sequencing during the first year of life could have a much greater impact on infant mortality than was recognized hitherto,” said Stephen Kingsmore MD, President/CEO, Rady Children’s Institute for Genomic Medicine, which was one of the additional study sites for the Tufts Medicine researchers.
Genetic testing is already used to predict infant health outcomes, but the Tufts study highlights further developments that could improve the process. Prenatal genetic testing can be utilized both through carrier testing to determine any potential genetic red flags in the parents, and during prenatal screening and diagnostic testing of the fetus.
When an infant presents symptoms after birth, rWGS can then be implemented to cast a broad net to determine the best course of treatment.
According to ScienceDaily, the Tufts study found rWGS “to be nearly twice as effective as a targeted gene sequencing test at identifying abnormalities responsible for genetic disorders in newborns and infants.”
However, the rWGS tests took an average of six days to come back, whereas the targeted tests took only four days, ScienceDaily reported. Also, there is not full consensus on whether a certain gene abnormality is actually the cause of a specific genetic disorder.
“Many neonatologists and geneticists use genome sequencing panels, but it’s clear there are a variety of different approaches and a lack of consensus among geneticists on the causes of a specific patient’s medical disorder,” Jill Maron, MD, Vice Chair of Pediatric Research, Tufts Medical Center, and a co-principal investigator of the Tufts study, told Science Daily.
rWGS Costs versus Return on Investment
Some also question the upfront cost of genetic testing. It can be high, but it’s coming down and Maron stresses the importance of the tests.
“Genome sequencing can be costly, but in this targeted, at-risk population, it proves to be highly informative. We are supportive of ongoing efforts to see these tests covered by insurance,” she told ScienceDaily.
Each of the doctors associated with the Tufts study emphasized the importance of this testing and the good that can be done for this vulnerable group. The potential value to the children, they say, far outweighs the drawbacks of the testing.
“This study provides further evidence that genetic disorders are common among newborns and infants,” Kingsmore told ScienceDaily, “The findings strengthen support for early diagnosis by rapid genomic sequencing, allowing for the use of precision medicine to better care for this vulnerable patient population.”
For clinical laboratories, there is also good news about reimbursement for rWGS. In a story published last fall KFF Health News wrote, “Since 2021, eight state Medicaid programs have added rapid whole-genome sequencing to their coverage or will soon cover it, according to GeneDX, a provider of the test. That includes Florida … The test is also under consideration for coverage in Georgia, Massachusetts, New York, and North Carolina, according to the nonprofit Rady Children’s Institute for Genomic Medicine, another major provider of the test.”
“Collectively, these developments are encouraging children’s hospitals, academic centers, and tertiary care centers to look at establishing their own rWGS programs,” wrote Michel in The Dark Report. “In settings where this is appropriate, hospital and health system-based clinical laboratories have an opportunity to take an active role in helping jump start a newborn rWGS program in their institutions.”
Pathologists should continue to monitor rWGS, as well as prenatal and carrier testing, to have a full awareness of its growing use in infant and young child cancer screening.
Initially thought to be an attack by a nation-state, actual culprit turned out to be a known ransomware group and each day brings new revelations about the cyberattack
Fallout continues from cyberattack on Change Healthcare, the revenue cycle management (RCM) company that is a business unit of Optum, itself a division of UnitedHealth Group. Recent news accounts say providers are losing an estimated $100 million per day because they cannot submit claims to Change Healthcare nor receive reimbursement for these claims.
The cyberattack took place on February 21. The following day, UnitedHealth Group filed a Material Cybersecurity Incidents report (form 8-K) with the US Securities and Exchange Commission (SEC) in which it stated it had “identified a suspected nation-state associated cybersecurity threat actor [that] had gained access to some of the Change Healthcare information technology systems.”
A few days later the real identity of the threat actor was revealed to be a ransomware group known as “BlackCat” or “ALPHV,” according to Reuters.
Change Healthcare of Nashville, Tenn., is “one of the largest commercial prescription processors in the US,” Healthcare Dive reported, adding that hospitals, pharmacies, and military facilities had difficulty transmitting prescriptions “as a result of the outage.”
Change Healthcare handles about 15 billion payments each year.
According to a Change Healthcare statement, the company “became aware of the outside threat” and “took immediate action to disconnect Change Healthcare’s systems to prevent further impact.”
Change Healthcare has provided a website where parties that have been affected by the cyberattack can find assistance and updated information on Change’s response to the intrusion and theft of its data.
“The fallout is only starting to happen now. It will get worse for consumers,” Andrew Newman (above), founder and Chief Technology Officer, ReasonLabs, told FOX Business, adding, “We know that the likely destination for [the Change Healthcare] data is the Dark Web, where BlackCat will auction it all off to the highest bidder. From there, consumers could expect to suffer from things like identity theft, credit score downgrades, and more.” Clinical laboratories are also targets of cyberattacks due to the large amount of private patient data stored on their laboratory information systems. (Photo copyright: ReasonLabs.)
Millions of Records May be in Wrong Hands
Reuters reported that ALPHV/BlackCat admitted it “stole millions of sensitive records, including medical insurance and health data from the company.”
The ransomware group has been focusing its attacks on healthcare with 70 incidents since December, according to federal agencies.
In a letter to HHS, AHA warned, “Change Healthcare’s downed systems will have an immediate adverse impact on hospital finances. … Their interrupted technology controls providers’ ability to process claims for payment, patient billing, and patient cost estimation services.”
“My understanding is Change/Optum touches almost every hospital in the US in one way or another,” John Riggi, AHA’s National Advisor for Cybersecurity and Risk, told Chief Healthcare Executive. “It has sector wide impact in potential risk. So, really, this is an attack on the entire sector.” Riggi spent nearly 30 years with the FBI.
Some physician practices may also have been impacted by the Change Healthcare cyberattack, according to the Medical Group Management Association (MGMA). In a letter to HHS, MGMA described negative changes in processes at doctors’ offices. They include delays in paper and electronic statements “for the duration of the outage.”
In addition, “prescriptions are being called into pharmacies instead of being electronically sent, so patients’ insurance information cannot be verified by pharmacies, and [the patients] are forced to self-pay or go without necessary medication.”
Here are “just a few of the consequences medical groups have felt” since the Change Healthcare cyberattack, according to the MGMA:
Substantial billing and cash flow disruptions, such as a lack of electronic claims processing. Both paper and electronic statements have been delayed. Some groups have been without any outgoing charges or incoming payments for the duration of the outage.
Limited or no electronic remittance advice from health plans. Groups are having to manually pull and post from payer portals.
Prior authorization submissions have been rejected or have not been transmittable at all. This further exacerbates what is routinely ranked the number one regulatory burden by medical groups and jeopardizes patient care.
Groups have been unable to perform eligibility checks for patients.
Many electronic prescriptions have not been transmitted, resulting in call-in prescriptions to pharmacies or paper prescriptions for patients. Subsequently, patients’ insurance information cannot be verified by pharmacies, and they are forced to self-pay or go without necessary medication.
Lack of connectivity to important data infrastructure needed for success in value-based care arrangements, and other health information technology disruptions.
Medical laboratory leaders and pathologists are advised to consult with their colleagues in IT and cybersecurity on how to best prevent ransomware attacks. Labs hold vast amount of private patient information. Recent incidents suggest more steps and strategies may be needed to protect laboratory information systems and patient data.
This is the third of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin Inc.
Automation and AI-Powered Workflow Paves the Way for Consistent, Optimized Molecular Diagnostics and Pathology RCM
Third in a three-part series, this article will discuss how sophisticated revenue cycle management technology, including artificial intelligence (AI) capabilities, drives faster, more efficient revenue reimbursement for molecular and pathology testing.
Financial and operational leaders of molecular testing laboratories and pathology groups are under pressure to maximize the revenue collected from their services rendered. This is no easy task. Molecular claims, in particular, can be especially complex. This article outlines the specific areas in which automation and artificial intelligence (AI)-based workflows can improve revenue cycle management (RCM) for molecular diagnostic and pathology organizations so they can better meet their operational and financial goals.
AI can play a number of important roles in business. When it comes to RCM for diagnostic organizations, first and foremost, AI can inform decision-making processes by generating new or derived data, which can be used in reporting and analytics. It can also help understand likely outcomes based on historical data, such as an organization’s current outstanding accounts receivable (AR) and what’s likely to happen with that AR based on historic performance.
AI is also deployed to accelerate the creation of configurations and workflows. For example, generated or derived data can be used to create configurations within a revenue cycle workflow to address changes or shifts in likely outcomes, such as denial rates. Suppose an organization is using AI to analyze historical denial data and predict denial rates. In that case, changes in those predicted denial rates can be used to modify a workflow to prevent those denials upfront or to automate appeals on the backend. This helps organizations adapt to changes more quickly and accelerates the time to reimbursement.
“Furthermore, AI is used to automate workflows by providing or informing decisions directly,“ says Clarisa Blattner, XiFin Senior Director of Revenue and Payor Optimization. “In this case, when the AI sees shifts or changes, it knows what to do to address them. This enables an organization to take a process in the revenue cycle workflow that is very human-oriented and automate it.”
AI is also leveraged to validate data and identify outcomes that are anomalous, or that lie outside of the norm. This helps an organization:
Ensure that the results achieved meet the expected performance
Understand whether the appropriate configurations are in place
Identify if an investigation is required to uncover the reason behind any anomalies so that they can be addressed
Finally, AI can be employed to generate content, such as letters or customer support materials.
Everything AI starts with data
Everything AI-related starts with the data. Without good-quality data, organizations can’t generate AI models that will move a business forward. In order to build effective AI models, an organization must understand the data landscape and be able to monitor and measure performance and progress and adjust the activities being driven, as necessary.
Dirty, unstructured data leads to unintelligent AI. AI embodies the old adage, “garbage in, garbage out.” The quality of the AI decision or prediction is entirely based on the historical data that it’s seen. If that data is faulty, flawed, or incomplete, it can lead to bad decisions or the inability to predict or make a decision at all. Purposeful data modeling is critical to AI success, and having people and processes that can understand the complicated RCM data and structure it so it can be effectively analyzed is vital to success.
The next step is automation. Having effective AI models that generate strong predictions is only as valuable as the ability to get that feedback into the revenue cycle system effectively. If not, that value is minimal, because the organization must expend a lot of human energy to try to reconfigure or act on the AI predictions being generated.
There is a typical transformation path, illustrated below, that organizations go through to get from having data stored in individual silos to fully embedded AI. If an organization is struggling with aggregating data to build AI models, it’s at stage one. The goal is stage five, where an organization uses AI as a key differentiator and AI is a currency, driving activity.
The transformation starts with structuring data with an underlying data approach that keeps it future-ready. It is this foundation that allows organizations to realize the benefits of AI in a cost-effective and efficient way. Getting the automation embedded in the workflow is the key to getting to the full potential of AI in improving the RCM process.
Real-world examples of how AI and automation improve RCM
One example of how AI can improve the RCM process is using AI to discover complex payer information. One significant challenge for diagnostic service providers is ensuring that the right third-party insurance information for patients is captured. This is essential for clean claims submission. Often, the diagnostic provider is not the organization that actually sees the patient, in which case it doesn’t have the ability to collect that information directly. The organization must rely on the referring physician or direct outreach to the patient for this data when it’s incorrect or incomplete.
Diagnostic providers are sensitive to not burdening referring clients or patients with requests for demographic or payer information. It’s important to make this experience as simple and smooth as possible. Also, insurance information is complicated. A lot of data must be collected or corrected if the diagnostic provider doesn’t have the correct information.
Automating this process is difficult. Frequently, understanding who the payer is and how that payer translates into contracts and mapping within the revenue cycle process requires an agent to be on the phone with the patient. It can be very difficult for a patient to get precise payer plan information from their insurance card without the help of a customer service representative.
This is where AI can help. The goal is to require the smallest amount of information from a patient and be able to verify eligibility through electronic means with the payer. Using optical character recognition (OCR), an organization can take an image of the front and back of a patient’s insurance card, isolate the relevant text, and use an AI model to get the information needed in order to generate an eligibility request and confirm eligibility with that payer.
In the event that taking an image of the insurance card is problematic for a patient, the organization can have the patient walk through a simplified online process, for example, through a patient portal, and provide just a few pieces of data to be able to run eligibility verification and get to confirmed eligibility with the payer.
AI can help with this process too. For example, the patient can provide high-level payer information only, such as the name of the commercial payer or whether the coverage is Medicare or Medicaid, the state the patient resides in, and the subscriber ID and AI can use this high-level data to get an eligibility response and confirmed eligibility.
Once the eligibility response is received, the more detailed payer information can be presented back to the patient for confirmation. AI can map the eligibility response to the appropriate contract or payer plan within the RCM system.
Now that the patient’s correct insurance information is captured, the workflow moves on to collecting the patient’s financial responsibility payment. To do that, the organization needs to be able to calculate the patient’s financial responsibility estimate. The RCM system has accurate pricing information and now has detailed payer and plan information, a real-time eligibility response, as well as test or procedure information. This data can be used to estimate patient financial responsibility.
AI can also be used to address and adapt to changes in ordering patterns, payer responses, and payer reimbursement behavior. The RCM process can be designed to incorporate AI to streamline claims, denials, and appeals management, as well as to assign work queues and prioritize exception processing (EP) work based on the likelihood of reimbursement, which improves efficiency.
One other way AI can help is in understanding and or maintaining “expect” prices—what an organization can expect to collect from particular payers for particular procedures. For contracted payers, contracted rates are loaded into the RCM system. It’s important to track whether payers are paying those contracted rates and whether the organization is receiving the level of reimbursement expected. For non-contracted payers, it’s harder to know what the reimbursement rate will be. Historical data and AI can provide a good understanding of what can be expected. AI can also be used to determine if a claim is likely to be rejected because of incorrect or incomplete payer information or patient ineligibility, in which case automation can be applied to resolve most issues.
Another AI benefit relates to quickly determining the probability of reimbursement and assigning how claims are prioritized if a claim requires intervention that cannot be automated. With AI, these claims that require EP are directed to the best available team member, based on that particular team member’s past success with resolving a particular error type.
The goal with EP is to ensure that the claims are prioritized to optimize reimbursement. This starts with understanding the probability of the claim being reimbursed. An AI model can be designed to assess the likelihood of the claim being reimbursed and the likely amount of reimbursement for those expected to be paid. This helps prioritize activities and optimize labor resources. The AI model can also take important factors such as timely filing dates into account. If a claim is less likely to be collected than another procedure but is close to its timely filing deadline, it can be escalated. The algorithms can be run nightly to produce a prioritized list of claims with assignments to the specific team member best suited to address each error.
AI can also be used to create a comprehensive list of activities and the order in which those activities should be performed to optimize reimbursement. The result is a prioritized list for each team member indicating which claims should be worked on first and which specific activities need to be accomplished for each claim.
Summing it all up, organizations need an RCM partner with a solid foundation in data and data modeling. This is essential to being able to effectively harness the power of AI. In addition, the RCM partner must offer the supporting infrastructure to interface with referring clients, patients, and payers. This is necessary to maximize automation and smoothly coordinate RCM activities across the various stakeholders in the process.
Having good AI and insight into data and trends is important, but the ability to add automation to the RCM process based on the AI really solidifies the benefits and delivers a return on investment (ROI). Analytics are also essential for measuring and tracking performance over time and identifying opportunities for further improvement.
Diagnostic executives looking to maximize reimbursement and keep the cost of collection low will want to explore how to better leverage data, AI, automation, and analytics across their RCM process.
This is the third of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin Inc. Missed the first two articles? www.darkdaily.com
Some hospital organizations are pushing back, stating that the new regulations are ‘too rigid’ and interfere with doctors’ treatment of patients
In August, the Biden administration finalized provisions for hospitals to meet specific treatment metrics for all patients with suspected sepsis. Hospitals that fail to meet these requirements risk the potential loss of millions of dollars in Medicare reimbursements annually. This new federal rule did not go over well with some in the hospital industry.
Sepsis kills about 350,000 people every year. One in three people who contract the deadly blood infection in hospitals die, according to the Centers for Disease Control and Prevention (CDC). Thus, the federal government has once again implemented a final rule that requires hospitals, clinical laboratories, and medical providers to take immediate actions to diagnose and treat sepsis patients.
The effort has elicited pushback from several healthcare organizations that say the measure is “too rigid” and “does not allow clinicians flexibility to determine how recommendations should apply to their specific patients,” according to Becker’s Hospital Review.
Perform blood tests within a specific period of time to look for biomarkers in patients that may indicate sepsis, and to
Administer antibiotics within three hours after a possible case is identified.
It also mandates that certain other tests are performed, and intravenous fluids administered, to prevent blood pressure from dipping to dangerously low levels.
“These are core things that everyone should do every time they see a septic patient,” said Steven Simpson, MD, Professor of medicine at the University of Kansas told Fierce Healthcare. Simpson is also the chairman of the Sepsis Alliance, an advocacy group that works to battle sepsis.
Simpson believes there is enough evidence to prove that the SEP-1 guidelines result in improved patient care and outcomes and should be enforced.
“It is quite clear that this works better than what was present before, which was nothing,” he said. “If the current sepsis mortality rate could be cut by even 5%, we could save a lot of lives. Before, even if you were reporting 0% compliance, you didn’t lose your money. Now you actually have to do it,” Simpson noted.
“We are encouraged by the increased attention to sepsis and support CMS’ creation of a sepsis mortality measure that will encourage hospitals to pay more attention to the full breadth of sepsis care,” Chanu Rhee, MD (above), Infectious Disease/Critical Care Physician and Associate Hospital Epidemiologist at Brigham and Women’s Hospital told Healthcare Finance. The new rule, however, requires doctors and medical laboratories to conduct tests and administer antibiotic treatment sooner than many healthcare providers deem wise. (Photo copyright: Brigham and Women’s Hospital.)
Healthcare Organizations Pushback against Final Rule
“By encouraging the use of broad spectrum antibiotics when more targeted ones will suffice, this measure promotes the overuse of the antibiotics that are our last line of defense against drug-resistant bacteria,” the AHA’s letter states.
In its recent coverage of the healthcare organizations’ pushback to CMS’ final rule, Healthcare Finance News explained, “The SEP-1 measure requires clinicians to provide a bundle of care to all patients with possible sepsis within three hours of recognition. … But the SEP-1 measure doesn’t take into account that many serious conditions present in a similar fashion to sepsis … Pushing clinicians to treat all these patients as if they have sepsis … leads to overuse of broad-spectrum antibiotics, which can be harmful to patients who are not infected, those who are infected with viruses rather than bacteria, and those who could safely be treated with narrower-spectrum antibiotics.”
CMS’ latest rule follows the same evolutionary path as previous federal guidelines. In August 2007, CMS announced that Medicare would no longer pay for additional costs associated with preventable errors, including situations known as Never Events. These are “adverse events that are serious, largely preventable, and of concern to both the public and healthcare providers for the purpose of public accountability,” according to the Leapfrog Group.
In 2014, the CDC suggested that all US hospitals have an antibiotic stewardship program (ASP) to measure and improve how antibiotics are prescribed by clinicians and utilized by patients.
Research Does Not Show Federal Sepsis Programs Work
He points to analysis which showed that though use of broad-spectrum antibiotics increased after the original 2015 SEP-1 regulations were introduced, there has been little change to patient outcomes.
“Unfortunately, we do not have good evidence that implementation of the sepsis policy has led to an improvement in sepsis mortality rates,” Rhee told Fierce Healthcare.
Rhee believes that the latest regulations are a step in the right direction, but that more needs to be done for sepsis care. “Retiring past measures and refining future ones will help stimulate new innovations in diagnosis and treatment and ultimately improve outcomes for the many patients affected by sepsis,” he told Healthcare Finance.
Sepsis is very difficult to diagnose quickly and accurately. Delaying treatment could result in serious consequences. But clinical laboratory blood tests for blood infections can take up to three days to produce a result. During that time, a patient could be receiving the wrong antibiotic for the infection, which could lead to worse problems.
The new federal regulation is designed to ensure that patients receive the best care possible when dealing with sepsis and to lower mortality rates in those patients. It remains to be seen if it will have the desired effect.
Researchers intend their new AI image retrieval tool to help pathologists locate similar case images to reference for diagnostics, research, and education
Researchers at Stanford University turned to an unusual source—the X social media platform (formerly known as Twitter)—to train an artificial intelligence (AI) system that can look at clinical laboratory pathology images and then retrieve similar images from a database. This is an indication that pathologists are increasingly collecting and storing images of representative cases in their social media accounts. They then consult those libraries when working on new cases that have unusual or unfamiliar features.
The Stanford Medicine scientists trained their AI system—known as Pathology Language and Image Pretraining (PLIP)—on the OpenPath pathology dataset, which contains more than 200,000 images paired with natural language descriptions. The researchers collected most of the data by retrieving tweets in which pathologists posted images accompanied by comments.
“It might be surprising to some folks that there is actually a lot of high-quality medical knowledge that is shared on Twitter,” said researcher James Zou, PhD, Assistant Professor of Biomedical Data Science and senior author of the study, in a Stanford Medicine SCOPE blog post, which added that “the social media platform has become a popular forum for pathologists to share interesting images—so much so that the community has widely adopted a set of 32 hashtags to identify subspecialties.”
“It’s a very active community, which is why we were able to curate hundreds of thousands of these high-quality pathology discussions from Twitter,” Zou said.
“The main application is to help human pathologists look for similar cases to reference,” James Zou, PhD (above), Assistant Professor of Biomedical Data Science, senior author of the study, and his colleagues wrote in Nature Medicine. “Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing, and education.” Leveraging pathologists’ use of social media to store case images for future reference has worked out well for the Stanford Medicine study. (Photo copyright: Stanford University.)
Retrieving Pathology Images from Tweets
“The lack of annotated publicly-available medical images is a major barrier for innovations,” the researchers wrote in Nature Medicine. “At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter.”
In this case, the goal “is to train a model that can understand both the visual image and the text description,” Zou said in the SCOPE blog post.
“Pathology is perhaps even more suited to Twitter than many other medical fields because for most pathologists, the bulk of our daily work revolves around the interpretation of images for the diagnosis of human disease,” wrote Jerad M. Gardner, MD, a dermatopathologist and section head of bone/soft tissue pathology at Geisinger Medical Center in Danville, Pa., in a blog post about the Pathology Hashtag Ontology project. “Twitter allows us to easily share images of amazing cases with one another, and we can also discuss new controversies, share links to the most cutting edge literature, and interact with and promote the cause of our pathology professional organizations.”
The researchers used the 32 subspecialty hashtags to retrieve English-language tweets posted from 2006 to 2022. Images in the tweets were “typically high-resolution views of cells or tissues stained with dye,” according to the SCOPE blog post.
The researchers collected a total of 232,067 tweets and 243,375 image-text pairs across the 32 subspecialties, they reported. They augmented this with 88,250 replies that received the highest number of likes and had at least one keyword from the ICD-11 codebook. The SCOPE blog post noted that the rankings by “likes” enabled the researchers to screen for high-quality replies.
They then refined the dataset by removing duplicates, retweets, non-pathology images, and tweets marked by Twitter as being “sensitive.” They also removed tweets containing question marks, as this was an indicator that the practitioner was asking a question about an image rather than providing a description, the researchers wrote in Nature Medicine.
They cleaned the text by removing hashtags, Twitter handles, HTML tags, emojis, and links to websites, the researchers noted.
The final OpenPath dataset included:
116,504 image-text pairs from Twitter posts,
59,869 from replies, and
32,041 image-text pairs scraped from the internet or obtained from the LAION dataset.
The latter is an open-source database from Germany that can be used to train text-to-image AI software such as Stable Diffusion.
Training the PLIP AI Platform
Once they had the dataset, the next step was to train the PLIP AI model. This required a technique known as contrastive learning, the researchers wrote, in which the AI learns to associate features from the images with portions of the text.
As explained in Baeldung, an online technology publication, contrastive learning is based on the idea that “it is easier for someone with no prior knowledge, like a kid, to learn new things by contrasting between similar and dissimilar things instead of learning to recognize them one by one.”
“The power of such a model is that we don’t tell it specifically what features to look for. It’s learning the relevant features by itself,” Zou said in the SCOPE blog post.
The resulting AI PLIP tool will enable “a clinician to input a new image or text description to search for similar annotated images in the database—a sort of Google Image search customized for pathologists,” SCOPE explained.
“Maybe a pathologist is looking at something that’s a bit unusual or ambiguous,” Zou told SCOPE. “They could use PLIP to retrieve similar images, then reference those cases to help them make their diagnoses.”
The Stanford University researchers continue to collect pathology images from X. “The more data you have, the more it will improve,” Zou said.
Pathologists will want to keep an eye on the Stanford Medicine research team’s progress. The PLIP AI tool may be a boon to diagnostics and improve patient outcomes and care.
Study findings could lead to new clinical laboratory testing biomarkers designed to assess for male infertility
Clinical laboratories are increasingly performing tests that have as their biomarkers the DNA and enzymes found in human microbiota. And microbiologists and epidemiologists know that like other environments within the human body, semen has its own microbiome. Now, a study conducted at the University of California, Los Angeles (UCLA) has found that the health of semen microbiome may be linked to male infertility.
The UCLA researchers discovered a small group of microorganisms within semen that may impair the sperm’s motility (its ability to swim) and affect fertility.
A total of 73 individuals were included in the study. About half of the subjects were fertile and already had children, while the remaining men were under consultation for fertility issues.
“These are people who have been trying to get pregnant with their partner, and they’ve been unsuccessful,” Sriram Eleswarapu, MD, PhD, a urologist at UCLA and co-author of the study, told Scientific American. “This latter group’s semen samples had a lower sperm count or motility, both of which can contribute to infertility.”
“There is much more to explore regarding the microbiome and its connection to male infertility,” said Vadim Osadchiy, MD (above), a resident in the Department of Urology at UCLA and lead author of the study, in a UCLA news release. “However, these findings provide valuable insights that can lead us in the right direction for a deeper understanding of this correlation.” Might it also lead to new biomarkers for clinical laboratory testing for male infertility? (Photo copyright: UCLA.)
Genetic Sequencing Used to Identify Bacteria in Semen Microbiome
Most of the microbes present in the semen microbiome originate in the glands of the male upper reproductive tract, including the testes, seminal vesicles and prostate, and contribute various components to semen. “Drifter” bacteria that comes from urine and the urethra can also accumulate in the fluid during ejaculation. Microbes from an individual’s blood, or his partner’s, may also aggregate in semen. It is unknown how these bacteria might affect health.
“I would assume that there are bacteria that are net beneficial, that maybe secrete certain kinds of cytokines or chemicals that improve the fertility milieu for a person, and then there are likely many that have negative side effects,” Eleswarapu told Scientific American.
The scientists used genetic sequencing to identify different bacteria species present within the semen microbiome. They found five species that were common among all the study participants. But men with more of the microbe Lactobacillus iners (L. iners) were likelier to have impaired sperm motility and experience fertility issues.
This discovery was of special interest to the team because L. iners is commonly found in the vaginal microbiome. In females, high levels of L. iners are associated with bacterial vaginosis and have been linked to infertility in women. This is the first study that found a negative association between L. iners and male fertility.
The researchers plan to investigate specific molecules and proteins contained in the bacteria to find out whether they slow down sperm in a clinical laboratory situation.
“If we can identify how they exert that influence, then we have some drug targets,” Eleswarapu noted.
Targeting Bacteria That Cause Infertility
The team also discovered that three types of bacteria found in the Pseudomonas genus were present in patients who had both normal and abnormal sperm concentrations. Patients with abnormal sperm concentrations had more Pseudomonas fluorescens and Pseudomonas stutzeri and less Pseudomonas putida in their samples.
According to the federal National Institute of Child Health and Human Development (NICHD), “one-third of infertility cases are caused by male reproductive issues, one-third by female reproductive issues, and the remaining one-third by both male and female reproductive issues or unknown factors.” Thus, learning more about how the semen microbiome may be involved in infertility could aid in the development of drugs that target specific bacteria.
“Our research aligns with evidence from smaller studies and will pave the way for future, more comprehensive investigations to unravel the complex relationship between the semen microbiome and fertility,” said urologist Vadim Osadchiy, MD, a resident in the Department of Urology at UCLA and lead author of the study, in a UCLA news release.
More research is needed. For example, it’s unclear if there are any links between the health of semen microbiome and other microbiomes that exist in the body, such as the gut microbiome, that cause infertility. Nevertheless, this research could lead to new biomarkers for clinical laboratory testing to help couples who are experiencing fertility issues.
