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· 10 hr 前 3m read

Uso de auditoría de Python para depuración de Python embebido

#Embedded Python #InterSystems IRIS

PEP 578 añadió hooks de auditoría de Python. Una gran variedad de eventos (carga de módulos, interacciones con el sistema operativo, etc.) generan eventos de auditoría a los que podéis suscribiros.

Así es como se hace. Primero, cread un hook de Python embebido:

Class User.Python
{

/// do ##class(User.Python).Audit()
ClassMethod Audit() [ Language = python ]
{
import sys
import time
def logger(event,args):
     if event=='import':
        module = args[0]
        print(f"Loading {module}")
        if module == "numpy":
            print(f"Module {module} forbidden. Terminating process in 3.")
            time.sleep(3)
            sys.exit(0)
     elif event in ('compile','exec'):
        print(f"{event}: {args}")
     elif event in ('code.__new__','open'):
        # do nothing for this event
        x=1
     else:
        print(f"{event}")

sys.addaudithook(logger)
}

}

En este ejemplo:

  • Terminamos el proceso si numpy comienza a cargarse
  • Mostramos el evento y sus argumentos para los eventos de compile/exec
  • Ignoramos los eventos de código
  • Registramos todos los demás eventos

Todo esto se escribirá en el STDOUT por defecto.

Para empezar la auditoría, basta con llamar a este método para registrar vuestro hook, o incluso podéis hacerlo automáticamente al iniciar el proceso (job):

%ZSTART
JOB
    new $namespace
    set $namespace = "%SYS"
    do ##class(User.Python).Audit()
    quit
LOGIN
    do JOB
    quit

Aquí tenéis un ejemplo de salida:

%SYS>:PY
Loading code
exec: (<code object <module> at 0x000001AB82A0F2F0, file "c:\intersystems\iris\lib\python\lib\code.py", line 1>,)
Loading traceback
exec: (<code object <module> at 0x000001AB82A173A0, file "c:\intersystems\iris\lib\python\lib\traceback.py", line 1>,)
Loading linecache
exec: (<code object <module> at 0x000001AB82A17A80, file "c:\intersystems\iris\lib\python\lib\linecache.py", line 1>,)
Loading tokenize
exec: (<code object <module> at 0x000001AB82A32030, file "c:\intersystems\iris\lib\python\lib\tokenize.py", line 1>,)
Loading token
exec: (<code object <module> at 0x000001AB82A323A0, file "c:\intersystems\iris\lib\python\lib\token.py", line 1>,)
compile: (b'lambda _cls, type, string, start, end, line: _tuple_new(_cls, (type, string, start, end, line))', '<string>')
exec: (<code object <module> at 0x000001AB82A32710, file "<string>", line 1>,)
sys._getframe
object.__setattr__
Loading codeop
exec: (<code object <module> at 0x000001AB82A32EA0, file "c:\intersystems\iris\lib\python\lib\codeop.py", line 1>,)
Loading __future__
exec: (<code object <module> at 0x000001AB82A472F0, file "c:\intersystems\iris\lib\python\lib\__future__.py", line 1>,)
 
Python 3.9.5 (default, May 31 2022, 12:35:47) [MSC v.1927 64 bit (AMD64)] on win32
Type quit() or Ctrl-D to exit this shell.
compile: (b'import iris', '<input>')
compile: (b'import iris\n', '<input>')
compile: (b'import iris\n\n', '<input>')
exec: (<code object <module> at 0x000001AB82A60870, file "<input>", line 1>,)
>>> import json
compile: (b'import json', '<input>')
compile: (b'import json\n', '<input>')
compile: (b'import json\n\n', '<input>')
exec: (<code object <module> at 0x000001AB82A60870, file "<input>", line 1>,)
Loading json
exec: (<code object <module> at 0x000001AB82A60DF0, file "c:\intersystems\iris\lib\python\lib\json\__init__.py", line 1>,)
Loading json.decoder
os.listdir
exec: (<code object <module> at 0x000001AB82A67710, file "c:\intersystems\iris\lib\python\lib\json\decoder.py", line 1>,)
Loading json.scanner
exec: (<code object <module> at 0x000001AB82A679D0, file "c:\intersystems\iris\lib\python\lib\json\scanner.py", line 1>,)
Loading _json
Loading json.encoder
exec: (<code object <module> at 0x000001AB82A71500, file "c:\intersystems\iris\lib\python\lib\json\encoder.py", line 1>,)
>>> x=1
compile: (b'x=1', '<input>')
compile: (b'x=1\n', '<input>')
compile: (b'x=1\n\n', '<input>')
exec: (<code object <module> at 0x000001AB82A60870, file "<input>", line 1>,)

Creo que esto os puede resultar útil para la depuración.

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お知らせ
· 12 hr 前

Aviso de mantenimiento de la comunidad de desarrolladores (21–22 de febrero)

#開発者コミュニティ公式

¡Hola, desarrolladores!

Tened en cuenta que la Comunidad de Desarrolladores se someterá a un mantenimiento programado los días 21 y 22 de febrero de 2026.

Durante este período, el sitio no estará disponible durante varias horas. Dependiendo de vuestra ubicación, podéis experimentar interrupciones temporales en distintos momentos dentro de este intervalo.

Este mantenimiento es necesario porque estamos migrando a una nueva versión del framework subyacente. Aunque la mayor parte de la interfaz y la funcionalidad seguirán siendo las mismas, es posible que notéis que algunos elementos se ven o funcionan de forma ligeramente diferente después de la actualización.

Tras el mantenimiento, podría producirse cierta inestabilidad temporal mientras los servicios vuelven a estar completamente en línea.

Si notáis algún problema, os agradeceríamos mucho vuestros comentarios; por favor, informad de ello aquí.

¡Gracias por vuestra paciencia y comprensión!

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記事
· 13 hr 前 7m read

Smart Approval Method for Laboratory Information Management System

#HealthShare

 

Introduction

In modern diagnostics and research environments, speed, accuracy, and compliance are critical. Laboratories process hundreds or even thousands of samples daily, making manual validation and approval processes inefficient and error-prone. A Laboratory Information Management System plays a vital role in automating workflows, improving traceability, and ensuring reliable results. One of the most impactful advancements in a laboratory information management system is the implementation of a smart approval method.

A smart approval method within a lims laboratory information management system enhances the verification and authorization of laboratory test results. By using automated validation rules, role-based access controls, and digital workflows, laboratories can reduce turnaround time while maintaining high-quality standards. This approach strengthens the reliability of laboratory data and improves operational efficiency.

Understanding Smart Approval in LIMS

Smart approval refers to an intelligent, automated result validation and authorization process embedded within a Laboratory Information Management System. Instead of relying entirely on manual review, the system uses predefined rules, quality checks, and alert mechanisms to evaluate test results before final approval.

In a traditional setup, laboratory supervisors manually review each result before releasing reports. While this ensures oversight, it can slow down operations and increase the risk of human oversight errors. A lab management information system with smart approval capabilities automates routine validations and flags only abnormal or critical results for further review.

The lims laboratory information management system can automatically:

  • Compare results against reference ranges
  • Detect inconsistencies or instrument errors
  • Highlight critical values
  • Route results to authorized personnel
  • Maintain digital audit trails

This structured approach ensures accuracy while accelerating reporting processes.

Workflow of Smart Approval Method

Automated Result Validation

When laboratory instruments generate test results, they are automatically uploaded into the Laboratory Information Management System. The system applies predefined validation rules, checking results against normal reference ranges and historical patient data. If results fall within acceptable parameters, they may be auto-approved.

Rule-Based Decision Making

The laboratory information management system uses configured approval rules to determine whether results require manual intervention. For example, abnormal findings, critical values, or inconsistent data are flagged for review. This minimizes unnecessary manual checks and ensures attention is given where it is most needed.

Role-Based Authorization

A smart approval method ensures that only authorized personnel can validate and release reports. The lims laboratory information management system assigns permissions based on roles such as technician, pathologist, or laboratory manager. This enhances accountability and strengthens compliance with regulatory standards.

Digital Audit Trails

Every action within the lab management information system is recorded, including edits, validations, and approvals. These audit trails support accreditation requirements and help laboratories maintain transparency during inspections.

Role LIMS Play in Healthcare

The role LIMS play in healthcare extends far beyond data management. Accurate and timely laboratory reports directly influence diagnosis, treatment decisions, and patient outcomes. By implementing a smart approval method within a Laboratory Information Management System, healthcare facilities can significantly reduce reporting delays.

Hospitals and diagnostic centers rely on laboratory results to guide clinical decisions. A laboratory information management system ensures that only verified and validated results are released. Automated alerts for critical values enable faster communication with physicians, potentially saving lives in emergency situations.

Additionally, improved approval workflows enhance patient safety by reducing the chances of reporting incorrect or incomplete results. This demonstrates how essential LIMS play in healthcare settings where precision and speed are paramount.

Benefits of Laboratory Information Management System with Smart Approval

There are numerous benefits of laboratory information management system implementation, particularly when smart approval methods are integrated.

Improved Accuracy

Automated validation reduces human errors and ensures consistent quality control. The Laboratory Information Management System verifies data before release, minimizing the risk of inaccurate reporting.

Faster Turnaround Time

Routine results can be auto-approved, significantly reducing delays. The lims laboratory information management system accelerates workflow while maintaining oversight for complex cases.

Enhanced Compliance

Audit trails, digital signatures, and role-based access controls ensure compliance with regulatory and accreditation standards. A lab management information system simplifies documentation and inspection readiness.

Better Resource Utilization

By automating routine approvals, laboratory professionals can focus on complex analyses and quality improvement initiatives. This improves productivity and operational efficiency.

ncreased Patient Safety

Smart alerts for abnormal or critical results ensure timely medical intervention. The Laboratory Information Management System strengthens communication between laboratories and healthcare providers.

Integration with Laboratory Billing Software

Smart approval methods also contribute to improved financial management when integrated with Laboratory Billing Software. Once test results are validated and approved in the Laboratory Information Management System, billing processes can be triggered automatically.

This integration ensures:

  • Accurate billing based on approved tests
  • Reduced revenue leakage
  • Transparent financial records
  • Faster claim processing

By connecting diagnostic workflows with Laboratory Billing Software, laboratories create a seamless link between clinical operations and administrative management.

Importance for Pathology Laboratories

Pathology laboratories handle complex diagnostic procedures that often require multi-level review. Pathology Lab Software integrated with a Laboratory Information Management System supports structured validation workflows tailored to histopathology, cytology, and microbiology testing.

Smart approval in Pathology Lab Software allows:

  • Multi-tier review processes
  • Digital slide integration
  • Automated result comparison
  • Secure electronic signatures

A lab management information system ensures that even high-volume pathology labs maintain strict quality control standards while improving reporting efficiency.

Security and Data Protection

Data security is a critical concern in healthcare environments. A robust Laboratory Information Management System ensures that only authorized users can approve and release results. Role-based permissions prevent unauthorized access, and encrypted storage protects sensitive patient information.

The lims laboratory information management system also maintains secure backups and disaster recovery protocols. These measures safeguard laboratory data and ensure continuity of operations.

Future of Smart Approval in LIMS

As technology evolves, smart approval methods are becoming more advanced. Artificial intelligence and machine learning algorithms are being integrated into the Laboratory Information Management System to enhance predictive analytics and anomaly detection.

Future developments may include:

  • AI-based result interpretation
  • Predictive quality control monitoring
  • Automated compliance reporting
  • Cloud-based approval workflows

These innovations will further enhance the benefits of laboratory information management system solutions and strengthen their role in healthcare.

Conclusion

The smart approval method in a Laboratory Information Management System represents a significant advancement in laboratory automation. By combining automated validation, rule-based workflows, and role-based authorization, laboratories can improve accuracy, efficiency, and compliance.

The integration of a lims laboratory information management system with Laboratory Billing Software and Pathology Lab Software creates a comprehensive solution that supports both clinical excellence and operational efficiency. The role LIMS play in healthcare continues to expand, ensuring reliable diagnostics and improved patient outcomes.

In today’s competitive and highly regulated healthcare environment, adopting a smart approval-enabled lab management information system is not just beneficial—it is essential for delivering secure, timely, and high-quality laboratory services.

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記事
· 13 hr 前 2m read
Open Exchange

Utiliser « IRIS Load Data » dans VSCode

#CSV #Docker #SQL #VSCode #InterSystems IRIS

L'utilitaire Load Data est un excellent outil pour charger des données depuis des fichiers CSV/TXT dans une table SQL IRIS. Auparavant, il était nécessaire d'envoyer le fichier cible au serveur IRIS et d'y insérer la commande Load Data pour importer son contenu. Désormais, il est possible de sélectionner un fichier dans VS Code, de définir la table de destination et de soumettre la requête. L'utilitaire vscode-load-data enverra le fichier au serveur IRIS et exécutera la commande Load Data. C'est très simple !

Installer l’extension vscode-load-data

  1. Téléchargez l’extension : https://github.com/yurimarx/vscode-load-data/raw/refs/heads/main/intersystems-load-data/intersystems-load-data-0.0.1.vsix
  2. Ouvrez n’importe quel projet InterSystems IRIS utilisant un conteneur Docker.
  3. Allez dans Extensions (Ctrl + Shift + X) : 
  4. Cliquez sur le bouton … en haut : 
  5. Sélectionnez l’option Installer à partir d’un fichier VSIX : 
  6. Vérifiez le nom de votre conteneur IRIS sur Docker Desktop (dans mon exemple c’est iris-cdc-sample-iris-1) : 
  7. Ouvrez votre fichier .vscode/settings.json et ajoutez les lignes suivantes à la fin du fichier :  
    "irisImporter.containerName": "iris-cdc-sample-iris-1",
    "irisImporter.namespace": "USER"
    Résultat final (pour moi, il s'agit de iris-cdc-sample-iris-1 et de l'espace de noms USER) : 
  8. Créez un fichier CSV/TXT dans votre projet VSCode : Exemple de contenu : 
    PersonName,PersonAge,PersonGender
Liam Johnson,25,Male
Olivia Smith,34,Female
Noah Williams,19,Male
Emma Brown,42,Female
Oliver Jones,56,Male
Ava Garcia,29,Female
Elijah Miller,31,Male
Sophia Davis,22,Female
James Rodriguez,45,Male
Isabella Martinez,67,Female
William Hernandez,38,Male
Mia Lopez,24,Female
Benjamin Gonzalez,51,Male
Charlotte Wilson,33,Female
Lucas Anderson,27,Male
Amelia Thomas,20,Female
Henry Taylor,48,Male
Harper Moore,26,Female
Alexander Jackson,39,Male
Evelyn Martin,60,Female
  9. Assurez-vous que votre conteneur Docker IRIS est en cours d’exécution.
  10. Assurez-vous que vous avez un fichier texte ou CSV à importer et une table de destination dans IRIS. Les noms de colonnes du fichier doivent correspondre à ceux de la table.
  11. Si vous n’avez pas encore de table SQL, créez-la, par exemple :
create table dc_cdc.SamplePerson (
    PersonName VARCHAR(200),
    PersonAge INT,
    PersonGender VARCHAR(10)
)

12. Cliquez à droite de votre fichier CSV et sélectionnez Importer dans le conteneur Docker IRIS :

13. Remplissez la table SQL qui recevra les données (dans mon cas, il s'agit de dc_cdc.SamplePerson) :

14. Cliquez sur le bouton Charger les données.

15. Le terminal affiche les résultats.

16. Utilisez SQL Explorer pour vérifier les résultats :

17. Profitez de votre nouvelle extension !

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