As healthcare systems continue to evolve towards more integrated and data-driven models, the need for structured task execution mechanisms has become increasingly evident. The article addresses this need by introducing a phase-wise and scalable approach to enabling healthcare task based workflows that will ensure enhanced workflow automation and efficiency in completing tasks by the various team members of a Patient Care Team.
While researching on the topic of enabling Agentic Ai within Healthcare, it becomes important to understand healthcare being a complex system, that doesnt work in a linear fashion. There is an inherent non-linearity in the way the work happens in context of healthcare, and therefore designing healthcare systems that are static, like the traditional EHRs and EMRs, ensures that the Healthcare Technology implementation undergoes endless change requests. And if solutions are not adapting to these changing requirements, they undergo major and often disruptive changes. Recently, in this context i came across this great article, references shared below the image sourced from the article: Some important excerpts from the Article I am adding to further add to the context of why Agentic Ai implementations should consider looking at healthcare systems in this context.
We don’t find that considering a health system in terms of groups of people (patients, doctors, administrators, and so on), an ecosystem of organisations (hospitals, insurers, etc.), or even a set of services or other initiatives (e.g. patient data standards, virtual wards) is that helpful. What we do find helpful is seeing health systems as composed of a set of worlds.
Image REF (Links below)
Health Worlds – Part 1: https://substack.com/home/post/p-156986403
Health Worlds – Part 2: https://substack.com/inbox/post/159469452
Assume here that you are startup or a healthtech company that wants to implement a Task Management Functionality, once you have put together the basic building blocks of EHR functionalities (and these could be mapped to your local geographies)
If you look at the EHR systems the Information Modelling for Healthcare Involves the following aspects (at a broad level); The Hierarchy of a Healthcare Data Structure and hierarchy of the way the information is stored (or the transactions developed for certain scenarios and events), within an EHR/ HIMS, etc, for the patient or citizen looks like this: (In brackets potential mapping to the Task based Framework)
Patient Registration (Project Type)
|___ Visit (OP, IP, Daycare) (Project Type)
|___ Visit Summary (Task Types assigned by role)
|___ Visit Orders (doctor)
|___ Consultation (doctor)
|___ Laboratory Reports (pathologist)
|___ Radiology Reports (radiologist)
|___ Nursing Interventions (nurse)
|___ Discharge Summary (nurse, doctor)
The concept being proposed here is to Map an existing EHR to a Task Management Solution. Therefore in this context, consider the patient-event information model, and let’s map it to a Task Management System’s model of a Project (assuming here Frappe Frameworks’ implementation of Projects or MindStaq).
It is important to shift from an approach where end users rely on various features to complete their activities for a patient, to a framework that offers a clear execution path. This framework should guide users while also providing the context in which specific tasks or groups of tasks are being carried out within the patient’s care journey.
In the healthcare context, let us map the following workflows, between EHRs and Task Management Software:
- Create a Project/ Plan/ Template whenever a Patient is “Registered” (Registration is an action that gets a unique ID created for a person within the system). On registration each organisation can define the standard set of Tasks within the project that needs to get created. E.g. Demographics Information, Insurance Details on record, etc
- Create a Project/ Plan/ Template when a Visit is registered within an EHR system. Once the Patient registration activity is completed in the EHR, the Patient Visit Registration is created within the EHR system.
- There can be different types of Visits and therefore the tasks (care plans) that need to be carried out for each visit type can be different.
- Create a Project based on the type Outpatient Visit or the Inpatient Visit or Daycare Visit. Within each of these Visit Types you would have tasks that are created based on a Standard of Care Plan, that can be defined on the basis of the Specialty, Department, Type of visit and many other similar categories of projects/ templates.
- Additional ad-hoc Patient Specific tasks will be added to the Visit Project as indicated by the Doctor for instance. Each task has a type that is performed by a Role within the Care Plan.
- The tasks get routed to the Team Members who are part of the Project. In the healthcare context, you basically have a Care Team and each of the team members are able to execute various types of tasks.
- Tasks and Task Types: When a Project has been created, you now have the ability to spawn tasks for that project type.
- Project Templates/ care plans: You have the ability to create Project Templates based on the Type of Project defined in the earlier two points – Patient Centered Project, Patient Visit Centered Projects.
- Tasks are activities that need to be performed during a visit. For instance, Visit Consultation is a Task that will be marked as Completed when an EHR Activity – Create a Visit Summary Document, is completed.
- Tasks can be standalone tasks that require completion OR they can be mapped to a connected EHR applications’ activity via an API integration
- One important aspect we enabled, that further helped integrating the workflows was something called as, task as a container. Basically meaning, that we enabled linking the completion of the task to completion of a feature within an EHR application. For instance, let’s say there is a visit note that needs to be created for a patient during an Outpatient Visit. The Visit note is a feature within the EHR application. Also based on our above stated logic, we have created a task called as Visit Note once a Visit has been registered for a patient. Now since the Visit Note creation is linked to the Visit Note Task completion, once the doctor opens the Visit Task it will open the Visit Note feature to record and complete (and Vice Versa).
- Status of the Tasks: Each task within the Task Plan/ Project can exist in one of several states:
- Pending: Waiting for execution.
- In Progress: Currently being executed.
- Paused: Temporarily on hold due to dependencies or manual intervention
- Completed: Successfully finished.
- Failed: Not completed due to errors or other constraints.
- Care Teams: The biggest benefit of such a linkage to the task planning solution, is the ability to link the care teams that should be participating in the care of the patient.
- The biggest benefit of such a system will be the ability to identify the type of care team members needed to complete the tasks for the patient within the context of the visit, thereby enabling the organisation to enable a more fuzzy methodology to add people to the care teams as on the day of the visit, vs a fixed approach of defining “schedules” for care team members.
- It is widely known in healthcare, while there exists working periods, the healthcare team members are very trained in adapting to the operational demands of day to day activities in a hospital setting.
- Instead of pre-assigning specific team members, team members can be now assigned when they take up a case and then proceed to collaborate on completing the tasks of the case that gets assigned to them based on their role they assume on that day.
Tracking and Mapping to Existing workflows in various Task Management Systems.
Most leading EHRs and EMRs have the in-built capabilities of creating care plans. They have the ability to create these care plans for outpatient or inpatient visit types. For inpatient care plans, the leading EHRs also provides the ability to create Nursing Care Plans that include the ability to create Nursing Interventions for each stage of an Inpatient Visit; i.e.
- Visit Stages
- Pre-Admission,
- Admission,
- Daily Inpatient Interventions,
- Pre-Surgery Interventions,
- Post-Surgery Interventions,
- Daily Doctor Rounds,
- Pre-Discharge Planning and
- Patient Discharge (to list a few)
Keeping the context and premise of this article in mind, to implement such a task driven and clinical intervention workflow that will enable the execution of concepts like the checklist manifesto or implement a care plan definition in your EHR system, you will be able to implement these functionalities in a very short period of time by mapping patient events identified above to Projects and Plans of a task management system. Once this mapping has been enabled, you will be able to provide the users the following functionalities that will help the users keep track of the various tasks that need to be completed by each of the users based on their roles as defined within the projects/ care plans.
- Task Tracking Functionalities
- Projects, Project Updates, Project Views (gantt, : based on the Patient Level projects or Visit Type Level Projects
- MyWork: gets created for each of the Team Members of the projects
- Team Dynamics: for coordinating task completion etc.
- Messaging: between team members for task or project specific coordination
Usecases Enabled by implementing a Task / Care Plan driven approach to augmenting the EHR transaction-driven workflows
- Use cases where such a Implementation can help in streamline the following workflows:
- A Healthcare Patient Navigation and Command Center Team to help coordinate between the patient and the care team and the family members for status updates, result reports, updates, planning, and many other tasks.
- Allowed the Clinical Care Team (Doctors and Nurses) to get to know the tasks they had to perform for a patient who is planned to be admitted for a surgery.
- Community Health Camp Activities
- Diagnostic Services Workflows
- Clinical Trials Processes and Workflow enablement
- Insurance Workflows and Finance Workflows
- Patient Admission and Discharge Planning
- Surgical Procedure Coordination
- Medication Administration Scheduling, closed loop medication administration
- Diagnostic Testing and Follow-up Actions
- Remote Patient Monitoring Workflows
- Automated Alerts for Health Deterioration
AI Agents & Tasks in Healthcare Approach
As part of the Phase 1 of making an upgrade to your EHR solution, you now have the ability to execute EHR Tasks & you have now integrated your solutions to an existing Task Planning Solution for a more efficient care planning workflow. As part of the Phase 2, probably an effort that can be done in parallel to the phase 1 activities, will be to enable the Agentic Workflows for your updated/ upgraded solution offerings. Each of the three aspects, EHR Workflows, Care Plan/ Task Planning Workflows and Agentic Workflows become feature differentiators for your end solution offerings and therefore can be charged differently.
To get started in the Agentic workflow integration to the solution, review this course to understanding concepts on Agentic Ai (there are many more elaborate and interesting courses and implementation that can be reviewed by you and your teams). While AgenticAi is seeing tremendous improvement in the past couple of months, moving from a traditional EHR functionality to a task driven approach and further to an Agentic Workflow implementation, It is important for the EHR companies and HealthTech startups to start identifying the tasks that needs to be done and which task is generally done by a person. And that aspect was completed as part of our Phase 1 implementation discussion above.
Additionally, it is important to identify the tasks that can be handed-off to the agent for completion, and this can then be informed to the end user as a data stream of actions that have been completed for the patient. While this topic in itself is worthy of an article by itself, i want to discuss this as an extension of the task based workflows and functionalities we want to implement in the existing EHR systems. This will help reduce the timeline to develop systems from scratch.
Mapping the Agentic Workflows to the Task Workflows begins with
- Identifying the tasks to be done and then assigning tasks to people who perform these roles. We have earlier defined how teams are added to Projects and each team member plays a role. This team in a Healthcare context can be called as a Care Team
- Care Team Members playing a specific role, get to know the tasks they are to complete, based on the type of the task or perhaps this is already defined in the project / care plan template.
- The Team member completes the task, the status of the task is updated as completed (or any other status)
- The Team member is presented with the next task to be completed.
Now, in context of Agentic Ai, A team of AI agents, a Swarm, can be created. This will allow you to define the following aspects of each agent (assistant agents, for instance) with these attributes:
- a specific role,
- Goal to be achieved, and
- a backstory for each agent. This backstory will break down the complex multi-step tasks and
- will assign these tasks to respective agents (users) that are customized to perform the tasks.
- The main idea regarding the Swarm implementation here is to let agent delegate tasks to other agents, while all agents share the same context (the patient visit/ project context/ care plan context).
Furthermore, the framework can set the Roles for each of the Agents/ Co-Pilots that will be created and linked to the Users. When the users are created in the system, the roles and responsibilities of each users are defined at the setup. The Agents/ Co-pilots will be able to review these roles and responsibility tasks and get an understanding of the various tasks they are allowed to perform for a user. They will be able to create Task lists based on this understanding.
- Front Desk Agent
- Concierge Agent
- OP Nurse Agent
- Doctor Agent
- Consultant Agent
- Meta Reviewer Agent: This agent will always be added to each of the patient teams to keep track of the various activities being performed for the patient in the context of the visit she is making to the doctor and healthcare facility. At any given time, Meta Agent will use Reflection for generating summaries for keeping track of the tasks are completed and yet to be completed for the patient during a visit.
- Project/ Careplan Author-reviewer agent that is responsible for assigning the tasks in the care plan that is created for the patient during a visit.
With the foundational setup established, AI Agents can now be designed to handle specific tasks related to healthcare workflows. These tasks include Appointments, Registration, and other critical events that occur both within the hospital and in the broader community.
Each AI Agent will be mapped to a particular set of responsibilities, ensuring that tasks are initiated, assigned, and managed efficiently. These agents will not only trigger necessary actions but also sequence them in an optimized order, ensuring smooth execution. For example, AI Agents can streamline inpatient workflows by automating key tasks. A Patient Admission Agent handles registration, room assignments, and notifications. A Diagnostic & Lab Coordination Agent schedules tests, retrieves results, and alerts doctors. A Medication Management Agent ensures prescriptions are checked, dispensed, and administered on time. A Nursing Task Coordinator Agent assigns care tasks, monitors patient status, and updates records. Finally, a Discharge Planning Agent prepares summaries, arranges follow-ups, and ensures smooth patient transitions. These AI-driven agents enhance efficiency, reduce administrative burden, and improve patient care coordination.
AI Agents in Healthcare: Enhancing Workflow Automation, Clinical Decision Support, and Patient Engagement
By automating and orchestrating these workflows, AI Agents reduce the manual workload on healthcare professionals, improve coordination among team members, and enhance overall efficiency in delivering patient care.
- Ai Agent Co-pilots: Each of the Team Members, once created as a Care Team Member within the application will get assigned an Ai Agent/ Co-Pilot. The task of the co-pilot will be to
- help the care team member complete tasks of the same type for multiple patients at one time, or
- help the team member complete all the tasks she needs to complete for a patient
- It will be responsible for creating the dynamic task lists that will be changing on a continuous basis based on tasks completed by other team members and status of the patient.
- The system will allow the users to define the level of automation they want to enable for these agent/ co-pilots (autonomous and context aware task execution)
- Fully Manual (only generate the task lists)
- Partially Autonomous (non-urgent tasks are executed, and team members are informed about this auto-execution
- Fully Autonomous, all the tasks are executed autonomously by the agent, up to the level of marking the task as completed. The team member will have to view the details of the tasks completed by the agent before marking them completed and therefore executed.
- Ai Generated Patient Progress Summaries:
- Ai Agents/ Co-pilots will create on-demand and scheduled Patient Summaries that will help the team members to handover patients
- Patient Summaries for clinical team ward rounds will be created using a conversational agent. The registrar in an oncology ward for instance can task the Ai Agent/ CoPilot to pull together relevant data points from the patient EHR records since the past 24 hours. Granularity of the information can be sent to the conversation agent as a prompt format previously created and the model is trained for generating the summary.
- The AiAgent/ CoPilot will also be able to provide the care team members the summary of the list of the tasks to be completed by hour, day or any other duration of time specified by the model training.
- Administrative & Workflow Automation
- Pre-appointment Coordination: AI Agent schedules patient appointments based on doctor availability, patient preferences, and urgency.
- Patient Check-in & Registration: AI auto-fills forms using EHR data and validates patient identity.
- Medical Billing & Insurance Processing: AI handles claim submissions, verifies coverage, and flags discrepancies.
- Automated Consent Management: AI ensures proper consent collection and documentation before procedures. Patient Consent Management at each stage of the admission created in the patient language of choice. The Agent will also allow the patient to ask in-context questions regarding what will the consent mean for the patient and patient family.
- Task Assignment & Delegation: AI assigns tasks to the right personnel based on availability and expertise.
- Clinical Decision Support & Diagnostics
- Triage & Initial Patient Assessment: AI evaluates symptoms and prioritizes cases for emergency vs. routine care.
- Diagnostic Assistance: AI suggests potential diagnoses based on patient data and historical cases.
- Lab Test Interpretation: AI interprets lab results and highlights abnormal values requiring review.
- Clinical Guidelines Enforcement: AI ensures adherence to evidence-based treatment protocols.
- Medication Reconciliation: AI cross-checks prescribed medications for interactions or contraindications.
- Remote Monitoring & Patient Engagement
- Continuous Patient Monitoring: AI analyzes real-time vitals from wearables and hospital sensors.
- Automated Alerts for Deterioration: AI notifies clinicians when patient conditions worsen.
- AI-driven Patient Communication: AI responds to common patient queries via chatbots or voice assistants.
- Behavioral Coaching & Compliance Tracking: AI nudges patients to take medications, follow diet plans, or attend follow-ups.
- Home Health & Post-Discharge Care: AI monitors recovery progress and flags issues needing attention. Can also keep track of possibility of readmissions.
- Surgery & Procedural Assistance
- Preoperative Task Checklist Automation: AI ensures that all pre-surgical requirements are met.
- Intraoperative Guidance & Support: AI provides real-time guidance based on best practices (e.g., robotic surgery assistants)
- Surgical Scheduling Optimization: AI allocates operating rooms and staff based on availability and complexity.
- Postoperative Monitoring & Risk Prediction: AI predicts complications and suggests interventions.
- Emergency & Critical Care AI Agents
- AI-Powered Emergency Triage: AI assesses incoming emergency cases for prioritization.
- Sepsis Early Warning System: AI detects signs of sepsis and alerts the medical team.
- Stroke & Cardiac Arrest Prediction: AI analyzes patient data to identify early warning signs.
- Real-time Decision Support in ICUs: AI suggests interventions based on multi-parameter patient monitoring.
- Data Processing & Research Support
- Clinical Trial Patient Matching: AI identifies eligible patients based on medical records and trial criteria.
- Automated Medical Transcription & Documentation: AI converts doctor-patient conversations into structured EHR notes.
- AI-assisted Literature Review for Clinicians: AI scans new medical publications and summarizes relevant findings.
- Predictive Analytics for Population Health: AI analyzes trends and forecasts disease outbreaks.
- Medical Imaging & Radiology AI Agents
- AI-assisted Image Analysis: AI detects anomalies in X-rays, MRIs, and CT scans.
- Automated Radiology Report Generation: AI generates preliminary diagnostic reports.
- AI-driven Quality Assurance in Imaging: AI flags low-quality scans requiring retakes.
- Lesion & Tumor Progression Tracking: AI compares past and present scans for disease progression.
- Personalized Treatment & Precision Medicine
- Genomic Data Interpretation: AI analyzes genetic markers to recommend personalized treatments.
- Oncology Treatment Planning: AI suggests chemotherapy or radiation regimens based on tumor profiles.
- Auto-Adjusting Medication Dosages: AI recalibrates dosages based on patient response data.
- AI-driven Lifestyle & Diet Recommendations: AI suggests personalized lifestyle interventions based on health data.
- Hospital Operations & Logistics Management
- AI-powered Bed & Resource Allocation: AI predicts bed demand and optimizes patient flow.
- Supply Chain & Inventory Management: AI monitors stock levels and automates reordering of medical supplies.
- Hospital Staffing Optimization: AI forecasts patient load and suggests optimal staff deployment.
- AI-assisted Waste Reduction: AI identifies inefficiencies in resource utilization and suggests improvements.
Conclusion and Next Steps
The integration of AI Agents within Electronic Health Record (EHR) workflows represents a transformative shift in healthcare task management, improving efficiency, reducing manual workload, and enhancing care team coordination. This article has outlined a Task Framework that links healthcare processes with AI Agent-driven automation, ensuring that patient care is delivered more effectively. The key takeaways include:
- AI Agent Co-pilots: Assigned to care team members, these agents dynamically generate task lists, optimize workload distribution, and allow varying levels of automation (manual, semi-autonomous, or fully autonomous execution).
- AI-Generated Patient Progress Summaries: AI-driven conversational agents create on-demand and scheduled patient summaries, aiding clinical handovers and ward rounds.
- Administrative & Workflow Automation: AI Agents streamline pre-appointment coordination, check-ins, billing, insurance processing, consent management, and task delegation.
- Clinical Decision Support & Diagnostics: AI assists in triage, diagnostics, lab test interpretations, medication reconciliation, and enforcing clinical guidelines.
- Remote Monitoring & Patient Engagement: AI-powered monitoring, automated alerts, patient communication, and behavioral coaching improve patient adherence and post-discharge care.
- Surgical, Emergency, and Critical Care AI Agents: AI optimizes surgical workflows, emergency triage, risk prediction, and ICU decision support.
- Data Processing & Research Support: AI enables clinical trial matching, automated documentation, literature reviews, and predictive health analytics.
- Medical Imaging & Radiology AI Agents: AI enhances image analysis, report generation, quality assurance, and lesion tracking.
- Personalized Treatment & Precision Medicine: AI interprets genomic data, recommends treatment plans, adjusts medication dosages, and suggests lifestyle interventions.
- Hospital Operations & Logistics Management: AI-driven bed allocation, staffing optimization, inventory management, and waste reduction improve hospital efficiency.
Next Steps
EHRs were traditionally designed as digital medical records, but the next evolution demands a task-driven, AI-augmented approach that actively assists healthcare professionals. By implementing a Task Management Framework with AI Agents, healthcare organizations can gradually transition from static documentation to dynamic, intelligent workflow automation.
To move forward, healthcare tech companies should:
- Identify key healthcare workflows that can benefit from AI Agent automation.
- Define AI-powered task hierarchies that align with EHR activities.
- Implement AI Agent Co-pilots in a phased manner, starting with high-impact use cases.
- Develop interoperability solutions to integrate AI-driven task management with existing EHR systems.
- Continuously iterate and refine AI Agents based on clinical feedback and real-world data.
By following a structured, phased implementation, organizations can reduce development timelines and accelerate AI adoption in healthcare, ultimately leading to better patient outcomes and more efficient healthcare delivery.
Addendum: An AI Agent-Driven Workflow based on a 4D Framework for Oncology
Using the 4D Framework (Early Detection, Diagnostics, Delivery of Care, and Data & Insights) mapped to an Oncology Scenario, this addendum details AI Agent-driven workflows aligned with the patient care navigation, improving efficiency, decision-making, and care outcomes. The 4D Framework—Early Detection, Diagnostics, Delivery of Care, and Data & Insights—organizes AI-powered workflows across the oncology care continuum. This structured approach ensures that AI Agents enhance every stage of the patient journey, from screening to survivorship.
1. Early Detection (Proactive Screening & Risk Assessment)
Early detection in oncology involves identifying at-risk individuals, flagging potential cancer cases, and optimizing patient outreach. AI Agents streamline this process by automating screening, risk stratification, and referral workflows.
AI Tasks & Workflows for Early Detection
- Population Risk Stratification Agent
- Analyzes patient history, genetics, lifestyle factors, and environmental exposure data to assess cancer risk.
- Flags high-risk patients for targeted screening programs (e.g., mammograms for high-risk breast cancer patients).
- AI-Powered Screening Outreach Agent
- Automates reminders for recommended screenings (e.g., colonoscopy, mammography, low-dose CT for lung cancer).
- Personalizes outreach based on patient risk profiles and screening eligibility criteria.
- Pre-Screening & Self-Assessment Agent
- Provides patients with AI-driven chatbots for self-assessment based on symptoms and medical history.
- Suggests follow-up screenings for concerning symptoms (e.g., unexplained weight loss, chronic fatigue).
- AI Imaging Pre-Screening Agent
- Analyzes routine medical imaging (X-rays, ultrasound) for incidental findings that require follow-up.
- Flags anomalies that might indicate pre-cancerous lesions or early-stage tumors.
- Referral & Care Coordination Agent
- Automatically schedules appointments with oncologists for patients flagged as high-risk.
- Notifies primary care providers (PCPs) and specialists about flagged cases for coordinated follow-up.
2. Diagnostics (AI-Assisted Precision Diagnosis)
Once a patient is flagged for suspicion of cancer, AI accelerates and enhances the diagnostic process by assisting in pathology, radiology, and molecular analysis.
AI Tasks & Workflows for Diagnostics
- AI-Enhanced Radiology Agent
- Detects abnormalities in MRI, CT scans, PET scans, and mammograms using deep learning models.
- Flags potential tumors and generates preliminary reports for radiologists.
- Tracks lesion and tumor progression over time with AI-based comparisons of historical imaging.
- AI Pathology Image Analysis Agent
- Analyzes biopsy slides to detect cancerous cells with high accuracy.
- Suggests cancer subtypes based on morphological features and staining patterns.
- Molecular & Genomic Profiling Agent
- Interprets liquid biopsies and genomic sequencing data to identify actionable mutations (e.g., BRCA1 for breast cancer).
- Recommends precision medicine treatments based on patient-specific tumor markers.
- Tumor Board Decision Support Agent
- Aggregates imaging, pathology, and genomic data into an AI-generated summary for multidisciplinary tumor board meetings.
- Suggests evidence-based treatment options aligned with national guidelines and clinical trial eligibility.
- Clinical Trial Matching Agent
- Identifies eligible patients for novel cancer therapies and immunotherapy trials based on molecular and clinical data.
- Automates outreach to patients and coordinates enrollment logistics.
- AI-Generated Patient Summary Agent
- Compiles a structured diagnostic summary (history, lab results, imaging, pathology reports) for oncologists.
- Provides an AI-powered conversational interface for physicians to query specific data points.
3. Delivery of Care (AI-Driven Treatment & Patient Management)
AI optimizes treatment planning, medication management, and real-time monitoring of oncology patients, ensuring precision in care delivery.
AI Tasks & Workflows for Delivery of Care
- AI-Driven Treatment Planning Agent
- Suggests chemotherapy, radiation, immunotherapy, and targeted therapy regimens based on patient-specific tumor characteristics.
- Integrates national cancer treatment guidelines for evidence-based recommendations.
- Chemotherapy & Drug Interaction Management Agent
- Cross-checks chemotherapy regimens for toxicity risks, drug-drug interactions, and contraindications.
- Suggests dosage adjustments based on renal function, genetic metabolism markers, and real-time vitals.
- AI Remote Monitoring Agent
- Tracks real-time patient vitals, weight, side effects, and treatment adherence using wearable devices and mobile apps.
- Flags early signs of treatment toxicity or disease progression for oncologist intervention.
- Radiotherapy Planning & Optimization Agent
- Assists in radiation dose calculations and treatment field planning for precision targeting.
- Monitors cumulative radiation exposure to minimize long-term toxicity risks.
- AI-Generated Patient Symptom Tracker Agent
- Enables patients to report side effects and quality of life metrics via mobile app.
- Notifies the oncology team of urgent symptom escalations (e.g., febrile neutropenia).
- Discharge & Palliative Care Coordination Agent
- Automates discharge planning for post-chemotherapy or post-radiation patients.
- Coordinates palliative and hospice care referrals for advanced cancer patients.
- AI Patient Navigation Agent
- Guides patients through insurance approvals, financial assistance programs, and social services.
- Assists in scheduling follow-up visits and supportive therapy sessions.
4. Data & Insights (AI-Driven Analytics for Outcome Optimization)
AI enables continuous learning by analyzing real-world oncology data, refining clinical decision-making, and improving care delivery.
AI Tasks & Workflows for Data & Insights
- Real-Time Tumor Progression Analytics Agent
- Aggregates and visualizes tumor size changes, metastasis risk, and therapy response trends.
- Identifies early signs of treatment resistance to prompt alternative therapy discussions.
- Predictive Analytics for Relapse & Recurrence Agent
- Analyzes post-treatment follow-up data to predict recurrence risk based on imaging, biomarker levels, and genetic data.
- Flags high-risk patients for intensified surveillance and secondary prevention measures.
- AI-Driven Cancer Registry & Population Health Insights Agent
- Collects de-identified patient data to track cancer trends, survival outcomes, and disparities in care.
- Generates automated reports for regulatory agencies and public health authorities.
- AI-Assisted Oncology Research Agent
- Conducts literature reviews on emerging cancer treatments and guidelines.
- Summarizes relevant publications for oncologists to keep up with cutting-edge advances.
- Automated Radiology & Pathology Report Generation Agent
- Converts unstructured medical imaging and pathology findings into structured, standardized EHR documentation.
- Enhances interoperability between different hospital systems and care providers.
- Genomic Data Aggregation & AI-Driven Treatment Discovery Agent
- Analyzes large-scale genomic datasets to identify novel biomarkers and therapy targets.
- Assists pharmaceutical companies in designing next-generation oncology treatments.
Conclusion & Implementation Strategy
By structuring AI Agent-driven workflows within the 4D Framework, oncology care can be proactive, precise, and patient-centric. Healthcare organizations and AI developers should focus on:
- Identifying the highest-impact AI use cases for early detection, diagnostics, treatment delivery, and data-driven insights.
- Building interoperable AI solutions that integrate with EHRs, imaging systems, and molecular diagnostics platforms.
- Developing AI Agents with explainability and clinical validation to enhance trust among oncologists and patients.
- Implementing AI in a phased approach, starting with pilot programs in screening, diagnostics, or remote monitoring before scaling system-wide.
By leveraging AI Agents in oncology, early detection improves survival rates, diagnostics become more precise, care delivery is optimized, and real-time data insights drive better outcomes for patients.
Disclosure:
Please note that i have used various LLMs like chatgpt, storm, claude to help me correct the grammar and spellings for the article. The process I followed was to collect the reference materials for the topic, used LLMs to help me collate the reference sources. These were then added in memory of the LLM and then presented to the LLM the various ideas that I had written about to elaborate from the sources shared.
Agentic Ai in Healthcare, Agents in Healthcare, AI in Healthcare,
References
A Visual Guide to LLM Agents https://newsletter.maartengrootendorst.com/p/a-visual-guide-to-llm-agents
Image Courtsey Bipin Rathod, ref. https://www.weforum.org/stories/2025/03/ai-healthcare-strategy-speed/
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