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Drizz raises $2.7M in seed funding •
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Featured on Forbes
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Drizz raises $2.7M in seed funding •
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Featured on Forbes
A prescription states a dose of 10 mg, and backend systems record this entry correctly, but the patient view in the application displays 100 mg.
All other parts of the application including the API function correctly and an automated test case that checks if the dosage field has a value passed.
None of these test cases attempts to correct the problem that the patient encounters.
To test a healthcare application, you must validate more than just the data going from one system to another. You must determine if the right patient gets the right information and is presented with the appropriate context.
A dosage next to the wrong medication, a lab result that gets truncated on a small screen, or a prescription associated with another patient's record, must each have their own validation.
Privacy must be addressed in the same way.
If a notification with prescription details is displayed on the lock screen of a shared device, sensitive information is exposed. Testing the notification to see if it was delivered is not enough to address this issue. The content of the notification and where it is displayed must be addressed along with the scope of the notification.
The risks mentioned should inform your test planning and your decision to release an application. Verify what patients see, follow important user flows, and check where sensitive data is displayed. Also define what violation to the patient in the software should be a showstopper. Failed dosage display validation or patient access validation should prevent a new software release to users, even if the rest of your test suite passed.
In this article, we’re going to touch upon critical flows, privacy risks, and release controls for testing healthcare mobile apps.
Healthcare apps process more than purchases or simply display information. They guide decisions relating to scheduling of appointments, prescriptions, lab results, treatment, insurance, and follow-up care. A broken checkout in a shopping app is an inconvenience. A prescription app with the wrong dosage, an incomplete lab report or a failed teleconsultation can affect a patient’s safety.
Healthcare apps have to adhere to privacy and regulatory requirements. Therefore, a defect in a healthcare app can cause both a reputation and legal issue in addition to the technical issue. The healthcare domain joins several different workflows:
Each step in the workflow is dependent on some other person, process, or system. Testing one mobile app in isolation will not capture the complete patient journey.
Healthcare compliance is not just about the privacy policies that are stored or data that is processed behind the scenes, it is the information that is presented within a clinical workflow.
A prescription might include the patient’s name, the doctor’s name, registration number, qualification, the name of the drug, dosage, frequency, time of dispensing, duration, and instructions. A lab report contains the result, the unit of the result, the reference range, the date of sampling, the lab information, and the patient’s identity.
A test should confirm if every field exists in the view hierarchy, but it should also confirm if the field contains information, if that information is legible, and if the field remains connected to the correct patient’s record and the clinical record. Regulatory checks should be incorporated in screen testing in the release of all clinical information. Missing, out of place, misplaced, or stale information should be a release defect.
Health data covers diagnoses, prescriptions, data pertaining to mental health, data pertaining to pregnancy, and test results. A breach that occurs during a test still creates exposure even if the data does not reach the production users.
For all test environments, use synthetic patient data. Generate fake yet realistic healthcare data for age, medical history, medications, and lab values without using real production data. Keep test accounts away from production accounts and make sure that test accounts can not be used to access production systems. Automated tests fail when patient information, appointment data, or values for reports are shown. Before storing logs or sending them to external tools, mask any sensitive information that was captured. Add deletion tests to verify that records are removed from active systems, archives, and backup copies based on your retention policy.
A teleconsultation involves more than a video call. Both the patient and the doctor must be able to connect, audio and video must remain stable, the session notes must be saved, and the patient must receive the prescribed medications at the end of the session.
The end-to-end user experience is most complete when all of the following are true:
Test cases include interruptions. These include duplicate submissions, backgrounding the application, calls dropping, touch interactions, APIs that are slow to respond, and making the user reconnect. Determine if the system saves the correct state and if the user has a clearly defined next action.
Healthcare payment flows include eligibility, authorization, provider networks, copayment, claim submission and rejection, and settlement. A single test case for successful payment essentially leaves all of this risk exposed.
Create different test cases for various plans and coverage limitations, the type of consultation, the status of the provider, the amount of the deductible, and the response of the insurer. Verify the amount displayed to the patient and the amount sent to the provider or insurer. Use contract tests for insurer integrations, then end-to-end tests in a controlled environment. Pay close attention to error messages as well as success messages. A rejected claim must provide the reason.
Patients, Doctors, Pharmacies, Labs, Insurers, and Delivery partners interact with different applications. One input field mistake in the doctor app could result in system failure in the patient app or pharmacy system.
Use shared test scenarios with controlled identities for each role. For example, create a prescription in the doctor app, check the structure of the prescription in the backend, present it in the patient app, and confirm if the pharmacy receives the same medicine, dosage, and duration. This exposes contract inconsistencies, stale data, permission problems, and application sync issues.
Patients may need prescriptions in hospital basements, rural clinics, elevators, or places with poor internet connectivity. Offline behavior should support critical functionalities, not just enhance convenience.
Check which records are available after a successful download. Test prescriptions, appointments, referrals, queue numbers, and lab reports. Test airplane mode, 2G with occasional connectivity, captive portals, cached data which has expired, and a connection which comes back during a transaction. The interface should explain what is available offline and what requires a connection. It should never show a success message for an action which the server has not confirmed yet.
Healthcare apps need to support older users, people with low vision, users with motor impairments, people with anxiety, and people in pain.
Run automated accessibility tests, and confirm if the app supports all tasks with VoiceOver, TalkBack, large text, screen magnification, color inversion, switch access, and voice input. Test if a screen reader announces the prescription in a proper order. Confirm if a patient is able to read the reference range without horizontal scrolling or the value being cut off by a screen magnification. Review tap target size, focus order, error recovery and timeouts. Conduct accessibility tests on the booking of appointments, the filling out of forms for consultations, the reading of prescriptions and the payment process as well as the accessing of reports.
An accurate number, although technically correct, can still confuse or scare a patient. In addition to the number, a Lab value should be presented with its unit, reference range, and its value in relation to the next step in the clinical process.
The presentation of test results should follow the rules for normal, abnormal, and urgent results. Results of critical importance should be presented with a clear next action instruction. Ensure the language is non-technical for lay people. Automated checks serve to confirm the presence of required fields and the correct formatting of data. A manual review is still necessary to accommodate the tone, the hierarchy of information, and the impact and emotion conveyed by the content.
Employ a layered pipeline in order to provide feedback at useful times.
Each Pull Request should run unit tests, static analysis, schema validation, accessibility linting, and focused API tests. These checks should be quick and block code review for obvious defects.
Install the release build on representative Android and iOS configurations. Verify signing, permissions, deep links, push notifications, database migrations, and feature flags. Run smoke tests for login, appointments, prescriptions, reports, and logout.
Test API contracts between patient, doctor, pharmacy, laboratory, insurance, and notification services. Validate authentication, authorization, retries, idempotency, and error responses.
Employ a risk-based device matrix covering supported operating systems, screen sizes, manufacturers, font settings, and network conditions. Of most importance, prioritize the screens that display clinical information. Visual testing tools add value here. Drizz is one example of an AI-powered tool for visual validation which can compare rendered mobile screens and identify changes like missing fields, ranges, dosage info, or incorrect co-pay placement. This reinforces the limitations of selector-based automation, which typically confirms the existence of an element.
Before production, execute the riskiest patient journeys, privacy checks, migration tests, and rollback validation. After release, observe crash rates, failed consultations, prescription generation errors, failed report access requests, failed authentications, and anomalous support volume. Associate alerts with the patient impact they create. A minor UI alteration on an admin screen involves less of a response than an inability to generate prescriptions.
There are more implications than just an inconvenience to a lost prescription or missed appointment booking. Your tests must contemplate
In your QA plan, build around the abovementioned risks.
Implement component and API tests to check data handling. Next, take a full end-to-end mobile workflow test to see if patients are presented with correct information and enabled to complete important tasks. Add privacy checks to the app wherever health data is stored, displayed or interacted with. Run these checks whenever app changes occur. An operating system update, a different sized device, a screen redesign, etc. all need regression tests.
Frequent changes to the interface just increase the burden of tests. Using Drizz's Vision AI testing automates mobile testing, and lessens that burden, allowing you to maintain coverage for the booking of needed appointments and access for needed prescriptions, and other critical workflows across each release.
Prioritize tests of each workflow based on the consequences of a failed test. Make clear what test failure consequences are set as 'blocking a release'. Your team must have reliable workflow checks of patient safety, privacy, and care as well as clear release consequences for failed tests.
Financial apps (fintechs) also handle sensitive data and regulatory compliance. However, healthcare apps also have to deal with clinical workflows (teleconsult → prescription → pharmacy), coordination with multiple stakeholders (patient + doctor + pharmacy + lab + insurance), with added emotional sensitivity to the data, and offline requirements in healthcare settings. Financial apps have a more predictable and defined workflow (transaction → confirmation → receipt).
Not necessarily. The tools for the automation framework are the same (Appium, Vision AI, etc.). However, testing strategies differ. The workflow to be tested has to account for regulatory compliance validation on each screen, the integration of the workflows of multiple stakeholders, management of synthetic patient data, and emotional/accessibility testing. The tools are the same, the test design is what differs.
The teleconsult-to-prescription pipeline. A failed consultation call that doesn't generate a prescription will block downstream orders of medicine and scheduling of follow up consults. This process integrates real time video conferencing, clinical documentation and pharmacy systems and is by far the most complex and riskiest flow in any health app.
In health app testing, realistic synthetic patient data is generated to match production data patterns (age, name, lab data, conditions, etc.) but devoid of any real patient data. Production data should never be copied to test environments, not even anonymized data. Dedicated test accounts should not have access to production patient records. Additionally, test runs that capture screen images should use screenshot sanitization.
Yes. Health apps contain a lot of information and densely packed data (prescriptions, lab reports, insurance summaries). Some data may require verification of how the data is presented, not just if the data is presented. Vision AI can help check if a prescription has all required fields, a lab report shows values with normal ranges, and the insurance co-pay is in the correct position. These may be structurally verified by a selector-based tool, but not contextually.
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