The Future of Software Testing in Healthcare Sector

Since COVID-19 swept across the globe, healthcare providers have rapidly expanded their digital care capabilities to ensure efficient and accessible care. Digital healthcare apps,from telehealth to remote monitoring devices, have been implemented to ensure care delivery while keeping their staff and patients safe. Additionally, due to shortages of personnel and medical resources, healthcare leaders recognized the need to enhance data sharing and collaboration across different healthcare settings.

As a result, the adoption of digital health technology has significantly accelerated. The integration of data across various settings, both within and outside of hospitals, has become increasingly crucial in this distributed network of care. Today’s healthcare software ecosystem, including electronic medical record (EMR) systems, picture archiving and communication systems, telehealth applications, and wearable, contributes approximately 30% of the world’s data. Digital transformation is now top of mind for healthcare leaders as they seek to build resilient and future-proof healthcare systems.

Yet challenges persist in ensuring the results of the fast-paced digital health innovations:

In 2021, one in three third-party data breaches targeted healthcare organizations.

A 2020 US research found that electronic medical records (EMR) systems failed to detect up to 33% of drug interactions and medication errors that could injure or kill patients.

Despite these gloomy statistics, there is hope. A recent study, titled “Vital Signs”by the Healthcare Information and Management Systems Society (HIMSS) and Keysight Technologies sheds light on three key considerations for solving critical quality assurance challenges and what the future of software testing looks like for healthcare systems.

#1 Transitioning from Manual to Automated Software Testing Practices

Key findings from the Vital Signs study reveal that traditional manual testing approaches are insufficient to meet the increasing testing requirements of a digitized healthcare system. In fact, only 6% of executive leaders surveyed expressed confidence in their current testing practices’ ability to minimize patient risk.

Figure 1. 94% of primary decision-makers in testing surveyed claimed insufficient software test coverage increasingly risks patient live.(Source: Vital Signs Study. HIMSS, 2023.)

While automation has been widely adopted in various industries, healthcare lags behind, with only 15% of healthcare providers utilizing test automation methods.

This lack of adoption presents significant challenges in ensuring the quality and reliability of electronic medical records (EMR) systems. Specifically, manual approaches face limitations in the following areas:

• Regression Testing: Customizations in EMRs, such as UI changes, modify the underlying codebase. Manual regression testing fails to effectively test complex graphical displays, dynamic values, and other modifications within the time constraints, thereby introducing patient safety issues.
• Integration Testing: Increasing integrations between EMRs, connected devices, and other healthcare applications pose challenges for manual integration testing. Each EMR system update can introduce bugs or vulnerabilities that impact interconnected systems. Manually identifying these changes with every update proves costly and inefficient. Moreover, additional tests are required to ensure interoperability across modern and legacy systems.
• Testing Over Virtualization: EMR testing becomes complicated when dealing with virtualization systems like Horizon and Citrix. While these systems enhance productivity by enabling remote access to patient data, traditional testing tools often struggle with inconsistent and erroneous results in such complex environments.

#2 Preparing for Healthcare Interoperability

At this point, it is clear that there’s a slow but steady march in healthcare toward interoperability.

In October 2022, the 21st Century CURES Act granted patients access to their medical records. Subsequently, as part of the Office of the National Coordinator for Health Information Technology’s (ONC) mandate to promote interoperability in healthcare, all U.S. healthcare providers were required to adhere to FHIR standards (Fast Healthcare Information Resources) by 2023.

Upgrading healthcare software testing capacity is crucial at this time to ensure secure and efficient data exchange among the diverse array of healthcare systems, smart devices, and at-home care applications for a digital health age.

According to the HIMSS’ Vital Signs study, respondents have identified key capabilities of a test automation platform, with interoperability testing ranking as the most important at 76%, followed by cross-platform testing and performance testing at 51% each.These three capabilities play a critical role in ensuring the proper functioning of healthcare software across different platforms and facilitating seamless data exchange. Here’s why:

• Interoperability Testing: This type of testing evaluates the ability of various healthcare software tools to exchange data smoothly and securely, regardless of the underlying technologies or architectures. It aims to optimize patients’ data accessibility and ensures informed clinical decisions by enabling the seamless flow of information between different health systems.
• Cross-Platform Testing: Healthcare software tools need to function correctly on different devices, platforms, and operating systems to provide consistent and reliable user experiences. By conducting thorough cross-platform testing, organizations can identify and address any compatibility issues that may arise due to differences in hardware, software, or configurations. This ensures that healthcare professionals and patients can access and utilize the software seamlessly, regardless of the device or platform they are using.
• Performance Testing: As the volume of patient data continues to grow exponentially, healthcare organizations must ensure that their electronic medical record (EMR) systems can handle increasing loads without sacrificing performance. By simulating high-usage scenarios and analyzing system behavior, organizations can identify any bottlenecks or performance issues and take proactive measures to address them. This helps prevent slowdowns, downtime, or data processing delays, ensuring that critical healthcare operations can be carried out efficiently.

As healthcare systems continue to evolve and rely heavily on digital technologies, investing in upgrading software testing to facilitate healthcare data interoperability becomes paramount to ensure high-quality care delivery and patient outcomes.

AI and Data-Driven Software Testing in Healthcare

In addressing the challenges faced by software testing teams in healthcare, the study emphasizes the potential of AI and machine learning (ML) algorithms. These technologies can significantly augment the capacity of testing teams and improve the overall quality and reliability of healthcare software systems.

Automation tools equipped with AI and ML capabilities offer a range of benefits. They can handle tasks such as test case generation, test execution, and reporting. What’s even more promising is that the study indicates that a majority of healthcare providers, 77%, have plans to adopt real-time analytics within the next five years. Real-time analytics enable AI and ML algorithms to analyze historical patterns, prioritize test cases, and automatically generate critical tests for different platforms, devices, and operating systems.

Figure 2. The percentage of providers adopting real-time analytics in software testing will increase to 77% in 5 years.(Source: Vital Signs Study. HIMSS, 2023.)

AI also plays a crucial role in test execution. By automating the execution of tests, AI and ML algorithms ensure that all tests are performed quickly and accurately. This not only saves time but also improves the overall efficiency of the testing process. Manual execution of tests can be time-consuming and prone to human error, whereas AI-powered automation provides consistent and reliable results.

Furthermore, AI and ML offer testers better insights into software product quality. These technologies can quickly identify potential defects and address them before they become significant issues. By analyzing test results and comparing them to expected outcomes, AI and ML algorithms can detect anomalies and deviations, allowing testers to take proactive measures. This proactive approach to quality assurance minimizes the risk of healthcare software failures and enhances the overall reliability of healthcare applications.

However, it’s important to note that the successful integration of AI and ML into the testing process requires careful consideration and planning. Healthcare organizations need to invest in the necessary infrastructure and resources to support these technologies. Additionally, proper training and skill development programs should be implemented to equip testing teams with the knowledge and expertise required to leverage AI and ML effectively.

Conclusion:

In conclusion, healthcare organizations are entering an era where automated software testing in healthcare is becoming the norm. This transition is crucial as patients require reliable healthcare solutions, staff shortages necessitate efficient processes, and advancements in technology facilitate the adoption of automation across the healthcare industry. Equipping QA teams with user-friendly test automation tools simplifies on-boarding and collaboration, empowering testers to focus on strategic initiatives. By embracing automation and leveraging AI/ML, healthcare providers can overcome the limitations of manual testing, enhance coverage, and prioritize patient safety.