EZIS Mobile: Simplifying Workflow for Nursing Staff

The main project for the Interface & User Experience Design minor consisted of a assignment from a design firm for an actual client. In our case the assignment came from Fabrique. We were asked to design a mobile application for the nursing staff of University Medical Center (UMC) Utrecht, to improve their daily workflow.

Patient data is stored in a centralized database: the Electronic Hospital Information System, or Electronisch Ziekenhuis Informatie Systeem (EZIS) in Dutch. At the moment of this writing, this information can only be accessed through a desktop or laptop computer. However, not all departments have Computers on Wheels (COWs) available, and in many cases they’re not even practical. So during rounds, most nurses rely on pen and paper to have an overview of their patients and keep track of all measurements they do throughout their shift.

Because this is impractical and error prone, the brief called for an application that would allow a nurse to enter new measurements directly into EZIS. It should also display previous measurements, so the nurse can also see trends for all measurements. During our research at the start of the project, it became clear that nurses not only write down measurements but also a lot of other patient data, like their history and medication. An app that focusses merely on measurements would only add to the tools used during the day, instead of actually simplifying a nurse’s workflow.

So we expanded the app’s functionality to include all data that nurses currently access during a shift. We present this data in several forms, depending on the nurse’s current activity. For instance, at the end of a shift a nurse discusses the patients with a nurse whose shift has just started. The app offers a brief overview of all patients, including measurements that have been marked as noteworthy.

Designs for EZIS Mobile

The original title of the project was NurseMapp, short for Nurse Measure App. We decided to go with a different name: EZIS Mobile. Though not extremely original, it communicates our intentions: ultimately this app should feel as a true and complete extension of the central EZIS. The brief described this app as the first of many. We believe a centralized but modular approach would better serve all staff within the hospital.

A “Making of” was designed for this project in the form of a one-page website, in Dutch. It describes the structure of the project and the decisions that were made along the way. You can find the Making of, including a working prototype of the final design, at http://umc.andra.nl.

INFPRJ04 – Mobile development – colorFinder

The fourth project was to build our very own mobile application. We were free to choose a platform. We choose for the Android platform for the practical reason that the majority of our group already owned an Android device, which would make testing a lot easier.

After some brainstorming, we stumbled onto the idea of a Color Finder: an application that would take a photo or other image, analyze it’s most dominant colors and assemble a color-palette that would best represent the colors in the original image. The application was predominantly aimed at graphic designers. They would be able to easily be able to create new palette whenever and wherever they saw a scene with a combination of colors they found interesting. The biggest flaw in that business-model was the fact that, though some creative friends had shown interest in the application, most designers own iPhones, not Android phones. Teachers suggested the application could be geared towards a larger audience by enabling the user to use the acquired color-palette to customize his or her phone interface.

During the project I mostly focussed on the algorithm that analyzed what the relevant colors in the image were. This entailed studying what elements of an image a human being would find interesting, determining what attributes of their color set these elements apart and figuring out how to sort all colors in the image to render a relevant color-palette. I ended up building a histogram of hues which showed what hues were most predominant in the image. Then I listed the hues that cause the highest peaks. For each hue I searched for the colors in that hue that had the most saturation and brightness. This is a gross simplification of the complete algorithm, but it give an idea of how it worked. I actually found a few ways of weighing the colors that rendered different palettes, each of which worked better in different circumstances. Unfortunately, there was not enough time to implement multiple versions in the final application.

We were (again) asked to produce a video as well, this time a promotional video. This is what we came up with: