The U.S. healthcare payer sector has many insurance companies, called payors. There are more than 900 payors in operation. Each payor has several phone numbers for different plan types, employer groups, and regional offices. This causes complicated phone menus and inconsistent naming in IVR systems. These systems try to reduce calls to live agents by using a list of prompts. But these prompts can be hard to understand, especially for automated systems.
Healthcare providers must call these IVR systems many times to verify benefits and get prior authorizations. Some calls last over an hour and require many back-and-forth steps. This adds a lot of extra work and makes up about 25% of healthcare administrative costs in the U.S. Long wait times and tough call menus make staff frustrated and slow down patient care. This causes problems for medical offices and payors.
General AI models, like those using GPT technology, can do basic tasks okay. But they do not work well with specialized topics like healthcare payor calls. Without specific knowledge, these AIs can make mistakes or fail to handle complex call flows and delays in IVR systems.
Domain-specific AI uses models made especially for healthcare payor tasks. Infinitus’ AI, for example, uses knowledge graphs. These graphs are organized data that show steps and connections for healthcare payor calls. They help the AI follow payor-specific phone menus and understand the context and tasks.
This mix of special knowledge and modeling helps the AI deal with unexpected situations like missing patient info or closed offices. It can manage timing and find correct responses fast without breaking the call. Knowledge graphs also lower mistakes and improve data accuracy. This is very important for verifying benefits and getting prior authorizations.
The AI system changes the IVR audio into text using speech-to-text tools. Then it uses knowledge graphs and AI methods to pick the right steps in the call. Unlike general AIs, this makes the system respond quickly and correctly to tricky prompts that slow down calls.
When on hold, the AI uses both audio (like music or ads) and text to check if it is still waiting or if a live agent joined. This helps the call go smoothly and lets humans join when needed. If the AI faces hard or unclear cases it can’t solve, people can take over easily. This keeps calls accurate and safe for patients.
Using AI this way makes calls shorter and reduces how often a live agent is needed. Staff can then focus more on patient care and important jobs.
Medical administrators in the U.S. see less manual work after adding domain-specific AI. Automating benefit checks and authorization follow-ups speeds up payments by cutting down delays and errors. Instead of spending many hours on long calls, staff can do more patient-related tasks. This improves work mood and office efficiency.
AI can be set up quickly, often in a week, without big changes to systems. When more integration is needed, APIs connect AI with Electronic Health Records (EHRs) and workflow tools like Salesforce Life Sciences Cloud using SMART on FHIR standards. This allows AI to work smoothly during normal office tasks.
Administrative work costs about one-quarter of the total healthcare spending in the U.S. Automating payor calls can save money and reduce errors. Infinitus’ AI agents handle thousands of calls daily for over 1,400 payors and 125,000 healthcare providers. This reduces costs, lowers mistakes, and shortens insurance approval waits that can slow treatment.
Automation helps providers give better and faster service. This improves patient access to needed treatments. Prior authorizations often come right after doctor visits or prescriptions. Faster approvals can speed up care.
AI in healthcare admin goes beyond call handling. It can automatically create call summary notes. After a call, AI pulls out key details like patient ID, benefits info, and authorization results from transcripts. This lowers the documentation work for staff, making things easier and records more accurate.
This fits well with the growing use of electronic records and systems that work together. Connecting AI with EHRs and billing systems means updates happen automatically. This cuts down manual errors and makes admin tasks smoother.
Healthcare IT managers see that automating routine tasks helps overall productivity. Staff have less repetitive work and feel less stressed, which can lower burnout. AI tools also let clinicians focus more on patient care and decisions instead of paperwork.
Human skills stay important despite automation. Experts handle tricky cases, decisions, and legal rules. AI systems built for payor calls include a “human-in-the-loop” system. This means humans step in when AI is unsure or faces unusual cases.
This teamwork keeps calls safe, correct, and within laws. AI does not replace staff but works like a helper, taking on boring tasks and leaving people to do more skilled work.
The move toward AI in payor calls is part of a bigger change in insurance. Many U.S. health insurers invest in AI chatbots, conversational AI, and data analysis to fix poor service and cut costs. Experts like Dr. Adnan Masood say member experience is now more important than just claims processing.
Medicare Star Ratings link over half of health plans’ scores to member satisfaction. Higher ratings affect money and competitiveness. Payors want AI that improves service, lowers call numbers, and gives personalized care. Conversational AI can reach out in helpful, customized ways, making payor processes easier and more patient-friendly.
For healthcare providers working with payors, this change helps communication and quick access to info. AI-driven platforms help meet rules, cut losses from delayed approvals, and support a smoother healthcare system.
Medical offices across the U.S. can improve daily work by adding AI call agents. Staff can expect:
Administrators and IT teams can set up AI call agents quickly. Often, no deep system changes are needed at first, making the switch smoother.
By using domain-specific AI paired with healthcare knowledge graphs, systems like Infinitus improve how payor IVR calls are handled. This reduces pressure on healthcare providers, speeds up benefit verification and prior authorization, and helps patients get better care access across the United States.
FastTrack™ AI goes beyond simple GPT wrappers by employing domain context, complex modeling, and intelligent engineering to handle unintuitive IVR flows, latency constraints, and avoid hallucinations, enabling efficient navigation of healthcare phone calls.
FastTrack processes the payor’s IVR audio via speech-to-text, then interprets the text to determine accurate actions considering task types and context, enabling it to navigate designedly challenging and unintuitive IVRs effectively.
IVRs are deliberately made non-intuitive to minimize live agent calls, requiring AI to instantly understand and respond to prompts, even if actions are unintuitive or time-sensitive, necessitating sophisticated AI capable of quick accurate decisions.
It recognizes edge cases such as missing patient data or closed payor offices and adapts its responses appropriately using its knowledge graph and contextual understanding, ensuring smooth call management despite anomalies.
Multi-modal AI analyzes both audio and textual input to differentiate music, advertisements, and live agent presence, enabling FastTrack to know precisely when to connect a human healthcare worker to the call.
It automates call summary note creation by identifying key transcript elements relevant to healthcare, reducing administrative tasks and allowing staff to focus on more critical duties.
FastTrack saves time by automating IVR navigation and note-taking, boosts productivity, eases tedious work, and improves morale by reducing repetitive tasks and long hold frustrations.
Employees can begin using FastTrack within one week without needing integration; however, APIs and integration with popular record systems are available for enhanced efficiency.
FastTrack combines speech-to-text, knowledge graphs, domain-specific modeling, latency-aware decision-making, and multi-modal audio-text AI models, enabling robust understanding and interaction within complex healthcare call environments.
By incorporating complex modeling and latency constraints into its decision-making algorithms, FastTrack rapidly processes prompts and acts immediately to prevent call termination or errors caused by delayed responses.
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