1. Patient intake via Google Sheet

A patient or pharmacy staff fills in lab values — HbA1c, LDL, BMI, blood pressure, medications, diagnoses. No app. No login. The Google Sheets Trigger polls every 60 seconds for new rows and fires the pipeline automatically.

2. SerpApi queries NIH PubMed in real time

Before the AI generates anything, SerpApi searches Google Scholar using the patient’s diagnoses as the query. Three current peer-reviewed publications are retrieved and injected into the agent prompt. This is the key differentiator: the agent works from 2024 and 2025 evidence, not frozen training data.

3. GPT-4o agent reasons over patient data, PubMed evidence, and RAG guidelines

The agent runs at temperature 0.1 — near-deterministic. It receives the patient’s lab data, the live PubMed results, and passages retrieved from a RAG knowledge base of six clinical guidelines. It produces a structured JSON brief: summary, primary diagnosis, risk level, numbered recommendations with evidence citations, drug interactions, and a confidence score.

4. Confidence gate decides routing

A deterministic IF node — not the model — evaluates the combined confidence score. Above 80%: the brief goes to Foxit eSign. Below 80%, or when extreme clinical values are present (HbA1c above 10%, LDL above 190 mg/dL, BMI above 40, systolic BP above 160), the case routes to human review. The agent cannot override this gate.

5. Foxit eSign delivers the signed brief

The clinician receives an HTML email with the full brief and a Foxit eSign signing request. The clinician must sign. The agent cannot sign on their behalf. That boundary — between what the agent does and what the human must authorize — is the core responsible AI design principle of this project.

1. Seed the knowledge base

Six open-access clinical protocol documents load into n8n’s in-memory vector store using OpenAI’s text-embedding-3-small at 1,536 dimensions, stored under the key clinical_protocol_kb. Sources: ADA 2024, ACC/AHA 2023, JNC 8, WHO Metabolic Syndrome, NKF KDIGO 2023, USPSTF 2024.

2. Retrieve on each run

The agent calls retrieve_clinical_protocols autonomously — typically 3 to 4 tool calls per execution. The query is the patient’s diagnosis and lab context. Top-3 matching passages are returned by cosine similarity.

3. Inject alongside PubMed evidence

Retrieved protocol passages and the SerpApi PubMed results merge into the agent prompt before generation. When the agent cites “ADA 2024” in a recommendation, that text came from the embedded document — not a training-data recollection.

✓ Clinical Brief Generated — Patient patient_001

Confidence: 86% | 2026-08-30 | Auto-signed via Foxit eSign

Summary: Patient presents with Type 2 Diabetes and Stage 1 Hypertension. HbA1c 7.2% indicates suboptimal glycemic control. LDL 145 mg/dL is above the ACC/AHA 2023 target for diabetic patients. Blood pressure 138/88 mmHg at Stage 1 threshold. Currently on Metformin 1000mg and Lisinopril 10mg.

Primary Diagnosis: Type 2 Diabetes with suboptimal glycemic control

Risk Level: Moderate

Recommendations:

1. Consider intensifying diabetes management — possibly adding a GLP-1 receptor agonist per ADA 2024 (HbA1c above 7% threshold).

Evidence: Prevention or delay of diabetes comorbidities — ADA Standards of Care 2024 [pmc.ncbi.nlm.nih.gov]

2. Initiate statin therapy for LDL management per ACC/AHA 2023 (LDL above 130 mg/dL in diabetic patients).

Evidence: ACC/AHA 2023 Cardiovascular Risk Guidelines — high-intensity statin threshold confirmed.

Medications: Metformin 1000mg twice daily, Lisinopril 10mg daily

Live evidence, not training data

SerpApi retrieves current PubMed publications before every brief. The agent works from papers published this year, not from a training snapshot that may be two years old. Clinical guidelines change. The evidence layer keeps up.

RAG-grounded, not hallucinated

Every recommendation traces to a retrieved protocol document or a live PubMed link. The output parser enforces the schema — if the agent returns free text or skips a required field, the parser catches it and the brief does not proceed.

Deterministic safety gate

The confidence gate is an IF node. It is not probabilistic, not model-driven, not overridable. A brief below the threshold does not reach Foxit eSign regardless of what the model would prefer. Extreme clinical values force a review path even when numeric confidence is high.

Fully inspectable in n8n

Every node in the pipeline is visible and clickable. A clinician, a regulator, or a developer can open any execution and trace any recommendation back to the exact PubMed paper and RAG passage it came from. No black box. No hidden inference.

Human signs — agent cannot

Foxit eSign is not decorative. The agent generates the brief. The clinician must sign it before it is sealed. That boundary is the point — responsible CDS means the human authorizes the output, not the model.