Agentic AI represents the next step in the transition to AI-powered enterprises. Moving from chat-based solutions to autonomous agents stands to power the next wave of technology evolution. Dive into our top 7 use cases for agentic AI to empower healthcare enterprises:
1. Medical Claims Processing and Adjudication
2. Healthcare Contact Center Operations
3. Prior Authorization Workflows
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The healthcare industry faces escalating pressures. Administrative costs, regulatory complexity, and patient demands for seamless experiences are straining resources and forcing organizations to rethink how they operate. Traditional manual workflows have become inefficient bottlenecks threatening both financial performance and quality of care.
Agentic AI, on the other hand, is driven by autonomous AI agents capable of learning, adapting, and executing independent decisions. This represents the breakthrough solution healthcare organizations require. Rather than rigid rule-based approaches, agentic automation provides adaptive, context-aware capabilities that substantially enhance operational performance while controlling costs.
Healthcare administrative expenses consume approximately 15-30% of total medical spending in the United States, with wasteful administrative spending alone accounting for $285-$570 billion annually. Organizations implementing AI-driven automation are achieving dramatic improvements in efficiency and cost reduction across their operations.
However, figuring out where to apply AI agents is still a tough decision. Our framework for deciding on agentic AI use cases provides a good place to start, but we know the reality is still a work in progress.
Below, we dive into seven use cases demonstrating how agentic AI produces real outcomes for healthcare companies.
Medical claims processing stands as one of healthcare's most labor-intensive operations, demanding detailed analysis of treatment codes, coverage policies, and payment regulations. Conventional claims workflows typically involve numerous systems, extensive manual review, and subjective decisions that produce inconsistent outcomes and payment delays.
Challenge: Manual claims operations create processing backlogs, inconsistent adjudication standards, and delayed reimbursements that strain provider relationships and erode patient satisfaction. Over 5 billion medical claims are processed annually in the United States, representing a massive opportunity for efficiency gains.
Agentic AI Advantage: Autonomous claims agents aggregate data from electronic health records, billing systems, coverage databases, and clinical documentation. These agents evaluate claims against policy provisions, identify coding errors, flag potential fraud indicators, and route complex cases for human expertise. The technology continuously improves through outcome analysis to enhance accuracy.
Potential Win: Health plans deploying agentic AI claims systems can significantly reduce processing time, transforming workflows that once took weeks into operations completed in hours or minutes. Major payers expect decreasing claims cycle times while maintaining high accuracy standards for standard submissions.
Contact centers serve as critical patient touchpoints, yet many healthcare organizations continue operating manual call routing, paper-based documentation, and fragmented information systems that frustrate patients and burden staff. Inadequate contact center performance directly impacts patient retention and satisfaction scores.
Challenge: Inconsistent patient interactions, extended wait times, and limited after-hours access damage organizational reputation while increasing operational expenses and patient churn.
Agentic AI Advantage: Contact center automation agents handle patient inquiries across multiple channels (phone, chat, email, portal), access complete patient histories, schedule appointments, verify benefits, answer billing questions, and provide medication information. These agents manage multilingual support and escalate appropriately to clinical staff when medical judgment is required.
Potential Win: Hospital systems can reduce average handle time while improving first-call resolution rates. Healthcare organizations report substantial improvement in patient satisfaction metrics and significant reduction in contact center staffing requirements through intelligent automation.
Prior authorization processes burden both providers and patients, with manual review requirements causing treatment delays and administrative frustration. These workflows involve complex policy interpretation, clinical criteria evaluation, and multi-party coordination that strain operational resources.
Challenge: Labor-intensive prior authorization procedures result in care delays, provider burnout, and abandoned treatments that compromise patient outcomes and organizational revenue. Physicians complete an average of 43 prior authorizations per week, spending 13 hours of physician and staff time on this process.
Agentic AI Advantage: Prior authorization agents automatically extract clinical information from medical records, compare treatment requests against coverage criteria, compile supporting documentation, submit authorization requests, track approval status, and notify providers of decisions. Advanced agents identify patterns in denial reasons and proactively address documentation gaps.
Potential Win: Provider organizations can dramatically reduce prior authorization processing time while achieving substantially higher approval rates on first submission. Health systems report significant decreases in administrative burden related to authorization management, freeing clinical staff to focus on patient care.
Patient scheduling involves coordinating provider availability, room capacity, equipment needs, and patient preferences while managing cancellations, no-shows, and urgent care requests. Manual scheduling creates inefficiencies, unused capacity, and access barriers that affect both revenue and patient experience.
Challenge: Fragmented scheduling systems, manual appointment coordination, and reactive capacity management increase operational costs while limiting patient access to timely care.
Agentic AI Advantage: Scheduling automation agents manage appointment requests across channels, optimize provider schedules based on historical patterns, send automated reminders, fill cancellation slots, coordinate multi-specialty visits, and maintain waitlists. These agents consider travel time, patient preferences, and clinical urgency while ensuring compliance with access standards.
Potential Win: Ambulatory practices can reduce scheduling labor substantially and improve capacity utilization significantly through intelligent schedule optimization. Healthcare organizations report major reductions in no-show rates through automated engagement strategies and proactive patient communication.
Revenue cycle operations in healthcare include patient registration, charge capture, coding, billing, collections, and denial management. Manual processes throughout this cycle create revenue leakage, extended collection periods, and compliance risks that directly impact financial sustainability.
Challenge: Disconnected revenue cycle workflows, coding errors, delayed follow-up on denials, and inconsistent collection practices erode margins while increasing days in accounts receivable. The industry benchmark for days in A/R is 30 days or less, yet many organizations struggle to achieve this target.
Agentic AI Advantage: Revenue cycle agents will verify insurance eligibility in real-time, ensure accurate charge capture, optimize medical coding, identify underpayments, automate denial appeals, prioritize collection activities, and manage payment plans. These systems maintain comprehensive audit documentation and ensure compliance with billing regulations.
Potential Win: Healthcare providers can decrease days in accounts receivable substantially and improve net collection rates toward the industry standard of 95-99% through comprehensive revenue cycle automation. Organizations achieve millions in annual improvements in cash flow performance while reducing operational overhead.
Clinical documentation and medical coding require precise interpretation of provider notes, accurate assignment of diagnostic and procedure codes, and compliance with evolving coding guidelines. Manual coding processes are time-consuming, error-prone, and fail to capture appropriate reimbursement levels.
Challenge: Incomplete documentation, coding backlogs, compliance risks, and missed revenue opportunities result from manual clinical documentation improvement and coding workflows.
Agentic AI Advantage: Documentation agents analyze clinical notes, identify documentation gaps, suggest query opportunities to providers, assign appropriate codes, ensure medical necessity support, and flag compliance concerns. These systems continuously update based on coding guideline changes and payer policy modifications.
Potential Win: Health systems can reduce coding backlogs dramatically while improving coding accuracy to exceptionally high levels. Healthcare organizations report substantial increases in case mix index and appropriate reimbursement capture through enhanced documentation practices, with substantial productivity improvements in coding tasks.
Healthcare technology teams face mounting demands for system integrations, application development, data analysis tools, and custom workflow solutions. Traditional development approaches struggle to keep pace with operational needs while maintaining security, compliance, and system reliability.
Challenge: Limited development resources, complex integration requirements, lengthy deployment cycles, and technical debt accumulation slow innovation and operational improvement initiatives.
Agentic AI Advantage: AI-powered development agents assist with code generation, automated testing, documentation creation, debugging support, and integration development. These agents accelerate API connections between clinical systems, payer platforms, and operational tools while ensuring HIPAA compliance and security standards. Development teams leverage AI agents to rapidly prototype solutions, refactor legacy code, and automate repetitive programming tasks.
Potential Win: Healthcare IT departments report substantial improvements in development velocity and significant reduction in time-to-deployment for new features. Organizations achieve faster integration of disparate healthcare systems while maintaining smaller development teams and reducing technical debt, enabling more rapid response to operational needs.
Learn more about Skan AI's developer productivity solution.
Agentic AI transcends operational enhancement. It constitutes a fundamental shift in how healthcare organizations deliver value, manage resources, and serve patients. The healthcare industry's traditionally cautious technology adoption is evolving toward recognizing that agentic AI is critical for sustainable operations.
However, implementing AI agents effectively begins with process intelligence software. Skan AI's solution helps enterprises create a baseline of current operations, enabling them to understand better where to implement agentic AI solutions to maximize business outcomes.
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