Case Study: AI-Powered Clinical Intelligence for Medical Oncology

Key Outcomes

→ 90% reduction in chart-review time (from 10-15 minutes to 60 seconds)
→ Enhanced clinical reasoning through AI-highlighted progression signals and anomaly detection
→ Increased OPD (Outpatient Department) throughput enabling hospitals to serve more patients
→ Transformed patient experience through WhatsApp-based report submission and deeper consultations
→ Creation of research-ready structured oncology registries from previously unstructured data

This case study answers and showcases a perfect use case on “How Agentic AI can be used in Cancer Treatment by Oncologists?”

Agentic AI technology was leveraged to create a multi agent Agentic AI platform developed by The Agentics, which was leveraged to create an innovative solution to streamline medical oncology workflows. By utilizing multi-agent systems, the technology converts massive amounts of unstructured clinical data, such as scanned reports and handwritten notes, into organized digital timelines for physicians. This automation addresses the information paradox in cancer care, where excessive data leads to doctor burnout and inefficient patient consultations.

Problem Statement

In a bustling oncology clinic in Mumbai, Dr. Sharma faces a problem that plagues cancer specialists worldwide. Her next patient, a 58-year-old with metastatic colorectal cancer, arrives with a bulging folder containing five years of reports comprising of pathology results, genomic tests, imaging scans, and discharge summaries from three different hospitals. Dr. Sharma has exactly 10 minutes to mentally reconstruct this patient's complex journey before making potentially life-or-death treatment decisions.

This is oncology's information paradox: modern medicine generates more data than ever before, yet doctors have less clarity at the point of care. The result? Cognitive burnout for physicians, rushed consultations for patients, and billions in lost productivity for healthcare systems.

Enter Agentics' Agentic AI Multi Agent Platform that's transforming this bottleneck into a competitive advantage.

The Hidden Crisis in Cancer Care

Healthcare generates an astonishing volume of data—doubling every 73 days. For oncologists managing multi-year patient journeys, this creates an impossible synthesis challenge. Each cancer patient generates hundreds of touchpoints: lab results, tumor markers tracked over months, treatment responses across multiple therapy lines, and genomic profiles with thousands of data points.

Yet 68% of this critical data remains trapped in unstructured formats—scanned PDFs, handwritten notes, and disconnected hospital systems. In India alone, with 1.4 million new cancer cases annually and a patient-to-oncologist ratio of 1,600:1, the problem reaches crisis proportions.

The impact cascades across three dimensions:

→ For hospitals: OPD throughput is capped at 15-20 patients per session when it could be 30-35. Valuable clinical data sits unused, preventing research, quality programs, and clinical trial participation. A typical tertiary cancer center leaves ₹50-100 lakhs in annual revenue on the table.

→ For doctors: Oncologists spend 10-15 minutes per patient manually piecing together histories under immense time pressure. This cognitive burden contributes to the 63% burnout rate among cancer specialists. Subtle trends—a slowly rising tumor marker, an emerging toxicity pattern—get missed. Time for actual patient care evaporates.

→ For patients: Long wait times culminate in rushed 5-10 minute consultations. Patients lug physical files between appointments, anxious about missing critical reports. Meaningful conversations about treatment options and prognosis get compressed into hurried exchanges.

→ Traditional solutions—better EMRs, documentation scribes, clinical decision support systems—nibble at the edges but don't solve the core problem: how to transform scattered, unstructured clinical data into actionable intelligence at the point of care.

The Agentic AI Breakthrough

Agentics' Agentic AI Multi Agent Platform takes a radically different approach using multi-agent AI - specialized AI "agents" that collaborate like a clinical team to solve complex problems.

The architecture has four pillars:

→ AI-Native Data Fusion combines structured data (hospital systems, lab interfaces) with unstructured data (scanned reports, handwritten notes, discharge summaries) into a complete 360° patient view. Advanced OCR extracts text from photos; computer vision understands document layouts; NLP models parse clinical narratives.

→ Multi-Agent System deploys specialist AI agents working in concert. Knowledge Agents read reports and extract clinical entities—diagnoses, staging, biomarkers, treatments, toxicities. Analytics Agents detect trends over time, identify anomalies, and flag concerning patterns. An Orchestrator Agent synthesizes everything into a coherent clinical summary and visual timeline.

→ Healthcare Semantic Layer ensures the AI truly understands oncology. It knows that "progression" can mean disease progression or treatment progression. It recognizes that CA 19-9 is a pancreatic cancer marker, FOLFOX is a chemotherapy regimen, and RECIST criteria measure treatment response. Generic AI models can't make these distinctions; domain-specific training is essential.

→ Secure AI Gateway processes all Protected Health Information within India-region data centers, respecting hospital access controls. The platform is model-agnostic, running on approved LLMs while maintaining strict data residency compliance.

The Patient Experience Transformed

The workflow is elegantly simple. Before their appointment, patients receive a WhatsApp message (487 million Indians already use WhatsApp daily). They photograph their reports at home and upload via WhatsApp. No apps to download, no technical complexity.

Within 10-20 minutes, the AI processes everything. OCR extracts data from images. Knowledge agents read reports and identify clinical entities. Analytics agents spot trends and anomalies. The orchestrator assembles a chronological cancer journey with color-coded treatment lines, tumor marker graphs, and a structured summary highlighting key concerns.

When the patient arrives, Dr. Sharma opens a dashboard on her tablet. In 60 seconds, she scans a complete synthesis: diagnosis and staging, treatment history across five years, current status, trend analysis showing a subtle uptick in CEA tumor markers over the past three cycles, and a flag that the patient is overdue for restaging scans.

The consultation begins with clinical discussion, not file hunting. Dr. Sharma addresses the rising markers, orders appropriate imaging, and has time to discuss the patient's anxiety about treatment toxicity. The interaction feels unhurried, thorough, comprehensive.

Results: The Triple Win

Early pilots demonstrate transformative outcomes across all stakeholders:

→ Hospitals see operational transformation: Chart review time drops 90%—from 10-15 minutes to 60 seconds. This enables oncologists to increase capacity from 18 patients per session to 30, a 67% throughput improvement. For a five-oncologist department, this translates to 6,240 additional consultations annually worth ₹12.48 million in incremental revenue.

Beyond immediate revenue, hospitals gain a research-ready structured oncology registry. Historical data previously trapped in scans becomes queryable, enabling real-world evidence generation, quality benchmarking, and clinical trial recruitment. This asset alone is valued at ₹5-10 million over five years.

→ Physicians reclaim cognitive capacity: The 8-13 minutes saved per patient accumulates to 322 hours annually per oncologist—equivalent to eight additional weeks of productivity. But the value transcends time. AI-highlighted trends help doctors catch subtle progression signals. Comprehensive histories prevent medication errors and guide evidence-based decisions. Most importantly, physicians redirect reclaimed time toward patient counseling, shared decision-making, and the therapeutic relationships that drew them to medicine.

Burnout metrics improve. Job satisfaction increases. Dr. Sharma reports: "I finally feel like I'm practicing medicine again, not just managing data."

→ Patients experience care transformation: WhatsApp submission eliminates the physical file burden. Wait times shorten. Consultations extend from 5-10 minutes to 15-20 minutes of meaningful dialogue. Patient satisfaction scores jump from 62% to 85%. Trust builds when patients see their doctor arriving fully prepared, their complex history already understood.

The Implementation Reality

Agentics designed an 8-week proof-of-concept enabling hospitals to validate value before committing.

• Weeks 1-2 establish infrastructure and collect anonymized sample cases.

• Weeks 3-4 build and train AI models, achieving >90% accuracy on clinical entity extraction.

• Week 5 deploys the physician dashboard and visual timeline interface.

• Weeks 6-8 run live pilots with 3-5 oncologists treating 50-100 real patients, measuring outcomes and iterating based on feedback.

The rapid timeline is possible because assistents.ai works with existing systems—no EMR replacement, no hardware installation, minimal workflow disruption. SaaS pricing (₹15,000-25,000 per oncologist monthly) delivers 3-5X first-year ROI, with 6-12 month payback periods.

Critically, the platform maintains physician authority as paramount. The AI summarizes; it never diagnoses. All clinical decisions remain 100% with the physician. Transparency features—source citations, confidence scores, drill-down capabilities—build trust. Data privacy is non-negotiable: end-to-end encryption, India-region data residency, comprehensive audit trails.

Beyond Oncology: The Broader Vision

If agentic AI can transform oncology, what else becomes possible? Cardiology synthesizing ECG, echo, and wearable data. Diabetes integrating continuous glucose monitoring with lifestyle data. Chronic disease management across multiple conditions. The architecture is specialty-agnostic; the approach scales.

Agentics envisions healthcare systems where cognitive burden shifts from humans to AI, where doctors focus on reasoning and relationships rather than data gathering, where patients receive attentive care even as volumes grow. This isn't about replacing physicians—it's about giving them AI superpowers.

The healthcare AI market races toward $188 billion by 2030. Success won't come from fancy technology seeking problems, but from solving real pain points with measurable impact. assistents.ai demonstrates the formula: deep domain expertise, elegant user experience, transparent safety, and genuine ROI.

The Human Impact

The true measure isn't technical sophistication—it's human outcomes. Physicians reclaiming time for clinical practice. Patients receiving attentive care in vulnerable moments. Healthcare systems delivering better quality with higher efficiency.

Dr. Sharma's experience captures the transformation. "I thought AI would make medicine more robotic," she reflects. "Instead, it made me more human. I have time to listen, to explain, to care. That's why I became an oncologist."

That's the future of healthcare: augmented, intelligent, and profoundly human.

Contact us to discuss how our Agentic AI-Powered Clinical Intelligence solution can help you transform your Medical Oncology practice through Intelligent Data Synthesis.

Email us at Hello@TheAgentics.Co.

————
Market Data: Global Healthcare AI Market Dynamics

The healthcare artificial intelligence market is experiencing exponential growth, projected to reach $188 billion by 2030 from $20.9 billion in 2024, representing a compound annual growth rate (CAGR) of 37.5%. This growth is driven by several converging factors:

Data explosion in healthcare:

→ Healthcare data doubles every 73 days, creating unprecedented challenges for clinical synthesis

→ Electronic Health Records (EHR) adoption has reached 96% in hospitals, yet interoperability remains fragmented

→ Genomic sequencing costs have dropped 99.99% since 2003, making personalized medicine data-rich but cognitively overwhelming

Clinical workforce crisis:

→ Physician burnout affects 63% of oncologists globally, with administrative burden cited as primary driver

→ Average oncologist consultation time has decreased 40% over past decade due to patient volume pressures

→ Cognitive load from information synthesis identified as top contributor to clinical errors

Quality and outcomes pressure:

→ Value-based care models demand better outcomes with lower costs

→ Precision medicine requires integration of multi-modal data (genomics, imaging, clinical, patient-reported)

→ Regulatory focus on reducing medical errors and improving care consistency

The Oncology-Specific Context

Medical oncology represents one of healthcare's most data-intensive and cognitively demanding specialties:

Complexity factors:

→ Multi-year patient journeys with hundreds of touchpoints across diagnostic, treatment, and surveillance phases

→ Integration requirements across 10+ data types: histopathology, molecular profiling, imaging (CT, PET, MRI), tumor markers, treatment response, toxicity assessments

→ Rapidly evolving treatment protocols with 50+ new FDA oncology approvals annually

→ Personalized treatment decisions requiring synthesis of genomic data, clinical trials eligibility, and patient preferences

Current state challenges in India and emerging markets:

→ Patient-to-oncologist ratio of 1,600:1 in India vs. 300:1 in developed markets

→ Hybrid paper-digital workflows with critical data trapped in scanned PDFs and handwritten notes

→ Limited EMR interoperability across diagnostic labs, imaging centers, and treatment facilities

→ Patients managing bulky physical files and bearing responsibility for data continuity

Market opportunity: For AI-powered Clinical Intelligence for Data Synthesis in Medical Oncology

India's oncology market valued at $2.7 billion, projected to reach $8.5 billion by 2030. 1.4 million new cancer cases diagnosed annually in India, expected to reach 1.9 million by 2030. Growing middle class demanding quality care and technology-enabled experiences.