In today’s healthcare software testing, tech burnout has become alarmingly prevalent.
One of the primary factors contributing to this issue is the staggering number of electronic medical record (EMR) systems that hospitals are operating—16 distinct platforms, according to recent data collected by HIMSS. However, the root causes go far beyond the mounting pressure from increased test volumes.
Figure 1: A typical US hospital has 16 EMR vendors in use on average
We will explore the three challenges of tech burnout in healthcare software testing, the potential of AI-driven automated EMR testing, and a 5-step model to seamlessly integrate AI into your software testing strategies.
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Root causes of software testing teams’ burnout
As more healthcare providers embrace digital health innovations, the increasing amount of software involved in a patient visit amplifies the persistent challenges faced by healthcare software testing professionals. Traditional manual testing approaches to these challenges exacerbate three issues for software professionals.
Draining repetitiveness of manual processes
Manual testing often leads to excessive workloads and inconsistent results, especially as EMR systems gain new features and integrate with other software. Consider the challenges of testing your EMR with various software updates from your lab, PACs, blood bank, and pharmacy systems. Fine-grained manual test scripts, which detail every step and test asset, present serious scalability problems. As a result, delays become inevitable when orchestrating multiple updates, which could add anxiety and demotivate the testers.
Anxiety about insufficient coverage
A recent survey indicates that over 60% of younger patients may switch healthcare providers due to poor digital experiences. However, user journeys in modern healthcare software are increasingly complex and connected, given the numerous combinations of devices, systems, and browsers. The sheer volume of possible workflow permutations is overwhelming, making it nearly impossible for manual testers to tackle, especially when pressed for time to meet release schedules.
Frustration over ineffective collaboration
Testers might feel burnout if the release process is not enabling them to get the work done. For example, in a waterfall model, testers sometimes are the last ones in the release cycle. They often don’t get the time they need, yet receive the blame when there are unstable releases and missed deadlines.
Revitalizing the joy in healthcare software testing with AI
The growing urgency to address the burnout of testers requires a more automated approach to testing healthcare software. By shifting more tedious manual work to AI (automation intelligence), healthcare software testers can dedicate their time to more meaningful work, rather than repetitive, error-prone processes.
Here’s how we’ve assisted customers in leveraging automated EMR testing to achieve more with less:
- Automate testing up to the design level, encompassing large parts of test creation, execution, model creation, and maintenance.
- Enhance software quality by testing all user journeys and discovering bugs that manual testers may not have time to find.
- Provide predictive insights on release readiness and pinpoint high-risk areas for optimal resource allocation.
A 5-step model: unleash automation intelligence in EMR testing
To successfully deploy AI-driven EMR testing, it is essential to assess your organization’s current test automation maturity.
We’ve introduced a comprehensive 5-stage model that helps you understand where your organization stands and how to identify priorities and effectively allocate resources to progress to the next level.
Figure 2: Test Automation Maturity (Start Your Assessment Now)
Planning
Healthcare organizations at this level deal with inefficiency due to manual testing processes. With EMR systems updating more frequently, testers struggle to keep up with every change across multiple platforms. To overcome these challenges, test automation has emerged as a strategic priority.
Experimenting
The experimenting level is a critical step to proving the value of automation and securing management’s buy-in. To achieve success at this stage, it is essential to adhere to test design principles and focus on areas that are easy to automate, such as unit and smoke testing.
Therefore, you can showcase the tangible benefits of automation, including increased efficiency, reduced testing time, and improved software quality. This evidence of success helps ensure continued confidence and investment in automation to progress to more advanced stages.
If you need help building your business case for automation, schedule a free consultation today with one of our technical consultants.
Establishing
In the establishing stage, the testing team can broaden the scope of automation beyond merely verifying that an application meets functional requirements. This expansion includes integration, UI (user interface), and vertical/workflow testing.
This level also focuses on the continuous improvement of standards and processes in a data-driven manner. Testers should define and analyze key performance indicators (KPIs) to understand the impact of automated testing and identify areas for further optimization. By doing so, organizations can refine their test automation framework and enhance overall efficiency and reliability.
Scaling
In the penultimate stage, the testing team can automate performance, load, usability, and accessibility testing. Testers will unlock automated continuous testing, testing every code change continuously throughout the CI / CD pipeline. Continuous testing merges traditionally siloed development and QA teams. This is essential to providing immediate feedback and detecting bugs as early as possible. As a result, the entire release cycle becomes more predictable and cost-effective.
Innovating
In the final stage, an AI-driven testing mechanism covers all applications, software, platforms, and technology layers. Test coverage extends beyond linear testing to include all possible user journeys across various devices. ML algorithms steer the process with high speed and accuracy, from data preparation and test case setup to post-testing checks. As the ML algorithm collects more data, it becomes smarter, further improving testing efficiency and giving more time back to human testers.
Figure 3: How Keysight Eggplant Test unleashes AI to test all possible user journeys
Conclusion
Of course, technology is only one part of the solution to combat software tester burnout in healthcare. But as these healthcare customer stories demonstrate, AI-driven testing could go a long way to delivering a more patient-centric digital experience while making software testing more people-oriented.
Dive into our latest eBook to learn Eggplant Test makes it easy to unlock the power of AI-driven EMR testing in minutes.