New vaccine could give clinical laboratories and antimicrobial stewardship programs the tool they need to dramatically reduce hospital-acquired infections
The innovative approach focuses on bolstering the patient’s immune system itself, rather than relying on proteins to fight infections, according to a USC Today article.
Developed by senior study author Brad Spellberg, MD, Chief Medical Officer at the Los Angeles General Medical Center, and colleagues, “The experimental vaccine takes an entirely different approach: It gooses the body’s preexisting supply of pathogen-gobbling immune cells called macrophages, which engulf and digest bacteria, fungi, and other bad actors. These activated fighters, found in all tissues, quickly neutralize incoming invaders which might otherwise multiply rapidly and overwhelm the body’s defenses,” USC Today reported.
“This is very different from developing new antibiotics,” Jun Yan, a doctoral student at Keck School of Medicine and the study’s first author, told USC Today. “This is using our own immune system to fight against different superbugs, which is a different approach than everybody else.”
To develop the vaccine [the USC researchers] formed a biotechnology startup called ExBaq LLC in Bethesda, Md.
“The pandemic stimulated unprecedented innovation in vaccine development, where federal funding and university-industry partnerships were game changers for translating promising discoveries from academic labs for the good of all,” said Ishwar K. Puri, PhD (above), senior vice president of research and innovation at USC. “We are both pleased and proud of the critical support the USC Stevens Center provided to enable the development of ExBaq’s experimental vaccine that protects vulnerable populations from serious infections.” Clinical laboratories that work with hospitals in the fight against hospital-acquired infections understand the importance of this discovery. (Photo copyright: University of Southern California.)
USC Vaccine Details
The USC team developed a “protein-free vaccine, composed of aluminum hydroxide, monophosphoryl lipid A, and fungal mannan, that stimulates the innate immune system and confers protection,” the researchers wrote in Science Translational Medicine.
“Tested in two independent labs, the vaccine works within 24 hours and lasts for up to 28 days. In lab models, the number of pathogen-eating immune cells in the blood increased dramatically, and survival time of invasive blood and lung infections improved. Early data suggest that a second dose could extend the window to prevent infection,” USC Today reported.
Unlike anything currently available, the new vaccine focuses on boosting the body itself instead of creating antibodies against certain pathogens. A mere dose of the vaccine is described to “provide rapid protection against nine different bacteria and fungi species,” USC Today noted.
“It’s an early warning system. It’s like Homeland Security putting out a terror alert. Everybody, keep your eyes open. Keep an eye out for suspicious packages. You’re alerting the soldiers and tanks of your immune system. The vaccine activates them,” Spellberg told USC Today.
“The vaccine acted through stimulation of the innate, rather than the adaptive, immune system, as demonstrated by efficacy in the absence of lymphocytes that were abrogated by macrophage depletion. A role for macrophages was further supported by the finding that vaccination induced macrophage epigenetic alterations that modulated phagocytosis and the inflammatory response to infection. Together, these data show that this protein-free vaccine is a promising strategy to prevent deadly antimicrobial-resistant healthcare-associated infections,” the researchers wrote in Science Translational Medicine.
“Patients who acquire infections from surgery spend, on average, an additional 6.5 days in the hospital, are five times more likely to be readmitted after discharge and twice as likely to die. Moreover, surgical patients who develop infections are 60% more likely to require admission to a hospital’s intensive care unit. Surgical infections are believed to account for up to 10 billion dollars annually in healthcare expenditures,” the CDC reports.
“All hospitalized patients are susceptible to contracting a [hospital-acquired] infection. Some patients are at greater risk than others: young children, the elderly, and persons with compromised immune systems are more likely to get an infection. Other risk factors are long hospital stays, the use of indwelling catheters, failure of healthcare workers to wash their hands, and overuse of antibiotics,” the CDC notes.
Therefore, USC’s new vaccine may be just what the doctor ordered to protect patients in hospitals and other healthcare settings from deadly HAIs.
Looking Ahead
There are currently no vaccines that are FDA-approved that treat “the most serious antibiotic resistant infections,” USC Today reported.
“Even if there were such vaccines, multiple vaccines would have to be deployed simultaneously to protect against the full slate of antibiotic-resistant microbes that cause healthcare-acquired infections,” Brian Luna, PhD, assistant professor of molecular microbiology and immunology at USC’s Keck School of Medicine, told USC Today.
Thus, USC’s new vaccine could be a boon to hospital antimicrobial stewardship programs. But so far, it has only been tested on mice.
“The next step is getting guidance from the US Food and Drug Administration (FDA) on the design of a clinical trial. The first such trial would be done in healthy volunteers to find the right dose of vaccine that is safe and triggers the same kind of immune response in people as seen in the mice,” USC Today reported.
ExBaq LLC has begun talking with potential larger partners who might be willing to help develop the vaccine into clinical testing.
For years hospitals and other healthcare settings—such as long-term care facilities, urgent care clinics, and clinical laboratories—have fought an uphill battle against superbugs. So, for a vaccine to be on the horizon that can prevent life-threatening hospital-acquired infections would be a game changer.
With antimicrobial stewardships being a requirement in all hospitals, medical laboratory managers and microbiologists may celebrate this new development and its potential to be a useful tool in fighting antimicrobial resistant bacteria in their facilities.
This is the second of a three-part series on revenue cycle management for molecular testing laboratories and pathology practices, produced in collaboration with XiFin, Inc.
Second of a 3-part series, this article will detail what molecular diagnostics and pathology groups need to understand about coding, billing, and denial management to maximize revenue and cash flow successfully.
In the first article, we discussed how molecular diagnostics and pathology groups can enhance the patient experience, physician engagement, and payer relations. Now, we will detail how denial management can successfully maximize revenue and cash flow. As we discussed in the last article, revenue cycle management (RCM) is much more than billing.
Today’s rapidly changing environment of directives and expectations from payers, patients, and health systems require deeper understanding, great agility, and strategy in every aspect of business. Creating opportunities to provide better service, adopt state-of-the-art technologies, and build robust processes and partnerships can make or break the long-term success of a laboratory or pathology practice.
Technical assessments are often required to establish clinical validity and utility to achieve payer coverage for novel genetic tests. Achieving payer coverage requires a deep understanding of how-to code tests compliantly and to facilitate reimbursement.
“We recommend that molecular diagnostics laboratories consult with coding experts to fully understand the coding requirements for each genetic test,” says Clarisa Blattner, XiFin Senior Director, Revenue and Payor Optimization. “Ensuring reimbursement requires knowing payer policies and to track denial trends by payer over time to identify changes.”
Blattner noted that payer policies and behavior are constantly changing. Labs, and their billing partners must stay abreast of changes to avoid lengthy delays that denials and subsequent appeals can cause. Understanding the documentation that is required with claims is invaluable. Knowing these requirements up front and submitting complete claims with all required medical records and documentation of medical necessity goes a long way toward facilitating reimbursement.
Payers are adopting increasingly rigid policies that are often inconsistent with others. Reimbursements continue to be cut while quality reporting requirements rise.
Diagnostics laboratories that conduct genetic testing must also overcome four common challenges:
Achieving and expanding payer coverage with coverage determination that defines reasonable and medically necessary services and tests.
Knowing how to code the tests correctly with medical nomenclature to report services and/or tests to a payer.
Ensuring payment/reimbursement for services/tests based on services/tests rendered and coverage determination.
Maintaining compliance and keeping abreast of billing compliance and having a voice in reform
“We also recommend that laboratories conduct internal audits that reconcile laboratory information system (LIS) data with RCM system data,” Blattner continued. “Labs with a robust business intelligence (BI) solution can proactively identify outliers, such as accessions that exist in one system but not the other.”
Maintain Your Billing System and Maximize Clean Claim Submissions
Laboratories should be sure that these four payer services are being handled appropriately, whether it is by the lab or an RCM partner:
1. Payer relations: An effective payer relations team monitors denials and coordinates with payers. This team reviews front-end payer rejections, coordinates with clients (i.e., ordering physicians), and identifies and updates edits based on payer policies and behavior changes.
2. Electronic data interchange (EDI) enrollment: This team handles monitoring and proactive enrollment for electronic submissions and helps ensure bidirectional transaction automation.
3. EDI analysts: Experts in healthcare EDI who investigate errors, participate in standards development and testing, as well as payer education and coordination.
4. EDI operations: These specialized technicians configure files and ensure the reconciliation of claim-level submissions.
Efficiently Upload and Store Medical Records and Documentation
Although laboratories do not directly control patient medical records, it is essential to understand that diagnosis codes alone are generally insufficient.
Laboratory sales representatives must work with clients and ordering physicians to ensure medical records have all the information required for payment. Ensuring that the payers expedite payment requires efficient uploading and storing of medical records and documentation:
Align with payers on clinical utility evidence requirements, current billing policies, and preferred coding approach.
Leverage the support and advocacy of key opinion leaders (KOLs).
Collaborate with clinicians on the prior authorization process.
Select an RCM partner that helps you maximize process automation and front-end edits.
Leverage a business intelligence (BI) system that simplifies the tracking of key performance indicators (KPIs), helps identify payer policy and behavior changes early, and highlights changes in key business trends.
The RCM system must be able to upload and store medical records and documentation. The required medical information typically includes the following:
Who? Ordering/referring provider.
What? What service(s)/test(s) is/are being ordered?
Where? Where is the specimen being sent?
When? What is the date of service (DOS)?
Why? What are the patient’s signs/symptoms, or what prompted the test to be ordered?
How? How are the test results used to manage the patient’s medical condition?
But even after including all of the correct medical information, denials are inevitable. There are important steps labs can take to streamline denial management.
The Importance of Patient Engagement in Maximizing Reimbursement
Patient engagement plays an essential role in facilitating reimbursement and maximizing cash collection. Patients expect transparency and ease of information access from their healthcare encounters, just as they experience in all other areas of their lives. Because most laboratory, pathology, and molecular encounters are not directly patient-facing, proven payment accelerating engagement tools are essential. Dynamic portals, electronic statements, and text messages are essential, especially when it comes to communication regarding errors and patient financial responsibility.
XiFin customer data show a substantive increase in patient payments received in the first 30 days of the dunning cycle after integrating texting and automated calls into the traditional process. For example, a XiFin customer collected 26.6% more of the revenue in the first 30 days after implementing a text reminder between the first and second paper statements. Prior to implementation, the customer followed a traditional three-statement dunning cycle:
42.6% of total payments received occurred after the first statement (within the first 29 days of the dunning cycle).
34.8% occurred after sending the second statement (between days 30-59 of the dunning cycle).
22.6% were received after sending the third and final statement (during days 60-90 of thedunning cycle).
The convenience of text messaging allows patients to connect to the call center or to the patient portal, where a payment can be made immediately. XiFin customers can customize their dunning cycle, depending on how their specific patient population responds to texts, paper statements, and the timing between billing cycles. Studying the behaviors of patient interactions at the client level, rather than only referencing the status quo of macro-level trending, empowers a more strategic approach to engagement and improving overall patient satisfaction.
Denial Trends Driving Reduced Revenue and Higher Costs
Denials extend time in accounts receivable, contributing to bad debt on services already rendered and laboratory expenses absorbed. Denials also often require the most attention from staff – increasing the cost of billing. Hard denials, such as Medical Necessity, make up the most challenging revenue to collect, comprising about 5-10% of total denials received. In addition to creating delays and revenue loss, denials illustrate how payers administer their policies, even when those policies are unpublished.
Fundamentally, an effective RCM process is rooted in the ability to file clean claims to the degree that is possible. Improving those outcomes requires focus on the exceptions – the dirty claims – the denials.
“At XiFin, we invest in front-end configurations and workflows to catch denials prior to submitting the claim to the payer,” continued Blattner. “As we monitor denial trends, we build more robust front-end workflows and add automation (such as integrating with insurance discovery and prior authorization vendors) to reduce the associated burden on billing teams.”
In addition, molecular testing coverage continues to expand, reducing non-covered denials. The stabilization of these medical policy-related denials is positive. The jump in demographic denials, however, requires additional consideration.
Paid vs. Denied by Payer Group
Denial patterns vary among payers. The percentage of claims denied also differs by segment, largely due to the type of testing performed.
Of the claims XiFin processes annually (approximately $50 billion in charges), 22.5% are denied. The graphs below demonstrate molecular testing’s higher propensity for denial (27.5% of claims billed), driven by non-covered, medical necessity, and prior authorization requirement challenges.
Routine pathology has closer to a 20% denial rate overall. The average percentage of billed claims that are denied by segment are:
■ Clinical: 13.62%
■ Molecular: 27.19%
■ Pathology: 19.82%
Molecular testing has a higher propensity for denial (27.5% of claims billed), driven by non-covered, medical necessity, and prior authorization requirement challenges. Routine pathology has closer to a 20% denial rate overall.
Clinical laboratory denial rates averaged 13.62% in 2021. As seen in the table below,clinical laboratories saw a significant decline in experimental/investigational denials between 2018 and 2021.
Denial Type
Molecular % of Total Denied 2018
Clinical % of Total Denied 2021
Variance (% change 2021 vs. 2018)
Benefit Maximum Reached
39.3%
29.7%
-24.4%
Claim Specific Negotiated Discount
17.6%
18.1%
2.8%
Coordination of Benefits
4.1%
16.3%
298%
Coverage Terminated
6.6%
13.4%
103%
Diagnosis Not Covered
11.3%
6.4%
-43.4%
Duplicate Denial
8.3%
3.4%
-57.8%
Experimental Investigational
0.1%
2.7%
2600%
HSA
2.1%
2.4%
14.3%
Incorrect Payer
0.9%
1.6%
77.8%
Non-Covered
2.2%
1.1%
-50.0%
Patient Cannot be Identified
0.7%
0.8%
14.3%
Patient Enrolled in Hospice
0.5%
0.5%
0.0%
Prior Authorization
0.2%
0.2%
0.0%
Procedure Code Inconsistent with the Modifier Used or a Required Modifier is Missing
1.6%
0.1%
-87.5%
Procedure Not Paid Separately
0.5%
0.1%
-60.0%
Service Not Payable per Managed Care Contract
0.1%
0.0%
-100%
Molecular claims continue to experience the highest denial rates of any laboratory segment. With an average denial rate of 27%, molecular continues to be arevenue recovery workflow heavy on the back-end. As a percentage of the total denial population, between 2018 and 2021, XiFin experienced increases in patient-coverage denials, such as coordination of benefits (298%), coverage terminated (103%), and experimental/investigational (2600%). Decreases in diagnosis not covered denials (-43.4%) and duplicate denials (-57.8%) are also recognized.
Exome/Genome Testing must be administered by specialized technicians with specificcredentials, creating potential backlogs. They can take 8, 12, or even 16 weeks to complete, depending on testing methodologies. This presents a high risk of timely filing denials for the many payers that have adopted 90-day timely filing limits. XiFin recommended practice: Explore amending your payer contracts to extend timely filing limits on these tests.
Denial Type
Pathology % of Total Denied 2018
Pathology % of Total Denied 2021
Variance (% change 2021 vs. 2018)
Prior Authorization
28.9%
36.1%
24.6%
Duplicate Denial
21.5%
21.2%
-1.9%
Non-Covered
14.1%
10.1%
-27.7%
Services Not Prov. By Network/Primary Care Provider
8.8%
8.5%
-3.4%
Procedure Not Paid Separately
4.4%
5.1%
15.9%
Services Not Authorized by Network/Primary Care Provider
3.6%
3.8%
5.6%
Procedure Code Inconsistent with the Modifier Used or a Required Modifier is Missing
1.5%
3.3%
120%
Coverage Terminated
2.2%
2.6%
18.2%
Coordination of Benefits
3.8%
2.4%
-34.2%
Patient Cannot Be Identified
3.1%
2.3%
-25.8%
Remark Code
5.9%
2.1%
-64.4%
Experimental Investigational
1.0%
1.2%
20.0%
Benefit Maximum Reached
0.4%
1.0%
175%
Patient Enrolled in Hospice
0.4%
0.1%
-75.0%
Incorrect Payer
0.0%
0.1%
100%
Service Not Payable per Managed Care Contract
0.2%
0.0%
-100%
Anatomic pathology denials have increased by approximately 5% from 2018 to 2021.As a percentage of the total denial population, the lack of prior authorization is the highestcontributor to this increase, having grown 24.6%. There was an increase inprocedure code inconsistent with modifier denials (120% increase) and a decreasein non-covered denials (-27.7%).
Importance of an Efficient and Effective Appeals Process
Front-end edits and configurations help mitigate backend denials. Capturing potential denial-related issues proactively are the most effective way to maintain a manageable AR and improve the propensity to pay. For example, payers that observe National Correct Coding Initiative (NCCI) and Medically Unlikely Edits (MUEs) will consider all Current Procedural Terminology (CPT) codes billed for that patient for the same Date of Service (DOS), even when not billed on the same claim form.
Denials are inevitable if your current billing process does not have edits in place to perform a historical review of charges for the same patient on the same DOS.
Denials are unavoidable, and not all known issues can be addressed on the front end of the process. An example of this is denial code CO252, which is an additional information denial. It indicates the payer is requesting additional documentation (i.e., clinical information, medical records, and test results) before issuing payment – essentially performing an audit to ensure the services billed are reasonable and necessary and medical necessity is justified and documented.
“These are not always complex molecular tests; they can be routine pathology claims,” said Blattner “Each time we receive a CO252 denial it has to be appealed with additional documentation found in the patient’s medical records. Though it is inevitable, we must wait on the denial before we can take action.”
Segment
Appeal-Payments as % of Total Insurance Payments Received
Average Payment Amount per Appeal
Clinical
0.11%
$121
Molecular
6.56%
$1,420
Pathology
1.12%
$327
Industry Average
3.39%
$623
Payment collection per appeal continues to be stable in the pathology (averaging 1-2%) and clinical segments, where appeals are less prolific. Revenue recovered by corrected claims is excluded since these claims follow a separate process and impact denial codes such as CO97 (Procedure or service isn’t paid for separately), CO18 (Duplicate), and CO234 (Procedure not paid separately). Further, a single appeal process is not sufficient. A robust appeals process here becomes critical. Specifically in molecular testing, appeals carry a heightening impact on revenue collection. In 2020, appeals accounted for 5% of the total revenue generated by XiFin customers. In 2021, that increased to 6.5%.
Appeal Success Rates by Payer Group by Segment
The next four charts show appeal success rates by payer group for 2021, overall and by market segment for clinical, molecular, and pathology. The fifth chart illustrates the incremental impact of multiple appeal attempts by market segment.
This assessment only includes activity related to revenue recovery through an appeals process. Some denials can be addressed by filing of a corrected claim and can be a much more efficient process. Although ideal, corrected claims are not always possible, depending on denial type and individual payer preferences.
% of Total Appeals Filed
% of AppealsPaid after 1st Attempt
% of AppealsPaid after 2nd Attempt
% of AppealsPaid after 3rd Attempt
Avg Paymentper Appeal
Clinical
17.4%
17.8%
9.9%
$ 276
Additional Information
70.1%
20.9%
20.3%
10.0%
$ 258
COVID Medical Necessity
8.9%
3.9%
50.0%
$ 78
Medical Necessity
4.8%
30.4%
18.4%
$ 553
Out of Network
6.9%
4.4%
2.4%
$ 594
Prior Authorization
0.0%
14.3%
0.0%
$ 421
Underpayment
9.3%
6.9%
6.3%
$ 10
The clinical laboratory segment maintains the lowest volume of denials. But this does not negate the need for robust editing processes. Implementing robust front-end logic and leveraging intelligent automation to correct potential issues dramatically streamlines the process from submission to payment, especially in the high-volume clinical laboratory segment.
% of Total Appeals Filed
% of AppealsPaid after 1st Attempt
% of AppealsPaid after 2nd Attempt
% of AppealsPaid after 3rd Attempt
Avg Paymentper Appeal
Molecular
21.4%
17.2%
19.4%
$1,420
Additional Information
47.7%
23.9%
20.7%
23.3%
$1,285
Medical Necessity
23.0%
17.6%
14.0%
12.8%
$1,518
Prior Authorization
11.4%
18.9%
11.7%
13.1%
$1,944
Experimental and Investigational / Non-Covered
5.6%
13.2%
9.0%
9.0%
$4,234
COVID Underpayment
3.8%
44.7%
24.6%
10.7%
$52
Timely Filing
3.5%
10.1%
8.3%
18.9%
$551
Out of Network
3.5%
14.0%
10.8%
8.4%
$2,513
Underpayment
1.1%
31.2%
17.8%
15.3%
$2,154
COVID Medical Necessity
0.4%
46.4%
27.0%
0.0%
$124
Appeal Trends: Molecular and Genomic Testing At $1,420, the average payment per appeal for molecular testing is more significant due to the high-dollar value of the testing. Additional information appeals account for 47% of the total appeals filed in 2021 in the molecular segment and have an average success rate of 23%. Another 23% of appeals are for claims denied for medical necessity, followed by prior authorizations at 11.4% of total appeals filed. Prior authorization appeal volumes have remained consistent year-over-year in this segment, averaging 10% in 2020, despite a higher volume of prior authorization requirements than pathology or clinical laboratory.
XiFin’s RCM platform has integrated automation with prior authorization partners, allowing claims meeting prior authorization criteria to be submitted to a prior authorization solution automatically. Utilizing “real-time data exchange” via application programming interfaces (API) without partners, XiFin can more quickly acquire the necessary prior authorization number and update the patient’s information in XiFin RPM upon those values being returned.
% of Total Appeals Filed
% of AppealsPaid after 1st Attempt
% of AppealsPaid after 2nd Attempt
% of AppealsPaid after 3rd Attempt
Avg Paymentper Appeal
Pathology
22.6%
20.6%
21.8%
$327
Additional Information
33.4%
28.8%
23.4%
27.9%
$337
Medical Necessity
19.0%
23.5%
23.4%
27.6%
$398
Out of Network
17.9%
17.6%
12.4%
17.7%
$318
Prior Authorization
12.2%
21.5%
32.9%
36.5%
$350
Experimental and Investigational / Non-Covered
9.2%
17.8%
8.9%
3.1%
$195
COVID Underpayment
5.8%
9.0%
3.4%
16.7%
$31
Timely Filing
2.5%
20.5%
15.6%
13.3%
$191
Underpayment
0.1%
52.2%
0.0%
$177
Appeal Trends: Pathology
Approximately 2% of the pathology accessions received into XiFin RPM require an appeal. Those appeals will be responsible for approximately 1-2% of the pathology practice’s revenue. As noted above, the revenue reclaimed is largely attributed to the first attempted appeal. A robust process that includes multiple attempts is critical in revenue recovery in the event the first appeal is not overturned.
If Not Documented, It Did Not Happen
Payer edits and guidelines can be difficult to follow, particularly if physicians, coders, or billing staff are expected to memorize those requirements.
Making the situation even more challenging is the fact that edits vary widely among payers and are constantly changing. RCM platforms should be updated routinely (XiFin RPM is updated monthly) with payer edit updates, while remaining configurable so that custom edits can be easily built to accommodate specific payer requirements.
Whether it is a payer audit or packaging an appeal, documentation in the pathology report and/or clinical notes should clearly outline the services provided and the medical necessity of those services. If it is not documented, it did not happen. Further, understand the various programs that drive payer edits and guidelines. These edits drive an increased need for discipline and documentation. Be conscious of payer-specific requirements. Cigna, Aetna, and UHC require proprietary forms to be completed when appealing claims.
Benchmarking Productivity
Proactively preventing a denial and avoiding the need to submit a corrected claim or file an appeal reduces the time to reimbursement by four to eight weeks, depending on the payer and type of denial. If denials are not addressed properly and manual workflows persist, diagnostic labs will continue to experience a loss of revenue, and staffing will be insufficient to keep up.
Productivity rates for anatomic and molecular billing teams historically average between 12,000-15,000 accessions per person per full-time equivalent (FTE) per year (clinical laboratory is often much higher). However, with the increases in denials, the resulting demands on back-end teams have increased substantially and impacted productivity rates. This holds particularly true for particularly non-covered, medical necessity, and prior authorization denials.
Further, speed to payment is also improved. By automating appeals, the turn-around-time on submitting back to the payer is reduced, on average, from 45 days to 1-3 days, as seen in the blue bar in the chart above.
By installing front-end edits to help maximize clean claims, up to an additional 54 days can be saved, moving from 135 days to just 30 days for full adjudication.
Automating Workflows with AI
Opportunities to automate the process will reduce time and labor and make decisions more consistent. Once there is a deep understanding of coding, billing, denial management, and strategic appeals, there is the ability to automate the important processes across the RCM process. Automation and AI-powered workflows pave the way for consistent, optimized molecular diagnostics and pathology RCM.
Part 3 will demonstrate how AI can be used in RCM to inform, accelerate, automate, validate, and generate.Watch for updates here at DarkDaily.
Best of all, the researchers say the test could provide an inexpensive means of early diagnosis. This assay could also be used to monitor how well patients respond to cancer therapy, according to a news release.
The protein had previously been identified as a promising biomarker and is readily detectable in tumor tissue, they wrote. However, it is found in extremely low concentrations in blood plasma and is “well below detection limits of conventional clinical laboratory methods,” they noted.
To overcome that obstacle, they employed an ultra-sensitive immunoassay known as a Simoa (Single-Molecule Array), an immunoassay platform for measuring fluid biomarkers.
“We were shocked by how well this test worked in detecting the biomarker’s expression across cancer types,” said lead study author gastroenterologist Martin Taylor, MD, PhD, Instructor in Pathology, Massachusetts General Hospital and Harvard Medical School, in the press release. “It’s created more questions for us to explore and sparked interest among collaborators across many institutions.”
“We’ve known since the 1980s that transposable elements were active in some cancers, and nearly 10 years ago we reported that ORF1p was a pervasive cancer biomarker, but, until now, we haven’t had the ability to detect it in blood tests,” said pathologist and study co-author Kathleen Burns, MD, PhD (above), Chair of the Department of Pathology at Dana-Farber Cancer Institute and a Professor of Pathology at Harvard Medical School, in a press release. “Having a technology capable of detecting ORF1p in blood opens so many possibilities for clinical applications.” Clinical laboratories may soon have a new blood test to detect multiple types of cancer. (Photo copyright: Dana-Farber Cancer Institute.)
Simoa’s Advantages
In their press release, the researchers described ORF1p as “a hallmark of many cancers, particularly p53-deficient epithelial cancers,” a category that includes lung, breast, prostate, uterine, pancreatic, and head and neck cancers in addition to the cancers noted above.
“Pervasive expression of ORF1p in carcinomas, and the lack of expression in normal tissues, makes ORF1p unlike other protein biomarkers which have normal expression levels,” Taylor said in the press release. “This unique biology makes it highly specific.”
Simoa was developed at the laboratory of study co-author David R. Walt, PhD, the Hansjörg Wyss Professor of Bioinspired Engineering at Harvard Medical School, and Professor of Pathology at Harvard Medical School and Brigham and Women’s Hospital.
The Simoa technology “enables 100- to 1,000-fold improvements in sensitivity over conventional enzyme-linked immunosorbent assay (ELISA) techniques, thus opening the window to measuring proteins at concentrations that have never been detected before in various biological fluids such as plasma or saliva,” according to the Walt Lab website.
Simoa assays take less than two hours to run and require less than $3 in consumables. They are “simple to perform, scalable, and have clinical-grade coefficients of variation,” the researchers wrote.
Study Results
Using the first generation of the ORF1p Simoa assay, the researchers tested blood samples of patients with a variety of cancers along with 406 individuals, regarded as healthy, who served as controls. The test proved to be most effective among patients with colorectal and ovarian cancer, finding detectable levels of ORF1p in 58% of former and 71% of the latter. Detectable levels were found in patients with advanced-stage as well as early-stage disease, the researchers wrote in Cancer Discovery.
Among the 406 healthy controls, the test found detectable levels of ORF1p in only five. However, the control with the highest detectable levels, regarded as healthy when donating blood, “was six months later found to have prostate cancer and 19 months later found to have lymphoma,” the researchers wrote.
They later reengineered the Simoa assay to increase its sensitivity, resulting in improved detection of the protein in blood samples from patients with colorectal, gastroesophageal, ovarian, uterine, and breast cancers.
The researchers also employed the test on samples from 19 patients with gastroesophageal cancer to gauge its utility for monitoring therapeutic response. Although this was a small sample, they found that among 13 patients who had responded to therapy, “circulating ORF1p dropped to undetectable levels at follow-up sampling.”
“More Work to Be Done”
The Simoa assay has limitations, the researchers acknowledged. It doesn’t identify the location of cancers, and it “isn’t successful in identifying all cancers and their subtypes,” the press release stated, adding that the test will likely be used in conjunction with other early-detection approaches. The researchers also said they want to gauge the test’s accuracy in larger cohorts.
“The test is very specific, but it doesn’t tell us enough information to be used in a vacuum,” Walt said in the news release. “It’s exciting to see the early success of this ultrasensitive assessment tool, but there is more work to be done.”
More studies will be needed to valid these findings. That this promising new multi-cancer immunoassay is based on a clinical laboratory blood sample means its less invasive and less painful for patients. It’s a good example of an assay that takes a proteomic approach looking for protein cancer biomarkers rather than the genetic approach looking for molecular DNA/RNA biomarkers of cancer.
Theranos founder and former CEO continues down the path she began by defrauding her investors and lying to clinical laboratory leaders about her technology’s capabilities
As a result of the ban, Holmes is “barred from receiving payments from federal health programs for services or products, which significantly restricts her ability to work in the healthcare sector,” ARS Technica reported.
So, Holmes, who is 39-years old, is basically banned for life. This is in addition to her 11-year prison sentence which was paired with $452,047,200 in restitution.
“The exclusion was announced by Inspector General Christi Grimm of the Department of Health and Human Services’ Office of Inspector General,” ARS Technica noted, adding that HHS-OIG also “excluded former Theranos President Ramesh “Sunny” Balwani from federal health programs for 90 years.” This is on top of the almost 13-year-long prison sentence he is serving for fraud.
“The Health and Human Services Department can exclude anyone convicted of certain felonies from Medicare, Medicaid, and Pentagon health programs,” STAT reported.
“Accurate and dependable diagnostic testing technology is imperative to our public health infrastructure,” said Inspector General Christi Grimm (above) in an HHS-OIG statement. “As technology evolves, so do our efforts to safeguard the health and safety of patients, and HHS-OIG will continue to use its exclusion authority to protect the public from bad actors.” Observant clinical laboratory leaders will recognize this as yet another episode in the Elizabeth Holmes/Theranos fraud saga they’ve been following for years. (Photo copyright: HHS-OIG.)
Why the Ban?
“The Office of Inspector General (OIG) for the Department of Health and Human Services (HHS) cited Holmes’ 2022 conviction for fraud and conspiracy to commit wire fraud as the reason for her ban,” The Hill reported.
“False statements related to the reliability of these medical products can endanger the health of patients and sow distrust in our healthcare system,” Grimm stated in the HHS-OIG statement, which noted, “The statutory minimum for an exclusion based on convictions like Holmes’ is five years.
“When certain aggravating factors are present, a longer period of exclusion is justified,” the statement continued. “The length of Holmes’ exclusion is based on the application of several aggravating factors, including the length of time the acts were committed, incarceration, and the amount of restitution ordered to be paid.”
Rise and Fall of Elizabeth Holmes
Readers of Dark Daily’s e-briefs covering the Holmes/Theranos fraud saga will recall details on Holmes’ journey from mega success to her current state of incarceration for defrauding her investors.
In November 2022, she was handed an 11-year prison sentence for not disclosing that Theranos’ innovative blood testing technology, Edison, was producing flawed and false results. Theranos had “raised hundreds of millions of dollars, named prominent former US officials to its board, and explored a partnership with the US military to use its tests on the battlefield,” STAT reported.
To get Holmes physically into prison was a journey unto itself. At one point, evidence showed her as a potential flight risk. “In the same court filings, prosecutors said Holmes and her partner, William Evans, bought one-way tickets to Mexico in December 2021, a fact confirmed by her lawyers,” Dark Daily’s sister publication The Dark Report revealed in “Elizabeth Holmes’ Appeal Questions Competence of CLIA Lab Director.”
Drama around her move into prison continued. “The former CEO’s attorneys are making last-minute legal moves to delay her prison sentence while she appeals her guilty verdict,” Dark Daily reported.
At the same time, Holmes appeared to be on a mission to revamp her public image.
In the Times piece, Holmes talked about her plans to continue to pursue a life in healthcare. “In the story, Holmes contended that she still thinks about contributing to the clinical laboratory field. Holmes told The Times that she still works on healthcare-related inventions and will continue to do so if she reports to prison,” The Dark Report covered in “Elizabeth Holmes Still Wants ‘To Contribute’ in Healthcare.”
In the meantime, her legal fees continued to mount beyond her ability to pay. “Holmes’ prior cadre of lawyers quit after she could not compensate them, The Times reported,” The Dark Report noted. “One pre-sentencing report by the government put her legal fees at more than $30 million,” according to The New York Times.
Apparently, this closes the latest chapter in the never-ending saga of Elizabeth Holmes’ fall from grace and ultimate conviction for defrauding her investors and lying to healthcare executives, pathologists, and clinical laboratory leaders.
According to an EADV press release, the AI software demonstrated a “100% (59/59 cases identified) sensitivity for detecting melanoma—the most serious form of skin cancer.” The AI software also “correctly detected 99.5% (189/190) of all skin cancers and 92.5% (541/585) of pre-cancerous lesions.”
“Of the basal cell carcinoma cases, a single case was missed out of 190, which was later identified at a second read by a dermatologist ‘safety net.’ This further demonstrates the need to have appropriate clinical oversight of the AI,” the press release noted.
AI is being utilized more frequently within the healthcare industry to diagnose and treat a plethora of illnesses. This recent study performed by scientists in the United Kingdom demonstrates that new AI models can be used to accurately diagnose some skin cancers, but that “AI should not be used as a standalone detection tool without the support of a consultant dermatologist,” the press release noted.
“The role of AI in dermatology and the most appropriate pathway are debated,” said Kashini Andrew, MBBS, MSc (above), Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust. “Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool. However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.” Clinical laboratories and dermatopathologists in the United States will want to watch the further development of this AI application. (Photo copyright: LinkedIn.)
How the NHS Scientists Conducted Their Study
Researchers tested their algorithm for almost three years to determine its ability to detect cancerous and pre-cancerous growths. A group of dermatologists and medical photographers entered patient information into their algorithm and trained it how to detect abnormalities. The collected data came from 22,356 patients with suspected skin cancers and included photos of known cancers.
The scientists then repeatedly recalibrated the software to ensure it could distinguish between non-cancerous lesions and potential cancers or malignancies. Dermatologists then reviewed the final data from the algorithm and compared it to diagnoses from health professionals.
“This study has demonstrated how AI is rapidly improving and learning, with the high accuracy directly attributable to improvements in AI training techniques and the quality of data used to train the AI,” said Kashini Andrew, MBBS, MSc, Specialist Registrar at University Hospitals Birmingham NHS Foundation Trust, and co-author of the study, in EADV press release.
Freeing Up Physician Time
The EADV Congress where the NHS researchers presented their findings took place in October in Berlin. The first model of their AI software was tested in 2021 and that version was able to detect:
85.9% (195 out of 227) of melanoma cases,
83.8% (903 out of 1078) of all skin cancers, and
54.1% (496 out of 917) of pre-cancerous lesions.
After fine-tuning, the latest version of the algorithm was even more promising, with results that included the detection of:
100% (59 out of 59) cases of melanoma,
99.5% (189 out of 190) of all skin cancers, and
92.5% (541 out of 585) pre-cancerous lesions.
“The latest version of the software has saved over 1,000 face-to-face consultations in the secondary care setting between April 2022 and January 2023, freeing up more time for patients that need urgent attention,” Andrew said in the press release.
Still, the researchers admit that AI should not be used as the only detection method for skin cancers.
“We would like to stress that AI should not be used as a standalone tool in skin cancer detection and that AI is not a substitute for consultant dermatologists,” stated Irshad Zaki, B Med Sci (Hons), Consultant Dermatologist at University Hospitals Birmingham NHS Foundation Trust and one of the authors of the study, in the press release.
“The role of AI in dermatology and the most appropriate pathway are debated. Further research with appropriate clinical oversight may allow the deployment of AI as a triage tool,” said Andrew in the press release. “However, any pathway must demonstrate cost-effectiveness, and AI is currently not a stand-alone tool in dermatology. Our data shows the great promise of AI in future provision of healthcare.”
Two People in the US Die of Skin Cancer Every Hour
According to the Skin Cancer Foundation, skin cancer is the most common cancer in the United States as well as the rest of the world. More people in the US are diagnosed with skin cancer every year than all other cancers combined.
When detected early, the five-year survival rate for melanoma is 99%, but more than two people in the US die of skin cancer every hour. At least one in five Americans will develop skin cancer by the age of 70 and more than 9,500 people are diagnosed with the disease every day in the US.
The annual cost of treating skin cancers in the United States is estimated at $8.1 billion annually, with approximately $3.3 billion of that amount being for melanoma and the remaining $4.8 billion for non-melanoma skin cancers.
More research is needed before University Hospitals Birmingham’s new AI model can be used clinically in the diagnoses of skin cancers. However, its level of accuracy is unprecedented in AI diagnostics. This is a noteworthy step forward in the field of AI for diagnostic purposes that can be used by clinical laboratories and dermatopathologists.
Clinical laboratories and pathology groups should be on the alert to this new digital threat; telehealth sessions and video conferencing calls particularly vulnerable to acoustic AI attacks
Banks may be the first to get hit by a new form of hacking because of all the money they hold in deposit accounts, but experts say healthcare providers—including medical laboratories—are comparably lucrative targets because of the value of patient data. The point of this hacking spear is artificial intelligence (AI) with increased capabilities to penetrate digital defenses.
AI is developing rapidly. Are healthcare organizations keeping up? The hackers sure are. An article from GoBankingRates titled, “How Hackers Are Using AI to Steal Your Bank Account Password,” reveals startling new AI capabilities that could enable bad actors to compromise information technology (IT) security and steal from customers’ accounts.
Though the article covers how the AI could conduct cyberattacks on bank information, similar techniques can be employed to gain access to patients’ protected health information (PHI) and clinical laboratory databases as well, putting all healthcare consumers at risk.
The new AI cyberattack employs an acoustic Side Channel Attack (SCA). An SCA is an attack enabled by leakage of information from a physical computer system. The “acoustic” SCA listens to keystrokes through a computer’s microphone to guess a password with 95% accuracy.
“With recent developments in deep learning, the ubiquity of microphones and the rise in online services via personal devices, acoustic side channel attacks present a greater threat to keyboards than ever,” wrote UK study authors Joshua Harrison, MEng, Durham University; Ehsan Toreini, University of Surrey; and Maryam Mehrnezhad, PhD, University of London.
Hackers could be recording keystrokes during video conferencing calls as well, where an accuracy of 93% is achievable, the authors added.
This nefarious technological advance could spell trouble for healthcare security. Using acoustic SCA attacks, busy healthcare facilities, clinical laboratories, and telehealth appointments could all be potentially compromised.
“The ubiquity of keyboard acoustic emanations makes them not only a readily available attack vector, but also prompts victims to underestimate (and therefore not try to hide) their output,” wrote Joshua Harrison, MEng (above), and his team in their IEEE Xplore paper. “For example, when typing a password, people will regularly hide their screen but will do little to obfuscate their keyboard’s sound.” Since computer keyboards and microphones in healthcare settings like hospitals and clinical laboratories are completely ubiquitous, the risk that this AI technology will be used to invade and steal patients’ protected health information is high. (Photo copyright: CNBC.)
Why Do Hackers Target Healthcare?
Ransomware attacks in healthcare are costly and dangerous. According to InstaMed, a healthcare payments and billing company owned by J.P. Morgan, healthcare data breaches increased to 29.5% in 2021 costing over $9 million. And beyond the financial implications, these attacks put sensitive patient data at risk.
Healthcare can be seen as one of the most desirable markets for hackers seeking sensitive information. As InstaMed points out, credit card hacks are usually quickly figured out and stopped. However, “medical records can contain multiple pieces of personally identifiable information. Additionally, breaches that expose this type of data typically take longer to uncover and are harder for an organization to determine in magnitude.”
With AI advancing at such a high rate, healthcare organizations may be unable to adapt older network systems quickly—leaving them vulnerable.
“Legacy devices have been an issue for a while now,” Alexandra Murdoch, medical data analyst at GlobalData PLC, told Medical Device Network, “Usually big medical devices, such as imaging equipment or MRI machines are really expensive and so hospitals do not replace them often. So as a result, we have in the network these old devices that can’t really be updated, and because they can’t be updated, they can’t be protected.”
But telehealth, according to the UK researchers, may also be one way hackers get past safeguards and into critical hospital systems.
“When trained on keystrokes recorded using the video-conferencing software Zoom, an accuracy of 93% was achieved, a new best for the medium. Our results prove the practicality of these side channel attacks via off-the-shelf equipment and algorithms,” the UK researchers wrote in IEEE Xplore.
“[AI] has worrying implications for the medical industry, as more and more appointments go virtual, the implications of deepfakes is a bit concerning if you only interact with a doctor over a Teams or a Zoom call,” David Higgins, Senior Director at information security company CyberArk, told Medical Device Network.
Higgins elaborated on why healthcare is a highly targeted industry for hackers.
“For a credit card record, you are looking at a cost of one to two dollars, but for a medical record, you are talking much more information because the gain for the purposes of social engineering becomes very lucrative. It’s so much easier to launch a ransomware attack, you don’t even need to be a coder, you can just buy ransomware off of the dark web and use it.”
Steps Healthcare Organizations Should Take to Prevent Cyberattacks
Hackers will do whatever they can to get their hands on medical records because stealing them is so lucrative. And this may only be the beginning, Higgins noted.
“I don’t think we are going to see a slowdown in attacks. What we are starting to see is that techniques to make that initial intrusion are becoming more sophisticated and more targeted,” he told Medical Device Network. “Now with things like AI coming into the mix, it’s going to become much harder for the day-to-day individual to spot a malicious email. Generative AI is going to fuel more of that ransomware and sadly it’s going to make it easier for more people to get past that first intrusion stage.”
To combat these attacks patient data needs to be encrypted, devices updated, and medical staff well-trained to spot cyberattacks before they get out of hand. These SCA attacks on bank accounts could be easily transferable to attacks on healthcare organizations’ patient records.
Clinical laboratories, anatomic pathology groups, and other healthcare facilities would be wise to invest in cybersecurity, training for workers, and updated technology. The hackers are going to stay on top of the technology, healthcare leaders need to be one step ahead of them.
