What You Should Know:

The healthcare revenue cycle is getting a major makeover, driven by a surge in automation and artificial intelligence (AI). A new survey by AKASA, a leading developer of AI-powered revenue cycle automation solutions, reveals that 74% of healthcare organizations are already automating some part of their revenue cycle operations, and 80% of those not currently using it plan to do so by the end of 2025.

The Rise of the Robots

– Automation is no longer a luxury, but a necessity: The survey highlights the widespread adoption of automation, with 74% of respondents already using it and another 80% planning to join the bandwagon within the next two years.

– AI is fueling the automation revolution: Nearly half (46%) of organizations using automation are already leveraging AI, showcasing its growing influence in streamlining revenue cycle processes.

Healthcare’s Drive for Automation

Survey respondents cited several reasons for prioritizing automation:

– Increased revenue yield: Automation streamlines processes, reduces errors, and improves claim accuracy, leading to higher revenue capture.

– Cost reduction: Automation eliminates manual tasks, freeing up staff for more valuable work and reducing operational expenses.

– Improved efficiency: Automated workflows optimize processes, leading to faster claim processing and improved overall efficiency.

– Enhanced staff focus: Automation frees up staff from routine tasks, allowing them to focus on more strategic initiatives and patient care.

The healthcare revenue cycle is undergoing a major transformation, and automation is at the forefront. As more and more organizations embrace these technologies, they will be well-positioned to achieve greater efficiency, improve financial performance, and ultimately deliver better care to their patients.

“Automation is no longer simply an option in the revenue cycle — it is an imperative. Technology has never been more capable of addressing these challenges, and organizations have access to automation options that harness AI and LLMs. These technologies are driving significant improvements in revenue yield, cost reduction, and staff efficiency, ultimately enabling organizations to focus on what matters most,” said Amy Raymond, senior vice president of revenue cycle operations and deployments at AKASA.

Survey Background/Methodology

Commissioned by AKASA, the survey fielded responses from more than 450 chief financial officers and revenue cycle leaders at hospitals and health systems across the United States through the Healthcare Financial Management Association’s (HFMA) Pulse Survey program.

What You Should Know:

– KAID Health, the AI-powered healthcare solutions pioneer, has secured a significant $9 million in funding led by Activate Venture Partners, Martinson Ventures, Boston Millennia Partners, and Brandon Hull, alongside KAID Health’s Board of Directors.

– With this new funding, KAID Health plans to expand its reach to more providers and their payer partners and develop new service offerings.

Unlocking the Power of Whole Chart Analysis

KAID Health’s flagship platform, Whole Chart Analysis, leverages cutting-edge natural language processing (NLP) to unlock the true potential of electronic medical records (EMRs). Unlike traditional NLP solutions that focus on specific data points, KAID Health goes beyond, analyzing every available piece of information within a patient’s chart, including notes, conditions, medications, and lab results. This comprehensive approach enables:

• Improved coding accuracy and capture: KAID Health demonstrably boosts coding accuracy, ensuring providers receive proper financial compensation while improving data-driven decision-making.
• Streamlined quality reporting: Effortlessly fulfilling complex quality reporting requirements, freeing up valuable time for clinicians.
• Enhanced care management: KAID Health identifies key patient needs, facilitating proactive care interventions and optimizing clinical outcomes.

Real-World Impact, Proven Results

KAID Health’s impact extends beyond theoretical promises. To date, the platform has already:

• Revolutionized Medicare Advantage coding for large provider groups nationwide.
• Simplified chart review for prior authorization and clinical trial enrollment.
• Outperformed physicians in identifying surgical risks at a major medical center.

“KAID Health continues to deploy technology that makes clinicians more efficient, translates that efficiency into more cost-effective care, and grows revenues,” explained Kevin Agatstein, CEO of KAID Health. “By combining best-in-class, scalable, flexible, secure technology with deep clinical workflow and market expertise, we can partner with each customer to meet their informatics needs while alleviating chronic staff shortages.”

What You Should Know:

– TREND Health Partners, a rapidly growing provider of AI-driven solutions, has acquired Advent Health Partners, an AI-driven platform identifies and standardizes clinical attributes from itemized bills and medical records, expediting critical information presentation and ensuring timely and appropriate reimbursement.

– This strategic combination brings together two industry powerhouses, creating a comprehensive platform for payers and providers seeking to optimize revenue and streamline operations.

Synergy through Expertise and Technology

Founded in 2010, Advent boasts CAVO®, a proprietary AI-powered platform specializing in medical record review for revenue cycle and payment integrity processes. This expertise aligns perfectly with TREND’s existing TRENDConnect platform, enabling seamless integration of clinical and non-clinical data and analytics. This holistic approach promises even greater value for clients, addressing a wider range of challenges in areas like denials management, utilization management, and DRG reviews.

Enhanced Value for Payers and Providers

The combined entity will serve over 50 health plans and nearly 1,000 hospitals, offering substantial benefits to both sides of the healthcare equation:

– Payers: Improved reimbursement accuracy, reduced administrative costs, and enhanced transparency in provider billing practices.

– Providers: Streamlined claim submission and processing, faster turnaround times, and clearer understanding of payer expectations.

AI-Powered Payment Accuracy

The TREND-Advent merger signifies a significant step forward in the evolution of healthcare payment accuracy. By leveraging AI and collaborative approaches, this combined entity is poised to transform the industry, ensuring fairer reimbursement, smoother operations, and ultimately, better care for patients.

“Rarely do you come across two companies with such complementary capabilities and aligned visions as what we found with TREND and Advent. CAVO® will help us extend our existing platform, TRENDConnect, to solve an even broader set of challenges for our payer and provider clients. We are very excited to welcome the Advent team into our organization,” said Sarah Armstrong, CEO of TREND.

What You Should Know:

– Ensemble Health Partners, a provider of revenue cycle management services, has released its 2024 Healthcare Business Trends Report, revealing a shift in priorities among healthcare executives.

– The report, based on a survey of over 100 senior executives, highlights a growing focus on outsourcing revenue cycle management and leveraging external expertise to drive performance, improve financial health, and achieve long-term growth.

4 Trends Driving the Next 4 Years

The report survey reveals four key trends driving the next four years for healthcare executives:

1. Implementation of alternative payment models, such as value-based contracts, will be a top strategic priority, with 63% of executives prioritizing this in 2028 (compared to 47% today).

2. Growth strategies will take precedence over cost-cutting, with increased prioritization of identifying acquisition targets and investing in talent and technology.

3. Increasing investment in managed services, with 25% of organizations planning to allocate over half their revenue cycle budgets to managed services in the next four years.

4. Value and performance will be primary factors driving revenue cycle management vendor selection, with 95% of executives expressing interest in end-to-end managed services.

Report Key Findings

– Outsourcing is on the rise: 95% of executives surveyed are considering or already using revenue cycle managed services.

– Performance trumps cost: More than 70% believe managed service providers deliver better results than in-house operations, making performance the primary driver of vendor selection.

– Focus on value-based care: 63% of executives plan to prioritize implementing alternative payment models like value-based contracts in the next four years.

– Investment in growth: Organizations are shifting focus from cost-cutting to growth, prioritizing acquisitions, talent development, and technology investments.

– Managed services budgets increasing: 25% of organizations plan to allocate over half their revenue cycle budgets to managed services within four years.

Judson Ivy, President, Founder, and CEO of Ensemble, notes the increasing challenges faced by healthcare organizations and the trend of seeking reliable partnerships to navigate financial strain, regulatory complexity, and cybersecurity challenges.

What You Should Know:

– R1 RCM Inc. (NASDAQ: RCM), a provider of technology-driven solutions for healthcare providers, announced a significant strategic partnership involving the acquisition of Acclara and a 10-year revenue cycle management agreement with Providence, one of the nation’s largest health systems.

– The acquisition of Acclara for $675M in cash and warrants will bolster R1’s offerings and position it as the “trusted partner of choice” in comprehensive revenue cycle management. Acclara’s technology platforms and experienced team will enhance R1’s capabilities in patient access, coding, billing, and more, ultimately improving financial performance for healthcare providers.

Strategic Partnership with Providence

As part of the deal, Providence has chosen R1 as its long-term revenue cycle management partner. This 10-year agreement marks a significant cross-sell opportunity for R1, leveraging its technology and service solutions to support an innovative healthcare system.

“This partnership demonstrates the confidence of a major health system in our solutions and our ability to support their mission. We are proud to be their trusted partner and look forward to welcoming Acclara to R1,” said Lee Rivas, CEO of R1.

Financial Highlights and Growth Potential

The combined revenue and Adjusted EBITDA are expected to exceed $625M and $185M by year five of the partnership, demonstrating strong financial potential. Additionally, R1 anticipates significant cost synergies of $30M by year three and $50M by year five.

Artificial intelligence (AI) captured the imagination of many in 2023. AI gets a lot of attention but little understanding or appreciation of what it can do to move revenue cycle management (RCM) forward, improve the patient experience, and answer the question, “Are you receiving the appropriate reimbursement?”  

More than 3 of 5 companies are still experimenting with AI, according to a recent survey by Accenture. Only one in four are innovating or achieving stated objectives) and only one in eight firms have advanced their AI maturity enough to achieve superior growth and business transformation. That percentage drops to only 3% for healthcare organizations. This is not surprising. Accenture argues healthcare companies are lagging behind because they tend to be late adopters of digital transformation technology for administrative purposes. But AI mature healthcare software-as-a-service (SaaS) vendors can offer ways to reduce the lag.  

AI is not new. There are numerous examples of AI taking a foothold in the healthcare and clinical diagnostics domain, including digital pathology and AI in NextGen Sequencing (NGS) analytics, as well as speech recognition and conversion to clinical notes. But there is still so much that can be achieved with AI.

Removing friction from the patient experience

The upfront information gathering from a patient is rife with friction. Prior authorizations, eligibility, benefits coverage determination and insurance discovery all generally require detailed information exchange. 

Consider for a moment a patient who has Blue Cross Blue Shield insurance. The patient provides the policy number at the time of service, but that number alone isn’t sufficient to confirm eligibility or benefits coverage, which is essential to providing an accurate estimate of potential out-of-pocket expenses. The insurance information required can be far more extensive than the patient has readily available.  

Complicating matters is that it can be difficult for a patient to put the right information in the patient portal or application that determines authorization, according to the American Medical Association. Reading the insurance card, hunting for the information being requested, typing it all in, and getting it right, according to Forbes, can be difficult for patients. Even just getting the correct payor name is not simple, according to WebMD. 

According to a recent poll, many are turning to Robotic Process Automation (RPA).   What is RPA?  London School of Economics Professor Leslie Willcocks describes RPA as “a type of software that mimics the activity of a human being in carrying out a task within a process. It can do repetitive stuff more quickly, accurately, and tirelessly than humans, freeing them to do other tasks requiring human strengths such as emotional intelligence, reasoning, judgement, and interaction with the customer.”

Unfortunately, RPA is limited to mimicking human actions, including the automated replacement of human keystrokes or application programming interface (API). RPA can help by automating keystrokes. But to truly remove friction from the patient experience, organizations need to look beyond RPA and adopt AI to remove keystrokes and other steps, such as putting the onus on the patient and the provider to provide the information. AI applied in the right places can uncover the underlying payor details are needed to process a claim.

Simplifying interactions with payors

For each payor response there is in many cases a need for manual intervention, requests for additional information, unnecessary cognitive load, and pressure to resolve not be impacted by timely filing deadlines. There are a myriad of acknowledgments, denials, and reason codes.

Understanding the payor requires having an agent on the phone to get the information from the patient. This is where AI can help. AI can uncover the underlying payor details, including eligibility, coverage, and patient responsibility for a particular claim. It can also discover the payor plan details for that claim to be processed without manual intervention.

How can we use the small amounts of information the patient has and get to where we need to be? Through optical character recognition (OCR). OCR can interpret the insurance card image and text data and feed that into an AI that can lead to eligibility determination. AI can discover the RCM payor and details for that claim so that can be processed without manual intervention.

Machine learning-based historic data models can also assist with healthcare claim acknowledgment responses and use natural language processing (NLP) to translate them to the appropriate reason codes. 

Translating payor responses into actionable next steps

Another AI financial game changer to the RCM process is the ability to determine how likely there is a problem with a particular claim, and proactively red flag or even solve the problem. 

AI reduces noise, accelerates resolution, and can automate parts of the RCM process that previously required manual intervention. 

An accurate picture of expected payor reimbursement is crucial to many RCM and financial functions. Contracted plans can be complex. Even harder to evaluate are non-contracted health plans. Machine learning models, trained on recently adjudicated claims, can overcome those challenges and provide accurate information based on rule history that may not be published:

• Expected allowed amount. 
• Estimated copay
• Estimated coinsurance
• Risk of coverage limitations
  

AI can also help with exception processing prioritization.  Imagine an AI engine that could assign and prioritize claim exceptions based on:

• Likelihood of payment collection
• Billing team member expertise or efficacy 
• Claim value
• Timely filing deadlines

With edited configurable rules, AI can determine if a claim is likely rejected because of incorrect or incomplete payor information or patient ineligibility and use automation to resolve many issues.

Rich and highly configurable AI can then quickly determine the probability of reimbursement to help prioritize the claims that still require intervention and then redirect those needing human attention to the best available team member.

Because there will be times when an agent is involved in handling exception processing that needs to be acted upon manually, AI-supported RCM can produce assignments daily to determine manual work.  

AI can prioritize and determine the billing team member who is most effective at resolving various denial types and route new denials to that person most likely to get the best outcome. AI-driven workflow automation significantly reduces the manual work required to escalate, mobilize, coordinate, and resolve claim disputes. 

RCM platform support for algorithms or AI can also drive efficient automation of workflow adaptation to payor changes. AI can help determine problems and then use those same models to provide updates and to determine if information is inconsistent.

Future-Ready RCM Infrastructure Saves Time, Money

Dirty or unstructured data leads to unintelligent AI. Purposeful data modeling in preparation for AI use requires constant vigilance in every step of the process to ensure the integrity of the data and the results.

AI reduces the number of claims needing to be touched by a billing team member and corrects input errors, and ultimately gives back time and focus to diagnostic leaders. AI helps teams eliminate pain points, shortens turnaround time, reduces cost, and enables easier dollar recoupment that was either previously lost or underpaid by payors.  

Saving time and money on tasks means a greater focus on what is most important to clinicians – the patient. It means better insights, less expense, and more opportunities to take on additional workloads and deliver better results. 

About Jeff Carmichael

Jeff Carmichael is the Senior Vice President of Engineering at XiFin, Inc., a provider of SaaS-based healthcare revenue cycle management (RCM) and workflow automation solutions. Jeff Carmichael’s engineering leadership spans over 20 years and encompasses networking, security, and Healthcare software and systems. He brings a career long focus on data-driven insights and prediction through advanced data modeling across several industries. Prior to joining XiFin, Jeff led worldwide software development for the network and security division of LSI Corp. He has held senior level leadership positions at several successful startups, and divisional leadership positions at Intel. Jeff holds a B.A. in Mathematics from San Jose State University.

What You Should Know:

– The Healthcare Financial Management Association (HFMA) has announced a strategic alliance with FinThrive, Inc., a healthcare revenue cycle management (RCM) software-as-a-service (SaaS) provider, to co-launch a peer-reviewed, five-stage Revenue Cycle Management Technology Adoption Model (RCMTAM).

– The RCMTAM is designed to help health systems leverage industry benchmarks to assess their current state of RCM technology maturity and build best-practice plans to optimize revenue cycle outcomes.

A Growing Need for RCM Modernization

Health systems increasingly signal a need to modernize their RCM technology stacks, initiating enterprise projects that favor vendor consolidation, automation and AI, and platform approaches. Nearly two-thirds of recently surveyed healthcare finance leaders expressed their intention to evolve to an RCM platform in the next two years.

“Our initial analysis of more than 30,000 individual data points shows the majority (42%) of health systems are at Stage 1,” said Hemant Goel, CEO of FinThrive. “This affirmed our hypothesis that although many health systems know they need to transform and automate their revenue cycles, they haven’t had a blueprint to guide them. Through collaboration with HFMA and health system early adopters, we are eager to contribute valuable insights that empower revenue cycle leaders on their path to financial excellence in healthcare.”

Key Features of the RCMTAM

The RCMTAM includes several key features that make it a valuable tool for healthcare organizations:

– Industry Benchmarks: The model sets technology adoption benchmarks for RCM leaders within the healthcare sector, providing a basis for organizations to assess progress against newly introduced industry standards.

– Correlation with Financial Outcomes: The adoption model leverages HFMA-defined MAP Key definitions to capture financial performance during the assessment and benchmark variance compared to the upper stages of the RCMTAM.

– Personalized Organizational Roadmap: RCMTAM presents a clear and customizable adoption roadmap that enables all healthcare organizations, regardless of their current RCM vendor landscape, to tailor digital transformation strategies to their unique needs and objectives.

Departure from Traditional RCM Technology Adoption Model

The RCMTAM is a departure from conventional models in that it prioritizes the deliberate implementation of technology within healthcare organizations.

“In a departure from conventional models, this approach prioritizes the deliberate implementation of technology within healthcare organizations. Positive change in healthcare demands innovative solutions,” said Senior Vice President at HFMA, Richard Gundling. “We believe in the enhanced problem-solving capabilities of using maturity models, particularly RCMTAM, to guide organizations toward sustainable and effective revenue cycle management.”

What You Should Know:

– Experian acquires Wave HDC, a healthcare technology leader that offers AI-enhanced data curation solutions to identify unknown insurance benefits coverage and critical patient demographics at the start of a patient’s healthcare journey.

– The acquisition bolsters the Experian Health business portfolio with more comprehensive and faster healthcare coverage identification and automation capabilities.

Wave HDC’s AI-Powered Solutions

Wave HDC offers several insurance “curators” including Coordination of Benefits, Eligibility and Medicare Beneficiary Identifiers, and serves hospitals, laboratories, billing companies and physician groups. The acquisition will enhance Experian Health’s leading patient access products to offer an advanced solution that captures all important registration data in real time from the start, as the patient checks in for an appointment. No longer will data need to be chased and updated post-registration. A single inquiry can search for all the essential insurance and patient demographics instantly, avoiding costly rework and delayed reimbursements.

Addressing Claim Denials and Enhancing Patient Registration

Patient registration is long overdue for an upgrade. When the industry fails to capture the right information from the start, a slew of other administrative headaches is created for healthcare providers. Claim denials continue to be a pain point for hospitals and medical practices contributing to more than $200 billion dollars per year in lost revenue. In a 2023 Experian Health survey, 42 percent of respondents said eligibility is a top reason for claim denials, while approximately one third of those surveyed cite patient information inaccuracy.

With rising claim denials along with increasingly complex regulations and constantly changing payer rules, healthcare organizations need innovative healthcare clearinghouses that deliver an accurate picture of a patient’s eligibility, coordination of benefits and insurance coverage quickly and seamlessly at the start of the registration and scheduling journey.

Acquisitions Boosts Experian Health’s Offerings

Wave HDC offers several insurance “curators” including Coordination of Benefits, Eligibility and Medicare Beneficiary Identifiers, and serves hospitals, laboratories, billing companies and physician groups. The acquisition will enhance Experian Health’s leading patient access products to offer an advanced solution that captures all important registration data in real time from the start, as the patient checks in for an appointment. No longer will data need to be chased and updated post-registration. A single inquiry can search for all the essential insurance and patient demographics instantly, avoiding costly rework and delayed reimbursements.

“Our mission is to simplify healthcare, and this move allows us to quickly scale our portfolio with advanced logic and AI-powered technology to help solve one of the biggest administrative problems providers face today, which is claim denials,” said Tom Cox, president of Experian Health. “With our vast clearinghouse data and Wave HDC’s expertise in insurance data capture processes and technology, we now offer the best eligibility and insurance identification products in the market.”

Financial details of the acquisition were not disclosed.

What You Should Know:

– PayGround, a Gilbert, AZ-based healthcare fintech payments platform, announced closing a $19.7M oversubscribed Series A financing led by SixThirty with participation from  Rally Ventures, IA Capital Group, FCA Venture Partners and Plug and Play Ventures.

– The funding will bolster the company’s rapid expansion into hospitals and health systems, building on its success in the ambulatory market, as well as grow its senior leadership team.

Simplifying Healthcare Payments

Founded in 2018, PayGround empowers individuals and families with a dedicated digital wallet in which they can manage and pay all their healthcare provider bills in one place. This not only simplifies the payment process for patients but also improves revenue cycle outcomes for healthcare providers.

“Most Americans have encountered the fragmented experience of paying healthcare bills. PayGround empowers individuals and families with a dedicated digital wallet in which they can manage and pay all their healthcare provider bills in one place,” says Drew Mercer, co-founder and CEO of PayGround. “Plus, by providing patients with an incredibly convenient way to pay for healthcare, paired with financial optionality, the revenue cycle outcomes for healthcare providers are naturally strengthened. It’s a win-win for all key stakeholders.”

PayGround plans to use the latest round of funding to enhance PayGround’s platform with additional value for hospitals, health systems, independent practices and patients that will further enable payor connectivity.

What You Should Know:

– Phare Health, a London-based startup, has secured $3.1M million in seed funding led by General Catalyst to transform hospital financial management in the UK and Europe.

– The company’s AI-powered solution bridges the gap between clinicians’ notes and hospital management, enabling better resource allocation and improved patient care.

Harnessing AI for Healthcare Efficiency

Phare Health’s groundbreaking AI system extracts valuable insights from clinicians’ notes, transforming unstructured data into structured information that can be utilized for administrative, operational, and financial purposes. This data-driven approach empowers hospital managers to make informed decisions, optimize resource allocation, and enhance cash flow management.

Closing the Gap Between Clinicians and Management

By converting unstructured medical data into a format that administrators can easily utilize, Phare Health fosters transparency and collaboration between healthcare professionals and hospital management. This newfound clarity empowers managers to allocate resources more efficiently, leading to a more accurate understanding of patient treatments and improved standards of care.

Experienced Team with Deep Healthcare Expertise

Phare Health’s team comprises experienced individuals with deep expertise in AI, healthcare, and finance. Co-founders and co-CEOs Dr. Martin Seneviratne and Lee Kupferman previously worked at DeepMind Health and Google Health, collaborating with the NHS on groundbreaking projects. Co-founder and CTO Tymor Hamamsy brings a wealth of experience in AI science from his time at Stanford and NYU.

What You Should Know:

– Maverick Medical AI, a provider of an innovative and autonomous AI-powered medical coding platform forms a strategic partnership with RadNet, Inc., a national provider of free-standing, fixed-site radiology diagnostic imaging services in the United States.

– This groundbreaking partnership will integrate Maverick’s Autonomous Medical Coding platform into RadNet’s extensive network of imaging centers, paving the way for significant advancements in revenue cycle management, operational efficiency, and reimbursement optimization.

Maverick’s AI-Powered Medical Coding Platform: A Game-Changer for RadNet

Maverick’s Autonomous Medical Coding platform has already been successfully deployed within RadNet’s network of imaging centers, demonstrating its ability to seamlessly integrate with DeepHealth, RadNet’s AI-powered health informatics system. This integration further strengthens RadNet’s commitment to innovation and its pursuit of groundbreaking advancements in care delivery.

Leveraging cutting-edge machine learning and generative AI, Maverick’s platform revolutionizes medical coding by autonomously analyzing clinical notes and reports to accurately generate reimbursement codes (ICD-10, CPT) in real-time. This capability significantly streamlines the coding process, enabling charges to be readily available to billing systems within seconds.

Key Capabilities Driving Efficiency and Accuracy

A cornerstone of Maverick’s platform is its robust auditing feature, empowering RadNet’s medical coding team to review pre-populated reimbursement codes promptly and efficiently. Additionally, the platform’s active learning capabilities enable real-time feedback collection from auditors and medical coders, fostering continuous adaptation without manual intervention. This dynamic approach results in a substantial boost in productivity and accuracy.

RadNet’s Rigorous Selection Process and Unprecedented Results

Prior to selecting Maverick as its AI-powered medical coding partner, RadNet conducted a thorough year-long assessment of various autonomous coding platforms. The company’s decision was driven by a comprehensive set of criteria, including the requirement for cutting-edge generative AI technology, support for MIPS quality measures, the development of a real-time radiologist interface for addendum creation, and the ability for the platform to continuously adapt to evolving coding guidelines and regulations.

To further validate Maverick’s capabilities, RadNet conducted a successful pilot of Maverick’s generative AI engine on 350,000 studies. The results were astounding, achieving an unprecedented 85 percent direct-to-bill rate, a testament to the platform’s exceptional precision and efficiency.

Transforming Medical Coding for the Future

“Our platform’s ability to seamlessly blend the power of deep learning generative AI and medical coding domain knowledge has proven to significantly transform medical coding efficiency,” stated Yossi Shahak, CEO of Maverick Medical AI. “Our integration with RadNet’s AI informatics platform, DeepHealth, will span all 360-plus imaging sites in their nationwide network, ensuring a superior direct-to-bill rate. By replacing legacy computer-assisted coding systems with Maverick Medical AI, we are not just streamlining medical coding processes, but also reducing errors and shaping the future of medical coding.”

What You Should Know:

Flywheel, a medical imaging artificial intelligence (AI) development platform, today announced the launch of its software-as-a-service (SaaS) data management solution on Microsoft Azure. This new SaaS offering will provide healthcare organizations with a secure, scalable, and user-friendly platform to manage, curate, and analyze their medical imaging data.

Flywheel’s SaaS solution is integrated with NVIDIA MONAI, part of the NVIDIA AI Enterprise software platform, and Azure Machine Learning Studio for the development and deployment of production-grade AI applications. This integration will make it easier for healthcare organizations to develop and deploy AI models that can be used to improve patient care.

Overcoming Barriers to Medical Imaging Breakthroughs

Healthcare breakthroughs have long been hindered by the difficulty of managing and analyzing medical imaging data. This data is often unstructured and siloed, making it difficult to access and use. Flywheel’s SaaS solution will help healthcare organizations overcome these barriers and accelerate the development of new AI applications for medical imaging.

Key Features of Flywheel’s SaaS Solution

• Secure and scalable: Flywheel’s SaaS solution is hosted on Microsoft Azure, which provides a secure and scalable platform for managing and analyzing medical imaging data.
• User-friendly: Flywheel’s SaaS solution is designed to be easy to use for healthcare professionals, even those with no prior experience with AI.
• Integrated with NVIDIA MONAI and Azure Machine Learning Studio: Flywheel’s SaaS solution is integrated with NVIDIA MONAI and Azure Machine Learning Studio, making it easy for healthcare organizations to develop and deploy AI models.

Enhancing Patient Cohort Discovery

In addition to the core features of its SaaS solution, Flywheel also announced that it will be integrating its solution with mPower Clinical Analytics by Nuance, a Microsoft company. This integration will allow Flywheel users to seamlessly enhance patient cohort discovery and enrich images with refined data from radiology reports.

Accelerating Discovery and Driving Innovation

“We are standing on the cusp of a revolution in healthcare, poised to leverage the vast potential of medical imaging data,” said Trent Norris, Chief Revenue Officer at Flywheel. “But harnessing this potential responsibly means ensuring the data is meticulously managed, accurately labeled, diligently curated, and rigorously tested. It’s a matter of trust. Our collaboration with Microsoft and NVIDIA epitomizes this commitment.”

What You Should Know:

– CodaMetrix has partnered with GE HealthCare to bring its AI-powered automated coding solution, CMX Automate, to GE HealthCare customers.

– This will enable GE HealthCare customers to benefit from CMX Automate’s ability to improve coding accuracy, efficiency, and revenue cycle management. CMX Automate is a cloud-based solution that uses machine learning, deep learning, and natural language processing to translate clinical notes into billing and diagnostic codes. Healthcare providers that have adopted CMX Automate have seen a significant reduction in claims denials and savings on coding costs.

Bringing AI-Powered Automated Coding to More Providers

CMX Automate is a cloud-based solution that uses machine learning, deep learning, and natural language processing to automatically translate clinical notes into billing and diagnostic codes. This can help providers to reduce coding errors and improve the efficiency of their revenue cycle management processes.

In addition to the benefits of CMX Automate, the partnership with GE HealthCare will also give providers access to GE HealthCare’s digital platform, which provides a secure, vendor-neutral environment for aggregating siloed healthcare data and enabling core application services.

CodaMetrix is committed to making high-quality, consistent coding affordable and reliable through the intelligent application of AI and automation. The company is excited to now offer even more providers access to CMX Automate at a time when coder workforce shortages and physician burnout are at all-time highs.

What You Should Know:

– AKASA, a leading developer of AI-powered automation for the revenue cycle, released new survey results showing that more than 70 percent of healthcare organizations are actively considering the use of generative AI.

– In the same survey of more than 250 CFOs and financial leaders at health systems and hospitals across the U.S., nearly 60% percent of respondents indicated they are actively considering these new technologies for their revenue cycle operations.

Today’s Healthcare Challenges

Healthcare organizations face challenges such as slim operating margins, staffing shortages, high costs, and growing volumes. These challenges are driving the urgency to embrace innovation, particularly in areas like the revenue cycle, where improvements can significantly increase revenue yield and reduce costs. Generative AI can now rapidly and accurately understand complex clinical documents, such as chart records, and extract meaningful information from them. This capability enhances the usability of clinical data across the revenue cycle.

What You Should Know:

– AAPC, a leading global solutions provider in healthcare revenue cycle management, has acquired Semantic Health, an early-stage healthcare technology company focused on creating AI-powered inpatient medical auditing software for health insurance plans and hospitals.

– This acquisition will allow AAPC to offer its members innovative tools to streamline auditing processes, reduce denials, and improve documentation quality.

AI-Powered Inpatient Medical Auditing

Semantic Health’s AI-powered inpatient medical auditing software has made significant strides in streamlining manual auditing processes, reducing denials, and improving documentation quality for auditing teams. The platform audits coded inpatient charts prospectively, validating, identifying, and specifying diagnosis and procedure codes with respect to clinical documentation.

Plans for Platform Expansion

The collective teams have ambitious plans to expand the platform’s functionality and elevate AAPC’s current product offerings. AAPC will work toward a seamless transition to continue supporting existing Semantic Health customers while growing the platform. The strategic move aims to keep AAPC’s 250,000 members at the forefront of healthcare’s digital transformation, ensuring their roles remain vital and effective in an evolving industry.

“I firmly believe this acquisition will help AAPC deliver immense value to our members and their employers, as this platform will empower them to continuously identify and prioritize cases with the highest revenue impact,” said Raemarie Jimenez, Chief Product Officer at AAPC. “We are committed to developing cutting-edge technology solutions that improve the efficiency of our members and helps make their complex jobs a bit easier.”