Case Study

Smart Pre-Visit Reports: Elevating Care with AI

A concise, trustworthy snapshot before the visit, so the clinician can focus on the right questions.

AI LLM EHR FHIR / HL7 Conversational Intake
15-20 min → <1 min Physician prep time per visit
One-page summary Trends, concerns, and insights before the visit

The Challenge

Doctors spend too much time prepping before visits, manually skimming forms, labs, and notes — it's easy to miss important details, and it drains time and energy. Two cases show the range of the problem:
  • A new patient with vague complaints like "feeling tired and stressed" provides limited details, forcing the physician to play detective — which can leave the patient feeling unheard.
  • For a patient with diabetes, hypertension, and heart disease, staff spend 15–20 minutes combing through years of records across specialists. This manual process risks overlooking critical updates, leaving the physician without a clear, actionable summary.

Proposed Solution

We designed an AI-powered system that collects patient information through conversational assistants and dynamic questionnaires, integrates it with EHR data, and generates a consolidated pre-visit dashboard that highlights key trends, concerns, and insights for physicians.
The patient engages with a conversational AI assistant via chatbot or audio bot to share details about lifestyle, sleep, diet, and stress. The bot asks intelligent follow-ups — probing sleep habits when fatigue is mentioned, for example — and Natural Language Understanding (NLU) extracts key medical entities from that unstructured dialogue and summarizes them for the physician. The patient then completes an AI-guided questionnaire on a secure portal or app, adding real-time symptoms and concerns; this is merged with the Electronic Health Record (labs, vitals, notes), and a medical Large Language Model synthesizes everything into a pre-visit dashboard highlighting trends, inconsistencies, and critical insights.
  • Conversational AI chatbot/audio bot to collect patient data.
  • Intelligent follow-up questioning using Natural Language Processing.
  • AI-guided questionnaire via secure portal or mobile app.
  • EHR integration to pull labs, vitals, notes, and diagnoses.
  • Medical LLM to synthesize patient and EHR data into one view.
  • HIPAA/GDPR compliance, end-to-end encryption, and interoperability with HL7/FHIR.

Architecture

Patient input from web/mobile and a conversational bot feed into an AI pipeline that produces a physician-facing dashboard.

Smart pre-visit report architecture showing patient inputs and conversational AI feeding intake and medical LLM processing, with EHR and data sources producing a physician-facing report.
Patient input, clinical sources, and AI processing converge into one pre-visit report for the physician dashboard.

Impact of the Solution

Before the visit, the doctor gets a one-page AI summary that surfaces the key problems and trends: possible links between stress and fatigue, a note about shortness of breath, and important changes like a BP spike, ankle swelling, or a higher A1C. Trusting that data, the doctor can start a focused, personal conversation and act faster — prep drops from 15–20 minutes to under a minute. The dashboard also suggests practical next steps, such as stress management, a sleep-apnea check, possible medication tweaks, and follow-up tests.

Tech Background

Python PyTorch Hugging Face Transformers FHIR HL7 v2 DICOM C# / .NET Core PostgreSQL MongoDB React.js Flutter Microsoft Azure

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