The Sandbox - Patient prediction in the OR

Envisioning anesthesia in 2032




University: Umeå Institute of Design

Duration: 10 weeks, fall 2017

Team: Minh-Huy Dang, Selvi Olgac & Toby Whelan

Collaboration partner: Maquet, Getinge Group



Anesthesia nurses play a vital role in the sensitive operation theatre environment. However, many of them are not familiar with the anesthesia machines full potential because of the complexity of these machines. In addition, the anesthesia nurses have limited opportunities to explore the full potential of the machines as they always are connected to the patient and no mistakes can be made that might harm the patient during surgery. This project explores the possibility for the nurses to experiment and deepen their knowledge concerning the anesthesia machines during the surgery autopilot phase, usually a more calm phase, without jeopardizing the patient.


Our product, the Sandbox, is a patient prediction device to be used in the operation theatre. With Sandbox, an interface proposal, anesthesia nurses can simulate drug adjustments, during the surgery autopilot phase, and predict individual responses of the patient before executing the alternative final changes onto the patient



How might we create a learning environment for exploration during autopilot phase, without losing the focus on the patient?

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Safe learning

Safe learning environment to explore and fail in the system, without harming the patient.

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The perfect team

A collaborative partnership between the nurse and the machine; each party learns from the other.

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Flexible future

Once the operation is underway, supervisors can leave trainee nurses to act independently, freeing expert time.

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Cooperative network

New data achieved could be united in a national and global cooperative network system for further research.


The Sandbox allows the nurse to explore different alternative parameters of the anesthesia drugs and their effects on the patient before executing changes. When the nurse adjusts the parameters, the patient’s values will be displayed as they are getting affected. In this way the nurse can have a safe learning environment to explore and fail in the system, without harming the patient. This allows the anesthesia nurse to use the autopilot phase more efficiently, by for example preparing the landing phase in advance.

The Sandbox concept is based on the collaborative partnership between the nurse and the machine; each party learns from the other. As the machines are getting more automatized, the nurses are running the risk of losing their manual skills. For example, today an anesthesia nurse can check out breathing rate with only their fingers, a tacit knowledge important to keep and include in the future. Furthermore, the new data achieved through the Sandbox could be united in a national and global cooperative network system for further research.

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The different modes in the Sandbox



Sleep mode is indicated by a lone ring on a black screen. This feedback signifies that the Sandbox is on and ready for use. As the nurse picks up the Sandbox and touches the screen, it will change into the next mode.


When adjusting parameters, simulated patient readings appear individually as they are affected, when tapping any of the 7 readings to highlight them in the central area. Once highlighted, more information is displayed.


Executing changes

When executing changes 4 fingers are placed at touch points on the outer edge of the Sandbox and holding on for 3 seconds, to avoid accidental changes to the selected values. The product provides the nurse with feedback through haptics and a green completing ring. 


Feedback to the system

In the final stage the nurse submits the observations of the patient's physical responses to the changes executed. In this stage the tacit knowledge of the nurse and the knowledge from the machine are united into the cooperative network system.



The design process


Hospital visits

3 different hospitals were visited during our research phase to explore how anesthesia nurses worked and how the facilities differed between the hospitals.


Attending surgeries

6 surgeries were attended by our team. Here we had the possibility to observe the nurses workflow and how the anesthesia machines were used in practice. 


Interviews with anesthesia nurses

9 interviews with anesthesia nurses were made in the different hospitals. Through ethnographic methodologies we learned how they experienced their role as a nurse and how their tasks could be improved.

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Analyzing screen information

During one of our hospital visits we analyzed the screen of the anesthesia machine together with one anesthesia nurses. We focused on what information that was most important for the nurses in their daily work.

The nurses' workflow

When visiting the hospitals we studied how the nurses workflow looked like, both before, during and after a surgery. Most of their work takes place before surgery. This work includes preparation of medical drugs and learning more about the patient through their journal.

Learning about machine settings

As the anesthesia machines are very complex and have a lot of different modes, we explored the machines' settings and the navigation of the system to learn more about the possibilities and the technical parts.

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Everyone has different
lungs, breathing and settings

Anesthesia nurse

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Mapping out findings

With our knowledge from research, interviews and hospital visits we structured and grouped our findings in order to search for patterns, problem areas and possibilities to get a starting point from where we could improve our design.

Research workshop

After collecting information and mapping out our findings, a workshop were organized together with our collaboration partner, colleagues and teachers. Based on our research findings we arranged different activities for them all to participate in. This gave us new thoughts on how to develop our material further.



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No scope for exploration

Currently the nurses do not know the full capacity of the anesthesia machines because of their complexity. There is also limited time for further exploration of and learning more about the machine during surgery.

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Double checking screen and patient

The nurses need to double check their readings against two sources, the patient and values and numbers from the master screen. 

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Less active during the autopilot phase

The autopilot phase is usually the less active part of a anesthesia nurses during surgery. Most of their work is carried out during the take off and landing phases.



Design sprints and concept development


Early concept ideation

Short sprints were made through quick drawings based on different keywords. Some of the sprints were made individually together with brainstorming on a whiteboard by the whole team to facilitate building on each others ideas.

Creating scenarios

With paper puppets and a cardboard box we created different scenarios of a surgery with a nurse. Our aim was to do a first tryout of our ideas as well as discovering new possibilities. 

Role-play with nurses

Role-play allowed us to test out first concepts and to brainstorm possible scenarios of how the operation theatre in the future might look like. Also to discover how the nurses interacted and used our prototypes in different situations.

Developing interactions

Another step in the process were to test different interactions by using lo-fi prototypes. In this way we were able to communicate our ideas and also see how our concepts affected the nurses' workflow.


Physical & digital prototyping

To visualize our ideas of the final concept we created simple prototype variations with cardboard and paper, to facilitate testing different interactions, sizes and colors.


The tactile feeling of precision

During our research process we understood the crucial role of the knob of the current anesthesia machines, from which navigations, settings and confirmations are made. With this knowledge as a starting point we saw the potential in translating the expression of the knob into our final concept. 

Transforming information to our interface

When deciding what information to include in our interface we used the analyzed screen from our research material. We contacted the nurses for further developing which patient values to include in the product and how to organize these values hierarchically. 

Testing interactions

We tested different interactions with focus on the tactile feeling of precision. It was important for us to make the product easy to navigate between the different values and parameters, as well as holding it in your hand simultaneously. 



User testing interface & form

We tested different sizes and interfaces of the product with both nurses and students at our school. Some of the results indicated that color differences between the values made it easier to read the interface. In addition the users mentioned the importance to understand the disposition of the values and how to read the span of the target values, current values and how the values were affected while adjusting the parameters.

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Visual identity of the interface

To create a clear connection between the readings on the existing master screen, we transferred the visual identity into our interface, with the intention to ensure a comparison between the simulated and live patient data. The patient’s values are organized hierarchically, based on feedback from the anesthesia nurses on how they rank the importance of each reading. The sleep and pain levels are values that can be read through brain waves, a parameter that could be connected to the anesthesia machine in a future scenario.

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In this project I was an active participant during the whole process, from research to execution, including field work, workshops, ideating, prototyping and the final model. 

Entering a for me completely new area as an operation theatre and understanding the complexity of the anesthesia machines, and the nurse profession as well, was very instructive for me. Other learning outcomes that I want to emphasize are the usage of design ethnography including professional-user observations, and insights in physical and cognitive ergonomics.