AI platform for clinical diagnostics
It helps to improve patient care and monitor the changes in the analysis.
It helps to improve patient care and monitor the changes in the analysis.
PCR is an artificial intelligence (AI) that checks for errors with 99.9% accuracy for clinical diagnosis. It automates one of the world's most popular methods for infection testing, qPCR, where unanticipated errors of 20% (or more) have been reported. Users can monitor and manage errors in real time with LJ plots.
The company was founded in 2009 to address patient safety issues caused by known diagnostic testing problems. Because diagnostic tests require manual input and analysis, this makes them time-consuming and expensive. Our client initially focused on identifying infections because misdiagnoses can lead to the spread of disease and the development of antimicrobial-resistant strains. Our client's work is based on the fact that most diagnostic problems depend on available data and require new approaches based on scientific evidence to solve them.
We were approached to redesign the overall PCR system. The main challenge was to create a user-friendly platform using UI/UX knowledge. The client has identified these goals to bridge the gap between technological innovation and global health needs. To play its part in the future of medicine, the client needs to apply artificial intelligence (AI) diagnostic methods that will make tests more automated, more accurate, and therefore safer. It is vital that the finished product is easy to use, has a straightforward interface for the new user, and displays the resulting data in the form of graphs and tables.
Our development team paid much attention to contrast and legibility, which the old design needed to have improved. This caused users to need to understand the data displayed, resulting in a poor user experience. Our design team developed a new version that is more efficient in collecting, storing, and displaying critical diagnostic data. We used UI/UX research methodology, JavaScript, and WordPress technologies to achieve the initial goal. As a result, our client got a well-designed platform that provides users with accurate data based on the entered diagnostic information and presents it nicely and conveniently. Moreover, the responsive design created by the designers is optimized for all devices, including mobile and tablet, allowing users to access the platform from anywhere, anytime.
Javascript
UX/UI
Healthcare is a specific industry that requires specific design solutions to create an understandable interface for each user and, as a result, improves the user experience. Our design team worked closely with the client to ensure their vision was realized and implement UI/UX design best practices. Our design solutions include advanced data visualization that allows users to identify potential errors and take corrective action quickly. That includes interactive graphs and charts, machine learning algorithms, and other advanced tools.
We created a design that matched the client's brand, using easy-to-read colors and typography. Overall, we designed the registration system to improve the user experience and make it easier for users to access, using iconography for easy navigation and an intuitive design.
The design solution of this section includes a developed interface with a list of studies, including the crucial information sections that allow users to systematize the collected diagnostic data.
Visualization of reports based on data entered by the user is one of the crucial functions of the platform. Graphs and numerical data allow the user to understand the data easily. All displayed information is intuitive, allows faster decision-making, and saves specialists time.
The healthcare AI platform has a core function of error checking in clinical diagnostics. It provides healthcare providers with a powerful tool to improve the accuracy and efficiency of diagnoses. The platform uses machine learning algorithms and other advanced technologies to quickly and accurately analyze large amounts of medical data. By comparing patient data with similar cases, the platform can identify potential errors or inconsistencies in diagnosis and provide recommendations for corrective action. Overall, the functionality of this platform ensures improved patient outcomes by providing more accurate and effective diagnoses.
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