carestack 0.1.22.18

SDK for EHR Services

Carestack Python SDK

A comprehensive Python SDK for integrating with Carestack's healthcare APIs, providing seamless access to electronic health records (EHR), AI-powered document generation, and healthcare professional registry services.

Features

• EHR Resource Management: Patient, Practitioner, Organization, and Appointment operations
• AI-Powered Healthcare: Generate discharge summaries, op summaries, radiology reports, care plan summary, partial uploads and FHIR bundles using AI
• Document Linking: Seamlessly link health documents and care contexts to the Patient's ABHA
• FHIR Bundle Generation: HL7 FHIR-compliant clinical documents
• ABHA & HPR Workflows: Streamlined healthcare identity management
• Type Safety: Built with Pydantic for robust data validation
• Async Support: Fully asynchronous operations for high performance

Installation

Install the Carestack SDK using pip:

# Create and activate virtual environment
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install development dependencies
pip install -e ".[dev]"

# Install Carestack SDK
pip install carestack

Configuration

The SDK requires a ClientConfig object with your API credentials:

from carestack import ClientConfig

config = ClientConfig(
    api_key="your_api_key",                    # Your API key
    x_hpr_id="your_hpr_id" # Required for Organization operations
)

Environment Variables (Recommended)

For security, store your credentials as environment variables:

export CARESTACK_API_KEY="your_api_key"
export CARESTACK_HPRID_AUTH="your_hpr_id"  # For HPR operations

Then load them in your code:

import os
from carestack import ClientConfig

config = ClientConfig(
    api_key=os.getenv("CARESTACK_API_KEY"),
    x_hpr_id=os.getenv("CARESTACK_HPRID_AUTH")  # Required for Organization operations
)

Quick Start

Here's a complete example to get you started:

import asyncio
from carestack import ClientConfig, Patient

async def main():
    # Configure the SDK
    config = ClientConfig(
        api_key="your_api_key",
        x_hpr_id="your_hpr_id" # Required for Organization operations
    )
    
    # Initialize patient service
    patient_service = Patient(config)
    
    # Create a new patient
    patient_data = {
        "idNumber": "123456789012",
        "idType": "Aadhaar",
        "patientType": "OPD",
        "firstName": "John",
        "lastName": "Doe",
        "birthDate": "1980-01-01",
        "gender": "M",
        "address": "123 Main Street",
        "mobileNumber": "[phone]",
        "emailId": "[e-mail]",
        "resourceType": "PATIENT"
    }
    
    try:
        # Create patient
        created_patient = await patient_service.create(patient_data)
        print(f"Patient created successfully: {created_patient.message}")
        
        # Fetch all patients
        patients = await patient_service.find_all()
        
        # Find specific patient
        if patients.patients:
            patient_id = patients.patients[0].id
            patient = await patient_service.find_by_id(patient_id)
            
    except Exception as e:
        print(f"Error: {e}")

# Run the async function
if __name__ == "__main__":
    asyncio.run(main())

Services Overview

The SDK provides the following main services:

Core EHR Services

Service	Description	Key Operations
Patient	Manage patient records and demographics	Create, Read, Update, Delete, Search
Practitioner	Handle healthcare provider information	CRUD operations, HPR integration
Organization	Manage healthcare facilities	Organization registry, master data
Appointment	Schedule and manage appointments	Booking, updates, availability

AI & Document Services

Service	Description	Key Operations
AiService	AI-powered document generation	Discharge summaries, OP Summaries, Radiology report generation, FHIR bundles, careplan generation, partial uploads
DocumentLinking	Link health documents with care contexts	Multi-step workflow orchestration
Encounter	Healthcare encounter workflows	FHIR generation, discharge processing

Healthcare Identity Workflows

Service	Description	Key Operations
CreateABHA	ABHA registration workflows	Multi-step OTP verification
CreateHPR	HPR registration processes	Professional registry enrollment

Detailed Service Guide

Patient Service

The Patient service provides comprehensive patient record management with full CRUD operations and advanced search capabilities.

Basic Operations

from carestack import ClientConfig, Patient
import asyncio

async def patient_operations():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    patient_service = Patient(config)
    
    # 1. Create a new patient
    new_patient_data = {
        "idNumber": "123456789012",
        "idType": "Aadhaar",
        "patientType": "OPD",
        "firstName": "Alice",
        "lastName": "Johnson",
        "birthDate": "1985-03-15",
        "gender": "F",
        "address": "456 Health Street, Medical City",
        "mobileNumber": "[phone]",
        "emailId": "[e-mail]",
        "emergencyContact": "[phone]",
        "bloodGroup": "B+",
        "maritalStatus": "Married",
        "occupation": "Teacher",
        "resourceType": "PATIENT"
    }
    
    created = await patient_service.create(new_patient_data)
    print(f" Created patient: {created.message}")
    
    # 2. Find all patients (with pagination)
    all_patients = await patient_service.find_all()
    print(f"Total patients: {all_patients.total}")
    print(f"Current page: {all_patients.page}")
    
    # Get next page if available
    if all_patients.next_page:
        next_page_patients = await patient_service.find_all(next_page=all_patients.next_page)
    
    # 3. Find patient by ID
    if all_patients.patients:
        patient_id = all_patients.patients[0].id
        patient = await patient_service.find_by_id(patient_id)
    
    # 4. Check if patient exists
    exists = await patient_service.exists(patient_id)
    
    # 5. Update patient
    update_data = {
        "address": "789 New Address, Updated City",
        "mobileNumber": "[phone]"
    }
    updated = await patient_service.update(patient_id, update_data)
    
    # 6. Search patients with filters
    search_filters = {
        "firstName": "Alice",
        "gender": "F",
        "patientType": "OPD"
    }
    filtered_patients = await patient_service.find_by_filters(search_filters)
    
    # 7. Delete patient (if needed)
    # await patient_service.delete(patient_id)
    # print(f"Patient deleted")

asyncio.run(patient_operations())

Expected Response Structure

# Create Patient Response
{
    "message": "Patient created successfully",
    "patient_id": "pat_123456789",
    "status": "success"
}

# Find All Patients Response
{
    "patients": [
        {
            "id": "pat_123456789",
            "firstName": "Alice",
            "lastName": "Johnson",
            "birthDate": "1985-03-15",
            "gender": "F",
            "mobileNumber": "[phone]",
            "emailId": "[e-mail]",
            "address": "456 Health Street, Medical City",
            "patientType": "OPD",
            "resourceType": "PATIENT",
            "createdAt": "2025-01-15T10:30:00Z",
            "updatedAt": "2025-01-15T10:30:00Z"
        }
    ],
    "total": 150,
    "page": 1,
    "per_page": 10,
    "next_page": "eyJpZCI6InBhdF8xMjM0NTY3ODkifQ=="
}

Practitioner Service

Manage healthcare provider information with full lifecycle support and HPR integration.

Complete Practitioner Workflow

from carestack import ClientConfig, Practitioner
import asyncio
from datetime import datetime

async def practitioner_operations():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    practitioner_service = Practitioner(config)
    
    # 1. Create a new practitioner
    new_practitioner = {
        "registrationId": "REG12345",
        "department": "Cardiology",
        "designation": "Senior Consultant",
        "specialization": ["Interventional Cardiology", "Cardiac Surgery"],
        "status": "Active",
        "joiningDate": "2020-01-15",
        "staffType": "Doctor",
        "firstName": "Dr. Sarah",
        "lastName": "Wilson",
        "birthDate": "1975-08-20",
        "gender": "F",
        "mobileNumber": "[phone]",
        "emailId": "[e-mail]",
        "address": "123 Medical Complex, Healthcare City",
        "pincode": "110001",
        "state": "Delhi",
        "qualifications": [
            {
                "degree": "MBBS",
                "university": "AIIMS Delhi",
                "year": "1998"
            },
            {
                "degree": "MD Cardiology",
                "university": "AIIMS Delhi", 
                "year": "2002"
            }
        ],
        "experience": 23,
        "languages": ["English", "Hindi", "Telugu"],
        "consultationFee": 1500,
        "resourceType": "Practitioner"
    }
    
    created = await practitioner_service.create(new_practitioner)
    
    # 2. Find all practitioners
    all_practitioners = await practitioner_service.find_all()
    
    # 3. Find practitioner by ID
    if all_practitioners.practitioners:
        practitioner_id = all_practitioners.practitioners[0].id
        practitioner = await practitioner_service.find_by_id(practitioner_id)

        # 4. Update practitioner
        update_data = {
            "consultationFee": 1800,
            "status": "Active",
            "address": "Updated Medical Complex Address"
        }
        updated = await practitioner_service.update(practitioner_id, update_data)
        
        # 5. Search practitioners by filters
        search_filters = {
            "department": "Cardiology",
            "status": "Active",
            "specialization": "Interventional Cardiology"
        }
        filtered = await practitioner_service.find_by_filters(search_filters)

asyncio.run(practitioner_operations())

Expected Response Structure

# Create Practitioner Response
{
    "message": "Practitioner created successfully",
    "practitioner_id": "pract_789012345",
	...,
    "status": "success"
}

# Find Practitioner Response
{
    "id": "pract_789012345",
    "registrationId": "REG12345",
    "firstName": "Dr. Sarah",
    "lastName": "Wilson",
    "department": "Cardiology",
    "designation": "Senior Consultant",
    "specialization": ["Interventional Cardiology", "Cardiac Surgery"],
    "qualifications": [
        {
            "degree": "MBBS",
            "university": "AIIMS Delhi",
            "year": "1998"
        }
    ],
    "experience": 23,
    "consultationFee": 1500,
    "status": "Active",
    "mobileNumber": "[phone]",
    "emailId": "[e-mail]",
    "resourceType": "Practitioner"
}

Organization Service

Comprehensive healthcare facility management with master data support.

Organization Management

from carestack import ClientConfig, Organization
import asyncio

async def organization_operations():
    config = ClientConfig(
        api_key="your_api_key",
        x_hpr_id="your_hpr_id" # Required for Organization operations
    )
    
    org_service = Organization(config)
    
    # 1. Get master data first (helpful for creating organizations)
    states = await org_service.get_lgd_states()
    
    # Get districts for a specific state
    if states:
        state_code = states[0]["stateCode"]
        districts = await org_service.get_lgd_sub_districts(state_code)
    
    # Get organization types
    org_types = await org_service.get_organization_type(ownership_code="PVT")
    
    # 2. Create new organization
    new_organization = {
        "basicInformation": {
            "facilityName": "Advanced Care Medical Center",
            "facilityType": "Hospital",
            ...
        },
        "organizationDetails": {
            "organizationType": "HOSP",
            "ownership": "PVT",
            ...
        },
        ...
    }
    
    created_org = await org_service.create(new_organization)

    # 3. Find all organizations
    organizations = await org_service.find_all()
    
    # 4. Find organization by ID
    if organizations.organizations:
        org_id = organizations.organizations[0].id
        organization = await org_service.find_by_id("facility_id", org_id)
        
        # 5. Update organization
        update_data = {
            "basicInformation": {
                "contactNumber": "[phone]",
                "bedStrength": 250
            }
        }
        updated = await org_service.update(org_id, update_data)
        
        # 6. Search organizations
        search_criteria = {
            "state": "Delhi",
            "organizationType": "HOSP",
            "specialty": "Cardiology"
        }
        search_results = await org_service.search(search_criteria)
        print(f"Search results: {len(search_results.organizations)}")

asyncio.run(organization_operations())

Expected Response Structure

# Organization Response
{
    "id": "org_456789012",
    "basicInformation": {
        "facilityName": "Advanced Care Medical Center",
        "facilityType": "Hospital",
        ...
    },
    "organizationDetails": {
        "organizationType": "HOSP",
        "ownership": "PVT",
        ...
    },
    ...
    "registrationDate": "2025-01-15T10:30:00Z",
    "status": "Active"
}

Appointment Service

Complete appointment lifecycle management with scheduling and availability.

Appointment Operations

from carestack import ClientConfig, Appointment, AppointmentDTO, AppointmentPriority, AppointmentType
from datetime import datetime, timedelta
import asyncio

async def appointment_operations():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    appointment_service = Appointment(config)
    
    # 1. Create a new appointment
    appointment_start = datetime.now() + timedelta(days=3)
    appointment_end = appointment_start + timedelta(minutes=30)
    
    new_appointment = AppointmentDTO(
        practitioner_reference="pract_789012345",
        patient_reference="pat_123456789",
        appointment_start_time=appointment_start,
        appointment_end_time=appointment_end,
        ...
    )
    
    created_appointment = await appointment_service.create(new_appointment)
    print(f"Appointment created: {created_appointment}")
    
    # 2. Get all appointments
    all_appointments = await appointment_service.find_all()
    print(f"Total appointments: {len(all_appointments.appointments)}")
    
    # 3. Find appointment by reference
    if all_appointments.appointments:
        appointment_ref = all_appointments.appointments[0].reference
        appointment = await appointment_service.find_by_id(appointment_ref)
        
        # 4. Update appointment
        from carestack import UpdateAppointmentDTO
        
        update_data = UpdateAppointmentDTO(
            reference=appointment_ref,
            status="CONFIRMED",
            notes="Patient confirmed attendance",
            priority=AppointmentPriority.HIGH
        )
        
        updated = await appointment_service.update(update_data)
        print(f" Updated appointment: {updated}")
        
        # 5. Check if appointment exists
        exists = await appointment_service.exists(appointment_ref)
        print(f" Appointment exists: {exists}")
        
        # 6. Cancel/Delete appointment (if needed)
        # await appointment_service.delete(appointment_ref)
        # print(f"Appointment cancelled")

Expected Response Structure

# Appointment Response
{
    "reference": "appt_abc123def456",
    "patient_reference": "pat_123456789",
    "practitioner_reference": "pract_789012345",
    "organization_id": "org_456789012",
    ...
}

# Appointments List Response
{
    "appointments": [...],  # Array of appointment objects
    "total": 45,
    "page": 1,
    "per_page": 20,
    "has_next": true
}

Summary Generation

Leverage AI for automated healthcare document generation.

AI-Powered Document Generation

from carestack import ClientConfig, AiService
import asyncio

async def ai_service_operations():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    ai_service = AiService(config)
    
    # 1. Generate Discharge Summary
    discharge_data = {
        "files": ["file123.pdf"],
        "publicKey": "-----BEGIN PUBLIC KEY-----..."
    }
    
    try:
        discharge_summary = await ai_service.generate_discharge_summary(discharge_data)
        print("Generated Discharge Summary:")
        print(f"Summary: {discharge_summary['summary'][:200]}...")
        print(f" Medications: {len(discharge_summary['medications'])} prescribed")
        print(f"Follow-up: {discharge_summary['follow_up_instructions']}")
        
    except Exception as e:
        print(f" Error generating discharge summary: {e}")

	# 2. Generate OP Case Sheet Summary
	    op_data = {
	        "files": ["file123.pdf"],
	    }
	    
	    try:
	        op_summary = await ai_service.generate_op_consultation_summary(op_data)
	        print("Generated OP Summary:", op_summary)
	        
	    except Exception as e:
	        print(f" Error generating op summary: {e}")
    
    # 3. Generate FHIR Bundle
    fhir_bundle_data = {
        "caseType": "DischargeSummary",
        "enableExtraction": True,
        "documentReferences": ["doc123", "doc456"],
        "recordId": "rec-789",
        "extractedData": {
            "patientName": "John Doe",
            "diagnosis": "Hypertension",
            "treatment": "Medication and lifestyle changes"
        },
        "publicKey": "-----BEGIN PUBLIC KEY-----...",
    }
    
    try:
        fhir_bundle = await ai_service.generate_fhir_bundle(fhir_bundle_data)
        print("Generated FHIR Bundle:")
        print(f"Resource Type: {fhir_bundle['resourceType']}")
        print(f" Bundle ID: {fhir_bundle['id']}")
        print(f" Entries: {len(fhir_bundle['entry'])} resources")
        
        # Display summary of included resources
        resource_types = {}
        for entry in fhir_bundle['entry']:
            resource_type = entry['resource']['resourceType']
            resource_types[resource_type] = resource_types.get(resource_type, 0) + 1
        
        print(" Bundle Contents:")
        for resource_type, count in resource_types.items():
            print(f"   - {resource_type}: {count}")
            
    except Exception as e:
        print(f"Error generating FHIR bundle: {e}")

	# 4. Generate Radiology report
		    input_data = {
		        "files": ["file123.pdf"],
		    }
		    
		    try:
		        radiology_summary = await ai_service.generate_radiology_summary(input_data)
		        print("Generated OP Summary:", op_summary)
		        
		    except Exception as e:
		        print(f" Error generating radiology summary: {e}")

	# 5. Generate Discharge summary for Parital upload
		    input_data = {
		        "files": ["file123.pdf"],
				"encounterId":"your-encounter-id",
				"date":"2025-04-12T23:24:57Z"
		    }
		    
		    try:
		        discharge_summary = await ai_service.partial_upload_for_discharge_summary(input_data)
		        print("Generated discharge Summary:", op_summary)
		        
		    except Exception as e:
		        print(f" Error generating discharge summary: {e}")

	# 6. Update Discharge Summary
		try:
			discharge_summary = await ai_service.trigger_discharge_summary("your-encounter-id")
			print("Discharge summary :", discharge_summary)

		except Exception as e:
			print(f"Error updating discharge summary: {e}")

	# 7. Generate care paln
		    input_data = {
		        "files": ["file123.pdf"],
		    }
		    
		    try:
		        care_plan = await ai_service.generate_careplan(input_data)
		        print("Generated care plan:", care_plan)
		        
		    except Exception as e:
		        print(f" Error generating care plan: {e}")

asyncio.run(ai_service_operations())

Expected Response Structure

# Discharge Summary, OP Consulation Response
{
    id='summary-abc-123',
    dischargeSummary={
        "patientName": "John Doe",
        "diagnosis": "Hypertension",
        "treatment": "Medication and lifestyle changes"
    },
    extractedData={
        "encounterDate": "2025-07-30",
        "doctor": "Dr. Smith"
    },
    fhirBundle={
        "resourceType": "Bundle",
        "entry": [...]
    }
}

# FHIR Bundle Response
{
    "resourceType": "Bundle",
    "id": "bundle_discharge_123456",
    "type": "document",
    "timestamp": "2025-01-15T14:30:00Z",
    "entry": [
        {
            "resource": {
                "resourceType": "Patient",
                "id": "pat_123456789",
                "name": [{"family": "Doe", "given": ["John"]}]
            }
        },
        {
            "resource": {
                "resourceType": "Condition",
                "id": "condition_123",
                "code": {
                    "coding": [{"code": "I21.9", "display": "Acute myocardial infarction"}]
                }
            }
        }
    ]
}

Health Document Linking

Orchestrate complex healthcare document workflows with multi-step processes.

Document Linking Workflow

from carestack import ClientConfig, DocumentLinking, HealthDocumentLinkingDTO, HealthInformationDTO, HealthInformationTypes, AppointmentPriority, AppointmentType
import asyncio

async def document_linking_operations():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    doc_service = DocumentLinking(config)
    
    # Create comprehensive linking DTO
    linking_dto = HealthDocumentLinkingDTO(
        patientReference="123e4567-e89b-12d3-a456-426614174000",
        practitionerReference="321e4567-e89b-12d3-a456-426614174000",
        patientAddress="123 Cedar Street",
        patientName="Jane Doe",
        appointmentStartDate="2025-08-15T14:00:00Z",
        appointmentEndDate="2025-08-15T14:30:00Z",
        appointmentPriority="ROUTINE",
        organizationId="Org987",
        mobileNumber="9998887776",
        hiType="OPConsultation",
        healthRecords=[rawFhir=True, fhirDocument={"key": "value"}, informationType=HealthInformationTypes.OPConsultation]
    )
    
    try:
        # Execute the complete linking workflow
        print(" Starting health document linking process...")
        
        # This orchestrates the entire workflow:
        # 1. Creates care context
        # 2. Updates visit records (if health records exist)
        # 3. Links care context with health document
        success = await doc_service.link_health_document(linking_dto)
        
        if success:
            print("Health document linking completed successfully!")
            print("Workflow completed:")
            print("Care context created")
            print(" Visit records updated")
            print(" Care context linked to health document")
        else:
            print(" Document linking completed but no health records were processed")
            
    except Exception as e:
        print(f" Error in document linking workflow: {e}")

# Individual workflow step examples
async def individual_workflow_steps():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    doc_service = DocumentLinking(config)
    
    # Step 1: Create Care Context (internal method example)
    print("Step 1: Creating care context...")
    # This is handled internally by link_health_document method
    
    # Step 2: Update Visit Records (internal method example)
    print("Step 2: Updating visit records...")
    # This is also handled internally
    
    # Step 3: Link Care Context (internal method example)
    print("Step 3: Linking care context...")
    # Final step handled internally
    
    print("All workflow steps would be orchestrated automatically")

asyncio.run(document_linking_operations())

Expected Response Structure

# Document Linking Response
{
    "success": True,
    "care_context_id": "cc_789012345",
    "visit_record_updated": True,
    "health_document_linked": True,
    "transaction_state": {
        "care_context_created": True,
        "visit_records_updated": True,
        "care_context_linked": True,
        "completed_at": "2025-01-15T14:30:00Z"
    },
    "message": "Health document linking completed successfully"
}

# Error Response (if failure occurs)
{
    "success": False,
    "error": "Care context creation failed",
    "transaction_state": {
        "care_context_created": False,
        "visit_records_updated": False,
        "care_context_linked": False,
        "failed_at": "care_context_creation",
        "error_details": "Patient reference not found"
    }
}

FHIR Bundle Generation

Orchestrate healthcare encounters with FHIR generation and discharge processing.

from carestack import ClientConfig, Encounter, EncounterRequestDTO, CaseType, OPConsultationDTO, OPConsultationSections
import asyncio

async def encounter_operations():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    encounter_service = Encounter(config)
    
    # Create encounter request for payload-based FHIR generation
    payload_request = EncounterRequestDTO(
        case_type=CaseType.OP_CONSULTATION,
        patient_details=PatientDetails(name="John Doe", ...),
        doctor_details=[DoctorDetails(name="Dr. Smith", ...)],
        document_references=["lab_report_1.pdf", "lab_report_2.pdf"],
        enable_extraction=True,
        dto=OPConsultationDTO(
            payload=OPConsultationSections(
              chief_complaints="Fever and cough",
              physical_examination=PhysicalExamination(...),
              ...)
        ),
    )
    
    try:
        # Generate FHIR bundle from sections
        print("Generating FHIR bundle from clinical sections...")
        fhir_bundle = await encounter_service.create(payload_request)
        
        print("FHIR Bundle generated successfully!")
        print(f"Resource Type: {fhir_bundle['resourceType']}")
        print(f"Bundle ID: {fhir_bundle['id']}")
        print(f"Total Resources: {len(fhir_bundle['entry'])}")
        
        # Analyze bundle contents
        resource_summary = {}
        for entry in fhir_bundle['entry']:
            resource_type = entry['resource']['resourceType']
            resource_summary[resource_type] = resource_summary.get(resource_type, 0) + 1
        
        print("Bundle Resource Summary:")
        for resource_type, count in resource_summary.items():
            print(f"- {resource_type}: {count}")
            
    except Exception as e:
        print(f"Error generating FHIR from sections: {e}")
    
    # 2. Generate FHIR Bundle from Files
    print("\n" + "="*50)
    print("Generating FHIR bundle from case sheet files...")
    
    # File-based encounter request
    file_based_request = EncounterRequestDTO(
        case_type=CaseType.OP_CONSULTATION,
        patient_details=PatientDetails(name="John Doe", ...),
        doctor_details=[DoctorDetails(name="Dr. Smith", ...)],
        document_references=["lab_report_1.pdf", "lab_report_2.pdf"],
        enable_extraction=True,
        dto=OPConsultationDTO(   
            caseSheets=["case_sheet_1.pdf", "case_sheet_2.pdf"],
        ),
    )
    
    try:
        fhir_from_files = await encounter_service.create(file_based_request)
        print("FHIR Bundle generated from files!")
        print(f"Processed files: Case sheets + Lab reports")
        print(f"Bundle ID: {fhir_from_files['id']}")
        
    except Exception as e:
        print(f"Error generating FHIR from files: {e}")
    

Expected Response Structure

# FHIR Bundle from Sections Response
{
    "resourceType": "Bundle",
    "id": "encounter_bundle_123456",
    "type": "document",
    "timestamp": "2025-01-15T14:30:00Z",
    "entry": [
        {
            "fullUrl": "Patient/pat_123456789",
            "resource": {
                "resourceType": "Patient",
                "id": "pat_123456789",
                "name": [{"family": "Doe", "given": ["John"]}]
            }
        },
        {
            "fullUrl": "Encounter/enc_789012345",
            "resource": {
                "resourceType": "Encounter",
                "id": "enc_789012345",
                "status": "finished",
                "class": {"code": "AMB"},
                "subject": {"reference": "Patient/pat_123456789"}
            }
        },
        ...
    ],
}

Healthcare Identity Workflows

Complete Ayushman Bharat Health Account registration with step-by-step workflow.

ABHA Registration Workflow

from carestack import ClientConfig, CreateABHA, AbhaSteps
import asyncio

async def abha_registration_workflow():
    config = ClientConfig(
        api_key="your_api_key",
    )
    
    abha_service = CreateABHA(config)
    
    # Complete ABHA registration workflow
    print("Starting ABHA Registration Process")
    
    try:
        # Step 1: Start registration with Aadhaar number
        print("Step 1: Initiating Aadhaar OTP...")
        aadhaar_number = "123456789012"  # Replace with actual Aadhaar
        
        step1_result = await abha_service.start_registration(aadhaar_number)
        
        txn_id = step1_result['data']['txnId']
        print(f"Transaction ID: {txn_id}")
        
        # Step 2: Enroll with Aadhaar OTP
        print("\nStep 2: Verifying Aadhaar OTP...")
        
        # In real implementation, you would collect OTP from user
        aadhaar_otp = "123456"  # Get from user input
        mobile_number = "9876543210"  # User's mobile number
        
        step2_result = await abha_service.registration_flow(
            AbhaSteps.ENROLL_WITH_AADHAAR,
            {
                "otp": aadhaar_otp,
                "txnId": txn_id,
                "mobile": mobile_number
            }
        )
        
        new_txn_id = step2_result['data']['txnId']
        
        # Step 3: Generate Mobile OTP
        print("\nStep 3: Generating Mobile OTP...")
        
        step3_result = await abha_service.registration_flow(
            AbhaSteps.GENERATE_MOBILE_OTP,
            {
                "txnId": new_txn_id,
                "mobile": mobile_number
            }
        )
        
        mobile_txn_id = step3_result['data']['txnId']
        
        # Step 4: Verify Mobile OTP
        print("\nStep 4: Verifying Mobile OTP...")
        
        mobile_otp = "654321"  # Get from user input
        
        step4_result = await abha_service.registration_flow(
            AbhaSteps.VERIFY_MOBILE_OTP,
            {
                "otp": mobile_otp,
                "txnId": mobile_txn_id
            }
        )
        
        print(f"{step4_result['message']}")
        print(f"Next step: {step4_result['next_step']}")
        
        verified_txn_id = step4_result['data']['txnId']
        
        # Step 5: Get ABHA Address Suggestions
        print("\nStep 5: Getting ABHA Address suggestions...")
        
        step5_result = await abha_service.registration_flow(
            AbhaSteps.GET_ABHA_ADDRESS_SUGGESTION,
            {
                "txnId": verified_txn_id
            }
        )
        
        for i, suggestion in enumerate(abha_suggestions[:3], 1):
            print(f"   {i}. {suggestion}")
        
        # Step 6: Create ABHA Address
        print("\n Step 6: Creating ABHA Address...")
        
        selected_abha_address = abha_suggestions[0]  # User selects first suggestion
        password = "SecurePass@123"  # User sets password
        
        step6_result = await abha_service.registration_flow(
            AbhaSteps.CREATE_ABHA_ADDRESS,
            {
                "txnId": verified_txn_id,
                "abhaAddress": selected_abha_address,
                "password": password
            }
        )
        
        print(f" {step6_result['message']}")
        print(f" Next step: {step6_result['next_step']}")
        
        # Step 7: Complete Registration
        print("\n Step 7: Completing ABHA registration...")
        
        final_result = await abha_service.registration_flow(
            AbhaSteps.CREATE_ABHA_ID,
            {
                "txnId": step6_result['data']['txnId']
            }
        )
        
        print(" ABHA Registration Completed Successfully!")
        
        return final_result['data']['ABHAProfile']
        
    except Exception as e:
        print(f"ABHA Registration failed: {e}")
        return None

# Run the workflows
if __name__ == "__main__":
    asyncio.run(abha_registration_workflow())

Expected Response Structure

# Step 1: Start Registration Response
{
    "message": "OTP sent to registered mobile number ending with XXX7890",
    "data": {
        "txnId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
    },
    "next_step": "ENROLL_WITH_AADHAAR",
    "next_step_payload_hint": {
        "required_fields": ["otp", "txnId", "mobile"],
        "otp": "6-digit OTP received on mobile",
        "txnId": "Transaction ID from previous step",
        "mobile": "10-digit mobile number"
    }
}

# Final Registration Response
{
    "message": "ABHA registration completed successfully",
    "data": {
        "ABHAProfile": {
            "ABHANumber": "12-3456-7890-1234",
            "ABHAAddress": "john.doe@abha",
            "name": "John Doe",
            "dateOfBirth": "01-01-1980",
            "gender": "M",
            "mobile": "9876543210",
            "email": "[e-mail]",
            "status": "ACTIVE",
            "createdAt": "2025-01-15T14:30:00Z"
        }
    },
    "next_step": "REGISTRATION_COMPLETE",
    "next_step_payload_hint": null
}

HPR Registration Workflow

Healthcare Professional Registry registration with multi-step verification.

from carestack import ClientConfig, CreateHPR, HprRegistrationSteps
import asyncio

async def hpr_registration_workflow():
    config = ClientConfig(
        api_key="your_api_key",
        x_hpr_id="your_hpr_id" # Required for Organization operations
    )
    
    hpr_service = CreateHPR(config)
    
    # Complete HPR registration workflow
    print("Starting HPR Registration Process")
    print("=" * 50)
    
    try:
        # Step 1: Generate Aadhaar OTP
        print(" Step 1: Initiating Aadhaar OTP for HPR...")
        aadhaar_number = "123456789012"  # Replace with actual Aadhaar
        
        step1_result = await hpr_service.start_registration(aadhaar_number)
        
        txn_id = step1_result['data']['txnId']
        
        # Step 2: Verify Aadhaar OTP
        print("\n Step 2: Verifying Aadhaar OTP...")
        
        aadhaar_otp = "123456"  # Get from user input
        domain_name = "allopathy"  # Medical domain
        
        step2_result = await hpr_service.registration_flow(
            HprRegistrationSteps.VERIFY_AADHAAR_OTP,
            {
                "otp": aadhaar_otp,
                "txnId": txn_id,
                "domainName": domain_name,
                "idType": "aadhaar",
                "restrictions": None
            }
        )
        
        verified_txn_id = step2_result['data']['txnId']
        
        # Step 3: Check Existing Account
        print("\n📋 Step 3: Checking for existing HPR account...")
        
        step3_result = await hpr_service.registration_flow(
            HprRegistrationSteps.CHECK_EXISTING_ACCOUNT,
            {
                "txnId": verified_txn_id
            }
        )
        
        print(f"{step3_result['message']}")
        
        if step3_result['data'].get('accountExists', False):
            print(" Existing HPR account found")
            existing_hpr_id = step3_result['data']['hprId']
            print(f"Existing HPR ID: {existing_hpr_id}")
            return step3_result
        
        # Step 4: Demographic Authentication
        print("\n📋 Step 4: Performing demographic authentication...")
        
        step4_result = await hpr_service.registration_flow(
            HprRegistrationSteps.DEMOGRAPHIC_AUTH,
            {
                "txnId": verified_txn_id,
                "demographic_data": {
                    "name": "Dr. Sarah Wilson",
                    "gender": "F",
                    "yearOfBirth": "1975",
                    "dayOfBirth": "15",
                    "monthOfBirth": "08"
                }
            }
        )
        
        auth_txn_id = step4_result['data']['txnId']
        
        # Step 5: Get HPR ID Suggestions
        print("\n Step 5: Getting HPR ID suggestions...")
        
        step5_result = await hpr_service.registration_flow(
            HprRegistrationSteps.GET_HPR_ID_SUGGESTION,
            {
                "txnId": auth_txn_id
            }
        )
        
        hpr_suggestions = step5_result['data']['hprIdSuggestions']
        print(f"Available HPR ID suggestions:")
        for i, suggestion in enumerate(hpr_suggestions[:3], 1):
            print(f"   {i}. {suggestion}")
        
        # Step 6: Create HPR ID with Professional Details
        print("\n Step 6: Creating HPR ID with professional details...")
        
        selected_hpr_id = hpr_suggestions[0]  # User selects first suggestion
        
        step6_result = await hpr_service.registration_flow(
            HprRegistrationSteps.CREATE_HPR_ID,
            {
                "txnId": auth_txn_id,
                "hprId": selected_hpr_id,
                "password": password,
                ...
            }
        )
        
        print(" HPR Registration Completed Successfully!")
        
        return step6_result['data']['hprProfile']
        
    except Exception as e:
        print(f" HPR Registration failed: {e}")
        return None


# Run the workflows
if __name__ == "__main__":
    asyncio.run(hpr_registration_workflow())

Expected Response Structure

# Step 1: Start HPR Registration Response
{
    "message": "Aadhaar OTP generated for HPR registration",
    "data": {
        "txnId": "hpr_a1b2c3d4-e5f6-7890-abcd-ef1234567890"
    },
    "next_step": "VERIFY_AADHAAR_OTP",
    "next_step_payload_hint": {
        "required_fields": ["otp", "txnId", "domainName", "idType"],
        "otp": "6-digit OTP received on mobile",
        "domainName": "Medical domain (e.g., allopathy, ayurveda)",
        "idType": "aadhaar"
    }
}

# Final HPR Registration Response
{
    "message": "HPR ID created successfully",
    "data": {
        "hprProfile": {
            "hprId": "HPR123456789",
            "fullName": "Dr. Sarah Wilson",
            "dateOfBirth": "15-08-1975",
            "gender": "F",
            "mobile": "9876543210",
            "email": "[e-mail]",
            ...,
            "status": "ACTIVE",
            "createdAt": "2025-01-15T14:30:00Z"
        }
    },
    "next_step": "None",
    "next_step_payload_hint": null
}

Response Examples

Common Response Patterns

All services follow consistent response patterns for better predictability:

Success Response Format

{
    "status": "success",
    "message": "Operation completed successfully",
    "data": { /* relevant data */ },
    "timestamp": "2025-01-15T14:30:00Z",
    "request_id": "req_123456789"
}

Error Response Format

{
    "status": "error",
    "error_code": "VALIDATION_ERROR",
    "message": "Invalid input data provided",
    "details": {
        "field": "email",
        "reason": "Invalid email format"
    },
    "timestamp": "2025-01-15T14:30:00Z",
    "request_id": "req_123456789"
}

Paginated Response Format

{
    "items": [ /* array of items */ ],
    "pagination": {
        "total": 150,
        "page": 1,
        "per_page": 20,
        "total_pages": 8,
        "has_next": true,
        "has_previous": false,
        "next_page": "eyJpZCI6InBhdF8xMjM0NTY3ODkifQ==",
        "previous_page": null
    }
}

Troubleshooting

Common Issues and Solutions

1. Authentication Errors

# Problem: 401 Unauthorized
# Solution: Check API credentials

async def diagnose_auth_issues():
    config = SDKConfig.from_environment()
    
    # Test configuration
    if not config.api_key:
        print("API key is missing")
        return
        
    print("Credentials are configured")
    
    # Test API connectivity
    try:
        sdk = CarestackSDK(config)
        patients = await sdk.patients.find_all()
        print("Authentication successful")
    except EhrApiError as e:
        if e.status_code == 401:
            print("Invalid credentials")
        elif e.status_code == 403:
            print("Insufficient permissions")
        else:
            print(f"API error: {e.message}")

2. Data Validation Issues

def validate_patient_data(patient_data):
    """Validate patient data before API call"""
    errors = []
    
    # Required fields
    required_fields = ["firstName", "lastName", "resourceType"]
    for field in required_fields:
        if not patient_data.get(field):
            errors.append(f"Missing required field: {field}")
    
    # Format validations
    if patient_data.get("gender") and patient_data["gender"] not in ["M", "F", "O"]:
        errors.append("Gender must be M, F, or O")
    
    if patient_data.get("birthDate"):
        try:
            datetime.strptime(patient_data["birthDate"], "%Y-%m-%d")
        except ValueError:
            errors.append("birthDate must be in YYYY-MM-DD format")
    
    return errors

# Usage
patient_data = {"firstName": "John", "lastName": "Doe"}
errors = validate_patient_data(patient_data)

if errors:
    print("Validation errors:")
    for error in errors:
        print(f"   - {error}")
else:
    print("Data validation passed")

Support

Documentation and Resources

• Python-3.10 SDK Documentation: [http://python-mkdocs-bucket.s3-website.ap-south-1.amazonaws.com/]

Getting Help

1. SDK Version: pip show carestack
2. Python Version: python --version
3. Error Messages: Full traceback if applicable

System Requirements

Minimum Requirements

• Python: 3.10 or higher
• Operating System: Windows 10+, macOS 10.14+, Linux (Ubuntu 18.04+, CentOS 7+)
• Network: HTTPS connectivity to Carestack APIs

Dependencies

httpx>=0.25.0
pydantic>=2.0.0
typing_extensions
pytest
pytest-asyncio
pydantic[email]>=2.0.0
python-dotenv>=1.0.1
cryptography>=3.0
python-jose[cryptography]>=3.3.0