Anamnesis is an AI-powered, patient-facing clinical history platform designed for high-throughput hospital outpatient departments (OPDs).
It enables patients to record their medical history through natural voice conversation and guided touchscreen interaction, digitize their existing medical documents, and receive a structured clinical history summary that can be reviewed by a physician before or at the beginning of the consultation.
The platform is designed to reduce the burden of manual history-taking and fragmented paper records while making clinical intake more accessible to patients with different levels of literacy, language proficiency, and digital comfort.
History taking is one of the most important activities in clinical medicine. However, in India's high-volume public hospitals, doctors often have only a few minutes with each patient.
Within this limited consultation window, physicians may need to:
- Elicit the patient's presenting complaint
- Take the history of the present illness
- Review past medical and surgical history
- Check medications and allergies
- Review family and personal history
- Review previous investigations and medical records
- Examine the patient
- Formulate a diagnosis
- Counsel and prescribe
High patient volumes can therefore result in incomplete history-taking, repeated questioning, overlooked information, and inefficient use of consultation time. The project specification identifies government and tertiary hospitals handling thousands of OPD patients per day as a particularly important setting for this problem.
The problem is further complicated by fragmented medical records. Patients may carry paper prescriptions, laboratory reports, discharge summaries, and other documents from multiple healthcare providers. These records can be handwritten, multilingual, unstructured, and arranged without a consistent chronology.
Ayurvedic clinical assessment can require detailed frameworks such as Trividha, Ashtavidha, and Dashavidha Pariksha, including parameters such as Prakriti, Vikriti, Agni, Koshtha, Ahara-Vihara, Nidana, and Samprapti.
Capturing this level of detail manually within a busy OPD environment can be difficult.
Anamnesis acts as a pre-consultation clinical intake layer between the patient and the healthcare provider.
Instead of replacing the physician, the system prepares structured information before the consultation begins.
PATIENT
│
▼
Identification & Consent
│
▼
Voice + Touch Interaction
│
▼
Adaptive Clinical History
│
┌───────┴────────┐
▼ ▼
Red-Flag Detection Document Scan
│
▼
OCR + Extraction
│
┌─────────────────┘
▼
Structured Patient Data
│
▼
Clinical Summary Generator
│
▼
Physician Review & Editing
│
┌──────┴───────┐
▼ ▼
HIS ABDM / ABHA
The intended outcome is simple:
The patient provides the history before the consultation, so the physician can spend more of the consultation on examination, clinical reasoning, and counselling.
Anamnesis provides a conversational interface through which patients can describe their symptoms naturally.
The system can ask follow-up questions based on the patient's responses rather than forcing every patient through the same static questionnaire.
For example:
Patient:
"I have chest pain."
↓
System:
"When did the pain begin?"
↓
"How would you describe the pain?"
↓
"Does the pain spread to your arm, shoulder,
back or jaw?"
↓
"Does anything make the pain better or worse?"
↓
"Are you experiencing shortness of breath,
sweating, dizziness or nausea?"
The history engine is designed around a clinical history ontology and complaint-specific questioning. For example, chest pain can lead to questioning based on the SOCRATES framework.
Every interaction is designed to be answerable through either:
Voice
- Natural spoken responses
- Preferred language
- Audio-guided interaction
- Speech recognition
Touch
- Multiple-choice responses
- Guided selections
- Icon-driven interaction
- Manual confirmation and correction
This dual-mode approach is intended to make the system usable across different levels of literacy, age, and digital familiarity.
The system dynamically determines relevant follow-up questions based on:
- Chief complaint
- Previous answers
- Associated symptoms
- Relevant medical history
- Clinical history structure
Rather than asking every patient an identical set of questions, Anamnesis aims to reproduce the structured information-gathering process of a clinical history interview.
Chief Complaint
↓
Relevant Questions
↓
Patient Response
↓
Next Relevant Question
↓
Associated Symptoms
↓
Complete History
Anamnesis includes a red-flag detection layer intended to identify potentially urgent symptoms during history collection.
For example:
Acute Chest Pain
+
Shortness of Breath
↓
Potential Red Flag
↓
Priority Alert
↓
Triage Staff
The purpose is to prevent a patient reporting potentially serious symptoms from simply entering the normal queu