Independent Case Study
October 2020

Healthcare Interface Charrette


UI Design




UI Screens, UX Flow
Healthcare Interface Charrette

Project Overview


This project is a case-study-meets-design-charrette focusing on the user research and visual design portions of user experience; the overarching premise for the study/charrette was born from the post-pandemic surge in healthcare-related topics. In this case, it focuses on the management of patient influx and the maintenance of patient differentiation/safety between COVID patients and non-COVID patients at an emergency room intake triage. Raw interview transcripts were provided but analysis and conclusions drawn are my own.



💡 THE GIST: This system's audience is a specific class of healthcare workers with a focus on the management of patients.

In the case of Citlali Clinic Group, there are three primary audiences to consider: the patients, the health care workers, and the health care system itself—Citlali Clinic Group.

For this exercise, I am focusing on the health care workers. This is the selected focus group because addressing their concerns stands to benefit the other groups, as well. In the case of the system, Citlali will benefit from the increase in their employees’ efficacy and job satisfaction. In the case of patients, many of their pain points are symptoms of the health care workers’ problems—for example, the nebulous wait times, room mix-ups, etc.

It’s important to note that though I’m focusing on the health care worker audience, my user interviews are only with nurses working outside the hospital in the screening/triage area—it would be useful to have access to interior nurses. Ideally I would know what their current system is and the feelings they experience as they go through their process, as it’s relevant and inter-related to the experiences of the external nurses.

Research "A-ha!"s

💡 THE GIST: Per the users, the current system's downfall is inadequate communication of information/data. Users wants better control over information.

Nurse Fatima is referred to as NF; Nurse Randy is referred to as NR. The number following the initials (ex. NF1) indicates the interview question from which the excerpt/point is drawn.

NF4: “We don’t always use the overflow room”
NR1: “In the triage we determine if they should go into the waiting room or into the overflow room”
Takeaway: There’s moment-to-moment flexibility regarding the use of the four waiting rooms

NF1: The patient is told verbally which room to enter
Takeaway: Rooms are assigned verbally

NF2, NR4: External nurses are not in communication with internal nurses
Takeaway: Lack of communication

NF3: Nurses aim for a 30 minute wait time
NR3: Patient wait time is “at least” 40 minutes
Takeaway: Numbers to later refer back to for initial KPIs

NF4, NF7, NR7: Patients have a tendency to find their way to the wrong waiting room
Takeaway: Further exacerbates the primary pain point (below)

NF4, NF6, NR4, NR6: The nurses are most bothered by the lack of information on how many people are in each waiting room; this is exacerbated by the tendency for people to enter the incorrect room
Takeaway: Lack of information

Ultimate takeaway: Exterior health care works face multiple challenges which stem from inaccurate (and non-existent) data resulting from inadequate information and communication.

User goals: Both Nurse Fatima and Nurse Randy want to know about the patient makeup of each room (how many patients, who is where, etc) so they can better distribute incoming patients.

Task brainstorming

💡 THE GIST: Based on the interviews, there are three core functions the interface has to have and a few "nice to have" features.

I used some light feature brainstorming and mapping based on my ultimate takeaway (“exterior health care works face multiple challenges which stem from inaccurate (and non-existent) data resulting from inadequate information and communication”) and considered those features in comparison to the user group’s goals (“both Nurse Fatima and Nurse Randy want to know about the patient makeup of each room (how many patients, who is where, etc) so they can better distribute incoming patients”).

Through consideration of these mini-brainstorming sessions and the user’s goal, these are the core tasks most essential to achieving that goal:

The defining tasks
Have to have
- View the number of people in each waiting room
- Update a person’s location (i.e. if they’ve been moved)
- Enable patients to confirm their room/location

Nice to have
- View the status of the last person to enter the building
- Message nurses (in this case, the ones working inside)
- View estimated queue processing time
- View daily data

Verbal storyboarding
This verbal storyboard follows the patient’s journey from sidewalk waiting line to waiting room assignment to seeing the doctor. Reading across at a particular bullet point will show you the key interactions or observations that occur for the exterior nurses (the focus of this application) and the interior nurses, whose contribution is necessary for accurate data management.

- The process of screening + triage is functional (per the nurses’ accounts) and doesn’t need to be significantly modified
- People are motivated by curiosity/impatience and will voluntarily scan a QR code for wait time estimates
- The majority of American adults own smart phones (81%, Pew Research)

The biggest unknown in this process is that of the interior nurses; ideally, I would like to know if the interior nurses are using paper tracking or a system of their own so I could determine whether this is a feasible system—whether they’re willing to scan patient QR codes, for example, or whether they could also benefit from the system’s data. For me, it’s a huge missing link that I don’t have any interview materials on their process and thus don’t know how difficult (or easy) it might be to achieve buy-in.


💡 THE GIST: Given the limited time frame (as it's a charrette, a few hours), the design process proceeded directly to sketching, wireframes, and then medium-high fidelity mockups using the tasks previously defined as required.

Using some rough sketches, my defining tasks, and the verbal storyboard, I created lo-fi wireframes of the screens/information the exterior nurses would encounter in addition to the overview of what a patient would see when confirming their room via web browser.

Those lo-fi screens then transitioned to these higher fidelity screens:

All of the “must have” and one of the “nice to have” tasks previously defined are addressed here:

- View the number of people in each waiting room
The overview panel shows the number of people in each waiting room in addition to adding visual separation between full, empty, and Covid-19 designated rooms.

- Update a person’s location (i.e. if they’ve been moved)
Interior nurses scan the patient-specific QR code when they are taken to be seen, thus removing them from the digital tally.

- Enable patients to confirm their room/location
Patients are encouraged to confirm their room by scanning the QR code on the door. If a user is in the incorrect room, scanning the QR code will update their location in the nurse-facing panel by switching their room assignment and updating the tally accordingly.

- View the status of the last person to enter the building
The bottom right status bar tells the status of and time elapsed since the last patient entered the building.

Note: In the interface, orange is always used to denote COVID-19; COVID-19 waiting rooms bear an orange flag, patients in the queue who test positive are marked by an orange dot, and the bottom right status bar clock is orange when the last patient to enter tested positive.


Evaluating success

To determine success, I would work with the product manager to determine the most viable and meaningful data points; the three that already stand out to me are:

(1.) the conversion rate of patients confirming their rooms via QR code,
(2.) the conversion rate of interior nurses scanning patient QRs when they take them back, and
(3.) the task completion time or number of patients processed within a specific time frame

Given the nurses’ commentary that they “try” to process a patient in the queue in 30 minutes (Nurse Fatima) but it takes “at least 40” minutes (Nurse Randy), an evaluation of average time it takes to process each patient over the course of the day would likely be ideal—with that number meeting or dipping below 30 minutes.

The pros of this system are that it offers the exterior nurses the data they most want, it offers patients data they would be interested in, and it mitigates the need for physical contact between interior and exterior nurses. Additionally, interior nurses may also be able to benefit from the use of digital patient profiles and location information, which could increase their buy-in. The cons include the fact that the interior nurses may not have the desire to use the system and patients may not be motivated enough to confirm their rooms; the participation of interior nurses is critical. Because buy-in from the interior nurses is necessary for data updates, having no interviews or information about their processes is a massive blindspot.

Note: A few screens from the flow map above are show below for easier viewing, in no particular order.