What You Should Know: 

 – Firstsource Solutions Limited (FSL), a leading global provider of Business Process Management (BPM) services, announced the acquisition of Quintessence Business Solutions & Services Private Limited (QBSS). 

– The strategic acquisition strengthens Firstsource’s position in the healthcare revenue cycle management (RCM) market, particularly in the United States. Financial details of the acquisition were not disclosed. 

Enhanced RCM Solutions for US Healthcare Providers

The acquisition of QBSS, a leader in outsourced RCM services and technology, allows Firstsource to offer a more comprehensive suite of solutions to US healthcare providers. These solutions leverage artificial intelligence (AI) to optimize revenue capture, improve efficiency, and boost overall productivity.

Meeting the Needs of a Changing Healthcare Landscape

The US healthcare market faces a unique challenge – navigating a post-pandemic environment with workforce changes and evolving patient care models. This acquisition allows Firstsource to cater to these evolving needs by:

• End-to-End RCM Services: Offering hospitals, physician practices, and integrated health systems a complete RCM solution.
• Proven Experience and Industry Expertise: Leveraging the combined experience of both companies in the RCM field.
• Streamlined and Personalized Care: Helping healthcare providers deliver high-quality care while managing their finances effectively.

“We are excited to be part of Firstsource, an enterprise with considerable depth and reach in complimentary market segments. The possibilities of deploying combined solutions, shared workflows, and opportunities to implement technology driven solutions to the larger healthcare market is enticing. We see a cultural fit that will be the key driver for working to enhance the competitiveness of our customer base, providing innovative solutions to the marketplace as well as enriching the roles of our employees,” saidNirmal Kumar Rajachandran, CEO, Quintessence. 

It’s a perfect storm of financial pressures facing healthcare provider organizations – from rising costs to labor shortages to constrained capacity – that stymies revenue growth. Growing challenges with payer payments only exacerbate these issues. According to a Kaufman Hall report, 73% of leaders surveyed said claims denials, which was the top revenue cycle issue in 2022, had increased in 2023.

The cost of denials is staggering. A recent data analysis revealed that providers spent nearly $20 billion in 2022 on efforts to resolve delays and denials across payers. More than half of the total – about $10.6 billion – came from denied claims that were appealed and ultimately paid. 

The traditional approach for providers has been to rely on medical professionals, including physicians and nurses, to help capture revenue at risk in denied claims by writing appeals. The need to hire clinical expertise to pursue such claims further adds to already substantial costs. The average cost to challenge a $43.84 denied claim will increase by $13.23 for a general inpatient stay and $51.20 for inpatient surgery. With an average of three rounds of appeals, providers are often waiting up to six months after care is delivered to receive payment, which can impact the ability for providers to maintain operational balance sheets. 

Moreover, there is a cascading effect on patients, increasing stress and detracting from the patient experience. For example, if a patient undergoes an outpatient procedure, such as a knee replacement, but then experiences complications that result in an overnight stay, the patient may not even realize their stay could be at issue. Even though the overnight stay was necessary to address complications and prevent deterioration, if the claim is denied, balances may become the patient’s responsibility, and the steps in the process are often confusing for patients. 

Prevent denials upfront

Providers now have the option to apply modern technology – including analytics, automation, and AI – to help improve their claims management processes to not only prevent denials upfront but also identify improvements to continuously hone processes, and efficiently resolve denials that can be overturned. Throughout the process, AI augments human expertise to reduce denials, reduce AR days and improve financial performance.  One method in which AI technologies can help organizations is with coding accuracy and compliance, so claims can be submitted along with appropriate documentation, resulting in fewer denials. 

The typical process for appealing a clinical denial is time-intensive, often requiring multiple rounds, resulting in long payment delays. Using AI technology to ingest, parse, and summarize text portions of the patient record can speed up and improve the entire process. AI-enabled analytics can pinpoint likely denials as well as identify trends by payer, clinical indication, etc. Providers can then focus more attention on those denied claims that are most likely to be successfully overturned. 

AI technologies also equip organizations with valuable data. Leveraging advanced analytics tools, organizations can identify areas for improvement to continuously optimize claims processes from start to finish. With a focus on preventing delays and denials, insights gained from both successful and unsuccessful appeals can be applied to better substantiate each claim upfront in the ever-changing payer landscape. 

Resolve denials accurately and efficiently

For each appeal, a clinician must formulate a strategy by reviewing a patient’s chart – potentially hundreds of pages of history, notes, and summaries – to assess the patient’s situation, treatment, existing conditions, and comorbidities. In minutes, AI can review the patient record and summarize all pertinent information for the type of appeal required, including the key identifiers, an accurate clinical summary, and the clinical argument to substantiate the claim. 

In addition, with manual chart reviews, people may miss key details or overlook important trends. Today’s AI technologies can efficiently and accurately go through the entire patient record in minutes to identify the points critical to depicting the complexity of the patient case. AI doesn’t get tired or experience stress – it can consistently and reliably pull together the data points needed for an effective appeal. 

Using AI in this way makes clinicians appeal editors rather than appealing authors. Instead of reading hundreds of pages and writing from scratch, clinicians review and fine tune the appeal drafted by AI to ensure it presents a compelling, accurate case to the payer. By integrating people services and technology capabilities, the time to resubmit claims can be reduced from hours to minutes – upwards of 75% in time savings. Such time savings on administrative and medical staff offers the added and critical benefit of enabling clinicians to focus on applying their expertise at the top of their license, which reduces their burden, relieves burnout, and improves job satisfaction and staff retention. 

Overcome AI adoption challenges 

Provider organizations evaluating AI solutions for the revenue cycle need to consider governance, change management, as well as policies and procedures to overcome common adoption challenges, including:

• Concern that AI will replace jobs: The best approach – proven by AI’s successful use by leading health systems today – is for AI to support, not replace, human decision-making. Providers should involve end users and other stakeholders right from the start to understand the most pertinent issues, build a solution that truly benefits end users, and gain buy-in along the journey. 
• Compliance and patient privacy: Publicly available solutions, such as ChatGPT, can put organizations at risk. However, adopting a robust framework and a closed environment enables providers to build upon their policies and procedures to productively manage patient privacy and compliance. 

Transform processes for sustainable growth

Healthcare organizations that adopt AI for high-value, high-cost administrative processes, such as clinical claims denials, will be better equipped to navigate today’s healthcare challenges. AI-enabled technologies can help organizations improve efficiency and clinician job satisfaction, more successfully resolve denied claims and apply their success to prevent future claim denials. In addition, AI can make the process more seamless for payers by making appeals more consistent and accurate, requiring fewer iterations. Most importantly, healthcare providers spend more time treating patients, and those patients receive better experiences – from care to cost. All told the resulting increases in revenue and cash flow can put healthcare organizations on stronger footing for sustainable growth, supporting better outcomes for all. 

About Steve Albert

Steve Albert is Executive Vice President and Chief Product Officer for R1. He joined R1 following the acquisition of Cloudmed where he also served as Chief Product Officer. Steve has over two decades of leadership experience in new market development and product innovation for enterprise-scale data management and analytics organizations. He leads R1’s product vision and roadmap, drives product innovation, and helps grow the company through expansion into new markets. Prior to joining Cloudmed, Steve held product and market development leadership roles at 1010data, Mastercard, Equifax, and GeoPhy. He has extensive experience leading and scaling go-to-market, product, and data science teams that delivered product-led revenue growth.

What You Should Know: 

– Ensemble Health Partners, a provider of end-to-end revenue cycle outsourcing (RCM) for healthcare organizations, has announced a significant expansion of its partnership with Microsoft. 

– The strategic collaboration aims to accelerate the development and adoption of Ensemble’s industry-leading revenue cycle intelligence platform, EIQ®.

EIQ®: Powering AI-Driven Revenue Cycle Management

EIQ® is a powerful platform that leverages Microsoft Azure’s advanced generative AI and machine learning (ML) capabilities. This empowers Ensemble to deliver a comprehensive suite of RCM solutions, including:

• Automation: Automating repetitive tasks across the entire revenue cycle, freeing up staff for more strategic initiatives.
• Machine Learning: Utilizing ML algorithms to identify trends and predict potential issues, allowing for proactive problem-solving.
• Artificial intelligence: Implementing AI tools to enhance decision-making and streamline workflows.
• Generative AI: Pioneering the use of generative AI for tasks like generating personalized appeal letters within seconds.

Ensemble has made significant investments in EIQ®, pouring over $100 million and 2 million development hours into building and refining this critical platform. Their dedication has resulted in eight U.S. patents for innovative RCM processes and data exchange functionalities.

“Each day, our operators work with an AI-optimized infrastructure that harnesses the richest data set in the industry to drive consistent, optimal outcomes our clients can rely on,” said Ensemble’s Chief Technology Officer Grant Veazey. “Our models are informed by thousands of variables and consume billions of transactions. The greater the volume and variety of data, the more complete and valuable models become – providing better and faster insights at the point of decision. Our partnership with Microsoft allows us to deploy hundreds of generative AI models quickly and safely to the millions of healthcare transaction we process daily.”  

What You Should Know:

–              Cohere Health, a recognized leader in clinical intelligence and prior authorization automation, announced that Humana Inc. (NYSE: HUM) will expand its use of Cohere’s prior authorization platform for diagnostic imaging and sleep services. 

–              In January 2021, Cohere and Humana began a pilot program in 12 states to improve the prior authorization process for musculoskeletal (MSK) services.

Transformative Prior Authorization Solutions: Cohere’s Nationwide Impact on Healthcare Access and Provider Satisfaction

The program effectively decreased turnaround times for prior authorization approvals, boosted provider satisfaction, and improved care delivery for MSK services. This success led to expansion across all 50 states in 2022. In January 2023, Cohere’s solutions were rolled out nationwide, encompassing cardiovascular and surgical services.

Cohere’s prior authorization platform expedites the approval process for providers and patients. Its unique combination of real-time analytics and evidence-based clinical intelligence reduces denial rates and facilitates quicker patient access to care.

Annually, Cohere handles 5.5 million prior authorizations, benefiting over 15 million health plan members and 420,000 healthcare providers nationwide.

“We’re pleased Humana will be utilizing our diagnostic imaging and sleep solutions, further strengthening our strategic and ongoing partnership,” said Siva Namasivayam, Chief Executive Officer of Cohere Health. “Our solutions are rooted in the latest evidenced-based guidelines from leading medical societies and use advanced technology to align patient services within care pathways through upstream moments of influence, ultimately enabling faster diagnoses.”

Medicare providers in hospitals and skilled nursing facilities (SNFs) are adjusting to new split/shared services documentation and billing regulations rolled out by the Centers for Medicare and Medicaid Services (CMS) as part of the 2024 Medicare Physician Fee Schedule (MPFS) final rule. The most notable change within the new regulations, which took effect on Jan. 1, 2024, is the finalization of CMS’s definition of the “substantive portion” of a split/shared evaluation and management (E/M) visit.

First introduced in 2022 in response to public comments asking CMS to allow either time or medical decision-making (MDM) to serve as the substantive portion, the 2024 definition of split/shared services  is: 

“More than half of the total time spent by the physician or nonphysician practitioner performing the split (or shared) visit, or a substantive part of the medical decision making,” 

Understanding who is considered to have spent the substantive portion of the visit is crucial in 2024 as it determines who will bill Medicare for such visits. 

The Definition in Action

The new split/shared services billing requirements apply only to services provided in hospital or SNF settings (no office visits) when patients are seen during a primary E/M visit by both a physician and a nonphysician practitioner (NPP) such as a physician assistant, advanced practice registered nurse, nurse practitioner, clinical nurse specialist, or other nonphysician provider. Under the previous year’s rule, physicians would typically bill for the time spent by any number of clinicians to see, examine, consult with, and treat the patient using an FS modifier to denote the shared charges. Now, however, the billing or listed provider must be the person who spent the majority of time or MDM on the patient encounter.

To determine who reports the split/shared visit and which code level is used, CMS relies on the list of activities included in CPT E/M Guidelines that count toward total time, according to The American College of Surgeons. Those activities include:

• Preparing to see the patient
• Obtaining and/or reviewing separately obtained history
• Performing a medically appropriate examination and/or evaluation
• Counseling and educating the patient, family, and/or caregiver
• Ordering medications, tests, or procedures
• Referring and communicating with other healthcare professionals, when those actions are not reported separately
• Documenting clinical information in the electronic or other health record
• Independently interpreting results that are not separately reported and communicating those results to the patient, family, and/or caregiver
• Care coordination that is not separately reported

According to CMS guidelines, physician and NPP time does not include the following activities:

• Performing other services that are reported separately
• Travel
• Teaching that is general and not limited to discussion required for management of a specific patient 

Finally, the AACP reports that documentation under the 2024 split/shared services rules should identify both practitioners. However, the medical record should be signed and dated only by the billing practitioner.

Taking Action

Complying with the 2024 split/shared services regulations will necessitate adopting new coding and auditing workflows. Coders and auditors will also need to outline documentation processes to ensure appropriate documentation is captured during patient visits and MDM – physician vs. NPP – must be appropriately reported to ensure compliant billing for split/shared services. While CMS does not define “appropriate documentation,” Coding Intel shares that “the only way for a physician and NPP to describe his/her contribution to the service is to document an individual note describing the portion of the service (they) performed.”

Physicians and NPPs will need to be trained on these documentation needs, which will also necessitate updates to claim scrubber software.  Finally, documentation processes must ensure time records don’t show overlaps between physicians and NPPs, who are likely to require credentialing with payors to obtain a billing identification number.  

Minimizing Challenges 

Successfully adjusting to 2024 split/shared services billing requirements calls for comprehensive education for auditors, coders, and providers, along with a proactive audit strategy and deployment of robust technology tools to support billing audits.

Given the broad impact of these new split/shared services policies, internal audit workflow tools capable of separating the servicing provider from the billing provider during the audit process would be a sound investment. These tools provide the granularity auditors need to identify instances when the service provider does not match with the service provided. They also enable an improved root cause analysis for ferreting out process errors and provide valuable feedback to informed targeted ongoing provider and coder education. 

The 2024 split/shared services billing rules provide greater transparency into care processes and better align reimbursement. Nonetheless, impacted providers and organizations are understandably apprehensive over the potential impact they will have on day-to-day workflows, timely compensation, audit risks, and the bottom line – all of which can be minimized with a proactive strategy encompassing education, audits, and workflow tools. 

About Shanta Lewis

Shanta Lewis, CPC, CRC, CPC-I is the Product Owner at MDaudit, a company that enables healthcare organizations to reduce compliance risk, improve efficiency, and retain more revenue streams by providing workflow automation, risk monitoring, and built-in analytics and benchmarking capabilities – all in a single integrated cloud-based platform.

What You Should Know: 

Wolters Kluwer Health, a leading provider of healthcare information and analytics, has introduced a new weapon in the fight against potential financial penalties for Medicare Advantage Organizations (MAOs) – the Regulatory Audit Module. 

– The AI-enabled tool leverages artificial intelligence (AI) to streamline the risk adjustment audit process, potentially saving MAOs millions of dollars.

The Challenge: A Complex and Costly Regulatory Landscape

Health plans, particularly MAOs, face a growing burden from complex regulations and stringent audits. The Centers for Medicare & Medicaid Services (CMS) estimates a staggering $4.7 billion in potential recoveries from Risk Adjustment Data Validation (RADV) audits through 2032. These audits assess the accuracy of risk scores assigned to Medicare Advantage beneficiaries, which directly impact reimbursement rates.

Manual Processes: A Recipe for Errors and Inefficiency

Traditional methods for preparing for risk adjustment audits are often manual and error-prone. Sifting through massive datasets to identify the most relevant medical records can be time-consuming and prone to human error. This can lead to missed opportunities to support accurate coding and potentially higher repayment liabilities.

The Regulatory Audit Module: Leveraging AI for Efficiency and Accuracy

Wolters Kluwer Health’s Regulatory Audit Module is designed to address these challenges head-on. This AI-powered solution offers several key benefits:

• Enhanced Accuracy: Clinically trained AI identifies an average of 5-7% additional codes that support accurate risk adjustment, potentially reducing repayment risks.
• Improved Workflow: Bypassing manual processes, the module automates tasks, presenting auditors with all relevant data points linked directly to the corresponding medical records.
• Prioritized Reviews: Embedded logic and AI prioritize charts based on a confidence score for each Hierarchical Condition Category (HCC). This allows auditors to focus on the most critical records for review, maximizing efficiency.
• Time Savings: The module streamlines the audit workflow by at least 25%, enabling MAOs to meet tight audit deadlines more effectively.
• Real-Time Insights: The module provides real-time data on coding and submissions, offering valuable insights into audit progress and potential financial impacts for all levels of leadership, from auditors to the C-suite.

“When we first launched Coder Workbench, we worked closely with our team of experienced clinical risk adjustment coders to deliver a solution that solved true pain points for both the end user and the C-suite. We’ve continued to lean on that expertise to develop the Regulatory Audit Module, combining essential industry insight with AI that, consistent with audit guidelines, recommends charts appropriate to submit to achieve higher validation rates and avoid costly penalties. There isn’t another tool on the market designed to help health plans effectively manage these challenges today,“ said Anne Donovan, Vice President and General Manager, Health Language, Wolters Kluwer Health.

Applying artificial intelligence (AI) solutions in hospitals and health systems can be overwhelming for leaders due to AI’s complexity (it’s not one technology, but several) and rapid proliferation. This is in addition to the usual technology hurdles within healthcare that include data quality and accessibility, interoperability, and clinical validation and adoption. Finally, with any new solution, there’s the concern of return on investment: Will implementing the latest solutions pay off for my organization? How will it be measured? 

Given these challenges, it’s advisable for organizations to initially apply AI in areas where there are fewer uncertainties and the value can be clearly ascertained. One such area is revenue cycle management (RCM), where the impact of AI can be easily measured, and hospital leaders can gain valuable insights into its potential. This knowledge can then be applied to more intricate and harder-to-measure domains like clinical care. To help RCM leaders build their AI strategy, here are three important steps: goal setting and impact measurement, assessing the true investment, and mitigating financial risk. 

Setting Clear Goals and Measuring AI’s Impact
Before investing in any new technology, it’s essential to establish a clear understanding of the value you aim to achieve and how that value will be accurately measured and attributed. New initiatives often can deliver on multiple fronts; for example, efficiency gains, cost savings and revenue capture. However, it is crucial to identify the primary goal from the beginning and ensure the methodology for measurement and communication is clearly defined to organizational leadership.

For instance, many organizations target cost savings through automation, such as reducing full-time equivalents (FTEs); however, employees may be reassigned to different tasks, potentially offsetting the savings achieved through automation. And sometimes automation reconfigures the workflow and shaves minutes off the total time but still requires the same roles to support. For example, if you automate the turning of the wheels in a taxi, you’ve technically automated ~80% of the work, yet you still can’t replace the driver. They still need to spot and interact with passengers, press the brake and gas pedals, and relay the fare. Healthcare leaders should be cautious of introducing automation to workflows that don’t actually realize savings in terms of the total number of positions.

A clear example where value attribution is well-defined is net new revenue. For instance, AI can automate prebill review by scanning charts for additional documentation and coding opportunities after final coding but before billing. With this approach, each dollar can be matched to added documentation and diagnosis codes, allowing hospitals to clearly measure and attribute financial results.

Assessing the Real Investment in AI
When implementing an AI solution, the true cost goes beyond what is paid to the vendor. It is crucial to consider other associated expenses such as staff time, training, system replacement and IT costs. For example, a change in physician workflow that requires education, monitoring and training for a large number of physicians can quickly escalate implementation costs. Leaders need to account for these costs in addition to the vendor fees.

Another often-misjudged cost is IT integration. A common assumption is that costs increase with the volume of data, but the actual scalability is determined by the depth of integration. Sending gigabytes of data periodically as one-way file transfers is typically easier compared to real-time bidirectional integration of even a few data fields. For clinicians using AI tools at the bedside, seconds matter, and thus near-instant patient data transfers and workflow integration are necessary. AI solutions focused on revenue cycle processes, however, can often work with data that’s minutes or even hours old, thus decreasing the requirements for direct integration.

Mitigating Financial Risk
Lastly, RCM leaders should explore contracting options to minimize financial risk when adopting AI solutions. The most prevalent model in the past decade has been Software as a Service (SaaS), which typically involves multi-year lock-ins and upfront costs. New models are emerging that offer alternative structures that meaningfully reduce financial risk. Some solutions operate on a use-based model, where the organization only pays for actual usage. Another option is a contingency model where the vendor is paid a percentage of the cost savings achieved or new revenue generated. By sharing the risks and rewards with the vendor, organizations stand to gain more from their AI solutions.

Laying the Groundwork for Future AI Applications
AI solutions hold tremendous potential in transforming healthcare. RCM leaders who approach AI implementation with a thorough understanding of its benefits and potential challenges are better positioned to strategically utilize these solutions for both short-term and long-term gains. With a focus on RCM processes, organizations can leverage AI’s ability to analyze vast amounts of data quickly and effectively, while also mitigating common risks. By focusing on narrower applications, organizations can take actionable steps to reduce costs, increase revenue and reinvest those gains into optimizing patient care.

About Michael Gao, MD

Michael Gao, MD, is the co-founder and CEO of SmarterDx, a clinical AI company that provides hospitals with a prebill safety net to capture net revenue opportunities and advance care quality. Prior to SmarterDx, he was an Assistant Professor of Medicine at Weill Cornell and Medical Director for Transformation for NewYork-Presbyterian where he led AI and automation projects across clinical, operational, and revenue cycle domains. Dr. Gao completed his BS at the University of California Los Angeles, his MD at the University of Michigan, and his Internal Medicine Residency and Silverman Fellowship for Healthcare Innovation at NewYork-Presbyterian/Weill Cornell.

What You Should Know: 

– ChrysCapital, India’s largest homegrown private equity (PE) firm, is reportedly exploring the sale of GeBBS Healthcare Solutions, a Los Angeles-based healthcare business process outsourcing (BPO) company. 

– This comes roughly six years after ChrysCapital acquired GeBBS in 2018. The potential $1 billion valuation reflects a significant increase from the acquisition price in 2018. This transaction could signal ChrysCapital’s strategy of entering a business, nurturing its growth, and then exiting at a healthy profit.

Potential Valuation and Gain for ChrysCapital

Multiple sources familiar with the development suggest a potential valuation of $800M to $1B for GeBBS. This would represent a significant return on investment for ChrysCapital. The firm acquired an 80% stake in GeBBS for $140 million in 2018, valuing the entire company at $175M. A sale at the reported valuation range would translate to a substantial profit for ChrysCapital.

Active M&A Landscape in Healthcare BPO

The potential sale of GeBBS highlights the active mergers and acquisitions (M&A) landscape within the Indian healthcare BPO space. Recent years have seen several big-ticket deals in this sector. Notably, EQT acquired the healthcare services arm of Hinduja Global Solutions (HGS) for ₹9,000 crore ($1.2B) in 2021, marking one of the largest PE deals in the Indian outsourcing industry. Interestingly, EQT itself is currently seeking to offload a significant minority stake in this BPO business, now known as Sagility.

What You Should Know:

• Tower Health and Ensemble Health Partners (Ensemble), the market leader in end-to-end revenue cycle outsourcing for mid-sized to large healthcare organizations, announced today that they will enter into a strategic partnership for the management of all revenue cycle operations.
• This partnership will allow Tower Health’s hospitals and physician practices to focus on delivering exceptional care in their communities while Ensemble manages day-to-day revenue cycle operations to ensure consistent financial performance for the organization.

Transforming Healthcare Revenue Cycle Performance: Tower Health’s Strategic Partnership with Ensemble

Tower Health, a regional integrated healthcare system dedicated to providing compassionate, top-tier healthcare and wellness services, has implemented a strategic turnaround plan. Central to this plan is the deliberate pursuit and successful execution of partnerships aimed at enhancing performance across key operational areas. Recognizing the need for greater scale, support, and advanced technology solutions to adapt to the evolving revenue cycle landscape, Tower Health’s leadership has forged a new partnership.

Through this collaboration, Ensemble and Tower Health have identified opportunities to enhance efficiency and effectiveness, ensuring a positive experience for both providers and patients throughout the healthcare journey. With this newly formed alliance, Ensemble now oversees $32 billion in annual net patient revenue, partnering with a greater number of health systems than any other firm to enhance revenue cycle performance through comprehensive managed services.

Ensemble prioritizes swift delivery of value and sustained high performance, achieving 100% of year-one client objectives, surpassing 102% of year-one cash collections, and consistently delivering a 5% average net revenue improvement annually across its clientele.

“We chose to partner with Ensemble because they are the industry’s top-ranked revenue cycle firm and they can ensure we’re optimizing our revenue cycle and incorporating best practices to better serve our patients. We are excited to partner with Ensemble and leverage their expertise and technological innovation in the revenue cycle industry,” said Michael Stern, COO at Tower Health.

What You Should Know: 

– A recent American Medical Association (AMA) survey reveals the ongoing crippling effects of the Change Healthcare cyberattack on physician practices across the United States. 

– The survey was conducted after UnitedHealth Group (UHG) said that claims would be flowing by the weekend of March 23. Despite UHG’s assurances, the attack, which began in February 2024, has disrupted claim processing and caused significant financial strain, jeopardizing patient care and potentially forcing practice closures.

– The AMA survey underscores the vulnerability of physician practices and the potential domino effect of disruptions in healthcare IT systems.

Key findings of the survey include: 

• Claims Processing Issues:
  • 36% of respondents reported a suspension in claim payments.
  • 32% were unable to submit claims altogether.
  • 22% faced difficulties verifying patient insurance benefits.
• Financial Strain:
  • 80% of practices reported lost revenue due to unpaid claims.
  • Over half (55%) dipped into personal funds to cover expenses.
  • 44% couldn’t purchase necessary supplies.
  • 31% were unable to meet payroll obligations.
• Impact on Patient Care:
  • The survey suggests potential implications for patient care due to disrupted services.
• Small Practices Hit Hardest:
  • Practices with 10 or fewer physicians appear to be disproportionately affected.

Despite Challenges, Practices Remain Open for Now

While facing significant financial hardship, only 15% of practices reported reducing their operating hours.  Many have sought financial relief through:

• Advance payments
• Temporary funding assistance
• Loans

“The disruption caused by this cyber-attack is causing tremendous financial strain,” said AMA President Jesse M. Ehrenfeld, M.D., MPH. “These survey data show, in stark terms, that practices will close because of this incident, and patients will lose access to their physicians. The one-two punch of compounding Medicare cuts and inability to process claims as a result of this attack is devastating to physician practices that are already struggling to keep their doors open.”

What You Should Know: 

– Availity, a real-time health information network launches Predictive Editing, a revolutionary feature within its Availity Essentials Pro™ platform. 

– The AI-powered tool developed in partnership with Anomaly Insights empowers healthcare providers to significantly reduce claim denials, leading to smoother revenue cycle management and improved financial performance.

The Problem: Costly Claim Denials

Denied claims are a major pain point for healthcare providers, resulting in lost revenue and wasted administrative time. Identifying and correcting errors before submission can significantly improve claim acceptance rates.

Predictive Editing: A Powerful Solution

Availity’s Predictive Editing tackles this challenge head-on with its intelligent AI engine:

• Proactive Denial Detection: The AI analyzes vast amounts of claims data, identifying patterns that indicate potential denials before claims are submitted.
• Impressive Accuracy: A recent study involving over 100 million claims showcased the tool’s effectiveness, flagging over $828 million in potential denials with a remarkable 97% precision.
• Machine Learning Expertise: Developed in partnership with Anomaly Insights, a leader in healthcare AI/ML solutions, Predictive Editing continuously learns and adapts to evolving payer rules and claim adjudication patterns.

Key Advantages of Predictive Editing

• Automates Upfront Edits: Eliminates the need for time-consuming manual review of denied claims.
• Handles Complexities: Goes beyond traditional edit engines, addressing intricate payer-specific scenarios.
• Complementary Integration: Works seamlessly with existing editing tools, enhancing their effectiveness and reducing manual rule maintenance burdens.

“Availity’s Predictive Editing tool works in real-time within the workflow and it can review each claim and permutation of the claim as its being created, generating predictions with virtually no latency and returning guidance via API,” said Linda Perryclear, Senior Director of Product Management for Availity Essentials Pro. “With Predictive Editing, providers have a tool that drives a more seamless payment process for providers nationwide through Availity Essentials Pro.”

What You Should Know:

– Talkiatry, a provider of in-network psychiatric care, has partnered with Cedar, a healthcare financial engagement platform, to optimize the billing experience for their patients.

– The strategic partnership prioritizes not only mental health treatment itself, but also the financial barriers that often prevent individuals from seeking or continuing care.

Mental Health Crisis Demands Solutions

The United States faces a significant mental health crisis, with one in five adults experiencing mental illness each year.  While stigma and provider shortages pose challenges, a major hurdle to obtaining care is often the financial burden. Even with health insurance, complex billing can discourage people from seeking or continuing mental health treatment.  Aligned with their focus on a caring and patient-centered approach, Talkiatry partnered with Cedar to improve the financial aspect of seeking mental healthcare.

Cedar Pay Streamlines Online Payments

Talkiatry implemented Cedar Pay in a mere 12 weeks, successfully transitioning nearly all payments online. This user-friendly system offers patients a stress-free experience, including:

– Personalized communication: Patients receive clear and timely updates at every step of the billing process.

– Transparency: Easy-to-understand statements and an online help center empower patients to find answers quickly.

– Control and Choice: Patients can choose when and how they pay, with convenient options like Apple Pay and card-on-file.

Positive Impact for Patients and Providers

The partnership has demonstrably improved the billing experience for Talkiatry patients.  Within the first year, the Cedar Pay platform achieved a remarkable 96% digital self-service payment rate and an impressive 86% patient satisfaction rating with the billing process.

“At Talkiatry, we’re committed to eliminating barriers that prevent patients from accessing quality psychiatric care and therapy,” said Todd Yu, Chief Financial Officer of Talkiatry. “Billing friction creates an extra layer of stress and anxiety at a time when patients should be focused on treatment. Cedar allows us to cater to patients’ needs.”

Autonomous inpatient coding. This may sound like a pipe dream for revenue cycle leaders, whose hopes were likely raised and crushed in the early aughts. But it’s coming sooner than most think.

Why is true inpatient coding automation the “holy grail” – as Kerry Gillespie, a former CFO at Intermountain Health and now an executive consultant at Warbird Consulting Partners, said to me at a Healthcare Financial Management Association roundtable last year?

For decades, inpatient services have been the backbone of many provider organizations and, simultaneously, a significant pain point for those managing hospital operations. First, the financial imperative is clear: although inpatient represents a much smaller share of patient volumes, it typically drives 60% of total revenues, according to MD Clarity.

Despite the financial magnitude, managing the revenue cycle for inpatient visits has proven more challenging. While providers have struggled with staffing shortages for medical coders of all stripes, certified coding capacity for inpatient admissions has been especially limited. According to recent Glassdoor data, this shortage is reflected in labor costs, with inpatient coder wages often 20%+ higher than outpatient wages. An MGMA Stat poll found that 34% of medical group leaders cited medical coders as the most difficult revenue cycle position to hire for.

With so much value on the line, coding accuracy and speed are much higher stakes. Inpatient coders must grapple with some of the most complex medical cases, typically involving longer stays and more complicated care plans – meaning a much larger volume of medical documentation to review and code. Fully automating coding for even a portion of inpatient cases would thus significantly reduce the burden on coders. Indeed, solving inpatient coding with AI, an elusive goal, would be a massive win for providers – perhaps one of the most impactful operational opportunities in a generation.

So what’s changed?

Tech advancements offer renewed promise

If you’re skeptical of meaningful automation on the inpatient side, you’d be right to think that way. But recent developments have upended what’s possible. To make sense of that, let’s take a quick look at what the past few decades have tried to deliver.

The history of coding automation is defined by four key phases:

• The arrival of natural language processing (NLP)
• The transition to ICD-10
• The advent of deep learning 
• The development of large language models (LLMs)
  

NLP is a type of AI that can be traced back to the 1940s. It enables computers to understand, interpret, and generate human language in a natural and meaningful way. In the early aughts, pioneers like CodeRyte and A-Life (now owned by other companies) led the way in coding productivity based on NLP, achieving rates of 70% automation for low-complexity encounters and 30% automation for moderate complexity. But these capabilities hit their limit – and then coding got harder.

On October 1, 2015, ICD-10 went live in the US. And with it, the number of possible diagnosis codes ballooned from 13,000 to 69,000. This explosion of codes crushed the promise of early NLP, as it couldn’t handle the larger volume and complexity of codes. As medical coding teams struggled to keep their heads above water and contend with 5x the number of codes, automation rates from available tools plummeted, negatively impacting the revenue cycle.

A light on the horizon appeared in 2018 with advances in deep learning. This approach combined mountains of data and extraordinary computing power to create AI that figures out its own rules, translating into super-high automation rates, significant cost reductions, and broad specialty coverage for health systems.

Underpinned by deep learning, autonomous coding has rapidly expanded across high-volume outpatient specialties, delivering automation rates of 85-90%+ or even pushing 99% in specialties like radiology. Prompted partly by broader AI agendas, autonomous coding is becoming the norm for many outpatient settings. This same technology is now turning to inpatient care.

But to make inpatient autonomous coding robust, the last boon has been the arrival of LLMs. This generative AI technology – known as the basis of ChatGPT and other popular tools – complements deep learning to handle last-mile issues, enabling near 100% automation rates. With deep learning and LLMs working in tandem, providers can at last realize true inpatient coding automation.

What to expect and how to prepare

From my vantage point, true autonomous coding for inpatient will arrive later in 2024, providing hyper-accurate automation for the majority of patient admissions. In 2025 and beyond, inpatient capabilities will catch up to where outpatient is today.

This advancement is not too far away. So, how can health system leaders ready their organizations to take on meaningful inpatient automation? A great place to start is by bringing autonomous coding to outpatient departments. Besides reaping the financial and operational benefits – including heightened accuracy, reduced labor costs, improved revenue capture, and decreased administrative burden – getting started on the outpatient side enables leaders to build a relationship with a trusted vendor and sets the stage for expansion to inpatient. Completing implementation for one or more outpatient specialties will also build confidence in the approach and enable revenue cycle and HIM teams to move more quickly on inpatient down the road.

In addition to paving the way for inpatient automation, securing experience with autonomous coding for outpatient helps to promote the organization’s broader AI ambitions. As providers pursue different models for building AI competency – hiring Chief AI Officers, forming cross-functional committees, or appointing in-house experts – locking in concrete projects such as outpatient automation helps to increase momentum and skill-building. Visibility into these AI efforts may even help systems to attract top talent – who, according to BCG, increasingly expect AI to help with their day-to-day roles – in tough labor markets.

Capturing the ‘holy grail’

The impending arrival of autonomous inpatient coding is a remarkable breakthrough for health systems. Recent advancements in deep learning and LLMs mean this transformative technology is closer than ever before. And starting now, the provider organizations that proactively set themselves up for this monumental shift will reap the most benefits.

About Andrew Lockhart

Andrew Lockhart is CEO of Fathom, the leader in autonomous medical coding. Andrew earned his MBA from Stanford University and his BA from the University of Toronto. He is an avid speaker and has presented at HFMA, Academy Forum, Stanford Medical School, and HBMA events.

What You Should Know:

– Sutter Health, a leading California healthcare system, announced a major collaboration with Abridge, a company specializing in generative AI for clinical documentation.

– The strategic partnership aims to transform the way physicians document patient interactions, ultimately improving patient care and reducing physician burnout.

Abridge: Streamlining Documentation and Freeing Up Physician Time

Physicians nationwide spend a significant amount of time on administrative tasks like documentation. Abridge’s solution has the potential to reduce this burden by 2-3 hours per day, significantly impacting physician well-being and reducing burnout. Abridge’s AI platform generates draft clinical notes in real time based on the conversation between physician and patient. This eliminates the need for lengthy post-visit documentation, freeing up valuable time for patient interaction. Additionally, Abridge supports over 14 languages and 50 specialties, catering to Sutter Health’s diverse patient population.

Patient-Facing Summaries: Empowering Patients

Sutter Health and Abridge are also collaborating on a new initiative: integrating patient-facing summaries into the electronic health record. This will empower patients to better understand and recall key details from their visits.

Recent Abridge Milestones

This partnership follows successful Abridge deployments at leading healthcare institutions like Yale New Haven Health and UPMC. Abridge recently secured $150M in Series C funding, which will fuel further research and development of their AI technology.

“There is tremendous momentum behind our collective efforts, and it has the ability to change lives for the better,” said Warner Thomas, president and CEO, Sutter Health. “We are highly focused on innovation as a lever to help us achieve our vision and mission of delivering safe, high quality, equitable care to all of our patients. Sutter is equally committed to the thoughtful integration of emerging technologies like AI in healthcare that support our physicians to help deliver on that mission.”

What You Should Know:

Zelis, a leader in healthcare technology solutions, has announced its newest weapon in the fight against opaque claim pricing: In-Network Pricing and Contract Modeling. This comprehensive solution empowers health plans to understand and manage claim pricing proactively, leading to more accurate cost estimates and improved financial control.

A Complete Claim Pricing Suite: Precision and Transparency

In-Network Pricing and Contract Modeling joins Zelis’ existing suite, offering a one-stop shop for all your claim pricing needs. This all-encompassing solution boasts several key benefits:

Clear Cost Expectations: Gain a precise understanding of claim costs before processing.

Automatic Pricing: Leverage a vast library of methodologies to price any claim in seconds.

Extensive Program Support: Handles commercial, Medicaid, Medicare, Tricare, and more.

Reduced Administrative Burden: Experts handle contract uploads and fee schedules.

Frictionless Provider Experience: Transparent line-by-line pricing formulas for providers.

Unique Features Drive Transparency for All

Unlike competitors, Zelis’ solution promotes collaboration between health plans and providers. Providers can access detailed pricing breakdowns, fostering trust and reducing the risk of balance billing. Additionally, payers can share this information with providers to ensure accurate pricing for all parties.

“We understand that navigating complex stakeholder relationships and evolving healthcare regulations requires more from a cost management partner than strictly managing cost,” said Jay Deady, President of Price Optimization for Zelis. “Platform solutions like this compound efficiencies, enhance the member experience, minimize payer-provider abrasion, and lower the overall cost of care for everyone involved.”

Partnership with Availity

Zelis announced a strategic alliance with Availity, the nation’s largest real-time health information network to streamline the entire financial experience, from claims processing to payments. The healthcare industry is notorious for its complex administrative processes, often leaving both payers and providers frustrated. Currently, navigating the healthcare financial system requires logging into multiple platforms and sifting through scattered information. This inefficiency not only wastes time but also creates confusion and errors.

Zelis and Availity’s partnership aims to solve this problem by combining their core functionalities. This will create a single, unified platform offering:

Turnkey enrollment and onboarding: Eliminate the hassle of complex onboarding processes.

Centralized access to payment and remittance data: Find everything you need in one place.

Real-time analytics and dashboards: Gain valuable insights into your financial performance.

Multiple disbursement options: Choose the payment method that best suits your needs.

“At Availity, we believe that patient-centric care starts with simplifying the administrative processes between payers and providers. Availity and Zelis, both trusted by payers and providers for digital connectivity, have identified ways to accelerate critical processes in the healthcare system,” shared Availity CEO, Russ Thomas. “This is a situation where two companies who play crucial roles in the administrative workflow and reimbursement process are coming together to join our core capabilities in a valuable way for our mutual clients, and it’s meaningful for reducing the administrative burden for the entire system by creating shared processes and shared value.”