AutoICD API
AI clinical coding platform for ICD-10 and SNOMED
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What is AutoICD API?
AutoICD API is a cutting-edge AI-powered Clinical Coding API that automatically converts unstructured clinical text and insurance claims into structured ICD-10-CM, ICD-11, and ICF diagnosis codes using advanced Natural Language Processing (NLP) and Machine Learning (ML) technology. The API supports both SNOMED-CT and ICD10 terminology standards, enabling healthcare providers, insurers, EHR/EMR systems, and healthcare AI developers to streamline their clinical coding processes.
Key features include Computed Assisted Coding (CAC) that reduces errors and saves time, accurate negation detection (correctly identifying when conditions are denied or absent), automatic generation of structured data, support for over 74,000 ICD-10 codes, PHI de-identification capabilities, ICD-10/ICD-11 crosswalk functionality, and official SDKs for TypeScript, JavaScript, and Python. The API provides a/restful endpoint at /api/code for coding clinical text and includes confidence scores for suggested codes.
AutoICD API is designed for medical professionals, healthcare providers, insurance companies, EHR/EMR vendors, healthcare AI developers building clinical workflows, startups creating healthcare tools, and organizations need to scan charts, identify diagnoses, group them into risk categories, and predict future healthcare costs more efficiently. It operates in North America and South America from Montevideo, Uruguay.
AutoICD API pricing
Pricing model: Freemium
AutoICD offers a flexible usage-based pricing model that allows healthcare organizations to scale their coding needs without incurring unnecessary costs. A free trial period is available for potential users to evaluate the platform's capabilities before committing to a subscription. Specific pricing tiers, per-request costs, or monthly plan details are not publicly disclosed on the website - users must sign up to obtain an API key and access pricing information.
AutoICD API pros
- High accuracy in assigning ICD-10 codes to clinical texts
- Successfully detects negation in clinical text (e.g., 'denies chest pain')
- Outperforms AWS Comprehend Medical and Azure Health Bot in accuracy
- Correctly codes complex conditions with history statements
- Supports ICD-10-CM, ICD-11, and ICF code systems
- SNOMED-CT enabled with high specificity and granularity
- Cloud-based API service with RESTful endpoints
- Flexible usage-based pricing model for scalability
- Free trial period available for evaluation
- Official TypeScript, JavaScript, and Python SDKs available
- PHI de-identification capabilities for data security
- ICD-10/ICD-11 crosswalk functionality included
- Searches 74,000+ ICD-10 codes programmatically
- Returns confidence scores for suggested codes
- Reduces coding errors and saves time for medical professionals
- MCP Server integration for AI assistants available
AutoICD API cons
- No publicly disclosed pricing details on website
- API key required - must sign up on website to obtain
- Limited to North America and South America regions
- No free tier mentioned, only free trial period
- Accuracy still requires human review for final call
- Cloud-dependent service (not available on-premise)
- Limited documentation publicly available
- Founder contact only (Federico Gonzalez), no support team listed
Frequently asked questions about AutoICD API
What is AutoICD API?
AutoICD API is a cutting-edge AI-powered Clinical Coding API that utilizes artificial intelligence and advanced Natural Language Processing (NLP) technology to automatically process unstructured clinical texts and insurance claims, generating structured data in ICD-10-CM, ICD-11, ICF, and SNOMED-CT code systems.
How do I get an API key for AutoICD?
You need to sign up on the AutoICD website at autoicdapi.com to obtain an API key. Once you have the key, you can add it to your configuration files or include it in request headers for authentication.
What code systems does AutoICD support?
AutoICD supports ICD-10-CM (ICD-10 Clinical Modification for US), ICD-11 (WHO International Classification of Diseases 11th Revision), ICF (International Classification of Functioning), and SNOMED-CT (Systematized Nomenclature of Medicine Clinical Terms) terminology standards.
How does AutoICD handle negation in clinical text?
AutoICD accurately detects negation in clinical texts, which is a major advantage over competitors. For example, when text states 'The patient denies any chest pain,' AutoICD correctly identifies the negation and does not assign a code for chest pain, while other solutions like AWS Comprehend Medical and Azure Health Bot would incorrectly assign the code.
What is the API endpoint for coding clinical text?
The AutoICD API provides an endpoint at /api/code for coding clinical text written in natural language to ICD-10 codes. You send POST requests with clinical text and receive JSON results with coded ICD-10 diagnoses.
Does AutoICD work with EHR and EMR systems?
Yes, AutoICD is designed for integration with EHR (Electronic Health Records) and EMR (Electronic Medical Records) systems. The API supports clinical coding, clinical documentation workflows, and can be integrated into existing healthcare software through RESTful HTTP requests.
What SDKs are available for AutoICD?
AutoICD offers official SDKs including TypeScript SDK (autoicd-js on npm), JavaScript SDK for healthcare AI workflows, and Python SDK (autoicd on PyPI). These SDKs make it easier to integrate AutoICD into healthcare applications without dealing with complex pipelines.
How many ICD codes can AutoICD search?
AutoICD can search through 74,000+ ICD-10 codes programmatically, allowing AI assistants and applications to perform ICD-10-CM diagnosis coding and code search functionality via the API.
What regions does AutoICD operate in?
AutoICD operates in North America and South America. The company is based in Montevideo, Uruguay at Canelones 1228, 11100.
Does AutoICD provide confidence scores for codes?
Yes, AutoICD returns JSON results that include confidence scores for suggested ICD-10 codes, allowing developers and medical professionals to assess the reliability of automated coding suggestions before making final coding decisions.