autoicd

v0.11.1 suspicious
4.0
Medium Risk

AI medical coding SDK - convert clinical text to ICD-10-CM and ICD-11 diagnosis codes with AI-powered NLP. Unified reference lookup for ICD-10, ICD-11, ICF, LOINC, SNOMED CT, UMLS, and RxNorm. Chart audit (HCC gap capture, RADV, specificity, denial risk), cross-standard translate, and PHI de-identification.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network activities and metadata concerns, though there is no concrete evidence of malicious intent.

  • network risk due to external API calls
  • metadata concerns including an insecure link and limited author activity
Per-check LLM notes
  • Network: The package makes network calls to an external API, which could be legitimate but requires scrutiny to ensure it's not leaking sensitive information.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags including an insecure link and an author with limited activity, but there's no clear evidence of typosquatting or other malicious intent.

📦 Package Quality Overall: Medium (6.4/10)

✦ High Test Suite 9.0

Test suite present — 2 test file(s) found

  • Test runner config found: pyproject.toml
  • 2 test file(s) detected (e.g. test_client.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://autoicdapi.com/docs
  • Detailed PyPI description (22117 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 97 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 5.0

Limited contributor diversity

  • 1 unique contributor(s) across 29 commits in fcggamou/autoicd-python
  • Single author but highly active (29 commits)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • self._http = http_client or httpx.Client(timeout=self._timeout) self.icd10 = ICD10Codes(self)
  • -> AutoICD: http_client = httpx.Client(transport=transport) return AutoICD(api_key="sk_test_123
  • om/", http_client=httpx.Client(transport=transport), ) assert custom._base_
  • import httpx raw = httpx.post( "https://autoicdapi.com/api/v1/code",
  • import httpx raw = httpx.get( "https://autoicdapi.com/api/v1/icd10/codes/I10"
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: autoicdapi.com>

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://id.who.int/icd/entity/1691003785
⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with autoicd 
Create a healthcare data analysis tool named 'MedCodeAudit' using Python and the 'autoicd' package. This application will serve as a comprehensive solution for medical coders and healthcare auditors, providing them with an efficient way to analyze patient records and ensure accurate coding practices. The tool will have the following core functionalities:

1. **ICD Code Conversion**: Users will input clinical notes or patient descriptions directly into the app. Using the 'autoicd' package, the application will automatically convert these textual inputs into corresponding ICD-10-CM and ICD-11 diagnosis codes.
2. **Unified Reference Lookup**: MedCodeAudit will offer a unified search feature where users can look up information across multiple medical coding standards such as ICD-10, ICD-11, ICF, LOINC, SNOMED CT, UMLS, and RxNorm. This feature will help users understand the relationships between different codes and terminologies.
3. **Chart Audit Tool**: The application will include a chart audit module designed to identify potential gaps in coding (e.g., HCC gap capture), evaluate the specificity of coded diagnoses, and assess the risk of claim denials due to insufficient documentation.
4. **Cross-Standard Translation**: To facilitate interoperability between different healthcare systems, MedCodeAudit will provide a translation function that converts codes from one standard to another (e.g., from ICD-10 to SNOMED CT).
5. **PHI De-identification**: In compliance with privacy regulations, the tool will also include a feature to de-identify protected health information (PHI) within the clinical text before processing it through the 'autoicd' package.

For each of these features, detail the user interface design, backend implementation strategy using 'autoicd', and any additional libraries or tools needed to support the functionality. Additionally, outline a plan for testing the application's accuracy and efficiency.

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