Healthcare software testing has some real challenges compare with other industries

How is Healthcare Software Testing Different?

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The healthcare IT market is on the rise. A recent report estimated that we can expect a growth up to $45.42 billion in 2022, from the current value of $18.38 billion. This exponential development is powered by increased government support and health records’ digitalization. More venture capital investments also act as incentives for more companies to join this market. Yet, since human lives are at stake, money should not be the only motivator. Quality is paramount, and it can only be achieved through rigorous healthcare software testing.

Healthcare software testing comes with specific requirements and challenges compared to regular quality assurance (QA). Evaluating these products includes making sure they comply with strict regulations, provide data privacy and security, are user-friendly, and can connect to existing infrastructure seamlessly.

The Challenges

The healthcare system consists of different categories of actors: providers (hospitals, clinics), receivers (patients), intermediaries (insurance companies), regulatory authorities and solution vendors. The primary challenge of a good software product is to please all these categories and provide timely and accurate responses – all while remaining compliant and safe.

Once approved in the development environment, the software needs to be tested in real-life situations to ensure proper integration with other platforms and prevent any dysfunctions that could make the system run slower or crash altogether. This is a countermeasure to installing any new software, since this is costly and takes time, thus making healthcare providers reluctant to changes and attached to legacy systems.

The Healthcare Software Testing Strategy

Following the general software testing approach, healthcare programs and apps should pass at least five (or six) different test sets:

  • Functional testing – this should ensure the product behaves as intended even in unlikely situations. This will also identify any gaps and validate scenarios before moving on to the next steps.
  • Performance testing – this checks if it responds fast enough to be useful, even when the network is not at its top parameters. This testing focuses on architecture, scalability, and tuning.
  • Penetration and vulnerability testing – this is required because healthcare programs handle information that is much more personal than financial records and more dangerous in the hands of hackers.
  • UX testing – this will reveal if the program is easy to use, intuitive, and has a short learning curve.
  • Localization quality testing (if applicable).
  • Automated testing – this will check if there are any reusable scripts that cover most likely situations. A1QA recommends using automation for each of the previous steps of testing to cut costs and time-to-market.

Healthcare Software Testing Peculiarities

The outlined testing strategy should include some specific objectives designed to ensure top quality of healthcare software. These include regulatory compliance, interoperability, security, and usability.

This is a highly regulated area; therefore, developers should work in close connection with medical staff, legal advisers, representatives of insurance companies, and other relevant stakeholders.

Compliance & Interoperability

The HIPAA framework has been in place for more than 10 years, but its implementation still raises practical obstacles in meeting the four objectives: privacy, security, enforcement, and breach notification. Each new software product that will be used in a healthcare setting must satisfy at least the minimum rules. These apply not only to healthcare providers but to solution vendors that handle patients’ personal information.

There are also compliance standards related to how the software communicates with other programs and hardware. The interoperability requirements and integration testing focus on correct data flows, from end to end.

The standards for e-health data are stipulated by HL7, the framework which provides automatic data validation and requires that the software is checked for data flows according to different scenarios.

Another industry standard is the DICOM, which ensures that medical imaging is interoperable. This free framework is designed to align with the evolving technologies and deals with formats, security, mapping, and even web services.


Medical teams are always in a rush to deliver the best care to patients, and most of the time they are outnumbered by the demand. Therefore, they greatly benefit using all the help they can get from digital tools. Particular attention should be given to verifying the software has fool-proof ways of ensuring data accuracy and patient safety. Errors or confusion in this area could result in a fatality. Also, systems need to run flawlessly and continuously. Patient records, previous treatments or administered substances must always be available, and there is no room for breakdowns here.

The best way to test this is to create relevant user stories and more prototypes of the intended software’s user interface. It’s recommended to run each scenario through each interface with real end users (nurses, doctors) and listen to their feedback.


The growing number of cyber-attacks triggers the medical world concerns about being the next target. Yet, there is still the balance to be found between encrypting everything and ensuring the necessary speed of operations.

Personal data should always be transmitted through encrypted channels, while stored records should be carefully protected, either on-premises or in the cloud. When developing a new software piece, the testing should focus on discovering its vulnerabilities. The assessment should take into consideration both the infrastructure (Wi-Fi and Bluetooth communications are the most vulnerable) and the points of entry into the system.

Gathering and Using Knowledge

Data is becoming a currency on its own. All the information collected through the software should not be used only once. It is supposed to be recycled and repurposed while remaining compliant with the previously mentioned standards and regulations. Through the use of machine learning and Big Data, after proper anonymization, data can be used for clinical studies and research.

Also, any response from healthcare software should be based on scientific evidence, and is expected to provide the proper references to this. For example, lab tests should indicate the method used, as methods differ in what they consider acceptable results.


A smart approach to software testing for healthcare starts with acknowledging the challenges, drawing up a strategy, making a clear inventory of the acceptance level for all the tests and cautiously applying them. The focus of the whole QA process should be on security, usability, functionality – since in the healthcare environments the staff’s attention should be on the patient, not the digital tools.

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Maria Weinberger

Maria Weinberger has over 5 years’ experience as a technology journalist; writing on subjects such as big data, AI and innovations for a range of publications.