AI with agency: a vision for adaptive, efficient, and ethical healthcare

Abstract

The healthcare industry continues to face significant operational challenges in patient care, resource allocation, and administrative processes. For instance, despite spending 16.8% of its gross domestic product on healthcare by 2015, the United States reported higher rates of preventable hospitalizations and lower life expectancy compared to countries that spent nearly half as much (Papanicolas et al., 2019). In fact, the average life expectancy in the United States was 78.8 years, falling short of the 80.6-year average among OECD countries (Papanicolas et al., 2019). Moreover, 73.2% of insured adults reported experiencing at least one administrative burden that led them to delay or forgo medical care (Kyle & Frakt, 2021). These inefficiencies stem not only from financial concerns but also from deeply embedded flaws in the administrative and technological infrastructure of the healthcare system. Such persistent inefficiencies highlight a need not merely for automation, but for intelligent, adaptive systems capable of navigating complexity in real time.A major driver of these challenges is administrative overhead. Healthcare institutions allocate approximately 20% of their budgets to administrative tasks, while American physicians spend around 13% of their work time on similar responsibilities (Cutler & Ly, 2011). Compounding this issue are fragmented workflows, excessive documentation, and poorly integrated clinical systems, which increase physician burnout and the likelihood of clinical errors (Zhang & Padman, 2014). For example, some computerized provider order entry systems are not tailored to patient needs, requiring 10% more physical effort than manual order selection (Zhang & Padman, 2014). Additionally, order sets can become outdated quickly, reducing their clinical value. These challenges underscore the need for a more intelligent system to ease administrative burdens and support effective clinical decision-making. What is needed is not just an AI system that follows static rules, but one that learns, evolves, and operates with autonomy.Agentic artificial intelligence (AI) offers a promising solution by autonomously managing complex healthcare tasks, reducing human error, and enhancing efficiency (Acharya et al., 2025). Using machine learning (ML) algorithms, agentic AI adapts to real-time healthcare environments (Jiang et al., 2017).Unlike conventional AI, which depends on fixed rules, agentic AI acts on its own to achieve healthcare goals and continuously updates its behavior as new information comes in. It can streamline workflows, enhance diagnostic accuracy, and reduce administrative workload (Jiang et al., 2017;Wubineh et al., 2014). Some agentic AI systems have been shown to lower cognitive workload by up to 52% (Zhang & Padman, 2014). Predictive models powered by agentic AI can identify patients at risk of disease progression or complications, resulting in fewer hospitalizations, reduced healthcare costs, and better outcomes (Khaleelullah et al., 2024). For instance, AI-based monitoring systems can detect subtle changes in vital signs, predict deterioration, and alert clinicians before critical issues develop, enabling timely intervention.Because agentic AI is goal-driven and adapts over time, it is especially well-suited to handle the complexity of hospital environments and ever-changing patient needs. In addition, agentic AI can optimize hospital resource management by dynamically adjusting staffing, supply distribution, and patient flow based on real-time data (Acharya et al., 2025). This perspective introduces an agentic AI framework designed to automate, optimize, and personalize medical services. Unlike traditional ML models, agentic AI continuously learns from routine data and adjusts its responses to match evolving healthcare demands. This combination of adaptiveness and autonomy makes agentic AI not just an innovation, but a necessity for the future of healthcare delivery. By reducing administrative burdens, enhancing clinical decision-making, and streamlining operations, agentic AI has the potential to transform healthcare delivery and improve both outcomes and cost-effectiveness.Reclaiming Time for Care: The Potential Administrative Power of Agentic AI AI enhances administrative efficiency by automating routine tasks such as medical documentation, insurance claims processing, patient scheduling, and staff coordination (Bekbolatova et al., 2024;Knight et al., 2023). In the United States, healthcare professionals spend approximately 25% of their working hours on administrative duties (Bekbolatova et al., 2024). This administrative burden contributes to physician burnout, which in turn affects the quality of care delivered to patients (Esmaeilzadeh, 2024). Automating repetitive and time-consuming administrative tasks can therefore free up valuable time for medical professionals to focus more on direct patient care. Agentic AI builds upon this by not only executing administrative tasks but also proactively refining and restructuring workflows. AI-driven automation addresses this issue by significantly reducing manual workloads, enabling healthcare providers to allocate their time more intelligently and efficiently (Alowais et al., 2023;Liu et al., 2023).One of the most time-consuming administrative tasks is the documentation of clinical history. Physicians often spend more than an hour documenting electronic health records (EHRs) for every hour spent with patients, contributing to both burnout and inefficiencies (Reddy, 2024). To address this, AI-powered natural language processing and voice recognition technologies are being integrated into EHR systems to assist with medical transcription (Sahithya et al., 2024). These tools allow physicians to dictate patient notes, which are then structured into standardized documents by AI. When these systems use agentic AI, they can learn each clinician's preferences over time to improve documentation quality. This reduces documentation errors, improves the accuracy of patient records, and minimizes delays in administrative processing (Al-Antari, 2023). These innovations also support interdepartmental coordination, improve hospital logistics, and enhance physician productivity (Tierney et al., 2024;Dicuonzo et al., 2023). In addition, automated documentation tools help ensure regulatory compliance by maintaining records that meet legal and ethical standards, thereby reducing the risk of malpractice claims and institutional penalties (Bekbolatova et al., 2024).AI also improves the processing of insurance claims by identifying errors, ensuring compliance, and detecting fraudulent activity. Traditional claims processing is often time-consuming, prone to human error, and financially inefficient, leading to delayed reimbursements and financial losses for healthcare providers (Wolff et al., 2020). AI-based systems can analyze large volumes of claims data, flag inconsistencies, and verify compliance with regulations, ultimately reducing administrative workloads (Senapati, Sarkar and Chen, 2024). These optimizations lead to fewer rejected claims, better cash flow, and improved patient satisfaction due to faster approvals (Erion et al., 2022). AI also detects suspicious patterns in claims, helping to prevent financial losses from fraud (Johnson, Albizri and Simsek, 2022). Agentic AI would let these systems spot fraud and errors more accurately by learning from feedback and by adjusting to new billing rules in real time. Thus, by automating insurance processing, AI strengthens the financial health of healthcare institutions and allows clinical staff to redirect their focus toward patient care. Additionally, it improves billing transparency and speeds up reimbursements (Francisco et al., 2024).In patient scheduling, AI increases efficiency by predicting no-shows, optimizing real-time appointment availability, and adjusting schedules accordingly. One study found that AI-supported reminders reduced no-show rates from 19.3% to 15.9%, which helped ensure timely treatment and better use of provider time (Knight et al., 2023). Furthermore, agentic AI would go even further by learning from patient behavior and clinic flow changes. It could adjust scheduling priorities and timing with minimal human input. These systems reduce waiting times, minimize administrative delays, and prevent resource wastage, improving the overall delivery of healthcare services. Virtual assistants powered by AI also help patients schedule appointments and handle inquiries, reducing the workload of administrative staff and improving the patient experience (Wu et al., 2023). These tools promote smoother operations, better coordination between providers and patients, and more effective resource use. Moreover, AI-powered assistants enhance accessibility by helping patients with disabilities or language barriers navigate healthcare services (Joshi et al., 2022).Finally, AI strengthens staff coordination by optimizing workforce management, automating administrative scheduling tasks, and projecting staffing needs based on real-time patient demand. AI scheduling systems analyze historical data, seasonal patterns, and patient inflow trends to assign personnel efficiently and maintain proper coverage during peak periods (Shahzad et al., 2023). This helps prevent employee burnout while ensuring that high-quality patient care is maintained (Cheng, Li and Xu, 2022). Predictive analytics can also assist administrators in anticipating staffing shortages and reallocating resources dynamically (Aminizadeh et al., 2024). Agentic staff coordination systems would respond to changing demands. It would reallocate staff and update schedules on their own in real time. This helps maintain workforce resilience and ensures effective clinical coverage. By integrating AI into staff coordination, healthcare facilities can enhance workforce efficiency, improve operational resilience, and raise the standard of care.However, it is crucial that AI is implemented with care, so that over-optimization does not result in staff reductions that compromise patient care (Wang et al., 2024). Agentic systems can be designed to prevent such over-optimizations by balancing administrative efficiency with patient-first policies.The application of AI in Clinical Decision Support Systems (CDSS) has enhanced diagnostic accuracy by reducing medical errors and improving patient outcomes (Ouanes & Farhah, 2024). AI-powered CDSS provides clinicians with real-time predictive data, evidence-based recommendations, and risk assessments.One study found that such systems led to a 5% change in treatment decisions, driven by more accurate diagnoses and improved decision-making processes (Al-Antari, 2023). By leveraging large datasets and integrating genetic, lifestyle, and environmental factors, AI enables the automation of personalized treatment plans that enhance patient care (Ilan, 2021). With agentic AI, these systems move beyond fixed rules by constantly updating their diagnostic models based on real-world clinical data, making their recommendations more accurate and tailored to current One of the most of AI in CDSS is diagnostic it has human in detecting et al., 2022). AI-powered tools have significantly improved in to is et al., 2022). AI has also by identifying at more thereby clinicians to timely and treatment et al., 2023). In AI helps detect and to and delays in et al., technologies are especially valuable in with to medical a and In for instance, automated diagnostic systems have accuracy in identifying and for in et al., 2022). Such systems reduce the need for manual diagnostic workflows, and effective care to By automating diagnostic agentic AI systems not only and diagnostic but also autonomously learn from each new which improves its accuracy over time. concerns be to and verify to ensure the and of diagnostic recommendations et al., 2023). Agentic AI systems therefore to and on just The that is not only from a technological and clinical but also from ethical and legal needs with and to detect flag and AI et al., AI-based CDSS has in in the of and on large these AI models enhance the accuracy of or contributing to alert et al., 2023). AI also in health care by enabling treatment and personalized recommendations for (Alowais et al., 2023). These systems can and trends timely and improved patient Agentic AI enables these systems to over time as they This improves the accuracy of health and reduces the data AI in health or care for (Francisco et al., 2024). Agentic models therefore have and their patient to ensure and AI clinical decision-making is in accuracy and AI can analyze patient and data to identify potential before they thereby reducing errors and patient risk (Wu et al., 2023). In complex among or patients, AI can detect and adjust accordingly. These systems are especially in are (Francisco et al., 2024). By AI into workflows, clinicians can minimize errors, improve and optimize Agentic AI would further improve this by continuously learning from patient recommendations, and evolving AI enhances clinical with learning can patient and current health data to identify and personalized treatment (Reddy, 2024). In care AI-powered monitoring systems continuously patient and can predict before critical et al., 2024). In AI support tools real-time recommendations based on and risk (Reddy, 2024). These that AI in clinical support beyond to enhance treatment efficiency, and overall clinical With agentic these systems patient data and treatment in real time. This of adaptiveness is for managing and changing AI also a in real-time disease monitoring and adaptive treatment et al., Agentic CDSS would the between and care by personalized treatment that changes in patient of the most operational challenges in healthcare is in the AI offers a solution by resource needs, data on historical seasonal patterns, and such as and et al., 2023). It also enables real-time by identifying patients and them for care. Agentic AI enhances this by autonomously refining in to evolving patterns and treatment this that the system learns from each and improves for future This reduces waiting for and improves overall healthcare by enabling to timely and effective care et al., 2023). on AI-based systems models to or in the of patient (Wang et al., the AI improves efficiency by automating tasks such as and Agentic AI could this by autonomously identifying learning from its and them in real time to the changing patient volumes with institutional AI-driven systems allow for real-time monitoring of patient with a of operational processes than data This enables administrators to identify and staff or resources dynamically in to patient et al., 2023). AI systems can also and resource reducing staff and optimizing hospital (Johnson, Albizri and Simsek, 2022). For example, the has an effective between and accuracy in clinical tasks, making it for real-time in environments et al., 2023). In AI can support scheduling to allocate minimize between and improve et al., 2022). When by agentic scheduling systems can become with clinical need and real-time these AI-based resource be with ethical to that compromise patient care quality (Esmaeilzadeh, 2024).AI also contributes to predictive of medical learning can analyze operational data from such as and systems to detect of This enables to schedule reducing the risk of and that can patient care (Senapati, Sarkar and Chen, 2024). Predictive reduces operational costs, and improves the of AI tools can also continuously to diagnostic delays, ensuring these tools are for critical use (Aminizadeh et al., 2024). with agentic AI, these tools can autonomously to environmental and could their schedules human AI-based systems significant which be for healthcare systems et al., addition, AI supply healthcare Predictive analytics enables AI systems to and prevent supply the systems a vital in efficiently and to the of need (Bekbolatova et al., 2024). quality systems also for and helping to ensure product of and reduces financial losses due to (Reddy, 2024). Agentic AI makes supply more and by adjusting and based on real-time patient needs, helping improve care in these tools enhance and reduce costs, healthcare by institutions over (Joshi et al., 2022). AI has also the of the enhanced and for (Alowais et al., 2023). These systems and allow to complex with In addition, AI with by patient and health records to personalized improving outcomes and reducing and 2023). can also routine enabling to focus on more critical of the et al., 2023). Agentic systems further and by learning from feedback and adjusting their the of its which increase in care institutions (Francisco et al., application of AI in healthcare has with it could between and to the healthcare system (Wolff et al., and 2023). these the of conventional AI The of agentic AI, capable of and adaptive even by healthcare systems to optimize in real from improved management, reduced hospital better resource and the automation of administrative By enhancing clinical decision-making and optimizing hospital operations, AI increases healthcare efficiency while medical (Bekbolatova et al., 2024). Agentic AI systems can this further by autonomously reallocating resources and dynamically updating based on institutional Thus, agentic AI can go beyond static optimizations to managing in to AI significant in and regulatory compliance, which can financial on healthcare providers (Aminizadeh et al., 2024). The of AI can help these challenges by resource to each healthcare In agentic AI can learn from to efficiency models that maintain care quality while operational of the AI reduces is disease and more AI-powered technologies have improved the of such as and reducing on and diagnostic et al., et al., 2023). also reduces the need for AI can and assist in identifying making the diagnostic more et al., 2023). in to AI technologies that institutions not experience these et al., et al., Agentic AI could help this by autonomously diagnostic to with the resources in AI-driven management also contributes to By patient and AI can predict which reduces and preventable hospitalizations, both of which are major to healthcare (Francisco et al., 2024). For such as and AI helps optimize and reduce thereby treatment and reducing from (Al-Antari, 2023). Agentic AI these by learning from patient outcomes and dynamically refining to prevent and optimize and hospital are significant in AI helps reduce these by improving diagnostic accuracy and ensuring that treatment is from the AI-based support tools have been shown to lower leading to fewer and (Ouanes and Farhah, 2024). For instance, AI can accurately between and reducing the need for (Johnson, Albizri and Simsek, 2022). In AI models disease risk and support which can prevent (Cheng, Li and Xu, 2022). Agentic AI further contributes by proactively care and adjusting recommendations as new patient data helping institutions care. AI also strengthens hospital supply management by improving and Predictive analytics can supply and maintain (Sahithya et al., 2024). the AI-driven systems efficiently and critical resources to (Aminizadeh et al., 2024). AI systems can also and to prevent and financial due to these tools support financial compromise patient care. in lead to that over outcomes for patients (Wang et al., 2023). is needed to ensure Agentic AI can help this by ethical into its so that not patient and automated billing are by AI. These systems can identify suspicious billing patterns, detect or claims, and prevent losses due to insurance fraud (Erion et al., et al., 2024). a from faster reimbursements and reduced administrative burdens, improving their overall financial health and 2021). Moreover, agentic systems can improve fraud by continuously refining models in to billing and fraud the AI contributes to healthcare system and improves reduces the need for and infrastructure costs, especially in or et al., 2021). Additionally, AI-powered and assistants can handle patient inquiries, clinicians to focus on more critical (Sahithya et al., 2024). With agentic these systems could into adaptive and support that could be capable of navigating complex patient needs healthcare also a in and clinical helping reduce and Traditional is time-consuming and often and of AI this by large datasets to identify promising more efficiently (Alowais et al., 2023). Agentic AI enhances this by and in real time, thereby reducing the of and improving This contributes to faster to and reduces the of innovation, making more AI offers a from traditional AI by and behavior into healthcare AI is to healthcare by enhancing administrative efficiency, improving clinical decision-making, streamlining operations, and In AI can scheduling, documentation, insurance claims, and which reduces the burden on physicians and minimizes human error, but these are enhanced AI systems with agentic that real-time based on changing priorities and feedback AI-powered Clinical Decision Support Systems can improve diagnostic accuracy management, and predict clinical deterioration, thereby reducing and medical AI can streamline hospital resource allocation, enables effective and enhances supply to ensure of medical When agentic AI these they become systems that in with and changes. AI can reduce healthcare by and improving fraud which would support better financial for healthcare AI-driven automation has the potential to healthcare delivery more and for human By automating repetitive administrative tasks, AI can allow healthcare to focus more on direct patient care. Agentic AI a of resilience to these systems by making them not only automated but continuously in real-world Agentic AI ensures that such automation adaptive even in the of tasks based on clinical AI-supported clinical decision-making can enhance patient care by that personalize and improve Predictive analytics can further operational efficiency by hospital optimizing patient flow, and enabling resource The of these can support the of a more and financially healthcare system. With agentic AI at its the healthcare system can become an adaptive that to meet both patient and provider its the of AI into healthcare concerns around patient and data a AI on large which increases the risk of data and of health Furthermore, AI on datasets can in and among with such as the and and the be and of AI In to regulatory compliance, and address ethical especially AI systems or responsibilities for errors is also especially systems clinical The for instance, healthcare AI tools as and around and these legal and to Furthermore, the legal for AI-driven clinical When an system contributes to a or it is with the the or the which a significant ethical and legal is healthcare institutions use EHR systems, making it to AI data between AI systems and infrastructure standardized and in technological In to face barriers to agentic AI. These the need for real-time data processing and tools that clinicians can systems are not designed to support learning models or recommendations, which in clinical real-time and with are These often processing, and AI which study of hospital AI the of an AI and time, at approximately resulting in a over a a despite reduced time for et al., 2024). These highlight that agentic AI systems significant financial and their efficiency can the this of more than just It infrastructure coordination and For even be of or concerns and between institutions that can to AI and that also coordination, and of time, an working and before is et al., 2024). These be for in and infrastructure especially for health systems on It is also critical to ensure that AI enhances than human clinical on AI could clinical decision-making among healthcare AI be as a support that human than as an Additionally, on AI result in reduced human the risk of the and the of are to ensure patient of agentic AI in clinical also and risk management such as and monitoring are vital for the and of agentic AI. Furthermore, is legal and at every to ensure systems and is the potential for AI to healthcare institutions are more to from AI while countries the infrastructure and to such in AI models, such as or treatment recommendations, can also patient These tools clinical and be continuously patient to ensure and AI addresses of these challenges by continuously to new data and by the of ethical into its decision-making processes. What sets agentic AI is its potential to dynamically ethical and at This allows healthcare to be and more and improve the of AI, models become more and AI can help clinicians and be from and ethical ensuring clinicians can both AI and their This especially in agentic AI, systems are not merely but are making that clinical In the of agentic AI, is clinicians have to be to not only the outcomes but also the processes that the AI also the potential to personalize by integrating genetic, lifestyle, and environmental data into care This is especially promising for disease and management, personalized can significantly improve The use of of and technologies makes health monitoring AI to patient data in real time. These support disease timely and improve disease the use of agentic AI, these systems would not only patient data but also autonomously adjust treatment recommendations in to real-time health changes. 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