Phages are miniscule, tripod-looking viruses that are genetically programmed to locate, attack, and eradicate a specific kind of pathogen. These microscopic creatures have saved lives and are being touted as a potential solution to superbugs, which are strains of bacteria, viruses, parasites, and fungi that are resistant to most antibiotics and other treatments utilized to counteract infections.
“These multi-drug-resistant superbugs can cause chronic infections in individuals for months to years to sometimes decades,” Dwayne Roach, PhD, Assistant Professor of Bacteriophages, Infectious Disease, and Immunology at SDSU told CNN. “It’s ridiculous just how virulent some of these bacteria get over time.”
Labs across the country are conducting research on phages in eradicating superbugs. Roach’s lab is currently probing the body’s immune response to phages and developing purification techniques to prepare phage samples for intravenous use in patients.
“There are a lot of approaches right now that are happening in parallel,” said Dwayne Roach, PhD (above), Assistant Professor of Bacteriophages, Infectious Disease, and Immunology at San Diego State University (SDSU), in a CNN interview. “Do we engineer phages? Do we make a phage cocktail, and then how big is the cocktail? Is it two phages or 12 phages? Should phages be inhaled, applied topically, or injected intravenously? There’s a lot of work underway on exactly how to best do this.” Clinical laboratories that test for bacterial infections may play a key role in diagnosis and treatment involving bacteriophages. (Photo copyright: San Diego State University.)
Building Libraries of Phages
When certain a bacterial species or its genotypes needs to be annihilated, a collection of phages can be created to attack it via methods that enter and weaken the bacterial cell. The bacteria will attempt to counter the intrusion by employing evasive actions, such as shedding outer skins to eliminate the docking ports utilized by the phages. These maneuvers can cause the bacteria to lose their antibiotic resistance, making them vulnerable to destruction.
Some research labs are developing libraries of phages, accumulating strains found in nature in prime breeding grounds for bacteria to locate the correct phage for a particular infection. Other labs, however, are speeding up the process by producing phages in the lab.
“Rather than just sourcing new phages from the environment, we have a bioreactor that in real time creates billions upon billions of phages,” Anthony Maresso, PhD, Associate Professor at Baylor College of Medicine in Houston told CNN. “Most of those phages won’t be active against the drug-resistant bacteria, but at some point, there will be a rare variant that has been trained, so to speak, to attack the resistant bacteria, and we’ll add that to our arsenal. It’s a next-generation approach on phage libraries.”
For the Baylor study, 12 patients were treated with phages customized to each individual’s unique bacterial profile. The antibiotic-resistant bacteria were exterminated in five of the patients, while several others showed improvement.
Clinical trials are currently being executed to test the effectiveness of phages against a variety of chronic health conditions, including:
Using a phage cocktail could be used to treat a superbug outbreak in real time, while preventing a patient from a future infection of the same superbug.
“The issue is that when patients have infections with these drug-resistant bacteria, they can still carry that organism in or on their bodies even after treatment,” Maroya Walters, PhD, epidemiologist at the federal Centers for Disease Control and Prevention (CDC) told CNN.
“They don’t show any signs or symptoms of illness, but they can get infections again, and they can also transmit the bacteria to other people,” she added.
The colorized transmission electron micrograph above shows numerous phages attached to a bacterial cell wall. Phages are known for their unique structures, which resemble a cross between NASA’s Apollo lunar lander and an arthropod. (Caption and photo copyright: Berkeley Lab.)
More Studies are Needed
According to CDC data, more than 2.8 million antimicrobial-resistant (AMR) infections occur annually in the United States. More than 35,000 people in the country will die as a result of these infections.
In addition, AMR infections are a huge global threat, associated with nearly five million deaths worldwide in 2019. Resistant infections can be extremely difficult and sometimes impossible to treat.
More research is needed before phages can be used clinically to treat superbugs. But if phages prove to be useful in fighting antibiotic-resistant bacteria, microbiologists and their clinical laboratories may soon have new tools to help protect patients from these deadly pathogens.
Forces in play will directly impact the operations and financial stability of many of the nation’s clinical laboratories
With significant regulatory changes expected in the next 18 to 24 months, experts are predicting a “Perfect Storm” for managers of clinical laboratories and pathology practices.
Currently looming are changes to critical regulations in two regulatory areas that will affect hospitals and medical laboratories. One regulatory change is unfolding with the US Food and Drug Administration (FDA) and the other regulatory effort centers around efforts to update the Clinical Laboratory Improvement Amendments of 1988 (CLIA).
The major FDA changes involve the soon-to-be-published Final Rule on Laboratory Developed Tests (LDTs), which is currently causing its own individual storm within healthcare and will likely lead to lawsuits, according to the FDA Law Blog.
In a similar fashion—and being managed under the federal Centers for Medicare and Medicaid Services (CMS)—are the changes to CLIA rules that are expected to be the most significant since 2003.
The final element of the “Perfect Storm” of changes coming to the lab industry is the increased use by private payers of Z-Codes for genetic test claims.
In his general keynote, Robert L. Michel, Dark Daily’s Editor-in-Chief and creator of the 29th Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management, will set the stage by introducing a session titled, “Regulatory Trifecta Coming Soon to All Labs! Anticipating the Federal LDT Rule, Revisions to CLIA Regulations, and Private Payers’ Z-Code Policies for Genetic Claims.”
“There are an unprecedented set of regulatory challenges all smashing into each other and the time is now to start preparing for the coming storm,” says Robert L. Michel (above), Dark Daily’s Editor-in-Chief and creator of the 29th Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management, a national conference on lab management taking place April 30-May 1, 2024, at the Hyatt in New Orleans. (Photo copyright: The Dark Intelligence Group.)
Coming Trifecta of Disruptive Forces to Clinical Laboratory, Anatomic Pathology
The upcoming changes, Michel notes, have the potential to cause major disruptions at hospitals and clinical laboratories nationwide.
“Importantly, this perfect storm—which I like to describe as a Trifecta because these three disruptive forces that will affect how labs will conduct business—is not yet on the radar screen of most lab administrators, executives, and pathologists,” he says.
Because of that, several sessions at this year’s Executive War College conference, now in its 29th year, will offer information designed to give attendees a better understanding of how to manage what’s coming for their labs and anatomic pathology practices.
“This regulatory trifecta consists of three elements,” adds Michel, who is also Editor-in-Chief of Dark Daily’s sister publication The Dark Report, a business intelligence service for senior level executives in the clinical laboratory and pathology industry, as well in companies that offer solutions to labs and pathology groups.
According to Michel, that trifecta includes the following:
Element 1
FDA’s Draft LDT Rule
FDA’s LDT rule is currently the headline story in the lab industry. Speaking about this development and two other FDA initiatives involving diagnostics at the upcoming Executive War College will be pathologist Tim Stenzel, MD, PhD, former director of the FDA’s Office of In Vitro Diagnostics. It’s expected that the final rule on LDTs could be published by the end of April.
Stenzel will also discuss harmonization of ISO 13485 Medical Devices and the FDA’s recent memo on reclassifying most high-risk in vitro diagnostics to moderate-risk to ease the regulatory burden on companies seeking agency review of their diagnostic assays.
Salerno will also cover the CDC’s efforts to foster closer connections with clinical labs and their local public health laboratories, as well as the expanding menu of services for labs that his department now offers.
Element 3
Private Payer Use of Z-Codes for Test Claims
On the third development—increased use by private payers of Z-Codes for genetic test claims—the speaker will be pathologist Gabriel Bien-Willner, MD, PhD. He is the Medical Director of the MolDX program at Palmetto GBA, a Medicare Administrative Contractor (MAC). It is the MolDX program that oversees the issuance of Z-Codes for molecular and diagnostic tests.
UnitedHealthcare (UHC) was first to issue such a Z-Code policy last year, although it has delayed implementation several times. Other major payers are watching to see if UHC succeeds with this requirement, Michel says.
Other Critical Topics to be Covered at EWC
In addition to these need-to-know regulatory topics, Michel says that this year’s Executive War College will present almost 100 sessions and include 148 speakers. Some of the other topics on the agenda in New Orleans include the following and more:
Standardizing automation, analyzers, and tests across 25 lab sites.
Effective ways to attract, hire, and retain top-performing pathologists.
Leveraging your lab’s managed care contracts to increase covered tests.
“Our agenda is filled with the topics that are critically important to senior managers when it comes to managing their labs and anatomic pathology practices,” Michel notes.
“Every laboratory in the United States should recognize these three powerful developments are all in play at the same time and each will have direct impact on the clinical and financial performance of our nation’s labs,” Michel says. “For that reason, every lab should have one or more of their leadership team present at this year’s Executive War College to understand the implications of these developments.”
Visit here to learn more about the 29th Executive War College conference taking place in New Orleans.
New non-invasive test could replace traditional painful spinal taps and clinical laboratory fluid analysis for diagnosis of Parkinson’s disease
Scientists at AXIM Biotechnologies of San Diego have added another specimen that can be collected non-invasively for rapid, point-of-care clinical laboratory testing. This time it is tears, and the diagnostic test is for Parkinson’s disease (PD).
The new assay measures abnormal alpha-synuclein (a-synuclein), a protein that is a biomarker for Parkinson’s, according to an AXIM news release which also said the test is the first rapid test for PD.
“The revolutionary nature of AXIM’s new test is that it is non-invasive, inexpensive, and it can be performed at a point of care. It does not require a lumbar puncture, freezing, or sending samples to a lab. AXIM’s assay uses a tiny tear drop versus a spinal tap to collect the fluid sample and the test can be run at a doctor’s office with quantitative results delivered from a reader in less than 10 minutes,” the news release notes.
“Furthermore, emerging evidence shows that a-synuclein assays have the potential to differentiate people with PD from healthy controls, enabling the potential for early identification of at-risk groups,” the news release continues. “These findings suggest a crucial role for a-synuclein in therapeutic development, both in identifying pathologically defined subgroups of people with Parkinson’s disease and establishing biomarker-defined at-risk cohorts.”
This is just the latest example of a disease biomarker that can be collected noninvasively. Other such biomarkers Dark Daily has covered include:
“With this new assay, AXIM has immediately become a stakeholder in the Parkinson’s disease community, and through this breakthrough, we are making possible new paradigms for better clinical care, including earlier screening and diagnosis, targeted treatments, and faster, cheaper drug development,” said John Huemoeller, CEO, AXIM (above), in a news release. Patients benefit from non-invasive clinical laboratory testing. (Photo copyright: AXIM Biotechnologies.)
Fast POC Test versus Schirmer Strip
AXIM said it moved forward with its novel a-synuclein test propelled by earlier tear-related research that found “a-synuclein in its aggregated form can be detected in tears,” Inside Precision Medicine reported.
But that research used what AXIM called the “outdated” Schirmer Strip method to collect tears. The technique involves freezing tear samples at -80 degrees Celsius (-112 Fahrenheit), then sending them to a clinical laboratory for centrifugation for 30 minutes; quantifying tear protein content with a bicinchoninic acid assay, and detecting a-synuclein using a plate reader, AXIM explained.
Alternatively, AXIM says its new test may be performed in doctors’ offices and offers “quantitative results delivered from a reader in less than 10 minutes.”
“Our proven expertise in developing tear-based diagnostic tests has led to the development of this test in record speed, and I’m extremely proud of our scientific team for their ability to expand our science to focus on such an important focus area as Parkinson’s,” said John Huemoeller, CEO, AXIM in the news release.
“This is just the beginning for AXIM in this arena,” he added. “But I am convinced when pharmaceutical companies, foundations, and neurologists see how our solution can better help diagnose Parkinson’s disease in such an expedited and affordable way, we will be at the forefront of PD research, enabling both researchers and clinicians a brand-new tool in the fight against PD.”
One of those tests was “a lateral flow diagnostic for point-of-care use that measures the level of lactoferrin proteins in tear fluid, which work to protect the surface of the eye. … Axim said that low lactoferrin levels have also been linked to Parkinson’s disease and that the assay can be used alongside its alpha-synuclein test,” Fierce Biotech noted.
“It made sense to try and look at the proteinaceous [consisting of or containing protein] constituents of tear fluid,” Lew told Neurology Live. “Tear fluid is easy to collect. It’s noninvasive, inexpensive. It’s not like when you do a lumbar puncture, which is a much more involved ordeal. There’s risk of contamination with blood (saliva is dirty) issues with blood and collection. [Tear fluid analysis] is much safer and less expensive to do.”
In Biomarkers in Medicine, Lew et al noted why tears make good biomarkers for Parkinson’s disease, including “the interconnections between the ocular [eye] surface system and neurons affected in Parkinson’s disease.”
The researchers also highlighted “recent data on the identification of tear biomarkers including oligomeric α-synuclein, associated with neuronal degeneration in PD, in tears of PD patients” and discussed “possible sources for its release into tears.”
Future Clinical Laboratory Testing for Parkinson’s
Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s. It affects nearly one million people in the US. About 1.2 million people may have it by 2030, according to the Parkinson’s Foundation.
Thus, an accurate, inexpensive, non-invasive diagnostic test that can be performed at the point of care, and which returns clinical laboratory test results in less than 10 minutes, will be a boon to physicians who treat PD patients worldwide.
Clinical laboratory managers and pathologists may want to follow AXIM’s future research to see when the diagnostic test may become available for clinical use.
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
