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Rising HDHPs and the Cure for RCM Providers: Multichannel Conversational AI

High Deductible Healthcare Plans (HDHPs) have become the preferred insurance option for many Americans primarily due to their lower premiums. They are also a popular health insurance plan offered by private-sector employers. In 2022, more than half of U.S. private-sector workers (53.6%) were enrolled in HDHPs.

However, HDHPs have a higher deductible than traditional insurance plans, meaning individuals must cover more healthcare expenses out of pocket before the insurance company starts contributing. 

In this blog post, we will discuss the rising popularity of HDHPs and their implications for RCM providers and Extended Business Offices (EBOs). Additionally, we will explain why Conversational AI technology is a game-changer for early-out collections.

Why Are High Deductible Healthcare Plans (HDHPs) Becoming Popular?

Rising Health Insurance Costs

With rising health insurance costs and hospital charges, people are opting for HDHPs, which have lower premiums.  The American Medical Association (AMA) reports that healthcare costs are climbing at approximately 4.5% annually. In 2019, healthcare spending in the United States increased by 4.6%, reaching a staggering $3.8 trillion nationwide, equating to an average of $11,582 per person. This increase aligns closely with the rates seen in 2018 (4.7%) and slightly surpasses those of 2017 (4.3%).

Besides the ongoing trend of healthcare costs inching upward, short-term factors have also played a significant role. Many U.S. residents experienced faster-than-average increases in their health insurance costs in 2021, as insurance companies and healthcare providers raised costs post-pandemic.

As a result, over half of all U.S. workers were enrolled in high-deductible health plans (55.7%). Enrollment for HDHPs has risen for the eighth consecutive year in 2023, the highest enrollment rate since 2012. 

HDHPs are Cheaper for Employers

Employers regularly seek strategies to offer stable benefits while reducing costs. This practice enhances their competitiveness in the job market while keeping expenses in check. 

According to a recent Mercer study, larger employers spend an average of $84 per month on High Deductible Healthcare Plans (HDHPs) per employee, compared to $132 per month for traditional Preferred Provider Organization (PPO) plans. This shift towards HDHPs translates to a significant 37% reduction in costs per employee, with greater savings realized by larger companies.

Flexible Coverages

Beyond cost savings, HDHPs offer enhanced flexibility in healthcare coverage. Unlike Health Maintenance Organizations (HMOs), HDHPs typically impose fewer restrictions, granting individuals greater freedom to select their preferred service providers. This increased flexibility removes hurdles from the healthcare decision-making process, empowering individuals to make more informed choices about their healthcare options.

HDHPs = Savings

HDHPs can also provide additional savings opportunities for individuals. HDHP is the sole Health Savings Account (HSA)-eligible health plan that helps with additional savings. With an HDHP, individuals can establish an HSA to benefit from tax-free saving, investing, and spending on healthcare expenses. HSAs offer several advantages, including the ability to carry over funds yearly without expiration. Unlike other types of accounts, HSAs are owned by the individual and can be transferred between jobs and healthcare providers. Furthermore, HSAs serve as an additional retirement account. 

This ownership of HSA funds provides stability amidst the ever-changing healthcare landscape, allowing individuals to retain their accounts even as they transition to different health plans each year.

What Does This Mean For RCM Providers and EBOs?

The increase in demand and adoption of HDHPs has significant implications for Revenue Cycle Management (RCM) providers and External Business Offices (EBOs) especially when collecting self-pay dues in early-out collections. 

Let’s explore how this trend affects early-out collections and alters the revenue cycle for these businesses.

Increased Patient Self-Pay Dues = Delayed Cash Flow

HDHPs typically come with higher deductibles, meaning patients are responsible for a larger portion of their healthcare expenses upfront before insurance coverage kicks in. As a result, patients may delay or struggle to pay their medical bills, leading to a higher volume of outstanding balances in early-out collections.

With this delay, the revenue cycle for RCM providers and EBOs may lengthen as they wait longer to receive patient payments. This impact on the cash inflow can strain liquidity and hinder financial planning efforts.

Challenges in Collecting Payments

RCM providers and EBOs may encounter challenges collecting payments from patients with HDHPs. The increased self-pay responsibilities require increased engagement efforts from RCMs/EBOs to reach patients and collect payments. There is also a higher denial rate for self-pay dues. This requires additional resources, such as spending time resolving billing disputes and answering queries. 

Need to Enhance Patient Communication

RCM providers and EBOs must prioritize communication to ensure patients are aware of their self-pay dues. This may involve explaining insurance coverage, clarifying billing statements, and offering payment plan options to facilitate timely collections.

Focus on Proactive Payment Strategies

RCM providers and EBOs must adopt proactive payment strategies to streamline billing and payment processes in response to the challenges posed by HDHPs. This approach facilitates the retrieval of self-pay obligations and fosters trust among patients.

How Does Conversational AI Help Expedite Early-Out Collections?

Skit.ai’s Multichannel Conversational AI solution can aid RCMs and EBOs by expediting early-out collections. Here’s how:

Bulk Outreach and Multichannel Engagement

Skit.ai’s AI bot can initiate outreach to patients and engage with them in the following days via multiple channels, such as phone calls (Voice AI), text messages, emails, and chatbots, ensuring effective communication and engagement from the outset. It can manage complex, multi-turn conversations with patients across all channels, maintaining context seamlessly. The Voice AI solution engages with patients in human-like conversations. This ensures meaningful engagement with them and, at the same time, offers scalability to RCM providers to reach out to numerous patients in bulk, thus helping mitigate potential payment delays and improving patient satisfaction.

More Than Just a Call; Available at Patient’s Beck and Call

Skit.ai’s AI bot can authenticate patients, clarify bill breakdowns, answer patient queries, facilitate on-call payments and text-based payment links, and even set up payment plans, enhancing convenience and reducing barriers to receiving payment.

Enhanced Cash Flow

With more outreach, faster query resolution, and seamless payment options (on-call payments and text-based payment links), Skit.ai enables RCM and EBOs to do more early-out collections of self-pay dues.

Improved Efficiency and Reduced Agent Costs

Skit.ai’s AI bot augments human efforts by automating repetitive and time-consuming tasks, enabling RCM staff to focus on resolving complex disputes and providing personalized patient assistance. 

Through automation, operational expenses are reduced, revenues are maximized, and overall productivity within the organization is enhanced. Additionally, this results in decreased staffing needs and reduced training costs for RCM providers and EBOs.


The surge in high-deductible healthcare plans (HDHPs) underscores a shift in early-out collections. The rise in patient self-pay dues under HDHPs requires increased engagement efforts and proactive payment strategies to streamline billing processes and enhance revenue cycles. Additionally, adopting Conversational AI solutions offers a promising avenue for overcoming these challenges. 

Conversational AI improves efficiency, enhances cash flow, and reduces costs for RCM providers and EBOs by facilitating bulk outreach, providing comprehensive patient assistance, and automating repetitive tasks.

Curious to learn more about how Conversational AI can help you gain a competitive edge over your competitors? Book a free demo with one of our experts.

Seamlessly Integrate Conversational AI with Your CRM Platform Using RPA

When you adopt a Conversational AI solution for your collection business, one of the first challenges is getting it to exchange information with your company’s customer relationship management (CRM) platform. In this article, we’ll explain how you can integrate Skit.ai’s solution with your CRM system using a robotic process automation (RPA) approach. This method can save you time and money while requiring minimal technical expertise.

The Importance of CRM Software for Collection Agencies

CRM software is essential to gather, organize, and manage your accounts’ information. The benefit of integrating your Conversational AI solution with your CRM system is to easily personalize calls and quickly fetch consumer data in order to achieve end-to-end automation.

Whether it’s an outbound call—and your bot is calling a consumer to collect payment—or an inbound call—in which a consumer may call to request information on their account—you’ll need the bot to have access to the data.

The Challenges of Achieving a Conversational AI & CRM Integration for Collection Agencies

Collection agencies that want to adopt Conversational AI have a options to give the solution access to their CRM data.

To get access to CRM data, flat-file transfers and middleware are two ways to avoid a complex integration requiring building new APIs. It’s important to note that flat-file and middleware are not considered actual integrations.

SFTP Flat-File Transfer:

  • What it is: Campaign files are transferred directly from one system to the other—from the agency’s systems to the Skit.ai servers—usually via a Secure File Transfer Protocol (SFTP).
  • The advantage of flat-file transfers: This approach is very simple to execute, especially for basic data exchanges, requiring no IT effort and resources.
  • The disadvantage of flat-file transfers: This method does not provide real-time updates and is not automated, requiring the collection agency to handle file uploads on a regular basis.

Middleware Approach:

  • What it is: The middleware approach enables the Conversational AI platform to access the collection business’ CRM platform and store its encrypted data in the platform’s database. Additionally, every time the AI solution handles an inbound call with a consumer, it creates an SFTP file on the call and then uploads it on the client’s server.
  • The advantage of the middleware approach: It’s a more scalable solution and it’s good for inbound use cases, as it enables the Conversational AI solution to access consumer data.
  • The disadvantage of the middleware approach: It requires setup and maintenance and does not provide real-time updates, as the transfers only occur at regular intervals (e.g., once a day). It also can’t be used for outbound use cases.

API Integration:

  • What it is: An Application Programming Interface (API) enables different software applications (such as a CRM and a Conversational AI platform) to communicate with each other.
  • The advantage of API: It allows the Conversational AI solution to seamlessly access CRM data in real-time and in a structured manner. The CRM is updated in real-time with the outcomes of each interaction. Another benefit is that once the API is built and implemented, no further manual intervention is needed.
  • The disadvantage of API: APIs need to be available or custom-built, and they require programming expertise to implement and manage. CRM platforms usually don’t provide ready-made API integrations. This method requires a longer go-live timeline.

Due to the disadvantages of each method, we’ve adopted an alternative approach to solving this challenge.

What Is Robotic Process Automation (RPA)?

Robotic process automation (RPA) involves using bots to automate repetitive tasks and workflows by mimicking human actions to interact with systems and applications.

With RPA, the Conversational AI platform can automatically access a CRM without requiring an API setup. The collection business grants the bot access to the CRM platform and whitelists it to ensure that the server is recognized as secure. The RPA bot functions as a live agent, logging into the CRM system and interacting with it directly, without the need for integration.

This approach requires no IT effort on behalf of the collection business utilizing Conversational AI, resulting in significant cost savings.

How Does an RPA Bot Impact Debt Collection Use Cases?

An RPA bot with access to a collection agency’s CRM can do the following:

  • Fetch account information such as due balance
  • Update the CRM with promise-to-pay (PTP), payment date, and other outcomes
  • Add notes to the CRM, e.g. reminder to call consumer on payment date

All this can be automated and executed without any human intervention.

The RPA method only works with cloud-based CRM platforms, such as Finvi, Collect!, and Debtrak. It does not work with on-premises CRMs, such as CollectOne, Debtmaster, Latitude, and Gcollect. 

Curious to learn how Skit.ai can integrate with your existing CRM? Request a demo with one of our experts!

SkiTalks: Abhinav Tushar on Machine Learning and Bias in Conversational AI for Collections

Abhinav Tushar, Skit.ai’s Head of Machine Learning, discusses how LLMs are reshaping automated consumer conversations in the collections space and their direct influence on enhancing the overall consumer experience.

What advancements in Conversational AI are you most excited about currently? 

At Skit.ai, we focus on helping businesses derive value from Conversational AI. We’re particularly interested in enhancing LLM capabilities to achieve difficult conversational goals. While LLMs are capable of having high-quality conversations, they still struggle with reliability in multi-turn conversations. We are focused on aligning with the goals of both the users and the businesses we serve.

How have LLMs changed the way we think about Conversational AI?

The current generation of LLMs has solved the problem of handling believable and natural conversations. Despite some factual issues and minor glitches, LLM bots can maintain the flow of a conversation. Apart from this, there are exciting upgrades for spoken conversations, such as the improved ability to model any behavior that can be meaningfully translated into text. This progress aligns with the promises of Artificial General Intelligence (AGI), and it’s exciting to see us move in that direction.

These advancements are prompting a reevaluation of the potential of automation. For a product like ours—goal-oriented bots—we expect a reduction in modeling complexity to increase the extent of automation, even for dialogs that used to be considered the forte of live agents. 

How do you envision the future of Conversational AI over the next few years? 

Over the next few years, we’ll see a focus on extracting value from this technology. While chat and voice bots have been around for quite some time already, the emergence of LLMs has marked the beginning of a brand new chapter, in which we will see more experimentation with conversational modality added as part of many interfaces. At a lower level, we expect multimodal models to dominate, along with a lot of effort going into integrating these virtual assistants with diverse data sources enabling us to further personalize the interactions with users.

What are some best practices that you follow while working with AI systems?

The most important thing to do is establish clear, measurable, goal-oriented metrics. Safety metrics, like the conversational compliance rate, are crucial in our domain. Without an effective system to monitor business value, we risk deploying products that are either harmful or not useful. This requires a thorough understanding of the Machine Learning (ML) model lifecycle, which remains unchanged despite advancements in LLMs, even though the other intermediate tools have evolved.

What sets Skit.ai’s approach to Conversational AI apart from others in the industry?

Our Conversational AI stack is powered by LLMs connected with speech systems in a complete duplex manner to achieve naturalness, which is an industry standard. However, here are a few elements that set us apart from other providers.

Firstly, at Skit.ai, we prioritize compliance and data security. We use guardrails, flow guarantees, red teaming, reinforcement learning, and other techniques to ensure compliance checks are considered at every stage of model development, deployment, and runtime.

Secondly, for us, goal completion often involves multiple conversations with a user, possibly across multiple channels.

Lastly, we support multi-modality and believe in speech-first Conversational AI. Our approach aims to acknowledge and leverage non-vocal cues from conversations, which have historically been overlooked in real-time Conversational AI.

What are some challenges that companies face in extracting value from AI? 

The two most common challenges in my experience have been (a) not thoroughly understanding how business metrics are connected with low-level models and (b) not respecting the model lifecycle once in production. With the rise of LLMs, executives in every company are pressured to incorporate them in some way, often leading to ineffective efforts.  What’s needed is a two-way conversation between understanding your product’s value chain and an LLM’s capabilities. Additionally, as this technology evolves rapidly, it’s crucial to have a clear vision of the future to avoid working on problems that may soon become irrelevant.

How do you handle AI’s shortcomings in terms of fairness and bias?

Most of the shortcomings can be handled with a little extra effort. The type of models used, and the nature of the product being developed carry more significant bias implications than the algorithm itself. We ensure that our product’s usage complies with AI ethics regulations and regional deployment guidelines. At a lower level, we monitor fairness metrics, prevent the misuse of protected attributes by any model, and select fair algorithms wherever we need them. This is a challenging objective, and new learnings often emerge as we go. We strive to lead the way as we address bias.

Can AI in collections enhance compliance with regulations? If so, how?

Yes, absolutely. While ongoing efforts are essential to ensure compliance with existing and upcoming AI regulations in the collections space, we’re confident that overall compliance regulations are on the rise and will continue to improve with AI adoption. This is not surprising. In fact, there are solutions built specifically to handle and monitor human compliance. Humans make mistakes naturally, and that’s one of the reasons why automation scales. At Skit.ai, we enhance collections with AI not only by adding and improving communication channels but also by interconnecting them and learning from data to create a superior, error-free engine.

Curious to learn more about how LLMs can enhance your collections strategy? Book a free demo with one of our experts.

Gain Competitive Advantage as a Small Third-Party Debt Collector

As a small third-party collection agency, it can be challenging to compete with larger firms. Companies often prefer to assign their accounts to larger agencies because they believe they can manage more accounts and achieve higher collection rates.

However, Conversational AI can change that for you and give you a competitive advantage over other collection agencies, including those larger than yours.

With Conversational AI, you achieve the following:

How AI Can Give an Edge to Small Third-Party Collection Agencies

Curious to learn more about how Conversational AI can help you gain a competitive edge over your competitors? Book a free demo with one of our experts.