July 15, 2026
Elevance Health is kicking off another round of quarterly earnings results for major insurers, posting $1.5 billion in profit for Q2.
This article was originally published on Fierce Healthcare.
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July 15, 2026
Elevance Health is kicking off another round of quarterly earnings results for major insurers, posting $1.5 billion in profit for Q2.
This article was originally published on Fierce Healthcare.
July 14, 2026
The health department on Tuesday rebuked a report that said an embattled proposal to add pediatric gender-affirming care service restrictions to Medicare and Medicaid conditions of participation was being shelved.
This article was originally published on Fierce Healthcare.
July 14, 2026
A new interoperability initiative will begin by targeting prior authorization cases that have the “greatest potential” to reduce administrative burden and aid patients in receiving timely care, the organization said.
This article was originally published on Fierce Healthcare.
Author: Juliet Ugarte Hopkins, MD, ACPA-C | July 13, 2026
Jan. 1, 2026 marked the start of the Centers for Medicare & Medicaid Services (CMS) plan to end the Medicare Inpatient-Only list.
Most musculoskeletal procedures were removed first, with the entire list expected to be eliminated as of Jan. 1, 2028. This policy change creates a need for careful, patient-specific status determinations, rather than automatic assumptions based only on the procedure being performed.
As in 2018, when total knee arthroplasty was removed from the list, removal of a procedure does not mean that every such procedure must be classified as outpatient. Instead, the clinical team must evaluate the individual patient’s medical condition, functional needs, home situation, and anticipated post-operative course.
The points below should guide decisions to place a patient into inpatient status when the surgeon or treating clinician reasonably anticipates that the patient will require at least two midnights of hospitalization during recovery before being safe for discharge on post-operative day two or later.
If the patient has any of the following conditions or needs, inpatient hospitalization may be appropriate when the reason is clearly documented in the medical record
An expectation that the patient will require transfer to a Skilled Nursing Facility (SNF) after surgery for skilled care supports inpatient hospitalization when the documentation explains why skilled care in a facility will be needed, and why discharge home with home services will not be appropriate after surgery:
Examples include the absence of family or friends to assist at home, multiple stairs, or other unmanageable obstacles within the home environment.
CMS anticipates that these patients will not be ready for hospital discharge until post-operative day three, which allows them to use their Medicare SNF benefit for covered charges when all requirements are met.
However, the clinician still must document what services the patient is receiving while hospitalized, why the patient cannot safely discharge home, and what medical needs require a SNF setting, on a daily basis. SNF transfer for custodial needs alone will not be covered by Medicare.
Procedure-Related Factors That May Support Inpatient Status
In addition to patient-specific medical and functional risks, the anticipated complexity of the procedure and the expected post-operative course should be considered before assigning status:
o If the procedure is expected to be technically challenging or difficult because of anatomical factors unique to the patient, hospitalize the patient as inpatient and document the reasons in detail.
o If the patient is expected to require at least two midnights of post-operative care that can only take place in the hospital, hospitalize the patient as inpatient and document in detail why the patient is not expected to be medically ready for discharge on post-operative day one.
This must be a case-by-case decision based on the factors associated with the individual patient.
This cannot be a blanket assessment for all patients.
When Outpatient Status Is More Appropriate
If the patient is in relatively decent health, takes no or few routine medications, or is expected to discharge on the day of the procedure, enter a status order for outpatient care. If complications arise during the procedure or recovery period, change the status to outpatient with observation services. If the patient’s clinical condition or post-operative care needs will not allow discharge on post-operative day one, change the status to inpatient.
Key Documentation Takeaway
The central lesson is that status determinations after removal from the Inpatient-Only list must remain individualized, clinically justified, and thoroughly documented. The record should explain not only the diagnosis or risk factor, but also why that factor creates a reasonable expectation for at least two midnights of hospital-level care, why discharge home is not safe, when applicable, and what ongoing hospital services are required. Clear daily documentation is especially important when SNF placement is anticipated, because Medicare does not cover SNF transfer for custodial needs alone.
This article was originally published on RACmonitor.
Author: Christine Geiger, MA, RHIA, CCS, CRC | July 13, 2026
As the summer heat rolls on, we continue our look at the FY 2027 Inpatient Prospective Payment (IPPS) proposed rule. Continuing with Part 2 of our proposed rule preview looking at new code additions for syndromes, we first will look at Li Fraumeni syndrome with proposed new code QA1.792.
Li Fraumeni syndrome is a rare genetic condition. Like Lynch syndrome previously discussed, patients with Li Fraumeni syndrome have an increased cancer risk.
According to Cleveland Clinic, people with Li-Fraumeni syndrome have a 90 percent chance of developing at least one type of cancer by age 60, and about half develop cancer before turning 40. Cleveland Clinic also notes that female patients are highly likely to develop breast cancer.
Li Fraumeni syndrome is due to a mutation in the TP53 gene which makes a tumor suppressing protein. When the mutation occurs, the protein isn’t made allowing cells to become cancerous.
Li Fraumeni is diagnosed through genetic testing with patients requiring an ongoing schedule of cancer screening throughout their life. Patients diagnosed as children will have a different screening schedule as they reach adulthood. Research has shown these screenings do improve survival rates.
The symptoms are related to the type of cancer that develops. Li Fraumeni syndrome is linked to many different types of cancer, but there are five that are commonly seen. These five core cancers are sarcomas, breast cancer, brain cancer, adrenocortical carcinoma and leukemia. An interesting note by the Cleveland Clinic is that patients with Li Fraumeni syndrome are more likely to develop cancers caused by radiation exposure. Since these patients may develop other cancers, it is important their providers know their diagnosis when developing their treatment plans.
This will be a new Alphabetic Index entry add for Li Fraumeni syndrome, with the proposed new code QA1.792. Tabular List will add QA1.7, Inherited neoplasm predisposition syndromes involving multiple systems, not elsewhere classified, as noted last week with Lynch syndrome. We will watch to see if this is finalized in the final rule.
Next, we will look at Loeys-Dietz syndrome with a proposed new code of Q87.A. This is also proposed to be a CC condition. Cleveland Clinic notes this is a genetic condition that affects the patient’s connective tissue, mainly the heart and blood vessels, bones and joints, the eyes and the skin. Loeys-Dietz is recent, being identified in 2005 by two physicians for whom the syndrome is named. Prior to this, this syndrome may have been diagnosed as Marfan syndrome because it also affects the connective tissue.
Cleveland Clinic also identifies four main features of Loeys-Dietz syndrome. The first is aneurysms which can occur in the aorta or other arteries. Second is arterial tortuosity, most often occurring in neck arteries. Third is ocular hypertelorism which is a distinctive feature of Loeys-Dietz. The patient’s eyes are spaced wider than in normal presentation. Fourth and final is a bifid or broad uvula, where the uvula is noted to be larger than normal or split. Cleft lip and palate, clubfoot and pectus excavatum or pectus carinatum are among the other physical findings that may also be noted.
There are five different types of Loeys-Dietz based on the gene change that is present. LDS-I mainly involves craniofacial while LDS-2 mainly involves skin. These are the two most common types. LDS-III mainly involves aneurysms and osteoarthritis. LDS-IV mainly involves Marfan syndrome-like features and aortic aneurysm issues. LDS-V mainly
involves thoracic and/or abdominal aorta aneurysms. LDS is diagnosed through genetic testing with treatment being related to disease involvement.
Q87.A will be an add to Q87, Other specified congenital malformation syndromes affecting multiple systems and will be a specific Alphabetic Index entry.
Li-Fraumeni Syndrome: Symptoms, Causes & Outlook
Loeys-Dietz Syndrome (LDS): Symptoms & Prognosis
FY 2027 IPPS Proposed Rule Home Page | CMS
This article was originally published on RACmonitor.
Author: Bryan Nordley | July 13, 2026
Question:
Which telehealth provisions were adopted in the Medicare Physician Fee Schedule (MPFS) final rule for CY2026?
Answer:
ICMS finalized its proposal to permanently adopt its waiver defining direct supervision for certain services—such as pulmonary, cardiac and intensive cardiac rehabilitation—to include virtual presence via audio/video real-time communications technology. Additionally, its waiver allowing federally qualified health centers (FQHCs) and rural health clinics (RHCs) to bill for telehealth services was extended through 2026. CMS did not propose to extend its waiver allowing teaching physicians to have a virtual presence for purposes of billing for services furnished by residents in teaching settings; however, this waiver was permanently adopted in response to public comments.
This article was originally published on RACmonitor.
Author: George Kelley | July 8, 2026
Behavioral health reimbursement has become increasingly complex as government agencies and commercial payers expand their audit activity. Organizations face compliance risk in three specific areas:
Although each area has unique billing requirements, the common theme is this: precise documentation and coding protect both revenue and compliance.
Organizations that establish consistent documentation standards and conduct regular internal reviews are better-positioned to reduce audit exposure while maximizing legitimate reimbursement.
I am going to touch on these issues more closely over the course of this article, but for more detail, Panacea has prepared an ebook you can download below.
Telehealth Services
Telehealth has become a permanent component of behavioral health delivery, but the regulatory requirements continue to evolve. Telehealth visits must follow the same coding principles as in-person visits, allowing providers to bill based on medical decision-making (MDM) or total time, when appropriate. However, virtual care introduces additional documentation requirements that must be consistently addressed.
There are four primary strategies for maintaining compliant telehealth programs:
Important documentation elements required for every telehealth visit include:
Audio-only visits continue to be allowable through Dec. 31, 2027, provided that the provider is capable of conducting video visits, the patient either cannot engage with or declines video technology, and the patient is located at home. Documentation should clearly explain why audio-only services were provided.
Psychotherapy with E&M Services
Psychotherapy combined with E&M services is one of the highest-risk billing categories in behavioral health. Audits frequently result in repayment demands reaching five or six figures because documentation often fails to adequately distinguish psychotherapy services from medical evaluation activities.
Some major audit findings are:
The aforementioned ebook will provide sample scenarios and additional detail.
Collaborative Care Model
The Collaborative Care Model is a structured, team-based approach to integrating behavioral health into primary care. Successful CoCM billing depends on clearly defined participant roles and detailed documentation.
CoCMs involve three essential participants:
Patient care is supported through the use of registries and validated assessment tools, with billing reported using CPT codes 99492, 99493, and 99494.
Three common documentation errors that frequently result in payment denials or recoupments are:
So, what steps can providers take to get ahead of these issues to reduce compliance risk and maintain entitled revenues?
These steps can go a long way to ensuring that your organization is prepared to face these compliance challenges. Feel free to download the ebook for additional detail here: https://go.panaceainc.com/l/1016042/2026-06-30/6prgr/1016042/1782834013eMY6vHhs/Solving_the_Behavioral_Health_Puzzle_eBook.pdf
This article was originally published on RACmonitor.
July 8, 2026
UnitedHealthcare is rolling out a new benefit option aimed at making it easier for employers to offer more personalized wellness programs.
This article was originally published on Fierce Healthcare.
Author: Tiffany Ferguson, LMSW, CMAC, ACM | July 7, 2026
In Part I of this series, we explored why the traditional utilization management (UM) model is no longer sufficient to meet today’s regulatory and operational healthcare demands. The proposed removal of the Medicare Inpatient-Only (IPO) List, continued expansion of Medicare Advantage (MA), and increasing payer scrutiny have shifted the UM process from a retrospective review function to one that must proactively support level-of-care decisions and denials prevention.
Typically, hospital UM programs assign utilization review (UR) specialists by nursing unit or service line, creating workflows in which clinicians spend much of their day moving from one patient to the next based solely on location, operating off a patient list. While this model is familiar and operationally comfortable, it often obscures the actual work requiring attention at any given time. It also assumes that every hospitalized patient requires the same level of UR intervention each day, which is rarely the case.
An adaptive UR model organizes staff according to the work being performed, rather than the physical location of the patient. Specialized teams focus on distinct operational responsibilities, such as pre-admission surgical review, emergency department and direct admission reviews, observation management, concurrent inpatient reviews, and post-discharge prebill denial and authorization reconciliation. Each function requires different clinical expertise, review cadence, and workflow priorities, allowing staff to develop greater proficiency while creating standardized processes across the organization.
One area where this redesign becomes particularly valuable is the surgical population. As reliance on the IPO List diminishes, UR will need to start shifting towards evaluating patients with significant medical complexity before surgery occurs, not just immediately after. A dedicated preoperative work queue, reviewed approximately two weeks before scheduled procedures, allows UR specialists to identify patients who may require inpatient hospitalization based on clinical factors, rather than procedure alone. Early collaboration with surgeons, physician advisors, and scheduling staff supports more accurate patient status decisions, timely MA authorizations, and stronger clinical documentation – before the patient ever enters the operating room.
Observation management represents another opportunity for specialization. Observation patients often require a much higher level of monitoring than traditional inpatients, because progression-of-care needs and conversion decisions frequently occur within hours. Assigning dedicated staff to observation services creates a simulation of an observation unit without having to necessarily collocate patients in the hospital. This works well, especially for hospitals where it is physically impossible to create a dedicated observation unit. Historically, observation workflows have focused heavily on identifying patients approaching the second midnight to determine whether inpatient conversion is appropriate. However, this represents only one component of observation management. Dedicated observation specialists can proactively address progression-of-care barriers, reduce unnecessary delays, help facilitate timely discharges, and improve patient throughput. This team is also well-positioned to oversee extended recovery patients and other outpatient-in-a-bed (OPIB) populations, creating consistency across bedded outpatient services.
Emergency department UR similarly benefits from a specialized approach. Evaluating patients at the point of entry allows medical necessity concerns, physician advisor consultations, and level-of-care determinations to occur before avoidable denials develop. Traditional models often delay these reviews, because staff believe that insufficient clinical information exists early in the encounter. However, advances in clinical decision support, artificial intelligence (AI), and real-time electronic documentation have made earlier intervention both practical and beneficial. Today’s UR specialist is no longer simply applying medical necessity criteria; they are serving as a real-time clinical resource, educating providers, collaborating with emergency physicians, and supporting accurate admission decisions before patient status becomes difficult to correct.
Building an adaptive model requires organizations to reconsider who performs the work. UR specialists should spend the majority of their time applying their clinical expertise, collaborating with physicians, physician advisors, and addressing complex medical necessity issues. Many administrative activities, such as authorization tracking, payer portal updates, fax management, scheduling peer-to-peer discussions, and post-discharge authorization reconciliation can often be delegated to trained UR technicians or other non-licensed professionals. This top-of-license approach allows the seasoned clinicians to focus on activities that require professional expertise while improving overall operational efficiency.
Technology serves as the foundation that connects these specialized workflows. Electronic medical records (EMRs) and UR technology are quickly replacing manual tracking spreadsheets with automated work queues and methods for prioritization that identify patients requiring review, notify physician advisors of escalation requests, monitor pending authorizations, and alert staff when status changes or payer requirements require action. Rather than relying on emails or secure chats, UR teams can build transparent electronic workflows for which each step of the review process is visible to case management, revenue cycle, clinical documentation integrity (CDI), and physician advisors. This level of visibility not only improves communication, but also creates valuable operational data that organizations can use to identify trends in denials, authorization delays, outpatient and observation utilization, physician advisor interventions, and throughput opportunities.
Healthcare delivery continues to evolve rapidly, and UM programs must be designed to evolve alongside it. Staffing models should reflect review demand, rather than traditional Monday-through-Friday schedules; incorporate remote and hybrid work environments, where appropriate; and adjust productivity expectations based on the complexity of each review function, rather than applying a single productivity standard across all roles. Success should be measured by meaningful outcomes such as reduced denials, improved authorization performance, shorter observation and bedded outpatient stays, cleaner claims, and stronger physician engagement.
This article was originally published on RACmonitor.
Author: Cheryl Ericson, RN, MS, CCDS, CDIP | July 7, 2026
Claims data that deviates from prior patterns or industry trends has been used to identify at-risk claims even before artificial intelligence (AI) entered the revenue cycle.
The Centers for Medicare & Medicaid Services (CMS) created the Program for Evaluating Payment Patterns Electronic Report (PEPPER) in 2002 to summarize provider-specific traditional Medicare data for target areas associated with improper payments due to billing, Diagnosis-Related Group (DRG) coding, and/or admission necessity issues. PEPPER is one of the unique resources that allows hospitals to compare their performances to those of other hospitals within their state, Medicare Administrative Contractor (MAC) jurisdiction, and the nation.
Whereas PEPPER is a resource for hospitals (it requires a secure portal for access), payers can easily access similar data using historical Medicare claims data (MedPar) or their own proprietary data set. A concept that was once used as a compliance tool has been weaponized by payers.
PEPPER uses the top and bottom 20th percentiles to identify outliers. Hospitals that rank within the top 80th percentile are classified as high outliers, compared to their cohorts within a target area. Those in the bottom 20th percentile are low outliers. The U.S. Department of Health and Human Services (HHS) Office of Inspector General (OIG) encourages hospitals to implement a compliance program that includes conducting regular audits to validate that Medicare is being billed correctly. Reviewing a sample of cases within any target area where the facility is a high outlier supports compliant Medicare billing, because these cases are at risk of being audited or denied. Conversely, being a low outlier may indicate revenue opportunities associated with underbilling.
As payers increasingly turn to AI for claims processing, they are gaining insight into billing trends and can more easily and readily identify outliers. An example of the power of modern data analytics emerged in 2018. A data analytic firm, Integra, brought a whistleblower case against two hospitals alleging upcoding. The firm was concerned that these facilities were over-reporting severe malnutrition, encephalopathy, and respiratory failure. Integra made their assertion because these clients deviated from CMS claims regarding the reporting of these major complications and comorbidities (MCCs) over a six-year period. The complaint was based primarily on a statistical analysis of Medicare claims data that demonstrated one of the hospitals submitted proportionally more claims with higher-paying diagnosis codes than comparable institutions. Claims with an MCC rate “more than twice the national rate” or “three percentage points higher than in the other hospitals” were flagged as fraudulent.
Ultimately, the lawsuits were dismissed. The judge ruled that educating physicians documenting using terms supporting the reporting of diagnoses classified as CCs or MCCs was “not in and of itself one to submit false claims.” The education was also determined to be consistent with efforts to improve hospital revenue through “accurate coding of patient diagnoses in a way that will be appropriately recognized and reimbursed by CMS.” Furthermore, the judge found that the medical records did not contain information that was “not justified” by physician judgement and medical opinion. The judge felt that the allegations of fraud overlooked an “alternative hypothesis:” that the hospital was “simply better than their peers in their efforts to ensure their medical documentation and coding maximized the opportunities for legitimate reimbursement from CMS.” It is reassuring that a judge did not jump to the conclusion of fraud, yet many payers are exploiting similar trends as a reason to remove impactful diagnoses from a claim, resulting in a DRG downgrade. This statistical outlier logic that once took years for a whistleblower to compute now happens instantly, at scale, via AI.
Diagnoses that have historically been targets for medical necessity denials or MS-DRG downgrades are more easily detected using AI tools that use past trends (e.g., diagnoses associated with denials) to predict future at-risk claims. The Office of the National Coordinator for Health Information Technology noted that 71 percent of hospitals reported using predictive AI integrated within their electronic medical record (EMR) in 2024. They also found billing to be one of the fastest-growing reasons hospitals are investing in predictive AI. The question is, when these types of claims are identified by the hospital, are they auditing these cases to validate accurate coding and billing?
These tools are becoming more common for both payers and hospitals. FinThrive designed one to help prevent denials by learning “from real-world adjudication outcomes from billions of institutional and professional claims.” This is a different approach than relying on payer policies. As discussed by Frank Cohen in his ICD-10monitor series about AI, payers are increasingly looking for pattern deviations among facilities. Cohen points out, “the audit that used to start with a single flagged chart now starts with a statistical model that has noticed something about your organization’s data. By the time a human auditor opens a chart, the suspicion has already been generated by an algorithm.”
CMS has acknowledged that there is nothing “inappropriate, unethical or otherwise wrong with hospitals taking full advantage of coding opportunities to maximize Medicare payment that is supported by documentation in the medical record.”[1] AI is a double-edged sword. It can identify opportunities for hospitals that can also be used as evidence of potential wrongdoing by payers. I recently highlighted a Blue Cross Blue Shield (BCBS) article in which the payer assumed that hospitals were upcoding acute blood loss anemia during admissions for delivery. Their analysis found that the increased volume of claims coded with acute posthemorrhagic anemia at the analyzed hospitals added $22 million to maternity admission costs in one year. However, their conclusion was biased by their limited definition of “appropriate treatment.” If the claim did not also have a procedure code for a blood transfusion, the diagnosis was considered invalid – yet blood transfusions are not considered a first-line treatment for this condition.
What should clinical documentation integrity (CDI) departments be doing differently? Do not blindly accept denials. An individual claim may be accurately billed, but associated with an at-risk trend. Business as usual is likely to result in more denials, since AI is trained on historical data. The solution:
Remember, AI is a tool to identify opportunities (or deficiencies), but keep the human in the loop to validate these recommendations and ensure that the context is accurate.
[1] Medicare Program; Changes to the Hospital Inpatient Prospective Payment Systems and Fiscal Year 2008 Rates, 72 Fed. Reg. 47,130, 47,180 (Aug. 22, 2007).
This article was originally published on RACmonitor.