Development of the Critical Values system redefined what STAT means in clinical laboratory testing turnaround times
Where did the concept of critical values and having clinical laboratories report them to referring physicians originate? How did the concept blossom into a standard practice in laboratory medicine? Given the importance of critical values, a lookback into how this aspect of laboratory medicine was developed is helpful to understand how and why this has become an essential element in the practice of medicine and an opportunity for labs to add value in patient care.
According to Stanford Medicine, critical/panic values are defined as “values that are outside the normal range to a degree that may constitute an immediate health risk to the individual or require immediate action on the part of the ordering physician.”
What you’ll read below is an insider’s account of the “birth of critical values reporting.”
According to Lundberg, an unaccompanied man was brought to the hospital in a coma and an examination revealed a laceration to his scalp. The patient was admitted to the neurosurgical unit where clinical laboratory tests were performed, including a complete blood count (CBC) analysis, urinalysis, and serum electrolytes. All the test results came back normal except the patient’s serum glucose (blood sugar level) which was 6 mg% in concentration.
“The hard-copy laboratory results were returned to the ward of origin within two hours of receipt of the specimens in the laboratory. However, the results were not noticed by the house officers who were busy with several other seriously ill patients. Ward personnel also failed to communicate the lab results to the responsible physicians,” Lundberg wrote.
When hospital staff did finally notice the test result the next morning glucose was immediately administered to the patient, but it was too late to prevent irreversible brain damage. The man soon passed away.
Following this incident, the hospital developed a “Critical Value Recognition and Reporting System.” The system generated new numbers that were termed “Panic Values.”
However, “critics complained that good doctors should never panic, so the name was changed to Critical Values,” Lundberg explained.
When any of these critical test values were out of the norm, “we required the responsible laboratory person to quickly verify the result and use the telephone (long before laboratory computers) to personally notify a responsible individual (no messages left) who agreed to find a physician who could quickly act on the result. All was documented with times and names,” he wrote.
“We understand that when a physician wants something, he/she wants it, no matter what. Well, in this patient-focused approach, the physician cannot have it, except as offered by the patient-focused approach, based on TAT [turnaround times of clinical laboratory tests],” wrote George Lundberg, MD (above), President and Chair of the Board of Directors of the Lundberg Institute, and Clinical Professor of Pathology at Northwestern University in an article he penned for the National Medical Journal of India (Photo copyright: Dark Intelligence Group. Shows Dr. Lundberg in 2011 addressing the Executive War College on Diagnostics, Clinical Laboratory, and Pathology Management.)
New Clinical Laboratory Standards
Recognition of the urgency to adopt new hospital standards related to certain clinical laboratory test results came swiftly. In 1972, Lundberg was invited to publish an article explaining the new Critical Value Recognition and Reporting System in Medical Laboratory Observer.
According to Lundberg, “most laboratory tests that are done do not need to be done; the results are either negative, normal, or show no change from a prior result. But some are crucial.”
The original set of Critical Values included the following testing results:
The list of values were later expanded to include “vital values.” These values describe lab results for which “action” is important, but where timing is less urgent. Examples of vital values include:
Lundberg and his colleagues went on to redefine what constitutes a laboratory test and what renders a test successful. They discussed laboratory procedures with committees of clinicians, lab personnel and patients, and reorganized hematology, chemistry, and toxicology based on the turnaround time (TAT) of tests.
“We ‘started the clock’—any and all days/times 24×7—when a specimen arrived at some place within the laboratory, and stopped the clock when a final result was available somewhere in the laboratory,” Lundberg wrote in NMJI. “We categorized all tests as: less than one hour, less than four hours, less than 24 hours, and more than 24 hours, guaranteed, 24×7. As a trade-off, we abolished the concept of ‘STAT’ orders … NO EXCEPTIONS. The rationale of each TAT was the speed with which a result was needed to render proper medical care that mattered to the welfare of the patient, and, of course, that was technically possible.”
Since then, very little has changed for the Critical Values System over the past 50 years. The majority of values added have fallen under the “Vital” category and not the “Critical” category. Today, most health systems and clinical laboratories create their own internal processes and procedures regarding which values need to be reported immediately (critical), which values are not urgent (vital), and how those results should be handled.
Groups representing academic publishers are taking steps to combat paper mills that write the papers and then sell authorship spots
Clinical laboratory professionals rely on peer-reviewed research to keep up with the latest findings in pathology, laboratory medicine, and other medical fields. They should thus be interested in new efforts to combat the presence of “research paper mills,” defined as “profit oriented, unofficial, and potentially illegal organizations that produce and sell fraudulent manuscripts that seem to resemble genuine research,” according to the Committee on Publication Ethics (COPE), a non-profit organization representing stakeholders in academic publishing.
“They may also handle the administration of submitting the article to journals for review and sell authorship to researchers once the article is accepted for publication,” the COPE website states.
In a recent example of how paper mills impact scholarly research, multinational publishing company John Wiley and Sons (Wiley) announced in The Scholarly Kitchen last year that it had retracted more than 1,700 papers published in journals from the company’s Hindawi subsidiary, which specializes in open-access academic publishing.
“In Hindawi’s case, this is a direct result of sophisticated paper mill activity,” wrote Jay Flynn, Wiley’s Executive Vice President and General Manager, Research, in a Scholarly Kitchen guest post. “The extent to which our processes and systems were breached required an end-to-end review of every step in the peer review and publishing process.”
In addition, journal indexer Clarivate removed 19 Hindawi journals from its Web of Science list in March 2023, due to problems with their editorial quality, Retraction Watch reported.
Hindawi later shut down four of the journals, which had been “heavily compromised by paper mills,” according to a blog post from the publisher.
Wiley also announced at that time that it would temporarily pause Hindawi’s special issues publishing program due to compromised articles, according to a press release.
“We urgently need a collaborative, forward-looking and thoughtful approach to journal security to stop bad actors from further abusing the industry’s systems, journals, and the communities we serve,” wrote Jay Flynn (above), Wiley EVP and General Manager, Research and Learning, in an article he penned for The Scholarly Kitchen. “We’re committed to addressing the challenge presented by paper mills and academic fraud head on, and we invite our publishing peers, and the many organizations that work alongside us, to join us in this endeavor.” Clinical laboratory leaders understand the critical need for accurate medical research papers. (Photo copyright: The Scholarly Kitchen.)
Using AI to Detect Paper Mill Submissions
Wiley acquired Hindawi in 2021 in a deal valued at $298 million, according to a press release, but the subsidiary has since become a financial drain for the company.
The journals earn their revenue by charging fees to authors. But in fiscal year 2024, which began last fall, “Wiley expects $35-40 million in lost revenue from Hindawi as it works to turn around journals with issues and retract articles,” Retraction Watch reported, citing an earnings call.
Wiley also revealed that it would stop using the Hindawi brand name and bring the subsidiary’s remaining journals under its own umbrella by the middle of 2024.
The service will incorporate tools to detect signs that submissions originated from paper mills, including similarities with “known papermill hallmarks” and use of “tortured phrases” indicating that passages were translated by AI-based language models, according to a press release.
These tools include:
Papermill Similarity Detection: Checks for known papermill hallmarks and compares content against existing papermills papers.
Problematic Phrase Recognition: Flags unusual alternatives to established terms.
Unusual Publication Behavior Detection: Identifies irregular publishing patterns by paper authors.
Researcher Identity Verification: Helps detect potential bad actors.
Gen-AI Generated Content Detection: Identifies potential misuse of generative AI.
Journal Scope Checker: Analyzes the article’s relevance to the journal.
The company said that the new service will be available through Research Exchange, Wiley’s manuscript submission platform, as early as next year.
Other Efforts to Spot Paper Mill Submissions
Previously, STM announced the launch of the STM Integrity Hub, with a mission “to equip the scholarly communication community with data, intelligence, and technology to protect research integrity,” Program Director Joris van Rossum, PhD, told The Scholarly Kitchen.
In 2023, the group announced that the hub would integrate Papermill Alarm from Clear Skies, a paper mill detection tool launched in 2022 with a focus on cancer research. It uses a “traffic-light rating system for research papers,” according to a press release.
In an announcement about the launch of Wiley’s Papermill Detection service, Retraction Watch suggested that one key to addressing the problem would be to reduce incentives for authors to use paper mills. Those incentives boil down to the pressure placed on many scientists, clinicians, and students to publish manuscripts, according to the research report from STM and COPE.
In one common scenario, the report noted, a paper mill will submit a staff-written paper to multiple journals. If the paper is accepted, the company will list it on a website and offer authorship spaces for sale.
“If a published paper is challenged, the ‘author’ may sometimes back down and ask for the paper to be retracted because of data problems, or they may try to provide additional supporting information including a supporting letter from their institution which is also a fake,” the report noted.
All of this serves as a warning to pathologists and clinical laboratory professionals to carefully evaluate the sources of medical journals publishing studies that feature results on areas of healthcare and lab medicine research that are of interest.
“The timing of EWC with the release of this policy couldn’t be better,” CEO and founder of Momentum ConsultingValerie Palmieri told Dark Daily in an interview at Monday night’s opening reception. “It’s a great conference to not only catch up with colleagues but really hear and have those difficult discussions about where we are today, where we’re going, and where we need to be.”
Final LDT rule ‘radically’ different than draft
Tim Stenzel, MD, PhD, former director of the FDA’s Office of In Vitro Diagnostics called the finalized rule “radically different” from the proposed rule. In some ways it is less complex: “The bar is lower,” he said, noting that he was voicing his personal views and not those of the federal agency. “I was convinced that there would be lawsuits, but I’m now not sure if that’s advisable.”
Still, laboratory teams will have to parse the more than 500-page document to determine how the final rule relates to their specific circumstances. After that, it won’t be as challenging, Stenzel said.
His advice: First, read the rule. Second, reach out to FDA for help—he’s sure, he said, that the office is geared up to respond to a “ton of questions” about the implications for individual labs and are standing by to answer emails from labs. And, he added in a discussion session, emailing the agency is free.
The final rule will be in force 60 days after it’s published. Stenzel provided a timeline for some of the milestones:
1 Year: Comply with MD(AE) reporting and reporting of corrections and removals.
2 Years: Comply with labeling, registration and listing, and investigational use requirements.
3 Years: QS records and, in some cases, design controls and purchasing controls.
3.5 Years: Comply with high risk (class III) premarket review requirements.
4 Years: Comply with moderate and low-risk premarket review requirements.
Executive Chair and CEO of XiFin, Inc.Lâle White welcomed the audience with a morning keynote entitled “Big Changes in Healthcare” on new regulations and diagnostics players poised to reshape lab testing.
The diagnostics business is in constant flux, she noted, from payer requirements to greater regulatory and compliance burdens on labs. Other factors include the growing senior population and increasingly complex health conditions, rising costs throughout the healthcare ecosystem, falling funding and reimbursement, and staffing shortages.
As for the economic challenges, consumers are increasingly making decisions based on cost, convenience and quality. The population is shifting to Medicare advantage, which is more cost effective. But changes to the star ratings system will mean lower pay for payer organizations. Those companies will, in turn, mitigate their losses by making changes to pre-authorizations and tightening denials, even for clean claims.
Still, White said, more money isn’t the answer.
White urged the audience to use technology, including artificial intelligence and advances in genetic testing, to manage these and other industry changes.
“We need to optimize the tests we order,” she said. “And if we did that, lab diagnostics really has the potential to change the economics of health and improve outcomes.”
The FDA, Stenzel added, is “very interested” in stimulating innovation, building on the laboratory industry’s success in responding swiftly to the COVID pandemic and outbreaks of Monkey Pox, for example.
He shared lessons learned from recent public health emergencies, talked about CDC’s efforts to engage with clinical labs to improve future public health readiness and response and provided an overview of the CDC’s first laboratory-specific center.
“Laboratories are fundamental to public health,” he said. The industry is on the “front lines” when it comes to identifying threats, responding to them, and preparing for future responses.
Robert Michel, Editor-in-Chief of The Dark Report wrapped up the day’s regulatory discussions with a general session on the “regulatory trifecta” that includes the LDT final rule, CLIA regulations, and private payers’ policies for genetic claims.
Tim Stenzel, MD, PhD, will discuss what clinical laboratories need to know about the draft LDT rule, FDA memo on assay reclassification, and ISO-13485 harmonization
Many clinical laboratories anxiously await a final rule from the US Food and Drug Administration (FDA) that is expected to establish federal policies under which the agency will regulate laboratory developed tests (LDTs). The agency released a proposed rule on Oct. 3, 2023, setting a Dec. 4 deadline for submission of comments. The White House’s Office of Management and Budget received a draft of the final rule less than three months later on March 1, 2024.
“Given how fast it moved through HHS, the final [rule] is likely pretty close” to the draft version, wrote former FDA commissioner Scott Gottlieb, MD, in a post on LinkedIn. Gottlieb and other regulatory experts expect the White House to submit the final rule to Congress no later than May 22, and perhaps as soon as this month.
Stenzel, who retired from the FDA last year, emphasized that he was not speaking on behalf of the federal agency and that he adheres to all FDA confidentiality requirements. He formed a new company—Grey Haven LLC—through which he is accepting speaking engagements in what he describes as a public service.
“I’m taking a wait and see approach,” said Tim Stenzel, MD, PhD (above), former director of the FDA’s Office of In Vitro Diagnostics, in an interview with Dark Daily. “The rule is not finalized. The FDA received thousands of comments. It’s my impression that the FDA takes those comments seriously. Until the rule is published, we don’t know what it will say, so I don’t think it does any good to make assumptions.” Clinical laboratory leaders will have an opportunity to learn how to prepare for FDA regulation of LDTs directly from Stenzel at the upcoming Executive War College in May. (Photo copyright: LinkedIn.)
FDA’s History of LDT Regulation
Prior to his five-year stint at the agency, Stenzel held high-level positions at diagnostics manufacturers Invivoscribe, Quidel Corporation, Asuragen, and Abbott Laboratories. He also directed the clinical molecular diagnostics laboratory at Duke University Medical Center in North Carolina. In the latter role, during the late 1990s, he oversaw development of numerous LDTs, he said.
The FDA, he observed, has long taken the position that it has authority to regulate LDTs. However, since the 1970s, after Congress passed the Medical Device Amendments to the federal Food, Drug, and Cosmetic Act, the agency has generally exercised “enforcement discretion,” he said, in which it declined to regulate most of these tests.
At the time, “many LDTs were lower risk, small volume, and used for specialized needs of a local patient population,” the agency stated in a press release announcing the proposed rule. “Since then, due to changes in business practices and increasing ability to ship patient specimens across the country quickly, many LDTs are now used more widely, for a larger and more diverse population, with large laboratories accepting specimens from across the country.”
Clinical Labs Need a Plan for Submission of LDTs to FDA
The FDA proposed the new rule after Congress failed to vote on the VALID Act (Verifying Accurate Leading-edge IVCT Development Act of 2021), which would have established a statutory framework for FDA oversight of LDTs. Citing public comments from FDA officials, Stenzel believes the agency would have preferred the legislative approach. But when that failed, “they thought they needed to act, which left them with the rulemaking path,” he said.
The new rule, as proposed, would phase out enforcement discretion in five stages over four years, he noted. Labs would have to begin submitting high-risk tests for premarket review about three-and-a-half years from publication of the final rule, but not before Oct. 1, 2027. Premarket review requirements for moderate- or low-risk tests would follow about six months later.
While he suggested a “wait and see” approach to the final rule, he advises labs that might be affected to develop a plan for dealing with it.
Potential Lawsuits
Stenzel also noted the likelihood of litigation in which labs or other stakeholders will seek to block implementation of the rule. “It’s a fairly widespread belief that there will be a lawsuit or lawsuits that will take this issue through the courts,” he said. “That could take several years. There is no guarantee that the courts will ultimately side with the FDA.”
He acknowledged that it is a controversial issue among clinical laboratories. Many labs have voiced opposition to the rule as well as the Valid Act.
Currently in retirement, Stenzel says he is making himself available as a resource through public speaking for laboratory professionals and other test developers who are seeking insights about the agency.
“The potential value that I bring is recent experience with the FDA and with stakeholders both inside and outside the FDA,” he said, adding that during his presentations he likes “to leave plenty of time for open-ended questions.”
In the case of his talks at the Executive War College, Stenzel said he anticipates “a robust conversation.”
He also expects to address other FDA-related issues, including:
A recent memo in which the agency said it would begin reclassifying most high-risk In Vitro Diagnostic (IVD) tests—those in class III (high risk)—into class II (moderate to high risk).
The emergence of multi-cancer detection (MCD) tests, which he described as a “hot topic in the LDT world.” The FDA has not yet approved any MCD tests, but some are available as LDTs.
A new voluntary pilot program in which the FDA will evaluate LDTs in situations where the agency has approved a treatment but has not authorized a corresponding companion diagnostic.
An FDA effort to harmonize ISO 13485—a set of international standards governing development of medical devices and diagnostics—with the agency’s own quality system regulations. Compliance with the ISO standards is necessary to market products in many countries outside the US, particularly in Europe, Stenzel noted. Harmonization will simplify product development, he said, because manufacturers won’t have to follow two or more sets of rules.
To learn how to prepare for the FDA’s future regulation of LDTs, clinical laboratory and pathology group managers would be wise to attend Stenzel’s presentations at this year’s Executive War College. Visit here to learn more and to secure your seat in New Orleans.
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.
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
