VOT is an iPad application we built for a global pharmaceutical company which helps sales representatives deliver various localized messages during sales calls and presentations. By combining vast data sources this tool helps model and calculate savings for specific sets of patients in various scenarios.
The tool consists of 2 major components:
1. iPad UI (which essentially serves as a frontend).
2. Data analytics platform, developed specifically for this tool, pulls a set of 6 distinct data sets that cover various aspects of the market structure. Combined together, these vasts data sets provide the information needed for VOT to model/calculate patient level savings for any US-based zip code.
A thorough Business Analysis determined the critical data that needed to be communicated. User research was also conducted during which we determined that the interaction design would need to accommodate two different types of end users simultaneously; the sales reps that would be using the app and their audience to whom they would present.
This analysis guided an extensive wireframing process that nailed down the information architecture and User Experience. Once that was completed we worked through several iterations before we came to a clean, user-centric UI that effectively displayed patient level savings by visualizing a complex array of distinct data sets.
The UX & UI
Because the app would need to communicate vital data in an engaging manner, during a short amount of time, the approaches we proposed all needed to be visually stimulating in order to retain viewers attention.
Our design team created screenshots as well as movement prototypes using After Effects. Screenshots and prototypes helped the client visualize the final product and guided the implementation of the apps UI, which was built as a native iOS application.In the beginning, we created a range of visual concepts to get the conversation started and provide our client with a range of options.
We always try to do one concept that is really different from the norm, and while a more conservative design direction was chosen our cartoon concept shown below drew a lot of interest.
The data platform was built primarily using Python with MongoDB as main data storage and MySQL as a storage for UI frontend. The platform was then integrated with proprietary data sets from IMS Health(specifically IMS Xponent Plantrak), FingerTip Formulary, Wolters Kluwer, and others. Additionally, we have developed certain data sets for bridging the data between these providers internally.