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How is Automation Testing Effective in Defect-Free Delivery?

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Testing is a method to validate if the delivered product is developed as per the requirement and if the expected functionality is achieved. There are two types of testing methods namely Manual and Automation.

Manual testing is validating manually for defects in the developed product. This requires time and more resources and delays the testing process as well. 

Automation Testing is a process where the product is tested using scripts, testing tools, or any framework to minimize manual intervention, human errors, and time. This is the best way to deliver the developed product in a short duration. But how is automation testing effective in Defect-Free Delivery? 

In this article, we will talk about the pros and cons of automation testing and delivering the developed product defect-free.

Automation Testing could be utilized to carry out Regression Testing, Load Testing, Performance Testing, and Repeated Execution for faster results.

It does not serve as a substitute for manual testing since some testing types, such as exploratory testing, usability testing, and ad-hoc testing, require manual verification to get the best results.

Why Automation Testing?

  • It is less time-consuming.
  • A list of consecutive sets of test cases called suites can be developed and executed any number of times.
  • On Regression testing, human error is eliminated.
  • Automation testing is best for Load and performance testing and repeated testing.
  • It is best for optimized use of the test scripts for different versions of the application.
  • Can be done even when a small modification is done to code on test suite execution.

Automation Testing Vs. Manual Testing

Automation testing is not required in all places, the decision has to be taken when to use manual or automation. When there is repeated testing of the scenarios to reduce repeated manual effort on execution, automation testing is required and when there is less repetition and the scripting takes a huge effort, then manual testing is the best.

Pros of Automation

  • Can be done to run repeated testing
  • Execution can be performed for the same scenario but with different inputs
  • Execution can be performed in various triggering points at the same time
  • To increase the accuracy and for quick test results.

Cons of Automation

  • Automation takes more effort in the beginning
  • There are some factors like visual appearances that cannot be automated
  • Maintenance of scripts is required when any updation takes place

Pros of Manual

  • Team interaction improvises between developers and testers.
  • Best suited for Ad-hoc testing

Cons of Manual

  • Requires more human resources
  • Delayed results in case of repeated testing
  • More occurrences of human errors
  • Depends on the resources’ presence while executing. 

Types of Automation Testing

  • Keyword-driven testing.
  • Integration testing.
  • Unit testing.
  • Smoke testing.
  • Regression testing.
  • Performance testing.
  • Security testing.
  • Data-driven testing.

Test automation life cycle

There are many phases/steps involved in the automation test life cycle:

  • Test planning
  • Analyse application under test
  • Setup test environment
  • Develop test scripts
  • Enhance test scripts
  • Debug the tests
  • Execute the tests
  • Analyze the test results
  • Defects/Reports

Implementing A Test Automation Strategy 

  • Define the Scope Of Automation
  • Decide On Testing Approach
  • Select A Test Automation Framework
  • Choose The Automation Tool To Use
  • Test Execution
  • Test Automation Maintenance 

Conclusion

Automation testing reduces time, and resources and also provides accurate results.

While it may help organizations deliver a great product defect-free and stay competitive, it has its own challenges. It can be difficult at first, but with the appropriate procedures and actions, it could be implemented successfully.

About The Author: 

Saranya N S is currently working in Mantra Labs as a QA manager. She has experience in multiple domains like telecom, IVR, and BFSI and is passionate about creating automation testing frameworks.

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Data Sharing: The Healthcare Superpower You Didn’t Know Was Needed

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Imagine a world where doctors can instantly access a patient’s entire medical history, from birth to the present, with just a few clicks. A world where researchers can rapidly analyze vast digital health records to discover groundbreaking new treatments. This seamless access to information isn’t just a dream—it’s the potential reality of effective data sharing in healthcare.

By breaking down the barriers that currently isolate crucial healthcare data, we can transform patient care, streamline operations, and accelerate medical advancements. The future of healthcare lies in the power of connected information, ensuring that every decision made is informed, accurate, and timely.

Barriers that are hindering Data Sharing in Healthcare

1. Data Silos: Healthcare providers often store patient information in isolated systems that do not communicate with each other. This fragmentation leads to a lack of coordination, duplicated tests, and gaps in patient care.

2. Interoperability Issues: Different healthcare organizations use various electronic health record (EHR) systems like Epic electronic health record, charm electronic health records and Cerner electronic health record, which are not always compatible. This lack of standardization makes it difficult to share data seamlessly across platforms.

3. Privacy and Security Concerns: The healthcare industry handles sensitive patient information. The risk of data breaches and unauthorized access creates reluctance among institutions to share data freely.

4. Regulatory and Compliance Barriers: Strict regulations like HIPAA (Health Insurance Portability and Accountability Act) in the US and GDPR (General Data Protection Regulation) in Europe mandate stringent data protection measures. While these regulations are essential for protecting patient privacy, they can also hinder data sharing.

5. Resistance to Change: The healthcare industry can be slow to adopt new technologies, and some providers may be resistant to changing their workflows to incorporate healthcare data analyst insights and data-sharing solutions data-sharing solutions.

Technological Innovations Transforming Data Sharing in Healthcare

By employing innovative tools and strategies, the industry can close the gap between isolated data systems and foster a more connected, efficient, and secure environment for patient care. Here’s a look at the key technological techniques making this possible:

  1. Interoperability Standards: Technologies like Fast Healthcare Interoperability Resources (FHIR) and Health Level 7 (HL7) are setting the foundation for seamless data exchange between different healthcare systems. These standards ensure that patient information can be shared across platforms without compatibility issues, eliminating data silos and enabling better coordination of care.
  2. Blockchain Technology:  According to a Deloitte report, 55% of healthcare executives consider blockchain a top-five strategic priority for enhancing data integrity and security.Blockchain offers a decentralized, secure way to store and share electronic health records. Its tamper-proof nature ensures that data remains unaltered and trustworthy, which significantly boosts confidence among healthcare providers when sharing sensitive information. This technology is crucial for maintaining the integrity and security of health records. 
  3. Cloud Computing: Cloud-based platforms allow healthcare providers to store and access health records remotely, ensuring that patient information is available to authorized users at any time, from anywhere. This flexibility improves collaboration between different healthcare entities and helps streamline patient care, especially in multi-location healthcare systems.
  4. Artificial Intelligence (AI) and Machine Learning: AI-driven tools are revolutionizing the way healthcare data is processed and shared. These technologies can standardize vast amounts of data, identify patterns, and enable predictive analytics. By automating data sharing and analysis, AI and machine learning reduce the burden on healthcare providers and improve decision-making processes.
  5. Telemedicine and Internet of Things (IoT): The rise of telemedicine and IoT devices has expanded the sources of digital health records. Wearable devices, remote monitoring systems, and telehealth platforms generate valuable patient information that can be shared in real-time with healthcare providers. This continuous flow of data allows for timely interventions and personalized care, bridging the gap between patients and providers.
  6. Health Information Exchanges (HIEs): HIEs facilitate the secure sharing of patient information among different healthcare providers. By connecting various systems, HIEs ensure that patient data is accessible where and when it’s needed, enhancing continuity of care across different providers and locations.
  7. Data Encryption and Anonymization: To address privacy concerns, data encryption and anonymization techniques are used to protect sensitive patient information. These methods ensure that data can be shared securely without compromising patient privacy, balancing the need for data access with stringent privacy regulations.
  8. Standardization of Data Formats: The adoption of standardized data formats, such as FHIR, allows for consistent and seamless data exchange across different platforms. This standardization reduces interoperability issues and ensures that healthcare providers can access and utilize patient data more efficiently.
  9. Collaboration and Partnerships: Collaboration between healthcare providers, technology companies, and regulatory bodies is crucial for overcoming data-sharing challenges. Initiatives like the CommonWell Health Alliance and the Sequoia Project are creating nationwide networks for data sharing, demonstrating the power of partnerships in advancing healthcare technology.
  10. Patient-Centered Approaches: Empowering patients to take control of their own health data is another technique used to bridge the gap. Through patient portals and apps, individuals can access their health records and share them with healthcare providers as needed. This not only improves patient engagement but also ensures that providers have the information they need to deliver optimal care.

Conclusion: The Path Forward

Bridging the data-sharing gap in healthcare is essential for improving patient outcomes, enhancing public health, and advancing medical research. While significant challenges remain, the combination of technological innovations and collaborative efforts is paving the way for a more integrated and efficient healthcare system.

As we continue to adopt and refine these technological techniques with the vision of a fully connected healthcare ecosystem, where data flows freely and securely between stakeholders and becomes increasingly attainable. By embracing these innovations and fostering partnerships, we are setting the stage for a future where healthcare is not only more accessible and personalized but also more proactive in addressing the complex challenges of tomorrow. The path forward is clear: by closing the data-sharing gap, we can unlock the full potential of healthcare and ensure better outcomes for all.

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