August 4, 2026
The settlement resolves allegations from 2020 to 2023 that the company submitted HCCs that were “not clinically valid” or not supported by medical records or treatment plans.
This article was originally published on Fierce Healthcare.
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August 4, 2026
The settlement resolves allegations from 2020 to 2023 that the company submitted HCCs that were “not clinically valid” or not supported by medical records or treatment plans.
This article was originally published on Fierce Healthcare.
Author: Christine Geiger, MA, RHIA, CCS, CRC | August 3, 2026
As we wait for the fiscal year (FY) 27 Inpatient Prospective Payment System (IPPS) Final Rule, now is a great time to review some new technologies, focusing on those that have an artificial intelligence (AI) or machine learning element.
First up is the Bayesian Health Sepsis Flagging Device. As the name suggests the application was submitted by Bayesian Health, Inc. Using AI and machine learning-based SaMD or Software as a Medical Device, this device aids in early detection and risk prediction of sepsis.
The purpose is to use this device, in addition to laboratory findings and clinical assessments, to detect or predict sepsis within the next four-day period.
Previously named, TREWS (Targeted Real-time Early Warning System), this is intended for use in patients aged 18 and older in the emergency department as well as in the intensive care unit (ICU). The Bayesian Health Sepsis Flagging Device works with data captured in the patient’s electronic health record and has a proposed add on payment of $61.84.
The VUNO Med-DeepCARS® is another AI-based technology that assesses patient risk factors.
Here the VUNO Med-DeepCARS® monitors and assesses the risk of an impending cardiac arrest for hospital inpatients within a 24- hour period. This would be for inpatients who are not currently in the ICU where other monitoring would be in use.
Like the Bayesian Sepsis Device discussed earlier, VUNO Med-DeepCARS® is used for adult patients aged 18 and older. It calculates a risk score based on systolic and diastolic blood pressure, heart rate, body temperature, respiratory rate, patient age, and the timeframe in which the data is collected. The application notes that the DeepCARS® Score should be interpreted alongside other clinical information. The proposed add-on payment is $236.66.
Next is the InVision Precision Cardiac Amyloid. This is a SaMD machine-learning disease detection algorithm that helps clinicians diagnose cardiac amyloidosis. This algorithm identifies patients with a high suspicion of this disease from routinely obtained echocardiogram videos. This new technology is also for adult patient use, but the age range is 65 and older. The application does note that this is a disease detection aid tool and patient management decisions should not be based solely on the results of the technology alone. The proposed add-on payment here is 162.50.
Finally, the BriefCase-Triage CARE Multi-triage CT Body. This has been submitted by Aidoc Medical Ltd., Inc. The acronym, CARE, stands for Clinical AI Reasoning Engine. Described as a “single-model, 11-clinical indication radiological triage device” with a described purpose of flagging and communicating “…suspected positive findings for a wide range of clinically actionable, time-sensitive conditions in the abdominopelvic region.”
This is another adult-use technology for patients aged 18 and older. It is a software product that analyzes both contrast and non-contrast CT images of the chest, abdomen and/or pelvis. The 11 clinical indications flags suspected positive findings for include: diverticulitis, abdominal-pelvic abscess, appendicitis, intestinal ischemia and/or pneumatosis, obstructive renal stone, small and large bowel obstruction, spleen, liver and kidney injury and finally pelvic fracture. The BriefCase-Triage CARE Multi-triage CT Body has a proposed add-on payment of $137.53.
This is just a sampling of the new technologies from the proposed rule. As you wait for the final rule, make certain that you and your coding team are aware of all the coding and MS-DRG changes that will be finalized.
October 1 is right around the corner!
FY 2027 IPPS Proposed Rule Home Page | CMS
This article was originally published on RACmonitor.
Author: Cheryl Ericson, RN, MS, CCDS, CDIP | August 3, 2026
A few months ago, I argued that clinical documentation integrity (CDI) needed to stop reviewing claims one case at a time because payers have already moved to population-level statistical modeling, powered by artificial intelligence (AI), to flag outliers and deny claims at scale. I did not expect to be revisiting that argument so soon, or from the other direction. The federal government is now doing what payers have been doing for years, and it has more money, more staff, and considerably less patience.
Healthcare fraud has always driven the largest share of False Claims Act (FCA) recoveries, but fiscal year (FY) 2025 set a new bar. Of the $6.8 billion the Department of Justice (DOJ) recovered in FCA settlements and judgments last year, $5.7 billion came from healthcare, roughly 84 cents of every dollar. The statute behind all of this, the False Claims Act (31 U.S.C. § 3729), imposes treble damages for any claim submitted with actual knowledge, deliberate ignorance, or reckless disregard of its falsity, a standard that population-level data analytics make far easier to meet at scale.
On April 7, 2026, the acting attorney general folded DOJ Health Care Fraud Unit into a new National Fraud Enforcement Division, giving it centralized leadership, expanded staff, and a broader mandate. Two months later, the 2026 National Health Care Fraud Takedown charged 455 defendants, including 90 physicians and other licensed clinicians, across 56 federal districts and 45 states, in connection with more than $6.5 billion in alleged false claims.
To date the focus has been on other healthcare sectors (e.g., labs, telehealth, hospice, home health, etc.) but it is only a matter of time before hospitals are in the crosshairs.
Taylor Chenery, at Bass, Berry & Sims the attorney representing providers in government investigations, told Chief Healthcare Executive in a recent article that federal agencies are now “extremely data focused,” and are no longer waiting for a whistleblower to hand them a case. Chenery’s advice to health systems is what CDI professionals should already be following: know your data.
This should sound familiar. I have written before about how a whistleblower complaint against Integra built its case on a simple statistical threshold. A facility’s rate of a targeted diagnosis more than double the national average, or three percentage points higher than its peers, was treated as evidence of a false claim in that complaint. It was based entirely on data. Calculations that at one time took months to complete can now be completed in a matter of hours by federal agencies looking for fraud.
Moreover, outlier-detection logic that flags claims for commercial payers has become standard equipment for federal prosecutors, but the consequences of a federal audit are far steeper than a payer denial.
What does this change for CDI and coding departments? Arguably nothing about the work itself, and everything about the stakes. If a federal analyst pulled your hospital’s coding data for the last eight quarters, would documentation patterns explain the trend? Would query practice hold up as clinically driven, or would it look to an algorithm with no clinical judgment, indistinguishable from a pattern of upcoding? Does your staff understand how their workflow tools make recommendations? Are CDI and coding staff allowed to override recommendations from technology, and if so, what is that process?
Are known issues with technology’s query or coding recommendations tracked? What efforts are made to remedy them? Hospitals must prove awareness and action; complacency is not an option.
Using artificial intelligence (AI) tools may be viewed as fighting fire with fire in the war against payer friction, but CDI and coding activities must still be grounded in compliant practices. The hospital is responsible for how they use these tools. Staff must avoid simply accepting AI recommendations. Perform due diligence by reviewing the complete health record. Is this truly an opportunity or simply the byproduct of natural language processing (NLP) which can take words out of context?
Is there corroborating clinical evidence beyond one abnormal finding? What is the quality of corroborating clinical evidence? As the federal government joins this battle, hospitals may soon be fighting the war on both the compliance and revenue fronts.
CDI departments were originally implemented to find incremental revenue. Although their scope has grown with maturity, current economic pressures are pushing many departments back to their original mission with the help of AI tools. There is nothing inappropriate about being paid accurately for the resources used to care for Medicare and Medicaid beneficiaries when done compliantly.
An anomaly within claims data flagged by an algorithm is not, on its own, evidence of noncompliance. It should be confirmed by chart review. Hospitals need to be aware that sudden shifts in Case Mix Index (CMI) or CC/MCC capture due to implementing a new documentation tool or education strategy can look like a coding anomaly to software with no context for why the number moved. Every process implemented for documentation integrity is now a strategy a federal investigator could misread.
Health systems that want to avoid learning about a problem from a federal audit or subpoena should start where the government will, with their own claims data. That means running the same outlier analysis a federal analyst would run. Examine trends by MS-DRG, physician, and service line, at least quarterly. Investigate anomalies before they become someone else’s discovery request.
This also means revisiting the Program for Evaluating Payment Patterns Electronic Report (PEPPER); the tool is not as sophisticated as AI analysis, but any area where a hospital sits as an outlier is still worth confirming for compliant, defensible coding.
Moreover, technology should not replace clinical or coding judgment. Staff cannot blindly follow AI recommendations. CDI and coding leaders need to know how recommendations are generated; staff need to be able to override technology when necessary; and known issues with query or coding logic must be tracked and corrected. A workflow tool that functions as a black box is a liability whether it ever produces an error or not.
The same discipline applies to documentation and query practice generally. Changes in process, whether from innovative technology, a new education initiative, or a consulting engagement, should be logged, defined in policy, and monitored for staff adherence. Be proactive so a shift in CMI or CC/MCC capture already has a documented, defensible explanation before a federal auditor ever asks for one.
The U.S. Department of Health and Human Services (HHS) Office of Inspector General (OIG) compliance program guidance has long framed this type of internal auditing as a baseline expectation, not an aspirational goal. That expectation is especially relevant to CDI, where the clinical narrative behind a coding trend is precisely what a data-only audit cannot capture. What story is your organization’s data telling?
None of this work is new for CDI professionals, who have spent careers translating clinical nuance into coded data. What is new is who sits at the other end of that data, and how quickly they can act on it. The DOJ no longer needs a whistleblower to notice when your hospital’s numbers look different from everyone else’s. It only needs your claims file, which they can easily access.
Organizations that actively monitor their own data, rather than managing it reactively, are the ones that will spend the rest of 2026 explaining their anomalies on their own terms, instead of the government’s.
This article was originally published on RACmonitor.
Author: Juliet Ugarte Hopkins, MD, ACPA-C | August 3, 2026
More than a decade after its October 2013 implementation, the Medicare Two-Midnight Rule should, in theory, be one of the simplest and most straightforward frameworks for assigning hospital status. In practice, however, it remains a persistent source of confusion for hospitals, clinicians, utilization managers, and even payers themselves.
Some Medicaid status determinations are also based on the Two-Midnight Rule, but this is not universal from state to state. Additionally, some states have other qualifiers for Inpatient status, which can be confounding from a medical necessity perspective because they may rely more on the length of hospitalization than on the true need for hospital services.
Commercial plans often align themselves with one of the two major criteria-set guidelines, and while these guidelines promote themselves as grounded in evidence-based practice, there is no way to care for patients as if following along in a cookbook.
Finally, status assignment associated with Medicare Advantage plans may be the most confounding of all, given their assigned obligation, per the Centers for Medicare and Medicaid Services (CMS), to utilize the Two-Midnight Rule, coupled with a lack of obligation to trust the attending clinician’s determination of medical necessity for continued hospital care.
Taking all of this into account, it makes sense that utilization managers have long served as an important resource for clinicians when deciding appropriate patient status. Whether at the time of hospitalization or a day or two later, physician advisors and utilization management leaders routinely emphasize to their hospitals’ medical staffs that clinicians should actively collaborate with, and rely on, utilization managers to assist with appropriate statusing of their patients.
What is not standard? The clinicians involved in these conversations about patient status, how the communication takes place, and how the decided-upon status order is entered into the electronic medical record. These variations from hospital to hospital may be justifiable, but at the end of the day, all must comply with CMS rules, in particular 42 CFR § 412.3 from the Code of Federal Regulations.
As I reviewed in an article in February of this year, the Rule explicitly states, “The expectation of the physician should be based on such complex medical factors as patient history and comorbidities, the severity of signs and symptoms, current medical needs, and the risk of an adverse event…” and, “…who is…knowledgeable about the patient’s hospital course, medical plan of care, and current condition at the time of admission.”
Do emergency medicine physicians meet this qualification when they decide a patient must be hospitalized and is not appropriate for discharge from the emergency department (ED)? I say no, since their scope of the patient’s care involves only the time spent in the ED. They are not considering the future hospital course or medical plan of care following the patient’s departure from the ED.
As such, I do not believe they can compliantly designate a Medicare patient as Inpatient or Outpatient with Observation services. However, standard operating procedure in many hospitals involves ED utilization managers working side by side with ED physicians and providing, among other things, suggestions on appropriate patient status.
Relatedly, even if the suggestions are not directed to the ED physicians but instead to the accepting or attending physicians, the practice can be just as problematic. There is an important distinction between collaborating with a physician to come to agreement on appropriate status assignment and dictating status assignment. Unfortunately, this is a scenario I am hearing about increasingly often. The utilization nurse manager, applying MCG or InterQual criteria or using their own clinical judgment to suspect that an at least two-midnight hospitalization will be required, communicates to the attending physician what status should be chosen. There is no discussion, no sharing of the points the utilization manager is considering to make the determination, just direction of Inpatient or “Observation.” Then, either the clinician places the order as directed, or, in even more egregious situations, the order has already been placed as pended in the electronic health record by the utilization manager and only needs the physician’s signature to complete and activate.
The common reasoning for this practice is that utilization managers are experts in patient statusing and are the best and most readily available resource for clinicians making this decision. I do not disagree with that. However, in virtually all the scenarios I have encountered across the country, the main reason is to take responsibility out of the clinicians’ purview entirely, with the aim of avoiding inappropriate status determinations.
In effect: do not bother teaching clinicians how to status patients; just tell them to order whatever status the utilization manager tells them to pick.
I believe this is a non-compliant practice and that it runs counter to 42 CFR § 412.3. In this instance, the clinician is not taking into account the factors the Rule requires to make the status determination. All they do is accept direction from the utilization manager and affix their signature to it.
How do I know this? Because when I ask clinicians working within hospitals that use this practice how they determine status for their patients, they tell me, “I have no idea; I just pick whatever the utilization manager tells me to pick.” This cannot be your medical staff’s answer. In the event of an audit, in theory, it could lead to a devastating retrospective review of months and months of Inpatient cases.
So what should hospitals do instead? I have long supported educating medical staff on medical necessity, the Two-Midnight Rule, and the practical application of the Rule to all patients. True, some of their decisions will be incorrect in cases involving commercial or even some Medicaid plans, but at least they will be following the direction of 42 CFR § 412.3 for the Medicare population. After that is when utilization managers come into play, reaching out with corrections when needed because a payor does not follow the Rule.
In real time, can utilization managers help clinicians make status determinations? Absolutely. But the key word is help. This interaction should be a discussion about the patient’s condition, the plan of care, and the medical factors supporting the conclusion. Utilization managers should be trusted collaborators and expert resources, not substitutes for the clinician’s required judgment.
The goal is not to remove clinicians from status assignment; it is to make sure they are prepared to make, understand, and own the determination in a way that is clinically sound and compliant.
This article was originally published on RACmonitor.
Author: Bryan Nordley | August 3, 2026
Question:
How can providers improve the likelihood of coverage or a successful appeal for testing reported with PLA code 0529U?
Answer:
Note that many payer coverage policies assign PLA codes a status of experimental/investigational, which may exclude coverage due to a lack of literature establishing clinical efficacy, safety, or applicability to clinical practice. Alternatively, laboratories may be required to submit documentation from the patient’s medical record to substantiate medical indications and support the medical necessity of testing defined by a PLA code. In the case of 0529U, such documentation might indicate a strong family history or specific high-risk situations, such as planning pregnancy, using estrogen/oral contraceptives, or undergoing major surgery. When seeking prior authorization or appealing a denied claim, it is beneficial to present chart notes supporting the intended use of results in directing treatment decisions, such as guiding anticoagulant duration after a clot or informing hormone use.
This article was originally published on RACmonitor.
Author: Elizabeth Casolo | July 31, 2026
Members may not think they are talking to AI when they call their health plan, but the person assisting them might be.
Some insurers have been leaning into member-facing AI capabilities. One of UnitedHealth Group’s 1,000 AI use cases has been using chatbots to handle customer calls, with an AI chatbot initially responding to more than 65 million calls in 2024.
However, there has been a growing trend of AI quietly working behind the scenes on customer service calls, too. These tools aim to boost the member experience by increasing efficiency and accuracy.
Stellarus is a healthcare technology company that spawned from a Blue Shield of California restructuring. Along with its tie to Blue Shield of California, Hawaii Medical Service Association and Blue Cross and Blue Shield of Kansas signed on as Stellarus co-founders, signaling Stellarus’ deep footprint with BCBS plans.
The company just launched its AI-powered Customer Service Representative Chat for health plan staff. CSR Chat is the company’s first product in its Compass suite, an AI-driven engagement platform for health plans.
“CSR Chat helps put the right information in front of representatives at the right moment, enabling more consistent service, faster resolutions and better experiences for the people they support,” Vanessa Colella, PhD, president and CEO of Stellarus, said in a July 30 news release.
But health insurers themselves have been rolling out these functions for even longer. SCAN Health Plan was one early adopter.
SCAN first identified call center operations as an area that could benefit from AI integration in 2023. The insurer incorporated AI in its call centers through a partnership with AI company Cresta. The tool worked with call center employees on real-time transcription, automated notes and guided workflows. The tool also gave recommendations for handling member inquiries, including questions about supplemental benefits or switching primary care providers.
“I’ve been able to peek in on the calls and when the hints pop up, sometimes the member might say something like, ‘Wow, how did you know that?’ They seem delighted by some of the experiences,” Corinne Stroum, SCAN’s senior director of emerging technologies, told Becker’s in 2024.
SCAN’s tool also sought to address employee burnout.
“It’s a big workforce change, and we need to manage this as a process and a personnel change as well,” Ms. Stroum said.
Independence Blue Cross kicked off a similar pilot in February 2025 with more than 40 customer service representatives. The company’s AI tool lowered the number of steps representatives had to take to retrieve essential information and increased the share of customers who got solutions on their first inquiry. IBX documented a 90% success rate among its representatives and planned to scale the tool for at least another year.
Humana has entrenched itself more deeply with Google Cloud through its rollout of Agent Assist in 2025 and 2026. While operating in the background with Humana’s 20,000 member advocates, Agent Assist is designed to output real-time conversation summaries, predict member needs and surface relevant information. Google Cloud’s Vertex AI, Gemini and Gemini Enterprise for Customer Experience power Agent Assist.
In the Agent Assist announcement, Humana confirmed ongoing monitoring to ensure the tool’s compliance. The tool is an extension of Humana’s existing partnership with Google Cloud, building upon a collaboration for cloud infrastructure and AI resources in 2024.
A 2024 survey from advisory firm Gartner found 64% of customers would prefer companies not use AI in customer service, with their top concern being difficulty accessing a person. However, given the spike in health plans deploying AI behind the scenes rather than as customer-facing bots, insurers are betting AI use itself is not members’ main issue. Instead, it is about keeping a human on the line.
The post Health plan customer service reps have AI in their ear appeared first on Becker's Hospital Review | Healthcare News & Analysis.
This article was originally published on Becker's Hospital Review.
July 31, 2026
The Inpatient Prospective Payment System (IPPS) final rule’s base pay increase is slightly below what was proposed earlier this year, though the agency said it now expects the rule’s changes to bring hospital payments hundreds of millions of dollars higher than its prior projection.
This article was originally published on Fierce Healthcare.
July 30, 2026
Cigna is raising its outlook for the year after beating the Street on both earnings and revenue in the second quarter of 2026.
This article was originally published on Fierce Healthcare.
July 29, 2026
Disproportionate share hospitals are expected to see a 5.8% net reduction in OPPS revenue under the proposal, while for-profits are expecting a 7.4% net increase, KFF highlighted in a recent analysis of CMS projections.
This article was originally published on Fierce Healthcare.
Author: ICD10monitor | July 29, 2026
The FY 2027 Inpatient Prospective Payment System (IPPS) Proposed Rule includes several updates that could affect hospital reimbursement, coding, documentation, and operational planning.
While many organizations may first focus on the proposed 2.4% operating payment increase, the rule also includes notable changes to MS-DRGs, ICD-10-CM/PCS codes, CC and MCC designations, quality reporting, and new technology payments. Although payment updates often receive the most attention, these operational changes frequently require the greatest amount of planning, education, and implementation before the October 1 effective date.
What’s Proposed?
CMS is proposing to create 14 new MS-DRGs and delete 18 existing MS-DRGs, with several changes tied to cardiac device procedures, spinal fusion, hip and knee revisions, and emerging technologies. The proposed MS-DRG revisions appear intended to more closely align payment classifications with the resources hospitals use to care for increasingly complex patients.
The Proposed Rule also includes 184 new ICD-10-CM diagnosis codes, four revised codes, and one new ICD-10-PCS procedure code. Many of these proposed changes focus on greater specificity, including osteomyelitis, pregnancy-related diagnoses, BMI reporting, neoplasms, and musculoskeletal conditions. For coding and CDI teams, these updates reinforce the need for accurate, detailed documentation.
CC and MCC changes are another important area to watch. CMS is proposing three additions to the MCC list, 56 additions to the CC list, and 22 deletions from the CC list. Several social determinants of health Z codes are proposed for removal from the CC list, signaling CMS’s continued focus on resource utilization tied directly to the underlying medical condition.
New technology payments are also a key part of the Proposed Rule. CMS proposes continuing New Technology Add-on Payments for 41 technologies, discontinuing 13, and reviewing additional applications for FY 2027. The agency is also proposing to eliminate the alternative NTAP pathway beginning in FY 2028.
How To Prepare?
Although the Final Rule will determine which proposals are ultimately adopted, hospitals should begin reviewing the areas most likely to affect their organization now. MS-DRG changes, ICD-10 updates, documentation needs, quality reporting, and new technology payment policies may all require education, workflow updates, and cross-department planning before the October 1 effective date.
For organizations looking for deeper analysis after the Final Rule is released, the 2027 IPPS Masterclass is a comprehensive 3-part webcast series designed to break down the most important FY 2027 changes and explain their real-world impact.
Learn more here: https://medlearn.com/icd10monitor/product/2027-ipps-masterclass-final-rule-update-with-expert-insights-and-analysis/
Sources
Centers for Medicare & Medicaid Services. (2026, April 10). FY 2027 Hospital Inpatient Prospective Payment System (IPPS) and Long-Term Care Hospital Prospective Payment System proposed rule—CMS fact sheet. U.S. Department of Health and Human Services. View the CMS fact sheet
Centers for Medicare & Medicaid Services. (2026). FY 2027 IPPS proposed rule home page. U.S. Department of Health and Human Services. View the FY 2027 IPPS proposed rule resources
Centers for Medicare & Medicaid Services. (2026, April 14). Medicare program; Hospital Inpatient Prospective Payment Systems for acute care hospitals (IPPS) and the Long-Term Care Hospital Prospective Payment System and policy changes and fiscal year 2027 rates; requirements for quality programs; and other policy changes. Federal Register, 91. View the full proposed rule
This article was originally published on RACmonitor.