Many people don’t realize just how long AI has been around in the healthcare industry—and are surprised to find out that it’s something that’s been relied on for 50 years already.

MYCIN, a computer-based model with machine learning capabilities, was developed by a team of researchers at Stanford University in the early 1970s. It marked a significant milestone in the intersection of artificial intelligence and healthcare. The project was led by Dr. Edward Shortliffe, who aimed to diagnose bacterial infections via MYCIN and recommend appropriate antibiotic treatments based on patient-specific data.

The system operated by asking physicians a series of questions about the patient’s symptoms and laboratory results, using a knowledge base derived from expert consultations.

The very first case MYCIN handled involved a patient presenting with meningitis. The computer-based model analyzed various factors and suggested a treatment plan that aligned closely with the recommendations of seasoned medical professionals, which showed the potential of AI to assist in critical decision-making processes in medicine.

Despite its promising results, MYCIN never made it to clinical use, largely due to regulatory barriers and concerns over liability. But its legacy undoubtedly laid the groundwork for subsequent AI developments in the healthcare field.

Over the last few years, AI’s rise throughout this industry and all other areas of life has been meteoric—sparking as much debate as it has interest. One such area is managing patient healthcare remotely. Can AI’s involvement in healthcare marry well with maintaining high quality assurance (QA) standards?

The challenges of managing patient healthcare remotely

One of the biggest advancements in recent years has been the increase in the number of healthcare professionals using RPM (remote patient monitoring) to enhance and improve patient care.

As many as 30 million U.S. citizens are currently using some form of RPM. It offers a host of benefits to improve QA practices and enhance safety in medical settings and for patients at home. However, there is a trade-off—ensuring that any QA methods used are much more robust than before.

The best and most successful QA strategies will consider all the following key points to make sure everything is secure and reliable within healthcare operations.

Adopting early testing approaches is key, and with it a shift to continuous testing practices. By utilizing both of these, healthcare organizations can nip any issues in the bud and take a streamlined approach to their developmental capabilities.

Taking an integrative approach to QA and AI together, and recognizing that with AI-infused components in RPM devices, both the AI algorithms themselves and their interaction with the overall software application are continually monitored to ensure optimum performance.

With data storage largely moving to the cloud, it’s paramount that system performance is validated, scaled appropriately, and regularly assessed. Following on from this is the need to have enhanced security and privacy testing. Making sure there are strong security measures at the app and device level is crucial—and testing these regularly with ethical hacking is critical to make sure any vulnerabilities are uncovered and dealt with.

There’s an increased need for enhanced regulatory compliance, too. Healthcare organizations have to abide by very strict healthcare regulations. Having a solid cloud assurance strategy alongside stricter regulatory compliance is something that is nonnegotiable for patients who have sensitive data stored in the cloud.

Better user experience (UX) testing is another key factor, too. Because RPM devices cater to a wide range of users, both young and old, more comprehensive UX testing is important to ensure that systems and devices are easy to use and offer maximum benefit. Using AI-driven analytics and user feedback loops can assist with better data integrity and privacy, enhancing algorithm accuracy and performance.

Tailored QA strategies are required, depending on what the usage scenario is. AI-based QA integration, discovering pain points and what the various usage contexts are, makes sure there’s reliability within a diverse range of scenarios—for instance, in remote locations as opposed to urban areas, or in-home care as opposed to inpatient care.

Quality assurance and patient data safety

One of the biggest concerns is patient data safety and whether or not it will ever truly be safe with AI and machine learning on the rise. Cybersecurity has been a huge area of debate within the healthcare industry—in particular, when it comes to safeguarding all the data of patients within systems.

It’s a multifaceted challenge that encompasses protecting any personally identifiable information as well as personal health information. Matters become further complicated by the challenges of navigating the sometimes confusing regulatory requirements set out by the Health Insurance Portability and Accountability Act (HIPAA), health industry cybersecurity practices (HICP), and healthcare interoperability.

These groups often set down strict mandates on health insurance companies and other healthcare providers, making sure they adhere to and support the people they service (i.e., the consumer/patient). Therefore, a solid QA practice has to be put in place to make sure any potential issues or vulnerabilities are dealt with as swiftly as possible. 

Adopting QA best practice methods

The fundamental best practice when it comes to dealing with QA in healthcare and the challenges of AI is to adopt DevSecOps principles (development, security, and operations). These principles integrate security into any software development pipeline, making security testing an integral part of validation.

Because many healthcare applications such as RPM are consumer-facing, it becomes even more essential to include techniques such as penetration testing and ethical hacking, too. Monitoring and tracking are increasingly important at every stage of these processes.

Lastly, using QA bots that can identify loopholes and standards that might otherwise be missing helps make sure that crucial aspects of security aren’t overlooked. With this comes an increased need for QA companies to work together and collaborate on best practices to further improve cybersecurity measures across the board.