Seventy-five healthcare systems will benefit from NETEC’s gift, applications open.
Infectious disease management is getting a significant lift, on October 15, 2025 the National Emerging Special Pathogens Training and Education Center (NETEC) announced their $37.5m grant aimed at High-Consequence Infectious Diseases (HCID), a NETEC press release announced. Labs working with High-Consequence Pathogen and Pathology will find this increased area of focus relevant and may look to encourage their employers apply to become one of the 75 facilities that will receive the grant in the US.
The grant, funded by the Administration for Strategic Preparedness and Response (ASPR) is slated to provide or upgrade the chosen 75 healthcare facilities within the US with a Level 2 Special Pathogen Systems (NSPS) status, NETEC added.
Selected hospitals will “receive up to $500,000 each to improve critical infrastructure, conduct staff training, and obtain specialized equipment aligned with NSPS Level 2 requirements,” according to the American Hospital Association (AHA).
“Level 2 centered are the backbone of a resilient, skilled response to special pathogen threats,” Shelly Schwedhelm, MSN, RN, NEA-BC, Executive Director of NSPS told NETEC. (Photo credit: UNMC)
“This initiative accelerates our mission to develop top tier care closer to communities nationwide while strengthening protective measures for our healthcare workers,” Shelly Schwedhelm, executive director of NSPS, told NETEC. The grant comes by way of the NSPS Level 2 Special Pathogen Treatment and Network Development (STAND) Award.
Positive Focus & Raising the Bar
The union of emergency providers and public health partners is crucial for disease maintenance, and NETEC’s vision paired with the grant aims to “provide safe, high-quality care during HCID outbreaks,” the press release noted.
Facilities moving into Level 2 status set the tone for more specialized care. These facilities are “specialized treatment centers that care for patients throughout the duration of illness,” the NETEC noted. To help facilities achieve this status is significant, John Lowe, PhD, NETEC co-principal investigator at University of Nebraska Medical Center, told NETEC. “This grant provides indispensable support for facilities striving to meet NSPS standards—from infrastructure upgrades to advanced training—making readiness both realistic and sustainable,” he commented.
The Importance of HDICs
HDICs, defined by the NETEC as having “high death rates and intense illnesses with limited remedies available,” are important to tackle for reasons beyond the obvious, NETEC noted.
“HDICs pose a significant threat to domestic and global security…some can be used as bioterrorism agents,” the Centers for Disease Control (CDC) noted on their site. They added that many HDICs spread from animals to humans and are contagious.
Looking Ahead
“We have a vision of a future with fewer infections and less suffering caused by high-consequence pathogens and disabling illnesses of unexplained causes,” CDC stated.
How to Apply
Labs interested in applying for the grant must have their employer submit proposals by 12/2/2025. Eligible hospitals must be in the US and offer inpatient services, emergency departments and critical care ability, as well as have airborne infection isolation capability. Both federal facilities and those already at Level 1 are not eligible.
With the government shutdown now stretching beyond two weeks, clinical laboratory leaders are beginning to feel the pinch. Experts warn that delays in Medicare payments could soon create cash-flow crunches and backlog claims well into November.
As the federal government shutdown stretches into its third week, laboratory leaders are warning of mounting financial pressure and potential payment delays that could disrupt operations and strain cash flow.
While clinical laboratories can continue to submit Medicare and Medicaid claims, the timing of reimbursements could soon become unpredictable. According to William Baus, a laboratory revenue cycle expert, who shared a visual on LinkedIn, “a government shutdown doesn’t stop you from submitting claims—but it can affect when you get paid.”
In his Oct. 11 post, Baus outlined the timeline of expected payment impacts. If the shutdown lasts fewer than 14 days (at the time this piece was written, the government shutdown entered its 17th day), Medicare reimbursements would have remained unaffected, since the Centers for Medicare and Medicaid Services (CMS) typically maintains a 14-day payment floor. But if the shutdown continues beyond that window (which it now has), the system begins to back up quickly.
For a 20-day shutdown, for instance, “payments are delayed about five business days,” Baus noted. Claims submitted October 1 would not pay out until October 21, creating a rolling backlog into November. “Bottom line,” he wrote, “a short shutdown = no impact. A longer shutdown = temporary cash-flow crunch.”
For independent laboratories and pathology groups, especially those with thin operating margins, these delays could create significant short-term liquidity challenges. Many smaller or privately owned labs depend on steady reimbursement cycles to cover payroll, reagents, and lease expenses. Even a week-long delay in large Medicare payments can tighten available cash.
Hospital and health-system labs may have more flexibility, but even they face potential ripple effects if system-wide financial operations slow down or if supply purchases and contractor payments need to be deferred.
Medicaid and ACA Impacts
Ann Lambrix, vice president of revenue cycle management at Lighthouse Lab Services, echoed those concerns in a LinkedIn post of her own, warning that providers should brace for payment delays as the shutdown continues. “Healthcare providers should prepare for potential delays in claim processing and payments from Medicare,” Lambrix wrote. She noted that while “Medicaid [is] funded through Q1 of next year,” proposed cuts to enhanced subsidies “may threaten ACA coverage for individuals choosing to obtain health insurance through marketplace plans.” Lambrix thanked William Baus for his visual summary of the shutdown’s financial ripple effects, underscoring how even temporary disruptions in federal operations can upend reimbursement timelines across the healthcare sector.
Operational Preparedness
Lab leaders should prepare contingency plans, including:
Closely monitoring accounts receivable aging reports for delayed remittances.
Reviewing cash reserves and establishing short-term credit options if needed.
Communicating with vendors and staff about possible timing issues.
Staying in contact with billing vendors and clearinghouses to track any system backlogs.
“Claims can still be submitted and processed electronically,” Baus emphasized, “but the payment cycle may slip depending on how long the shutdown lasts.”
The Takeaway
In the short term, laboratories should brace for administrative slowdowns rather than outright denials. Yet as the shutdown continues, payment backlogs could cascade, especially for labs heavily reliant on Medicare revenue.
For now, experts recommend vigilance, conservative spending, and clear communication with financial teams. As the shutdown persists, even well-run labs could feel the pinch of delayed federal payments before November begins.
Genetic, toxicology, and even routine panels can create pitfalls for clinical laboratories.
Clinical laboratory professionals involved with diagnostic billing and coding should double check claims submitted for routine, toxicology, and genetic testing. Those three testing types are inviting private payer scrutiny and possibly worse.
“Between audits, denials, and government crackdowns, the risks are higher than ever,” said Jamel Giuma, founder and CEO at JTG Consulting Group, a laboratory IT consulting company.
Routine Panels Can Create Headaches for Lab Billing
Giuma explained that when it comes to diagnostic billing and coding, three testing areas often get oversized attention from commercial payers and the Medicare program:
Routine panels. Lab professionals should beware of overordering these types of tests, such as lipid or metabolic panels. High volumes have attracted payer audits, Giuma said. The US Department of Health and Human Services’ Office of Inspector General (OIG) has previously noted investigations where lipid panels were billed with direct low-density lipoprotein cholesterol tests to the same patient on the same day, which the OIG said was medically unnecessary.
Toxicology tests. Some drug testing panels to detect pain management or substance abuse have received scrutiny in 2025 for their ordering frequency.
Genetic testing. Expensive DNA and molecular assays may get an extra look from insurers, particularly services that get tagged with CPT code 81479. The code is a vague catch-all for unlisted molecular pathology procedures. The Dark Report has noted that using 81479 is essentially begging a payer to review the claim. Giuma added that this can be a tricky area for labs developing investigational tests.
Giuma said based on data he has reviewed, payers initially deny one in five clinical lab billing claims. “That is staggering,” he noted.
Jamel Giuma noted that one of every five clinical lab billing claims gets denied by payers, making the submission process a thorny one for laboratories. (Photo credit: JTG Consulting)
Investigators Eye Laboratory Test Fraud
Meanwhile, the OIG, auditors from the Centers for Medicare and Medicaid Services, and investigators from the US Department of Justice also scrutinize diagnostic billing and coding patterns.
“They’re looking for repeat offenders or systematic over-coding,” Giuma said.
Earlier this year, as part of the largest healthcare fraud bust in US history, dozens of clinical laboratories were charged with Medicare fraud for alleged telemedicine and genetic testing schemes where deceptive telemarketing campaigns targeted Medicare beneficiaries.
Even the most scrupulous labs should heed the indictments from the fraud investigations. All laboratories can run into trouble if they don’t stay on top of compliance efforts to detect fraud risks.
Documentation of billing code justifications and claims submissions are a solid first line of defense, Giuma said.
Also, labs should closely monitor prior authorization processes and understand associated rules from payers, he added.
Giuma’s advice emphasizes that diagnostic billing and coding is an area that clinical labs can unnecessarily get snarled in if providers are not careful about how and when they order tests.
A new analysis shows why models fall short in practice, how liability and equity issues slow adoption, and what lab leaders should consider as AI becomes a growing part of diagnostic workflows.
Artificial intelligence (AI) has made notable advances in medical imaging, but radiologists are not being displaced. For laboratory and diagnostic leaders, a recent analysis in Works in Progress highlights why AI has not replaced human expertise in radiology—and what this means for managing technology adoption in labs and hospitals.
In 2016, AI pioneer Geoffrey Hinton declared that “people should stop training radiologists now.” Since then, more than 700 FDA-cleared radiology AI models have entered the market, covering everything from stroke detection to lung cancer screening.
Companies such as Annalise.ai, Lunit, Aidoc, and Qure.ai offer tools that can identify dozens of diseases across modalities, reorder worklists, or generate structured draft reports. “On paper, radiology looks like the perfect target for automation,” the article noted, citing its reliance on digital images, pattern recognition, and quantitative benchmarks. Yet demand for radiologists has never been higher. In 2025, US residency programs offered a record 1,208 positions, and vacancy rates remain high as well.
Why Hasn’t AI Taken Over?
For leaders overseeing diagnostic services, three key elements are why AI has not replaced radiologists.
First, models struggle in real-world deployment. “Performance can drop by as much as 20 percentage points” when systems trained on narrow datasets are applied across different scanners, imaging protocols, or patient populations, the article explained. What works in a benchmark test may falter in a hospital with diverse workflows.
Second, liability and regulatory hurdles remain high. Assistive models that require physician review face fewer barriers, but autonomous systems must self-abort on poor image quality, identify unfamiliar equipment, and withstand rigorous scrutiny. Insurers have also drawn hard lines: one malpractice policy states that “coverage applies solely to interpretations reviewed and authenticated by a licensed physician; no indemnity is afforded for diagnoses generated autonomously by software.” Another bluntly imposes an “Absolute AI Exclusion.” For labs, this underscores the importance of risk management before deploying AI tools.
Photo credit: “Artificial Intelligence – Resembling Human Brain” by deepakiqlect is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/?ref=openverse.
Photo credit: “Cancer” by davis.steve32 is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/?ref=openverse.
Third, radiologists do much more than read scans. “Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians,” the commentary pointed out. Oversight of imaging protocols, interdisciplinary consultations, and patient communication all fall outside the reach of algorithms. Even as AI improves, demand for imaging may increase rather than decrease—a version of the Jevons paradox where greater efficiency leads to higher use. “The better the machines, the busier radiologists have become,” the article observed.
For laboratory leaders, the takeaway is not to fear replacement but to prepare for integration. AI tools are proving valuable in triaging urgent cases, flagging abnormalities, and drafting reports, but they remain narrow in scope—stroke, lung cancer, and breast lesions account for about 60% of models, yet represent only a fraction of total imaging work. As the article concluded, “Models can lift productivity, but their implementation depends on behavior, institutions and incentives.”
The challenge for labs is to create environments where AI augments human expertise rather than attempts to replace it. That means aligning technology adoption with clinical needs, providing training for staff, and working with insurers and regulators to ensure coverage and compliance.
For now, radiologists and the labs that support them are not going away. They are adapting, and AI will be a partner in that evolution.
A UCLA microbiology lab used whole-genome sequencing to trace a carbapenem-resistant Pseudomonas outbreak to a single ICU sink, revealing how biofilm and plumbing can silently harbor superbugs.
A routine culture from an ICU patient at UCLA Health sparked an investigation that ultimately uncovered a silent, domestic outbreak of a highly resistant strain of Pseudomonas aeruginosa. The discovery was led by the Molecular Microbiology and Pathogen Genomics Laboratory and highlights the critical role clinical laboratories play in outbreak detection, antimicrobial resistance surveillance, and environmental tracking.
The initial isolate appeared typical: P. aeruginosa, a common hospital-associated pathogen. But further analysis revealed something more troubling, the presence of NDM-1 (New Delhi metallo-β-lactamase), an enzyme that breaks down carbapenems and other powerful beta-lactam antibiotics, rendering them ineffective.
“This was the first time we’d ever seen an NDM-1-producing Pseudomonas strain in our hospital—and in a patient with no international travel,” said Shangxin Yang, PhD, director of UCLA Health’s Molecular Microbiology and Pathogen Genomics Laboratory.
Shangxin Yang, PhD, director of UCLA Health’s Molecular Microbiology and Pathogen Genomics Laboratory noted, “While NDM-1 is prevalent in Asia, Europe and the Middle East, it remains rare in the United States. That’s when we knew this wasn’t imported. This was something domestic—and very concerning.” (Photo credit: UCLA)
Sporadic Cases, Elusive Source
Over the next 18 months, seven additional patients were identified with the same rare resistance pattern. The cases were sporadic—spread across time and units—and did not follow conventional outbreak patterns, complicating source identification.
In collaboration with UCLA Health’s infection prevention team, the lab launched a detailed investigation. Routine epidemiologic methods failed to identify commonalities between the cases. Shared equipment, staffing patterns, and care protocols were ruled out. With limited leads, the microbiology team turned to whole-genome sequencing (WGS).
Whole-Genome Sequencing Connects the Dots
WGS became the turning point. By sequencing all eight patient isolates and comparing them to environmental samples, Yang’s lab determined that seven of the eight clinical isolates and two environmental strains shared an almost identical genomic profile. Only one isolate, from a patient previously treated in Iran, was genetically distinct.
“Whole-genome sequencing gave us the clarity we needed,” said Yang. “It allowed us to move from hypothesis to high-resolution confirmation—pinpointing the genetic relatedness of these organisms with certainty.”
The team had uncovered a clonal outbreak of NDM-1-producing P. aeruginosa, likely stemming from a single environmental reservoir.
Unexpected Reservoir: An ICU Sink
During a third round of environmental testing, the lab isolated the same NDM-1-producing strain from a contaminated sink drain and P-trap in one ICU room. Notably, two of the eight patients had been admitted to that room more than a year apart.
The persistence of the organism was attributed to biofilm formation in the sink plumbing. Pseudomonas is known for forming robust biofilms that adhere to moist surfaces and resist standard disinfection methods.
“This wasn’t just about surface contamination,” said Yang. “This was a deeply embedded reservoir that conventional cleaning protocols couldn’t touch.”
Lab-Driven Response and Mitigation
Once the lab identified the environmental source, targeted interventions were put in place:
Weekly disinfection of ICU sinks using Virasept, a biofilm-effective agent
Plumbing replacement, including P-trap components known to harbor persistent biofilms
Engineering modifications to faucet angles to reduce splash-back and droplet spread
Expanded environmental surveillance to monitor other sinks for colonization
The lab continued to monitor the situation post-intervention, and no further cases of NDM-1-producing P. aeruginosa have been identified since the changes were implemented.
Lessons Learned
This case reinforces the value of whole-genome sequencing in resolving complex outbreaks, linking patient isolates to an environmental source that traditional methods missed. It highlights the need to include plumbing and other biofilm-prone areas in environmental sampling. Most importantly, it shows how microbiology labs through genomic, phenotypic, and molecular tools can lead outbreak investigations, especially when paired with strong cross-department collaboration.
“This is a clear example of the power of the clinical lab when genomic tools and environmental surveillance are used strategically,” said Yang. “Without WGS, this would have remained an unsolved mystery.”
New program draws bipartisan criticism and concern from patients and doctors.
Shrewd labs will keep an eye on the latest Centers for Medicare & Medicaid Services (CMS) prior authorization pilot that leans on artificial intelligence (AI) to determine treatment options for Medicare patients. While the Wasteful and Inappropriate Service Reduction Model pilot (WISeR) doesn’t directly mention lab tests, staying on the pulse of this growing trend will keep labs thinking ahead on how to minimize impact on bottom line, paperwork, and workflows when these pilots infiltrate lab testing.
An article from POLITICO reported that CMS will start a pilot version of the program as early as January 2026 in six states including Ohio, Texas, Oklahoma, Ariz., N.J., and Wash. Private AI companies will assist and focus on “services that have been vulnerable to fraud, waste and abuse in the past,” the article noted. The voluntary model is slated to span six years through December 31, 2031, according to the Centers for Disease Control and Prevention (CDC).
Among the types of procedures encumbered by the pilot program are knee arthroscopy for osteoarthritis, skin and tissue substitutions, and electrical nerve stimulator implants, CMS noted. All outpatient and emergency services would currently be excluded, they added, as well as “services that would pose a substantial risk to patients if substantially delayed.”
“All recommendations for non-payment will be determined by appropriately licensed clinicians who will apply standardized, transparent, and evidence-based procedures to their review,” CMS added.
The premise of the pilot is to eliminate wasteful spending, with CMS citing 25% of US healthcare spending falling in this category. “According to the Medicare Payment Advisory Commission Medicare spent up to $5.8 billion in 2022 on unnecessary or inappropriate services with little to no clinical benefit,” their website noted.
A Sour Reception
The pilot program is receiving a less-than-warm welcome from both parties—doctors, and patients alike, Politico noted. “It’s been referred to as the AI death panel. You get more money if you’re that AI tech company if you deny more claims. That is going to lead to people getting hurt,” Greg Landsman (D-Ohio) said during the committee hearing.
Landsman noted in the article from POLITICO that a bipartisan desire to put a halt to the program exists among growing concerns about patient harm coming from the program. Landsman “called for the program to be shut down until an independent review board could be erected to review the liability questions and ensure the AI prior authorization pilot doesn’t harm patients.”
“I’m concerned that this AI model will result in denials of lifesaving care and incentivize companies to restrict care,” Frank Pallone (D-N.J.) and House Energy and Commerce Committee ranking member said at the subcommittee meeting on the use of AI in health care held on Sept. 3.
“We have pretty good evidence that prior authorization as a process itself is fraught, adding that AI’s ability to improve the process for patients remains unproven,” Michelle Mello, Stanford University health law professor and witness at the hearing, said.
Looking Ahead
The involvement of AI in healthcare will only continue, and learning what aspects positively impact healthcare versus cause damage will continue to evolve.
Worth noting, there are already two unrelated lawsuits, against UnitedHealthcare and Cigna, that challenge the safety of AI use to deny patient care, POLITICO noted in the article.
Laboratory leaders should keep their eyes open and their ears to the ground on not only the pilot but all AI healthcare trends.
