The integration of Artificial Intelligence (AI), automation, and data analytics into the revenue cycle management (RCM) of healthcare facilities marks a transformative leap toward operational excellence. In an era where financial sustainability is as crucial as clinical outcomes, these technologies are pivotal in optimizing processes from patient intake to final billing, ensuring that healthcare providers not only survive but thrive in a competitive market.

At our infectious disease lab, the implementation of AI and data analytics has revolutionized how we manage our revenue cycle. By automating routine tasks, we have freed up valuable time for our staff to focus on more complex, value-added activities. Automation of data entry and claims processing reduces the likelihood of errors and speeds up the turnaround time, directly impacting our cash flow and reducing the days in accounts receivable.

One of the most significant advantages of using AI is its ability to analyze vast amounts of data to identify trends and patterns that would be impossible for a human to discern. This capability allows us to anticipate issues before they become problematic, such as identifying which claims are likely to be denied based on historical data. With predictive analytics, we are proactive rather than reactive, which not only increases our revenue but also reduces the stress on our staff and improves our relationships with patients and insurers.

AI transforms data into actionable insights, enhancing efficiency and profitability in healthcare.

Moreover, machine learning (ML) models within our AI systems continuously learn from new data. As they become more sophisticated, they offer increasingly accurate forecasts and deeper insights into our lab’s financial operations. This ongoing learning process is crucial for adapting to the ever-changing landscape of healthcare regulations and insurance policies.

Our organization has also capitalized on data analytics to fine-tune our pricing strategies and to ensure compliance with billing regulations. By analyzing the outcomes of thousands of past transactions, we can set competitive prices that maximize our revenue without compromising patient care. Furthermore, compliance monitoring through AI-driven systems ensures we adhere to all billing regulations, reducing the risk of costly penalties and legal issues.

The integration of these technologies extends beyond internal operations to enhance patient interactions. Our patient portal, powered by AI, offers personalized experiences where patients can easily access their billing information, understand their payment options, and communicate with billing representatives seamlessly. This not only improves patient satisfaction but also expedites payments, positively affecting our cash flow.

In addition to these operational improvements, AI and data analytics significantly enhance our strategic decision-making capabilities. With access to real-time data and advanced analytical tools, our management team can make informed decisions quickly, addressing potential financial discrepancies and optimizing overall financial health.

Furthermore, the ability of AI to integrate with other technological advancements, such as electronic health records (EHRs), further streamlines our operations. This integration ensures that all patient data is synchronized across platforms, minimizing the risk of data silos, and ensuring that every department has access to the same accurate and updated information. This seamless integration helps in maintaining consistency in billing practices and patient care services.

Our commitment to leveraging AI extends to training our staff to effectively utilize these tools. By holding regular training sessions and workshops, we ensure that our team is not only comfortable but also proficient in using the latest technologies. This empowerment enables them to contribute actively to our ongoing efforts to refine and improve our revenue cycle processes.

Additionally, AI tools help us manage the complexities of insurance verification and eligibility checks with greater accuracy. By automating these processes, we reduce the instances of claim rejections due to coverage errors. This not only speeds up the billing process but also decreases the burden on our patients, who can be confident that their coverage is correctly verified at the outset of their healthcare journey.

Moreover, AI-driven analytics assist us in identifying inefficiencies in our billing and service delivery models, allowing us to make necessary adjustments. These adjustments are often predictive rather than reactive, positioning us to address potential issues before impacting our operations. This foresight saves time and resources and supports our strategic goals of maintaining financial health and patient satisfaction.

The adoption and continual advancement of these technologies in our revenue cycle processes illustrate a commitment to innovation and excellence in healthcare management. As these tools evolve, so too does our ability to meet the needs of the patients we serve and the staff we support, ensuring a future where healthcare and technology work hand in hand for the betterment of all involved. As we continue to harness these powerful technologies, we not only foresee a more robust financial footing for our lab but also a greater capacity to provide exceptional care to our patients.

Through ongoing investments in AI and data analytics, we not only optimize our current operations but also pave the way for future innovations. These technologies allow us to stay at the forefront of the healthcare industry, continually improving our services and outcomes. By embracing AI and automation, we not only enhance our operational efficiencies but also ensure a higher standard of care, which is the cornerstone of our mission in healthcare.

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In the dynamic landscape of healthcare, Geisinger’s Steele Institute for Health Innovation is pioneering initiatives that harness automation to reshape patient care and streamline clinical workflows. Through cutting-edge technology and innovative approaches, Geisinger is ushering in an era of efficiency and excellence.

A pioneering initiative at Geisinger involves automating compliance audits for patient charts, targeting critical areas. This initiative aimed to revolutionize auditing, ensuring timely completion while upholding stringent quality standards. Digital algorithms are assigned to navigate electronic health records (EHRs) almost in real-time to identify patients under specific conditions, such as those requiring non-violent restraints or those on suicide watch. These algorithms then check for necessary orders and flowsheet completions per policy requirements to determine compliance.

Automations streamline the review of admitted patients, identifying and addressing missing information, and ensuring it’s promptly accessible online as needed by policy. This enables real-time access for nursing leadership. Additionally, compliance dashboards offer valuable insights into audit compliance rates across various units, hospitals, and regions, empowering nurse leaders to support documentation compliance within the EHR, thus enhancing patient safety. It’s crucial to emphasize the importance of documentation and our partnership with native systems, which helps us give time back to clinicians by managing tasks efficiently. This avoids inconsistencies and leads to quicker turnaround times for audit requirements and a better understanding of compliance rates.

Geisinger’s automation journey represents a paradigm shift in CDS. By harnessing the power of technology, they have revolutionized internal processes and set a new standard for excellence in patient care.

Through meticulous automation driven by Robotic Process Automation (RPA) technology, Geisinger ensures that individual chart audits are completed close to real-time, enabling the organization to achieve a nearly 100% completion rate in quality audits. This achievement represents a significant milestone in Geisinger’s journey toward excellence in patient care. The RPA technology employed by Geisinger plays a pivotal role in eliminating delays and errors inherent in human manual processes.

By creating a standardized method for completing each chart audit for all patients in all hospitals at Geisinger, the organization ensures consistency and adherence to the same high-quality standards across the board. This standardization enhances efficiency and facilitates seamless auditing processes, further contributing to the organization’s ability to achieve real-time completion of chart audits.

Leveraging cutting-edge technology to extract and analyze data from electronic medical records (EMRs), Geisinger maximizes the effectiveness of its automation initiatives. By automating routine tasks and streamlining workflows, Geisinger enables nursing managers and leaders to focus their time and expertise on initiatives to improve patient outcomes.

With RPA technology leading the way, Geisinger empowers its nursing managers and leaders to focus on driving positive change and innovation in patient care delivery, maximizing their potential. This strategic approach boosts operational efficiency and underscores Geisinger’s dedication to providing the highest quality of care to every patient.

Geisinger’s automation initiatives have far-reaching effects, yielding concrete improvements in patient safety, nursing compliance, and job satisfaction. By automating routine tasks, nurses and nurse managers gain valuable time to dedicate to direct patient care, elevating the standard of healthcare delivery. Geisinger places a premium on the well-being of its nursing and clinical staff, understanding that relieving them of mundane duties can significantly enhance job satisfaction and promote a healthier work-life balance. Through proactive efforts to streamline workflows and automate administrative processes, Geisinger reaffirms its commitment to fostering the professional growth and overall well-being of its healthcare professionals.

Geisinger’s proactive approach to addressing compliance challenges sets a precedent for other healthcare organizations and serves as a model for best practices in the industry. By leveraging automation to ensure adherence to regulatory standards, Geisinger enhances operational efficiency while promoting transparency and accountability across the organization. Geisinger’s commitment to utilizing cutting-edge technology to streamline processes benefits its operations and contributes to raising the standard of care and compliance across the healthcare sector.

Geisinger’s automation journey represents a paradigm shift in CDS. By harnessing the power of technology, they have revolutionized internal processes and set a new standard for excellence in patient care. As healthcare organizations worldwide embrace automation, Geisinger’s experience is a guiding light, illuminating the path toward a future where automation and innovation converge to create a healthier world for all.

Intelligent Automation Hub: Enhancing Efficiency and Patient Care

At Geisinger Health System, the Intelligent Automation Hub stands as a transformative force, revolutionizing internal processes to drive efficiency and, ultimately, elevate patient care. Collaborating closely with internal teams, the Automation Hub pioneers automated solutions for various processes and tasks, significantly enhancing operational efficiency. By enabling clinicians and care team members to concentrate on delivering high-quality patient care, we drive efficiencies and give valuable time back to the clinical staff, alleviating them from unnecessary tasks and fostering a more effective healthcare environment.

The Automation Hub comprises a dedicated team of business analysts and programmers who partner with data scientists to integrate various levels of technology. This collaborative effort enables the research and implementation of leading-edge automation tools throughout the organization.

To support its mission, the Automation Hub focuses on delivering business processes with tangible benefits, continuously improving the automation development process, and creating a robust platform for process automation. The goal is to add layers of intelligence to processes through enhanced automation, freeing up employees to engage in higher-value work.

The bot creation process spans six stages, from discovery to production, ensuring seamless integration and efficiency. Geisinger’s automation initiatives have yielded remarkable outcomes, notably emphasizing time savings and efficiency enhancements. For instance, the implementation of chart audit automation has resulted in approximately $6.5 million in cost avoidance measures and a staggering 100K hours saved, with a total transaction count of 300K. This underscores the avoidance of inconsistencies and translates into a swift turnaround for audit requirements and a deeper understanding of compliance rates.

In summary, Geisinger’s Intelligent Automation Hub represents a beacon of innovation in healthcare. By harnessing the power of automation, Geisinger continues to drive efficiency, improve patient care, and set new standards of excellence in the industry. As healthcare organizations worldwide embrace automation, Geisinger’s journey serves as a testament to the transformative potential of technology in shaping the future of healthcare delivery.

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Automation is taking the healthcare industry by storm. Every organization is exploring opportunities to invest in Robotic Process Automation (RPA), Machine Learning (ML), or Artificial Intelligence (AI). Even though it is now the trend in our industry, it has been used since 2012, when it was introduced to me upon my arrival at a health system based out of Pennsylvania. It was presented to me by an analyst. He lobbied the organization to purchase a program where he could automate the perfunctory functions of the Business Office. He could program accounts to flow to a particular work queue with a Claim Adjustment Reason Code (CARC) from the 835, program the bot to log into different applications to capture data, develop an eclectic spreadsheet, and go to websites to verify eligibility. The main problem was that several computers had to be utilized to perform each function. We had to have desktops allocated to workstations and continually active to perform programmed tasks. Although it was an advancement in our technology journey, much more remained desired.

AI could only accurately predict how long the account will take to achieve a zero balance, but “TRUE” AI should tell you how we got there.

As the emergence of automation grew, the buzzwords that became popular were RPA, ML, and AI. The problem is that many individuals proposing these advancements do not realize the vast differences between each form of technology. Concerns began to grow in the industry because individuals who invested in the wrong tool for a perceived outcome became disbelievers of healthcare innovation. However, the industry dynamics have changed how doubters view automation in the healthcare environment. The need for more automation is hitting us in the face! We have payors who consistently use inaccurate responses that require manual intervention to resolve simplistic issues such as additional documentation, claim edit rejections for minor discrepancies in formatting, and staffing shortages with increased volume. Each of these tools can be used to solve a particular operational issue.

RPA is what was described in my first interaction with healthcare automation. This is the most primitive form of automation. However, it is still an achievement for some organizations without experience with automation. Advantages of RPA include scalability and the ability to perform more overall tasks. RPA bots performing as intended will also provide more accuracy for mundane functions such as work queue assignments, and there will be cost savings. Imagine having five bots resolving Additional Documentation Request (ADR) versus individuals manually retrieving medical records. Not only would you be able to do it more quickly, but you also will not need the number of full-time employees (FTE) required to maintain the volume of work. For instance, three bots may be able to do the same work as 6-8 FTEs. RPA requires constant attention to any rule break that will alter the bot’s actions. An individual must monitor any inconsistencies or gaps in the process, review where the break occurred, and then reprogram it. This can become laborious if systems are constantly being updated.

ML is more intuitive. The bot remembers behavior and adjusts its programming based on actions previously used to achieve the desired outcome. A great epitome of ML is a bot programmed to send a particular response from a payor to a work queue. However, the machine realizes that any time response code 754 goes to the assigned work queue, it is removed and sent to a different system for processing. ML will adjust its behavior and reprogram itself to respond accordingly to response code 754 without manual intervention. The challenges exist when the majority cannot be the rule. For Instance, ADRs are actually clinical denials and require a written appeal. Situations as described are cautionary boundaries for automation in healthcare. However, it is not a reason to deter your organization from developing technological capabilities that will enable your team to be more productive and efficient.

Some skeptics reject the notion of AI in healthcare for good reason. AI can be as harmful as it can be helpful. I don’t believe enough data exists to gain advancements in AI because of the inconsistency, lack of standardization, and perversity of healthcare-adjudicated claims. However, I would prefer to separate clinical and financial AI objectives. I believe there is an opportunity to predict patient outcomes based on comorbidities presented by previous patients’ armamentarium available. We should be able to see what care was provided and the outcomes and conclude. The difference with healthcare financial outcomes is the level of subjectivity on claims with a zero balance. Many inaccurate actions could have occurred to resolve the account; AI could only accurately predict how long the account will take to achieve a zero balance, but “TRUE” AI should tell you how we got there. Conventional wisdom tells us that payors will change their denial behavior once we have made operational improvements. The best example I can provide for AI would be predictive denials. It would be efficacious if a technology existed that could determine the payor plan that begins with a “U” would start to deny future claims based on their pattern and reason codes from previous denials, predicting the change in the pattern.

Innovation in healthcare is required to guide us through the challenges we have ahead of us. It needs to be embraced and managed appropriately to ensure its efficacy.

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In the ever-evolving healthcare landscape, RCM stands as a critical aspect that ensures financial stability for healthcare providers. However, the journey of optimizing revenue cycles is fraught with challenges, and one of the most pressing concerns is the need for automation. 

The Need for Automation in RCM

The healthcare industry has recognized the urgency of embracing automation to enhance efficiency, reduce errors and address other day-to-day challenges. However, the path to automation is often obscured by a lack of clarity on where to start. The myriad of options, from optimizing EMR to implementing third-party vendor solutions, can be overwhelming. Additionally, the definition of automation varies depending on who you speak with, adding to the confusion.

Value of Automation

Automation, in the context of RCM, employs technology to streamline and optimize financial processes, minimize manual intervention, reduce errors, and enhance overall operational efficiency. This transformative force in RCM delivers benefits, enhancing efficiency by streamlining workflows, reducing processing time, and optimizing resource utilization.

Additionally, it significantly minimizes billing, coding, and claims processing errors, ensuring precision and reliability in financial processes. Cost savings are a notable advantage as automation eliminates manual handling of repetitive tasks, freeing up resources that can be redirected to critical areas within healthcare. Moreover, automation plays a vital role in ensuring compliance with industry regulations and mitigating the risk of non-compliance and associated financial penalties.

The positive impact of automation extends further, expediting claims processing and billing for faster reimbursement, making it a multifaceted solution that improves efficiency, accuracy, cost-effectiveness, regulatory adherence, and overall patient satisfaction in RCM.

By integrating automation, we embark on a transformative journey that optimizes processes, empowers our people, and enhances the overall quality of care for our patients.

Key Questions Before Approaching Automation

Before venturing into the realm of automation in healthcare RCM, it’s imperative to delve into the existing processes. Understanding the nuances of the current revenue cycle procedures is the foundational step. This involves thoroughly assessing how automation can strategically optimize these existing processes. By identifying bottlenecks and areas for improvement, organizations ensure that automation is applied judiciously for maximum impact.

Moving beyond mere budget considerations, evaluating the ROI becomes paramount. How automation will influence the broader business landscape, encompassing efficiency, cost-effectiveness, and overall performance, forms a critical facet of the pre-implementation assessment. This holistic understanding ensures that the introduction of automation aligns seamlessly with the organization’s overarching business objectives.

Integration with existing systems and scalability are intertwined considerations. Organizations need to evaluate whether automation can effortlessly integrate into the current system or if there’s a necessity for integration with a new system. This assessment encompasses both scalability and the potential impact on existing workflows. Deciding whether to add automation within an existing system or integrate a new one becomes pivotal to ensure a smooth transition without disruption.

Furthermore, it’s essential to consider the impact on end-users. Will automation simplify their tasks or make their jobs more complex? Striking the right balance is key to ensuring that automation enhances the user experience rather than causing unnecessary complications. This user-centric perspective is crucial for the successful implementation and acceptance of automation within the healthcare revenue cycle.

Some Lessons Learned when Implementing Automation

Learning from the lessons of organizations that have successfully implemented automation in Healthcare RCM reveals valuable insights for those considering a similar path. These lessons underscore key considerations. 

Automation does not have to be an all-or-nothing scenario. Starting small and gradually scaling has proven to be a successful approach. Many organizations initiate their automation endeavors with pilot projects targeting specific processes. This allows them to cautiously test the waters, identify challenges, and refine their approach before expanding the scope.

Collaboration with stakeholders, especially frontline staff, is paramount. Involving key personnel in the decision-making process fosters a sense of ownership and ensures that the chosen automation solution aligns seamlessly with organizational goals and day-to-day workflows. Input from those directly involved in operations is instrumental for a smooth transition.

Know that automation is not a one-and-done solution. Successful organizations emphasize continuous monitoring and optimization because automation is not a one-time fix. Regular reassessment of the effectiveness of automation ensures adaptability to evolving challenges, maintaining agility in response to dynamic healthcare landscapes.

Flexibility and adaptability are cornerstones of effective automation strategies. Acknowledging the dynamic nature of the healthcare industry, organizations that thrive in automation embrace solutions capable of evolving with changes in regulations, technology, and organizational growth.

Contrary to common assumptions, the impact on staffing, measured in Full-Time Equivalents (FTEs), is nuanced. While automation can free up staff from mundane tasks, its implementation does not always equate to reducing FTEs. Acknowledging this, organizations recognize that automating a broken process may expose a need for more FTEs if underlying issues are not addressed. Automating a flawed system is akin to masking a problem rather than solving it, highlighting the importance of thorough evaluation before implementation.

Lastly, the overarching lesson emphasizes the necessity to evaluate existing processes before introducing automation. Adding automation to a broken or inefficient process is not a panacea. A critical evaluation of workflows is essential, identifying areas that need improvement. Automation should strategically enhance well-defined and optimized processes, contributing positively to the overall efficiency and effectiveness of healthcare RCM.

Taking steps toward implementing automation in RCM is marked by challenges, uncertainties, and the need for careful consideration. By defining automation, understanding its value, asking critical questions, and drawing insights from successful implementations in other organizations, healthcare providers can navigate this complex landscape with confidence.

As the healthcare industry continues to embrace automation, there is hope for a future where revenue cycle processes are more efficient, accurate, and the evolving needs of patients and providers alike. Embracing these challenges of automation as opportunities for growth and improvement will undoubtedly pave the way for a more streamlined and resilient healthcare system. By integrating automation, we embark on a transformative journey that optimizes processes, empowers our people, and enhances the overall quality of care for our patients.

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I’m not sure whom to credit for the now universally accepted terminology to explain the technology adoption curve, but it does apply to automation. The innovators are the first out of the gate – they see a need, develop it, test it and bear a higher degree of risk than those who follow. Followed by early adopters who see the success that others have had and are willing to venture out onto that same limb. The risk is still high, but the gains established by those innovators before them make it worth the risk. The early and late majority are squarely in the middle and if we’re thinking about this statistically, they make up the majority of the bell curve in a normal distribution. Followed by laggards who adopt the innovation as late as possible and get on board when it’s already established as common practice.

Our organization has a fairly robust automation footprint. Let’s start with the workhouses of automation – bridge routines, edits and batches. Bridge routines can transform our data to accomplish things like changing/duplicating information, allowing us to split claims or follow payor-specific rules. Batches allow us to post large volumes of transactions simultaneously and gives an option to reverse. Edits let us create rules to route, modify or hold a claim. They help us implement thousands of rules for compliant governmental or payor-specific billing and coding.

The next wave in automation is robotic process automation (RPA), or bots for short. It can follow a set of directions and complete a prescribed action. Banner has around 40 bots running revenue cycle tasks at all times and continues to assess the value of these automations. Some are a bridge to tide us over while we wait for a permanent solution. For example, if you have an HL7 interface that’s failing and the programming solution is evasive, a quick fix is to have a bot key the information from one system into another while you work through the complexities of an interface redesign with systems that vendors have to update on their own timeframe. Can’t wait for the Q3 2024 release to get your interface working? Get a bot! Keep track of these quick fixes and ensure you sunset them. They are not long-term solutions, but when the pressure is off to find the permanent solution because you have a workaround, you might find the long-term solution pushing out over and over. Vendors are more likely to implement a solution in the next quarterly release that serves the needs of the majority of their client base, and your broken interface might be at the bottom of their list.

Everyone has experienced a bot doing something unexpected at some point.

Application Program Interfaces (APIs) are stepping in where bots have an Achilles heel. A bot can go out and get data from a specific location and make a point-to-point transfer of information. But what happens when you add a column in the middle of a dataset? Rename a field? If you don’t update the bot, it runs anyway and your information moved from point to point can quickly become very wrong. APIs allow you to just transfer the data. They’re a reliable form of information transfer.

Predictive analytics leveraging Machine Learning (ML) or predictive AI is driven by statistics. It will be very commonplace to use ML to drive workflow in the next two to three years. ML can take a set of data from your collections team and assess the actions taken, the outcomes and all the covariates or variables contributing to that outcome. A robust ML model can reduce the number of times we take an action that does not yield a result.  

When we hear about bias in ML models, we often hear about it in clinical terms. It’s not just a social determinants of health style bias opportunity that exists in an ML model. It’s a data bias that can cause an ML based model to emulate an incorrect action that happens frequently – like accepting an underpayment that should be pursued. You should heavily audit and constantly retrain your models to attain accuracy. Don’t implement them universally. Look at each population as a separate implementation. Establish thresholds for model accuracy with your compliance partners, vendors and programmers.

While predictive AI uses rules and data as a reference and is constrained by those things, generative AI has no such constraints. It is somewhat constrained by the data it has been trained on, but can create new content from its base of information. The value of generative AI will be found where there is written content that can be created faster and possibly more accurately by the generative AI than by the human being responsible for the content. There are health systems testing generative AI in writing appeal letters. A recent JAMA’s article (Ayers, 2023) says that responses to patient questions written by AI chatbots were more empathetic than those written by physicians. We are likely to see generative AI in patient facing communications and provider documentation in the near future.

If your revenue cycle scope includes clinical documentation improvement (CDI) or clinical denial appeals, you may find a use case for generative AI in the near future. The regulations in this space will play a crucial role in determining its fortune.

Everyone has experienced a bot doing something unexpected at some point. The more complex the actions for a bot, the more opportunities for a break based on an unknown. Consider whether a bot should drive all the actions or serve as a component of an automation. Perhaps you could have one bot create a batch file and have another bot post a batch file rather than keying it directly into a system. That is certainly easier to reverse.

Payers are using automation today with far more success than providers. That puts providers in an interesting position because we need to rely on more automation to keep up with their automation. But, we also have a front-row seat to the scrutiny our judicial system is applying to the practice. AI governance is in its infancy and we don’t have enough formal guidance on what will be allowed. We cannot rest on our laurels while payers automate their workflows and bombard us with additional tasks to resolve before payment, so we have to automate. Health systems need to be mindful of how and where we follow them into the breach.

Transforming Healthcare Through Technology Insights

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In the rapidly evolving world of healthcare finance, Infinx Healthcare is emerging as a key innovator, helping transform revenue cycle management (RCM) through cutting-edge automation and AI technologies. Infinx’s journey began almost a decade ago as the founding team began building a tech-enabled, end-to-end RCM solution. “We believed technology could be a force-multiplier in achieving the desired healthcare outcomes for our clients,” reflects Infinx’s Chief Product Officer, Navaneeth Nair.

The Evolution of Infinx’s Healthcare Revenue Cloud ^TM

They set out to build a comprehensive, cloud-based platform to power their RCM service offerings and tackle the fragmented, burdensome aspects of the revenue cycle. Infinx chose prior authorization as its initial challenge, understanding that manual-only methods were insufficient for solving this complex problem. They soon realized the need for human involvement in any effective technological solution. This insight set the direction for their product development roadmap: to amplify and orchestrate human efforts with technology, rather than replace them. Nair shares, “Our objective was to assist our in-house specialists with technology, supporting and amplifying their capabilities.”

Simply automating tasks was not enough. It needed other components to adequately cover all that is required to truly overcome revenue cycle obstacles their healthcare clients faced. Over the years, the platform evolved to intelligently automate tasks and assign tasks efficiently when manual intervention is required. It continues to learn and predict. The platform displays relevant revenue cycle analytics, proactively assesses and suggests next steps. Critical for adoption and continued use by Infinx’s clients, interoperability with provider systems and connections with payers is prioritized. The platform’s foundation on secure AWS for Health cloud ensures HIPAA compliance and scalability, allowing providers, large and small, to scale their operations without needing additional hardware or licenses.

Revenue Cycle Automation Journey

Continuing with their philosophy of augmented human performance, Infinx developed its own robotic process and cognitive automation technology. Initially, they designed automations for activities requiring minimal cognitive effort. However, they noticed that when healthcare providers attempted to automate entire workflows, the automation would fail at some point, leading to backlogs as staff reverted to manual processes for task completion. Infinx’s approach aimed to create a system that would avoid such pitfalls​​.

In developing automations for specific workflows, breaking down tasks based on their cognitive requirements was essential. “We focused on automating ‘no reasoning’ tasks and combining automation with AI for ‘low level’ reasoning tasks, leaving clinical responsibilities and higher reasoning tasks to humans,” explains Nair. “These ‘digital workers,’ capable of operating unattended, transform the way routine tasks like eligibility and benefit checks, prior authorization initiation and follow-up and claim payment follow-ups are handled, reducing the need for human intervention in these areas.

For those tasks that required manual intervention, they created “human-in-the-loop” or attended automations, wherein bots executed mundane, repetitive tasks, while human expertise is utilized for critical clinical decisions and data input. “This synergy ensures not only efficiency and clinical accuracy but also compliance with healthcare regulations,” shares Nair.

As the number of automations developed increased, Infinx launched a standalone Revenue Cycle Automation platform. This platform enables providers to subscribe to a variety of pre-built automations, ranging from EHR and payer automations to claims management and credentialing, allowing clients to implement their automations within weeks.

Solid Artificial Intelligence Foundation

Early on, Infinx recognized the significance of a platform that evolves and learns over time. They formed a data science team to harness the wealth of data processed by their solutions, initially focusing on machine learning to decode payer behavior in prior authorization—a task traditionally handled by manually updated rules. Infinx’s AI dynamically adapted to the rapid changes in payer policies, enhancing decision-making accuracy and timeliness. Their engineers have also worked on bringing order to the unstructured data prevalent in revenue cycle processing. With much of this data still existing in paper form, they developed document capture AI technology to process it effectively.

Nair expresses his enthusiasm: “I’m most excited about the fact that every Infinx platform is going to be a self-learning platform. This means our solutions will continuously improve from human-data interactions, becoming increasingly intelligent in supporting and augmenting user work.”

Their central thesis revolves around the synergy of technology and team effort in executing revenue cycle functions. This is the essence of Infinx’s Healthcare Revenue Cloud ^TM  platform.

The AI functionality in Infinx’s Patient Access Plus platform is a testament to their commitment to continuous learning and improvement. Its machine learning algorithms evolve with each processed prior authorization request, becoming smarter and quicker. The AI assists in determining the necessity of prior authorization, predicts turnaround times, identifies gaps in required information, and drives regular follow-up for updates​​.

Infinx leverages AI in its A/R Recovery and Denial Management (ARDM) platform. By analyzing historical claims data, their algorithms quickly detect patterns and trends associated with claim denials, enabling better prediction and mitigation of future denials. Predictive analytics within the platform proactively adapt to payer behavior and policy changes, providing billing teams helpful information to optimize claim submissions to prevent denials.

A national radiology group streamlined over $100 million in accounts receivables, achieving a 28 percent increase in collections, reduced 120-day aging A/R by 60 percent and accomplished a 20 percent reduction in 90+ days aging A/R in just two months. Nair recalls, “These results demonstrate the power of our technology in transforming revenue cycle management. For this client, ARDM effectively identified A/R most likely to win reimbursement, proving to be a reliable tool for them to recoup earned revenue due to them.”

Robust Revenue Cycle Analytics

Despite so much data generated from revenue cycle interactions being available, providers continue to try to solve the same problems and rely on spreadsheets or out-of-box BI tools to make sense of the data. While EMR systems may show RCM data, it is difficult to drill-down to assess root causes and use that data to power optimization initiatives.

Infinx built its own analytics pipeline so they could take any data source and convert it into more actionable information for their users. Their robust analytics dashboards provide on-demand insights across all revenue cycle stages, enabling data-driven decision-making.

Optimizing Human and Automation Synergy with Intelligent Workforce Management

Infinx has seamlessly integrated intelligent workforce management on their platform, enabling effortless orchestration of human intervention and automated efficiency. Advanced work algorithms effectively identify the number of resources required and then dynamically assigns and manages work. Nair elaborates, “This system is designed to maximize resource utilization, enhancing both productivity and efficiency across workflows.”

Moreover, the platform enables providers to adapt to fluctuating case volumes or staffing challenges. This flexibility is important for their software-only enterprise clients, providing them with the ability to effortlessly scale their resources and manage workloads efficiently, with the ability to assign a subset of cases to Infinx’s specialists to work when desired.

Looking Ahead

While AI and machine learning-led solutions in the healthcare ecosystem have been gaining momentum, the prevalence of disparate, legacy systems and manual data entry lead to data siloes and human error challenges.

Infinx’s central thesis revolves around the synergy of technology and team effort in executing revenue cycle functions. This is the essence of their Healthcare Revenue Cloud ^TM platform. “This holistic approach positions Infinx as a forward-thinking, solution-oriented SaaS provider in the healthcare industry,” said Nair. “The integration of AI, automation, and human collaboration with enhanced interoperability between different systems will result in a more efficient, patient-centric healthcare system.”

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Automation in the revenue cycle is no longer a futuristic concept but a present necessity. Emerging from the challenges posed by COVID-19, such as quiet quitting, an aging workforce, knowledge shifts and recruiting competition, organizations are grappling with the need to identify resources that don’t exist. In the face of this scarcity, terms like RPA, AI, and BI have become interchangeable. Innovations such as ChatGPT and partnerships announced by EMR giants signal a shift towards streamlining clinical documentation, aiding decision-making, and alleviating administrative burdens.

Revenue cycle technology, regulatory guidelines and payer rules are constantly evolving. Organizations must scale their knowledge bank to shorten training timelines, improve employee onboarding, and reduce errors.

For revenue cycle departments and hospitals to stay competitive with sufficient resources and appropriate scalability, the time to embrace automation is now, if not yesterday. To embark on this transformative journey, key elements need careful consideration, organization, and execution.

Moving from Idea to Execution

Emphasizing efficiency, cost savings, return on investment (ROI), and scalability becomes imperative in streamlining workflows. Build the “why” and the “how” for automation within your organization and work to secure executive support. Network with other top organizations to share demonstrations of key processes that have been successfully automated. To execute, you must have a dedicated team. Embarking on the journey of revenue cycle automation within a hospital necessitates a phased approach, aligning with the crawl, walk, run methodology. Initially, identify the pain points in the revenue cycle and assemble a diverse team to brainstorm feasible solutions. Start at the “crawl” phase by prioritizing manageable tasks that can be easily automated with minimal investment, such as streamlining billing documentation or optimizing claims processing. Implement a proof of concept (PoC) or pilot project to test the effectiveness of these automation solutions. Subsequently, move to the “walk” phase by evaluating the outcomes, measuring efficiency gains, and gathering feedback to refine and scale successful initiatives. As successes accrue and confidence in automation builds, transition to the “run” phase, gradually expanding automation across more complex revenue cycle processes. This phased approach, akin to a journey from crawling to walking and ultimately running, allows for steady progress, learning, and optimization at each stage of automation implementation in the hospital’s revenue cycle.

Building the Future Pipeline

To build your initial ideas, seek out workflows that require minimal build but yield maximum benefit. Examples include basic payment posting workflows, insurance verification, account adjudication, and vendor file processing. After gaining momentum, take these use cases and share demonstration sessions with key leaders and staff to initiate brainstorming sessions. Leaders and staff may have difficulty coming up with initial use cases; however, after seeing examples, those same individuals will begin to form connections to other workflows.

Designate key leaders and engage staff in each area of the revenue cycle as your automation leads. By having these individuals identified, you can secure buy-in, new ideas and gain their support to get staff on board with the concepts and ideas. Staff must see and feel the value, as questions about self-preservation will quickly surface. It is important to provide psychological safety for team members to realize that the goal is not staff replacement, but workflow capacity extension, staff augmentation, and process optimization. By creating a culture of psychological safety, you will create a think tank that will continue replicating new ideas.

Creating a Center of Excellence

After obtaining executive buy-in, designated teams, vendors, partners, key staff, and a pipeline, it will be important to manage stakeholder expectations. By having individuals completely assigned to automation build, you can generate momentum to create subject matter experts who can socialize new builds, workflows, and ideas with revenue cycle team members. It is important to bring your automation team into the revenue cycle as an official team and partner. To achieve additional scalability, find an automation software platform and a 3rd-party developer who specializes in the platform (if services are not offered by the software company). This approach creates a training ramp period to help build and scale workflows quickly while giving your newly dedicated team a chance to learn. Communicate the value that is being driven by the initially built automations. Show the time saved, ROI, and pipeline with future build, testing, and implementation dates. It will also be important to create a simple business case template that can document the process, the why, the time and money saved, the revenue generated, and the number of steps involved. This structure will help shape a standardization to quickly replicate new ideas and build the pipeline. By creating a branded center of excellence with designated areas, you can begin to construct an automation team that can expand beyond the revenue cycle into other critical clinical and administrative departments. Demonstrating and celebrating the successes along the journey will further solidify the value created and efficiency gained. You must evangelize the new culture and impact.

Where do we go from here?

The future is shifting to a scalability framework and new insight creation. Vendor platforms offer the ability to monitor end-users in live workflows to capture the most efficient processes. This innovative technology will be critical for converting tribal knowledge to documented steps that can be shared for employee training, future automation build, and mitigating the information knowledge atrophy that comes with an aging workforce and employee turnover. It is imperative to capture the most efficient processes from the highest performing employees to further create insights that can be used to insulate the risk that comes with daily staffing management. Revenue cycle technology, regulatory guidelines and payer rules are constantly evolving. Organizations must scale their knowledge bank to shorten training timelines, improve employee onboarding, and reduce errors. These benefits are crucial in a world of uncertainty and rapid change. We must learn to adapt.

The post Revenue Cycle Automation – Not If, Not When, But Now appeared first on HealthTech Magazines.

We are all tasked to do more complex work as we face staff challenges across the healthcare industry. Our team at Ochsner Health has been working internally and with our vendor partners collaboratively to elevate the work that our staff does. At the same time, we are automating work within our revenue cycle that does not require human intervention. The road to automation was sometimes bumpy, but we got here utilizing our information services guiding principles and organizational values. I’ll share our best practices to help your team as you work through the process.

As an organization, we view automation not just as bots, but as any creation of efficiencies in the process using technology. This can include interfaces, functionality within our EMR, and vendors outside of our EMR. We also utilize bots via an internal and external automation team.

Critical to our success as a revenue cycle automation team has been our partnership between IS team, revenue cycle team, and vendors. Our best ideas come from our team. We work with our operational teams to gather ideas for optimizing existing EMR build as often as we can. As an Ochsner team, we work together to govern what items should be automated and align with our strategy as well as our values and guiding principles. Looking back, our team agrees that we could have better defined the strategy for the external automation team earlier in the process. This led to lots of discussions regarding the scope of the bots in our EMR. It is worth suggesting that your team have those discussions early to decide the limits of automation in your system with your legal and compliance teams and what your appetite for risk will be. As an organization, we ultimately landed on having our bots work through low-risk and high-reward projects.

We have found that having a project manager with an IS and revenue cycle background has been helpful in holding teams accountable to tasks and understanding the complexities of these projects.

We also review the ROI for any automation. As an organization, we do time studies for the work being automated. Sometimes, based on these studies, it has not made sense to automate due to the limited ROI. A lesson learned for our team is that the ROI should take into consideration the added costs that may be incurred by your information services team for quality assurance, interfaces, reporting, or additional staff to monitor the automation vendor or your EMR. You will also want to account for ongoing maintenance related to system upgrades and testing. As your system changes, your bots’ access to the system will need to change.

As it relates to a bot’s access to the EMR, we have taken the approach that a bot should have only the minimum necessary access to the record and perform the necessary actions. This has its pros and cons. The pros are that the bot cannot create new patients or unnecessary actions. Another pro is that because our workflows are standardized, we can provision new bots to do the same workflow for another site quickly. We recently did this for large-scale deployment following testing. The cons can be that creating user templates and additional access sometimes takes longer than our revenue cycle partners would like.

It is also important that your organization have safeguards in place for data governance. As mentioned above, quality assurance is important for automation as this employee works a longer shift than any of your others. Oversight is critical at all stages. Your organization will need to make sure that the vendors you are working with meet all requirements for data sharing as they will be managing a large amount. All new applications at our organizations must be reviewed for information security safety and any vendor we share data with must also be reviewed.

Maintenance of our automation programs are just as important as implementation and governance.  Our EMR is updated at least twice yearly, but also takes system updates between that timeframe. As changes are made, our IS and revenue cycle teams work with vendors to do regression testing on our bots and other automation to ensure that they will work with the changes to the system.  Therefore, it is also critical that you create a robust testing, quality assurance, and notification process and a business continuity plan in the case of downtimes. Even our bots have sick days.

As we work with many vendors and teams to make revenue cycle automation successful, project management is key. We have found that having a project manager with an IS and revenue cycle background has been helpful in holding teams accountable to tasks and understanding the complexities of these projects. Our project manager regularly leads meetings with technical stakeholders, vendors, and senior leaders. That person also documents successes and opportunities in progress reports.

As your organization works on revenue cycle automation, I hope these best practices are helpful in making work easier and more efficient for your teams. 

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As a 16-hospital, an integrated health system serving 67 counties in Missouri and Kansas, Saint Luke’s Health System has faced challenges: increased demand for care, higher patient acuity, and critical staffing shortages.

Addressing these challenges was critical to helping us achieve our vision of becoming a digital health system. As part of this, we embarked on a strategic throughput initiative to improve operational efficiency and integrate care progression across the system. We believed that if we could unlock efficiency for patients in the inpatient setting, we would create downstream capacity and improve access for new patients coming in the door.

Extending the EHR with Enterprise-wide Predictive Capabilities

Over the past few years, we have invested significantly in our Epic EHR capabilities. Our teams now use many advanced features and functionality to maximize value from the system. And as a system of record, the EHR enables our teams to capture clinical data and equips them with dashboards and insights to understand the current situation.

As we embarked on our strategic throughput initiative, we first explored how to further leverage our EHR. But even after implementing recommended enhancements, our teams were still managing patient flow from the “rearview mirror.” We used EHR throughput dashboards to set discharge goals, but these only used population averages, such as GMLOS, rather than recommending targets specific to a patient’s condition and circumstances. We also tried using EHR checklists to track discharge plans, but these created ongoing, manual documentation for our teams that still required much back-and-forth communication.

Through these activities, we realized that we needed new predictive capabilities. This system had to meet several criteria: it had to integrate with the EHR and leverage real-time patient data; it needed to automate processes wherever possible or make it easy for frontline teams to take the right action at the right time; and ultimately it had to reduce the workload for physicians and staff. 

We quickly recognized that building these capabilities in-house would be too time-consuming and cost-intensive, so we decided to partner with a vendor who uses AI and ML to automate care operations. This gave us access to top data science, behavioral science, and clinical operations experts to help us operationalize AI models and maintain the technology.

We originally started working with our vendor in the early phase of the pandemic to help us with systemwide planning for COVID-19. Our work together quickly evolved to bring automation capabilities to our inpatient and perioperative settings, as well as to our systemwide command center, Central Patient Logistics.

Creating Inpatient Bed Capacity

We focused first on creating inpatient bed capacity. In the past, our teams managed care progression within the EHR, but there were so many moving pieces to discharge a patient that we experienced a lot of variabilities. To better manage throughput across the system, we needed to optimize discharge planning at the patient level. 

Working alongside our vendor’s clinical and process improvement experts, we set up new multidisciplinary discharge rounds (MDRs). To reinforce these process changes, the software makes it easier for our teams to consistently establish and update discharge plans. Integrating with the EHR, it ingests patient-specific clinical data and then uses AI and ML to auto-populate or recommend key elements such as the estimated date of discharge (EDD), disposition, and likely barriers to discharge for each patient.

With the discharge plan and barriers captured during MDRs, the system automatically coordinates with ancillary services to resolve those barriers. Instead of the first-in, first-out EHR worklist used in the past, our vendor helps our ancillary services teams prioritize patients based on discharge.

The system also helps our leaders be more proactive in addressing issues in real-time. For instance, it lets them know when a patient identified as a morning discharge is still in a bed at noon so they can help remove barriers to getting that patient safely discharged.

By shifting discharge planning upstream, we have reduced excess days by 0.3 to 0.6 days per patient at two of our largest facilities. This creates a significant amount of new bed capacity to care for patients.

Unleashing Perioperative Growth

Like many health systems, we rely on surgical cases to strengthen our margins and support other services. To accommodate growing surgical demand — while having fewer available staff due to staffing shortages — we needed to maximize value on our OR schedules. The challenge was that teams continued to rely on manual processes and tools that kept a significant amount of OR time locked up.

Our perioperative team partnered with our vendor to automate surgical scheduling processes so we could unlock OR time, increase case volume, and improve utilization. ML identifies blocks unlikely to be used and then encourages surgeons and schedulers to release the time — weeks in advance. AI then automatically offers newly-released time to the specific surgeons and schedulers predicted to be the best fit, so we can strategically grow our volume instead of just filling the time with any available case. There’s also a reservation interface that surgeons and schedulers can use to view and request time — without logging into the EHR.

In the first six months using the solution, we’ve unlocked more than 170 hours, which is increasing at an accelerating rate each month as we drive greater use of the system. Blocks that would have previously gone unused are now used more than 37% of the time. In the last month alone, we had 109 cases scheduled through this system.

Improving Operational Efficiency Systemwide

With improved operational performance in inpatient and perioperative settings, we needed to optimize resources across the health system and improve care coordination between sites. Previously, we had co-located core system functions and teams in our system command center, Central Patient Logistics. However, our centralized teams still operated in a manual, reactive manner, and when exploring new approaches, we found that traditional command center technologies only replicated EHR capabilities.

To optimize our systemwide resources and processes, we realized this was another opportunity to use predictive capabilities. Our teams now have real-time situational awareness across facilities, service lines, and units. ML predicts census hours in advance, giving our teams more time for planning and decision-making. Looking ahead, the system will also help balance demand across acute care and community hospital settings.

With this foundation of predictive capabilities in place, we are far ahead today. Already, these capabilities have provided us with a distinct advantage in the market. We also see a broad range of new opportunities for care operations automation on the horizon, including integrating care delivery across new settings, such as virtual care or hospital-at-home, and optimizing staffing and scheduling.

The post The Real-time Digital Health System: Automation for Care Operations appeared first on HealthTech Magazines.

BOTS! Love them or hate them, but they have been in existence for ~ 50 years based on some estimates. In simplistic terms, a Bot is a software robot (“Robotic”) that is given instructions (“Process”) to perform (“Automation”). Therefore, RPA is written rules/instructions you tell a software to do in an automated manner.

In this day and age, you have probably recently encountered software robots as chatbots. These chatbots may have scheduled an appointment for you or even performed a task such as booking a hotel room. Beyond chatting, you might have tried to buy tickets to a concert/game or purchase a certain clothing item (such as the notorious streetwear brand Supreme) only to get beat out by someone who deployed a bot. Leveraging bots allows you to tell the application software to complete a transaction in <1 second. My personal online transaction time is about 15-20 seconds, so I often do not get the hyped item I intended to buy. Despite the annoyances many encounter with bots, they play a significant role in automating repetitive mundane tasks (copy-pasting text into structured fields on a template). Let’s explore how this applies to healthcare…

Right now, there is a lot of interest in the “Digital Front Door” for patient care. How can I prep a patient before they see a physician? Scheduling quickly comes to mind, but you can also have the RPA engine copy/paste certain health information to tee up your data at the moment you will see a physician. Imagine having your latest labs, outside records, images, and medications ready to go so that your encounter is meaningful and full of decisions in your health journey! This has yet to be realized in our current health systems. Often, we scramble to gather information at the right moment, looking through many tabs and unstructured data. Leveraging RPA could be a link to realize functional interoperability without true interoperability. You also remove having humans pull open charts and re-chart in new documentation packages. All of these mundane tasks can be completed in split seconds in the background. As electronic record keeping in healthcare continues to be burdensome, deploying Bots with RPA technology can really get the right information to the right person at the right moment in a workflow. That is the holy grail with regards to healthcare applications.

Mundane task elimination can be realized through deployed RPA. Therefore, RPA integration will be part of the new world order in healthcare that brings back the human touch!

Looking beyond the digital front door, there has been a success with the use of chatbots for procedure-related preparation. For those of you who have had a colonoscopy, you are aware that the most difficult part is the preparation, not the procedure itself. Engaging patients with automated messaging and specified “conversation” can help better troubleshoot or understand the process of preparing for any procedure. Think of this as a real-time directed FAQ based on your medical procedure. If your prep is done right, your procedure will go smoother and be of higher utility. My definition for this type of technology is simply “patient engaging applications”. Patients are being engaged in a relevant manner on the topic of interest, such as bowel prep. As you can imagine, the development in this space is red hot as you can create pathways and algorithms for so many different medical conditions, procedures, preparation, etc. Everyone wants very minimum no-shows and improved compliance with personalized health delivery.

Connecting the dots, RPA is what leads to the realization of promises of early diagnosis and improved quality of care. When health information is prompted in a structured manner to the right person, prioritization of care delivery can be realized (think of timely cancer screening) as the computer can automate the names of patients with the most risk factors. Everything comes down to how you leverage RPA for your desired outcome. Are you looking only to schedule more efficiently and prevent “no-shows” or are you seeking a wider efficient whole health approach to how your system/network delivers care? Once you determine how RPA can be leveraged, it’s up to you how you want to connect the dots.

What does the future hold for RPA? Obviously, more and more “simple” tasks will be automated. However, as we have all experienced frustrations of being on hold or misdirected by a chatbot (think credit card company calls), RPA will be a part of the wider advanced technology ecosystem that brings back the human touch. Just as “google it” hasn’t replaced expert consultation, bots can only go so far before human intervention is needed to weigh the risks/benefits of decision-making. Medicine is an art and what to do with diagnosis is complex depending on very personal circumstances. Mundane task elimination can be realized through deployed RPA. Therefore, RPA integration will be part of the new world order in healthcare that brings back the human touch!

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