One question for revenue cycle leaders: do you know why your payments are regularly delayed?
Annually, hundreds of millions of people turn to providers and hospital systems for care, treatment, appointments, and emergencies – and with that need for medical assistance comes the administrative burden of tracking patients from scheduling their first appointment to paying their final bill.
Understanding the obstacles in place that are preventing timely and reliable payment plans is the single best way to optimize your revenue cycle management strategy.
Conversation data (call center conversations, including voice, text, chat, patient portal messages) is unlocking trends and insights revenue cycle leaders need to improve speed to value in receiving and processing payments. This means improving:
- MoM remittance rates
- Speed-to-payment metrics
- First call resolution and decreased call volume
- Overall patient experience to incentivize retention
In leveraging data you already have, intelligence tools are equipping teams to aggregate trends and look at digital interactions. With the help of AI, establishing reliable and timely repayment plans become effective and frictionless for the patient.
Improve Revenue Cycle with AI
When considering revenue cycle, errors and confusion often lead to delays in payment or reimbursements. However, it’s often unclear where or how to monitor these interactions inside the customer service journey.
The first step towards optimization is to gain insight into the current revenue cycle experience. That visibility exists across your customer service centers, such as billing and payment call lines. With the help of AI, you can leverage insights from recorded patient conversations to monitor topic trends and identify points of friction.
Artificial intelligence capabilities, like conversation summarization and aggregated reporting, can help provide context on why delays are occurring, what issues are emerging in the process, and how revenue cycle steps can be optimized and improved.
Collect and Analyze Conversation Data at Scale
Is there a current way to aggregate and share trending data to highlight anecdotal evidence of patients getting stuck? Investing in software that can rapidly analyze a large volume of conversations at scale can lead to reduced time spent on:
- Work denials
- Re-bill
- Credit balances
- Refunds
- Incorrect reimbursements
And this software works by identifying friction points in the service journey that often get overlooked due to workload burden and complex processes.
The more comprehensive and detailed the data, the stronger the foundation is for strategic decision-making. Having a comprehensive view of various data channels improves tracking and gives a clear, holistic view of patient care management. Tech innovations, like artificial intelligence and machine learning, are making it possible to listen to and analyze large amounts of data. This encompassing data oversight can help optimize revenue cycle teams to position themselves to respond proactively and improve speed to payment.
Identify Root Causes
To implement change, the first step is to understand with a high degree of confidence the root causes of negative experiences and barriers to cash flow. Conversation data is one of the best sources for leading indicators of frictions because it can provide data on frequency and context of recurrent challenges.
Listening at scale with AI pinpoints where issues originate, such as: disruptive claims processes, claim denials, human coding errors, missing or incorrect patient information, etc. Presenting data clearly and efficiently allows decision-makers to understand gaps in care and provides a path towards resolution. Letting errors and friction points go unresolved or unfound has a direct effect on patient experience and health, as well as potentially lost or wasted revenue.
Systematically Respond to Social Determinants of Health (SDOH)
Consider systems currently in place to support patients and their payment plans. In leveraging conversation data that includes all voices of a patient population, revenue cycle leaders can prioritize which processes in place would best improve efficiency. One outcome may be to reduce call volume by focusing on complex calls coming into the contact center (rather than transactional ones). Optimization tactics might include proactively improving your digital front door strategy with additional education materials and channels to seek support.
In particular, factors linked to a lack of socioeconomic resources are commonly attributed to higher readmission rates for patients at hospitals. Being able to develop a system that is responsive to the needs of diverse populations can address social determinants that are specific to the surrounding community and provide a higher quality of care.
Employee Onboarding and Training
Staff training is a common pain point for many healthcare organizations. Many revenue cycle issues stem from human errors, such as improper coding, missing items in a patient’s account, and insurance eligibility issues that lead to delays in treatment and lost employee time solving the errors. Identifying examples of success and opportunities for improvement have been linked to better return on investment, such as lowering turnover rates and reducing medical errors. Review conversations, increase first call resolution, lower call abandonment and deflections to reduce unnecessary call volumes.
Conversational AI can identify previously unrecognized blind spots within the revenue cycle. Listening at scale with AI gives health systems the ability to address issues at the root cause. This has a profound impact on reducing a hospital system’s administrative burden, providing a high-quality patient experience, and increasing speed to value with optimized payment processes.
Quantify what patient barriers are costing you with Authenticx to improve revenue cycle
Prevent no shows, improve time to revenue, and drive operational efficiencies with AI and machine learning purpose-built for healthcare providers. With Authenticx, view recurrent and frequency trends over time as well as in real-time with generative AI capabilities.
Revenue cycle leaders need to feel confident in their quality scores and hospital metrics. Authenticx was founded to surface meaningful insights from conversation data. Our blend of NLU, LLM, and deep learning models ensure the full context present in every conversation is captured, so you can address the root cause of any issue. Go beyond vague labels like “billing confusion” and drill down into the what and why – with the data needed to drive decisions.
About Authenticx
Authenticx was founded to analyze and activate customer interaction data at scale. Why? We wanted to reveal transformational opportunities in healthcare. We are on a mission to help humans understand humans. With a combined 100+ years of leadership experience in pharma, payer, and healthcare organizations, we know first-hand the challenges and opportunities that our clients face because we’ve been in your shoes.
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