Agent as a service (AaaS) goes beyond SaaS to offer greater automation, fewer errors, and to perform more tasks in real time while requiring less effort from humans. For specific healthcare use cases, it can be very effective. This article explores the basics of Agentic AI and the ways in which it is more capable than a typical SaaS-based architecture.
Agentic AI basics
Agents are essentially autonomous or semi-autonomous codebases that perform three key sub-tasks:
- Accessing different data sources and synthesizing data in real time
- Automating decision-making process(es) based on analysis of the data
- Automating routine tasks with process automation tools, integrating to/from different systems and orchestrating workflows
Single as well as multiple agents can collaborate on interconnected tasks. Single-agent systems can automate stand-alone processes like claims validation, patient scheduling, and appointment reminders. Multi-agent systems, for their part, handle more complex episodic events and workflows across multiple teams and systems, like a care transition for knee surgery that involves hospitals, payers, different physician teams, and community health team members.
In a multi-agent system, for example, one agent is responsible for integration (APIs, batch-based ETLM processes, real time connection to EHRs, etc.), another handles data analysis and memory retention, and a third performs task orchestration. This improves coordination among payers, providers, CBOs, patients, and others.
Agentic AI-based systems give physicians, nurses and caregivers enhanced capabilities of diagnosis, knowledge, and task automation while still ensuring the human component of healthcare remains intact.
A case for AaaS in lieu of SaaS
Most SaaS-based applications have a business logic tier that handles the different CRUD (Create, Read, Update and Delete) operations over relational and/or non-relational data stores.
Given the pace of AI advances, much of this business logic function soon will be handled by AI agents. Once achieved, there is really no need for a traditional SaaS-based model. AI agents will be able to understand what users want/need, anticipate their requests, and eliminate the need for the current model of SaaS applications.
AaaS use cases in healthcare
Use cases addressed in a typical SaaS-based implementation for value-based care (VBC) include:
- Care engagement (tracking patient data, sending reminders, referral management, analyzing trends for high-risk patients, etc.)
- “Network of networks” implementation
- Contract builder, contract modeler, and contract management
- To/from integration with different systems (EHRs, different source systems for payers/providers/employers)
- Patient longitudinal health record
- Outcomes reporting using analytics over different datasets
Many of these activities require healthcare team members to set goals/objectives, analyze data, release payments, and take different types of actions. In an AaaS architecture, some decision-making processes and actions can be automated. Here are four examples of how AaaS performs tasks better:
- Task: Identifying at-risk patients and appointment scheduling
SaaS: Patients who missed appointments or have worsening vitals are flagged. A caregiver/nurse reviews the list, decides who to contact, and schedules appointments for follow-ups.
Agentic AI: Automated identification of at-risk patients, automated contact via text/email/WhatsApp, appointment scheduling, and respective entries into different systems
- Task: Claims processing
SaaS: Issues resulting in claims denials are identified, but it still requires action from healthcare workers to trigger different resolution workflows
Agentic AI: Automatic validation of claims, identification of any missing information, trigger any workflows that require resolution, and reduce denials. AaaS uses LLMs for clinical documents interpretation and extraction/matching for coding accuracy.
- Task: Chronic conditions management
SaaS: Produces reports showing which diabetic patients need better glucose control. The physician or care team member reviews and decides the next steps.
Agentic AI: Automated identification of the patient, preparation and communication of personalized dietary advice, order blood tests (if needed) and alerts the care team if a patient’s condition does not improve. Since AI agents are context and memory aware, they can recall previous case adjustments for patients and provide the information to case managers.
- Task: Transitioning care between teams
SaaS: A majority of the task hand-offs across care teams are manual, and workflow-based tools do not integrate across care settings.
Agentic AI: AaaS-based platforms facilitate real-time coordination, making seamless transitions for inpatient, outpatient, and post-acute settings.
Outperforming SaaS
Agentic AI brings automation, personalization, and adaptive learning to healthcare – transforming traditional SaaS tools into proactive care solutions. Instead of just presenting insights, Agentic AI acts on them, improving efficiency and patient outcomes.
The key technologies powering Agentic AI in healthcare are:
- Large Language Models (LLMs): For understanding medical notes and automating communication
- Computer vision: For analyzing medical imaging
- Reinforcement learning: For optimizing care pathways by learning from outcomes
- RPA (robotic process automation): For automating repetitive tasks like data entry and appointment booking
Conclusion
While a traditional SaaS implementation gives healthcare teams data and insights (analytic outputs), an AaaS-based implementation can analyze, decide, and then act on the data, essentially automating much of the process and thus helping improve patient outcomes. It can provide proactive care, automate repetitive tasks, personalize patient experience, and prevent serious issues, which reduce costs.
Platforms, solutions, tools and utilities that utilize Agentic AI architectures enhance productivity, reduce errors, and provide better care while reducing physician burnout. These cannot replace healthcare workers but act as powerful assistants in providing better healthcare.
The success of AaaS-based platforms and solutions depends upon the measurable results aligned with the needs of the business, while the biggest challenges are going to be in co-existing with current applications, while the transition happens for specific use cases.
About Rahul Sharma
Rahul Sharma is chief executive officer of HSBlox, an Atlanta-based technology company empowering healthcare organizations with the tools and support to deliver value-based care (VBC) successfully and sustainably.
