Digital technology has touched every conceivable sector of the economy including healthcare. The multiplicity of healthcare apps doing the rounds is a testimony to the technological advancements made by the digital ecosystem in enabling attributes such as ease of access, convenience, and cost-effectiveness. If the pandemic can be a yardstick to measure the success of such apps then one cannot but agree that these did help innumerable patients in reaching out to the doctors or paramedics and get treated. When social distancing has been the norm, more so during the lockdown phase, healthcare apps came as a beacon of hope and convenience for a large number of people, including senior citizens.
Since healthcare apps collect vital health parameters of patients such as blood pressure, heart rate, etc., their accuracy of results is of paramount importance. Imagine a scenario where a patient has a surging blood pressure but the wearable app does not show any alert but gives the impression that everything is normal. This may delude the patient into believing that nothing is wrong with them leading to a sudden fatal medical condition like a stroke or heart attack. Had the wearable device shown accurate results, the patient (or others around them) could have taken a timely call of consulting a physician and start the much-needed treatment. This instance shows how the quality of a healthcare app is so important in saving the life of a patient. Healthcare app testing should be an integral and critical part of the SDLC wherein every aspect of the app ought to be validated against set parameters.
Role of Artificial Intelligence (AI) in Quality Assurance of Healthcare Apps
Healthcare apps generate a huge amount of data by reading the vital parameters of a patient for processing. The accurate and comprehensive processing of the data by the app is critical for the patient’s family to arrive at the right decision of seeking medical help. Taking such a quick and accurate data-driven decision is possible with AI as the technology can examine a huge quantum of data quickly to gather accurate and deep insights. AI can help the healthcare app makers to optimize performance, improve resource utilization, increase productivity, and cost-efficiency.
The healthcare ecosystem is slowly but steadily incorporating AI-powered tools to enhance the quality of healthcare management. Artificial Intelligence in healthcare can lead to significant cost reductions arising out of fewer hospitalizations, infrequent doctor visits, and less treatment. AI-based healthcare apps can help people to stay healthy by continuously monitoring the vital health parameters and offering early diagnosis, efficient follow-ups, and tailored treatments. The AI-driven global healthcare market is expected to touch USD 13 billion by 2025 (Source: wired.com.) Let us understand the need for AI-led quality assurance.
AI-powered chatbots: Juniper Research states that chatbots are likely to save around $ 11 billion per annum by 2023 (https://www.juniperresearch.com/press/press-releases/chatbots-to-deliver-11bn-cost-savings-2023) for many sectors including healthcare. Chatbots can ensure quick resolution of queries and entail huge cost savings. For example, chatbots can allow doctors to diagnose the ailments of patients and suggest treatment. This is done by asking the patients to choose an option from a predefined set of options. Juniper Research also claims that chatbot interactions without human interventions shall rise to 75% in 2022. Applications of AI in healthcare will aid in the learning process of a chatbot by accumulating and analyzing the series of questions and answers received by the chatbot solution.
Aid in automated testing: Testing of every healthcare app involves activities such as manual testing, automated testing, and non-functional testing. Artificial Intelligence in healthcare app testing can auto-explore the apps on real devices to ensure the functionalities and navigation are working normally. This way, AI can identify any bugs or glitches in the app. The AI-driven tools can be leveraged by the healthcare app testing teams to supplement their testing efforts. AI also helps QA to conduct fast-paced testing by covering a wide scope with precision.
Saving time: When the race to remain competitive entails developing and delivering a greater number of healthcare apps within short turnarounds, AI can come to the aid. AI-based tools can ensure the execution of some routine tasks in healthcare software testing and free up the time of testers. The free time can be utilized by QA specialists to conduct exploratory testing thereby speeding up the release of apps.
Conclusion
The use of AI has the potential to revolutionize the healthcare sector through increased automation and data analytics. Its role in healthcare domain testing in terms of streamlining the workflow, identifying outliers, and speeding up the delivery of apps is likely to grow.