Voltaire, Winston Churchill, and most recently, Uncle Ben from Spiderman, all highlighted the concept of greater responsibility that comes with greater power. In the field of healthcare application testing, this concept holds a simple yet profound meaning: if there’s an opportunity to prevent something bad from happening, seize it.
In today’s digital healthcare landscape, where patient welfare intersects with technology, ensuring the quality of the software is of life-critical importance. The healthcare industry is now saturated with software, with nearly every activity leaving a digital footprint that contributes to a broader understanding of enhancing patient outcomes. From leveraging big data to facilitating telehealth services, up to 80% of US health systems are planning to increase their investment in digital technologies over the next three years.
For healthcare IT (HIT) leaders, the primary focus has been on maximizing the business value of software while improving the efficiency of quality assurance. In this article, we delve into the challenges faced by healthcare institutions during the software testing phase and explore the emergence of AI-driven testing as a promising solution. By harnessing the power of artificial intelligence, healthcare organizations can overcome these obstacles and pave the way for better software quality and ultimately, improved patient care.
The Wider Adoption of EMR and Emerging Quality Issues
Electronic medical record (EMR) systems are vital components of modern healthcare, seamlessly integrating into nearly every aspect of clinical workflow. They act as comprehensive repositories for a wide range of patient data, encompassing diagnoses, treatment plans, laboratory results, allergy information, imaging data, medication records, and more. The adoption of EMR systems has experienced substantial growth, with approximately 80% of office-based physicians in the United States utilizing certified EMR systems as of 2021.
While the widespread implementation of EMR systems has facilitated efficient data management and improved healthcare delivery, it has also introduced new challenges and patient safety risks. The reliance on digital technologies, including EMR systems, has exposed vulnerabilities such as software defects, user errors, and other potential pitfalls that can impact patient well-being.
To address these challenges, rigorous testing methodologies and robust quality assurance processes are essential. By prioritizing rigorous testing and continuous quality improvement, the healthcare industry can harness the full potential of EMR systems to enhance patient outcomes and improve the overall delivery of healthcare services.
The State of Software Testing in Healthcare
The recent study”Vital Signs” by the Healthcare Information and Management Systems Society (HIMSS) highlights a concerning trend in software testing among healthcare organizations, with a staggering 82% still relying on manual or do-it-yourself (DIY) software testing methodologies. Such reliance on outdated approaches poses significant risks, as 94% of testing decision-makers in US healthcare providers agree that inadequate testing increasingly endangers patient lives.
The study revealed that thirty-eight percent of providers still use manual testing approaches, where the software testing teams go through repetitive tasks from test case creation to maintenance to identify defects. The manual approaches are ill-equipped to scale and meet the swiftly evolving and complex demands of healthcare. In the surveyed manual testers, only a third feel their organizations can scale testing to align with the ever-changing demands of healthcare.
Transitioning to automated testing from scratch isn’t a walk in the park, though. The Do It Yourself (DIY) testing methodology, favored by over 40% of the survey’s respondents, presents its own set of inefficiencies and hurdles. The scarcity of technical expertise required for developing and maintaining in-house testing solutions proves to be a significant impediment. The complexity of healthcare applications, the diversity of automation frameworks, and programming languages necessitate specialized skill sets. Moreover, hidden infrastructure costs associated with DIY solutions can quickly escalate, placing a financial burden on IT organizations. Consequently, only 38% of respondents utilizing DIY methods expressed satisfaction with their organization’s ability to enhance QA team satisfaction and productivity.
Transforming Software Testing for Critical Medical Systems
Despite these roadblocks, there’s a promising outlook.
To overcome these challenges, healthcare organizations must recognize the limitations of manual and DIY testing and embrace automated testing solutions. The HIMSS report suggests that 75% of healthcare providers plan to shift to test automation within half a decade.
Early adopters are already experiencing the benefits, with improved resilience to future challenges. Organizations that have embraced automated software testing report higher satisfaction rates in scaling testing to meet emerging requirements. For instance, those who have already made the leap are reaping its benefits, with 80% reporting satisfaction with their enhanced testing capabilities.
The Role of AI in the Future of Software Testing
The integration of AI and ML algorithms has the potential to revolutionize software testing by augmenting the capabilities of testing teams. By leveraging historical patterns and real-time user behavior insights, machine learning-powered testing tools can automate the execution of critical test cases across various platforms, devices, and operating systems. This convergence of human expertise and machine learning enables enhanced productivity and comprehensive testing coverage within time constraints.
One significant application of machine learning in software testing is the automation of test case generation for electronic medical record (EMR) systems. Building test cases for these complex systems can be a challenging and time-consuming task. Machine learning algorithms can analyze vast amounts of data, including past test results and user behavior, to prioritize test cases based on their risks of uncovering defects. This not only accelerates the testing process but also allows human testers to allocate their time and efforts to more value-added activities.
Conclusion
In the age of digital healthcare, my mentor’s words have never been more pertinent. As healthcare software testers, we need to scrutinize the “what” and “how” of our testing processes. We must remember that testing isn’t an end goal; it’s a means to ensure safety, efficiency, and adaptability in an ever-evolving industry.
Looking ahead, it’s clear that the healthcare industry stands on the precipice of significant change. For software testers in this field, the shift towards automation will be a central part of this transformation. However, the journey ahead is a nuanced one, filled with challenges as well as opportunities.
