Product BackgroundThe product allows hospitals to automate the workload and distribution of studies for interpreting radiologists. To meet the SLA time frames, the app’s AI engine defines exam priorities, locates qualified doctors (based on their preferences), and assigns a specific exam to a particular expert. ChallengeThe main difficulties in the previous version of the app, which became the source of customer disappointment, were poor operational performance combined with a confusing UI.Medium to large hospitals with a significant study turnover rate reported that slow app responsiveness and overall balancing inefficiency were affecting the number of medical exams reviewed. SolutionAfter the execution of highly detailed performance tests, our developers reviewed all API calls and database requests and challenged every query’s adequacy.As a result, we tightened data retrieval and increased the volume of acquired data every step of the way. A large portion of data was cached in order to speed up access to repeatedly queried but rarely changing medical data. ResultsAfter initial months of working around the clock, the AI engine’s performance and API back-end responsiveness has improved significantly. Presently, the initial infrastructure can efficiently support hospitals with up to 10 million studies per annum.Furthermore, the business-logic of studies distribution was challenged and fixed throughout. Together with UI updates, these achievements lowered the overall page responsiveness to less than one second. Our collaboration has lasted for over 2 years and continues successfully as of the time of writing of this case study, making it possible to polish the product with innovative and competitive features.
