Author: Emily Yan | Industry Marketing Manager at Keysight Technologies
The rapid acceleration of digital transformation in the healthcare sector, fueled by the pandemic and the surge in virtual connectivity, has presented a unique set of challenges and opportunities for healthcare organizations. With the potential to tap into $1.6 trillion in business value, organizations are in a high-stakes digital race to gain a competitive edge through the effective utilization of new technologies like virtual care, data analytics, and artificial intelligence. However, as exponential software growth becomes the norm, the pressing need for robust software testing strategies has been exposed to ensure patient safety amid disruption.
A recent study, titled “Vital Signs” conducted by the Healthcare Information and Management Systems Society (HIMSS) sheds light on the critical testing challenges faced by healthcare IT leaders and provides a path to navigate the digital storm. One of the key findings is the poor scalability of manual testing in the healthcare sector. While automation has been widely embraced in various industries, healthcare lags behind, with a staggering 41% of healthcare providers still relying on manual testing methods. This poses significant challenges as electronic medical records (EMR) systems become increasingly complex and customized. In fact, only 6% of executive leaders surveyed expressed confidence that they were testing enough to minimize patient risk.
Poor Scalability of Manual Testing
While automation has been widely embraced in various industries, the healthcare sector lags behind, relying heavily on manual testing methods. The study reveals that a mere 15% of healthcare providers have adopted modern test automation platforms, while a staggering 41% still rely on manual testing. Despite three-quarters of the providers surveyed investing in software testing to meet their bottom line, nearly two-thirds reported feeling inadequately resourced for future test requirements. The limitations of manual approaches in terms of scalability were evident from the survey results. Only 33% of manual testers were satisfied with their organizations’ capacity to scale testing to meet new requirements, and even fewer were satisfied with the availability of clear testing analytics and the ability to develop test coverage across all devices and browsers.
Figure 1. Percentage of respondents using manual approaches who are satisfied with their organizations’ capabilities in specific areas
Hidden Challenges in Do-It-Yourself (DIY) Solution
One alternative approach that may seem attractive to healthcare organizations is the do-it-yourself (DIY) testing solution. While the DIY testing approach may appear attractive to over 40% of the healthcare organizations in the study, the difficulty in maintaining a robust DIY testing approach cannot be ignored.
One major hurdle is the lack of technical talent required for an in-house testing solution. Modern healthcare applications and compliance requirements demand specialized expertise in programming, test automation, and test requirements. Moreover, the hidden costs associated with DIY testing, such as recruiting specialized personnel and investing in infrastructure, often exceed initial budgets.
Automating Healthcare Software Testing for Resilience
In light of these challenges, the study highlights the potential of machine learning (ML) algorithms in augmenting the capacity of software testing teams. ML-powered testing tools can analyze historical patterns, prioritize test cases, and auto-generate critical tests for various platforms, devices, and operating systems. By combining human expertise with machine learning capabilities, healthcare organizations can enhance the productivity of their QA teams and achieve broader testing coverage within time constraints.
Encouragingly, the study reveals that 75% of healthcare providers plan to adopt test automation within the next five years, marking a significant increase from the current rate. Early adopters have already experienced the benefits, reporting improved resilience to future challenges and higher satisfaction rates in scaling testing to meet emerging requirements. By leveraging ML-powered testing tools, organizations can automate critical tests across different platforms, devices, and operating systems, thereby streamlining the testing process and allowing testers to focus on more value-added activities.
In conclusion, healthcare organizations are entering an era where healthcare software testing will become predominantly automated. Those who adopt automated software testing early stand to gain the most. It is crucial for organizations venturing into automation to stay informed about evolving healthcare testing requirements and evaluate automation vendors against critical regulatory standards. Adopting a technology-agnostic solution ensures comprehensive test coverage across all platforms and boosts efficiency. Furthermore, equipping QA teams with user-friendly test automation tools simplifies onboarding and collaboration, empowering testers to focus on strategic initiatives. By embracing automation and leveraging the power of machine learning, healthcare providers can overcome the limitations of manual testing, enhance testing coverage, and ultimately safeguard patient well-being.