Lisa Jo Elliott

1.1 Tow Lot Auctions. The City of Kansas City Missouri holds car auctions each month. The city officials are concerned that only a few bidders attend each month. They believed that if they reached a broader cross section of the population, they would attract more bidders and gain a higher return on the auction. They asked for an app. I did user testing, I discovered that the users were overwhelmed by the amount of data and did not know how to attend a government auction. The users suggested two YouTube videos instead of an app. One video should describe how to use the data on the open data portal to choose a car. The second video should describe how to attend an auction, register, bid, and purchase a vehicle. The city official agreed that this was more cost effective approach to initially address the challenge. https://data.kcmo.org/Traffic/Kansas-City-Monthly-Car-Auction/2uje-k9n5

1.2 tow lot user testing wireframe.
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1.3 Tow Lot Auctions. More user testing notes. Along with the videos, I was able to show him what the issues were and were not.

2.1 Homeless KC - this is a website that lists resources for social service providers in the city of Kansas City. The user interface, UI, was designed by Oleh K. during a prototype jam in St. Louis. He won fourth prize for the UI. I wondered how to take his ideas forward to build an Android app for homeless who own low cost Android smart phones. Here is a screen shot of website that I started from.
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2.2 Homeless KC website user testing notes.

2.3 Homeless KC. Here is the flow diagram developed from the user testing sessions with the website. The flow diagram has the link to the Axure site.

3.2 Initial user testing of the mobile wireframe.
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3.3 Land Bank Mobile App Flow and link to Wireframe.

4.1 Planning the Que Paso application. One of the data managers from University of Kansas Med., and someone from Kansas City Digital Drive, wanted to enter a Medical Application challenge. We submitted two entries to the challenge. Mine was research based. The other interaction designer chose a chat box approach.

4.2 Planning the Que Paso application. Some of the challenges that the KU med person mentioned were on my planning board; he also added that one purpose of the application was to increase adherence to doctors' orders. Without access to the user population, I used research for the basis of the wireframe app for Android. I found research on medication adherence by health psychologists DiMatteo and her colleagues. DiMatteo's articles noted that ethnic minorities and young blacks had the lowest adherence rates and experienced the highest mortalities correlated to this low adherence.

4.3 More planning the Que Paso application.

4.4 Planning the Que Paso application.

4.5 Planning the Que Paso application.

4.6 Planning the Information Architecture of the Que Paso application.

4.7 Planning the Information Architecture of the Que Paso application.

4.8 Planning the Information Architecture of the Que Paso application.

4.9 Planning the Information Architecture of the Que Paso application.

4.10 Planning the Information Architecture of the Que Paso application.

4.11 Planning the Information Architecture of the Que Paso application. This is where I started putting the screens into Axure.

4.12 Planning the Information Architecture of the Que Paso application.

4.13 Planning the Information Architecture of the Que Paso application. This is the final version of the IA flow before I finalized the screens. It took several iterations before I was satisfied with the screens. Some of the features that I wanted to include were not practical at this time: a. recording the doctors visit, b. pushing information from the doctor's EMR to this app, and c. having it connect to a database.

4.14 IA Flow of the Que Paso application.