The Care and Condition Monitor (CCM) is a tablet-based, networked visual analytics tool for collecting, structuring and analyzing informal and qualitative healthcare data. Building off research into application usability and best practices for communicating complex information, CCM illustrates how visual analytics tools coupled with social communication within teams of caregivers enables capturing of longitudinal informal data that would otherwise go unrecorded. This expanded scope of information can support medical decision making by making it possible to analyze informal and qualitative health care data,creating a multi-dimensional holistic picture of a person‟s health care and condition over time

Arunachalan, Bhuvaneswari

Diamond, Sara

Pridham, Hudson

Stevens, Anne

Szigeti, Steve

OCAD University Open Research Repository

  1  Fostering Insight and Collaboration in Long-Term Healthcare through Collection and Visualization of Qualitative Healthcare Data  Abstract The Care and Condition Monitor (CCM) is a tablet-based, networked visual analytics tool for collecting, structuring and analyzing informal and qualitative healthcare data. Building off research into application usability and best practices for communicating complex information, CCM illustrates how visual analytics tools coupled with social communication within teams of caregivers enables capturing of longitudinal informal data that would otherwise go unrecorded. This expanded scope of information can support medical decision making by making it possible to analyze informal and qualitative health care data, creating a multi-dimensional holistic picture of a person‟s health care and condition over time. Author Keywords Health informatics, informal data, long term care, mobile platforms, qualitative data, visual analytics.  ACM Classification Keywords B.4.1 Data Communication Devices [Mobile Device]; H.5.2 User Interfaces [Prototyping]; J.3 Medical information Systems [Health]; C opyright is  held by the author/owner(s ). Mobile HCI 2014 Sept   23 – 26, Toronto, Ontario, CanadaA C M XXX-X-XXXX-XXXX-X/XX/XX.   Hudson Pridham OCAD University 100 McCaul St. Toronto, ON, M5T 1W1, CA hp12pk@student.ocadu.ca  Anne Stevens OCAD University 100 McCaul St. Toronto, ON, M5T 1W1, CA astevens@faculty.ocadu.ca  Steve Szigeti OCAD University 100 McCaul St. Toronto, ON, M5T 1W1, CA sszigeti@ocadu.ca  Sara Diamond OCAD University 100 McCaul St. Toronto, ON, M5T 1W1, CA sdiamond@ocadu.ca  Bhuvaneswari Arunchalan OCAD University 100 McCaul St. Toronto, ON, M5T 1W1, CA barunachalan@ocadu.ca    2  Introduction The Care and Condition Monitor (CCM) is a tablet based visual analytics tool that uses a simple voting strategy to collect and structure informal and qualitative healthcare data, presenting it in a circular visualization format to represent the data. Belden et al. established the following usability design principles relevant to healthcare delivery: (a) minimize caregiver cognitive load, (b) design for UI simplicity, naturalness and consistency, (c) make color meaningful, and (d) preserve context [1]. These principles guided the development of the application, ensuring it presents information in a simple and intuitive manner to caregivers: in the CCM, visualizations of data are prominently displayed in order to prompt user analysis, and visual and input complexity is reduced by only displaying relevant menus and functions to the task at hand, providing cues as to when a particular action is available. CCM was tailored to support the existing practices of caregivers at the partner facility, Clemente Ferreira CAIS hospital in Lins, Brazil, a long term care residential institution for people with a range of congenital mental and physical conditions. In CAIS, like many care facilities, in addition to traditional medical forms and notebooks, caregivers rely on informal communication practices, such as conversations at shift change, to analyze the full picture of a resident‟s status. The design of the application and research focused on capturing and translating informal practices into a system of qualitative data analysis that supports the caregivers‟ culture and practices, reinforces CAIS ‟s sense of community, improves collaboration across professional categories, and improves overall understanding of care and condition data. The last two points, care and condition, were identified through the research of the CCM team as components of resident well-being which were necessary to record, visualize, and foster collaboration around. Condition is measured by five metrics (general health, morale, cognition, mobility and form) that were derived from CAIS‟s records and are standardized for all residents.  Care, on the other hand, is individualized and organized around care goals tailored to the needs of each resident and translated into action that are described as treatments.  The Prototype Clearly communicating recorded condition and care data became the core of the application design effort. The main component of this, the visualization/analysis system for the application, was guided by the work of Yi, et al. On insight formation Yi, et al. speculate that, “an insight does not appear suddenly “out of the blue,” but instead there are paths or procedures leading to the insight” [3]. Two procedures Yi, et al. identifie bear specific relevance: Provide Overview and Adjust. Through Provide Overview users understand the larger body of relevant data. The Adjust process facilitates that exploration. This is achieved by changing perspective on the data through adjustments to the level of abstraction and data range selected. [3]. Taking these processes of insight generation into account, combined with Belden et al.‟s principles, the core structure of the application was designed to provide a clear overview of data wherever possible. For example, by coupling the core navigation system for the app with the data visualization the user is rewarded for navigating through the application‟s various screens. She or he is always presented with a new unique visualization on each screen. Navigating down into the app from a user‟s profile into a particular treatment acts to filter the data range being viewed in the accompanying visualization, switching from an overview visualization of the patients care/condition to only visualizing that one treatment. This system provides the user with a clear conceptual model of how the data relates and assists in making more informed judgments. As Don Norman notes in his seminal book,  3  The Design of Everyday Things, “conceptual models allow us to predict the effect of our actions” [5].    (a) (b) Figure 1(a) & (b): Care and condition visualizations. The visualizations of caregivers‟ votes for treatments and condition metrics use a simple input system consisting of three possible states: green for positive, yellow for neutral, and red for negative. These are then aggregated and mapped along the green/yellow/red spectrum in the large coloured dot in the centre of the display.  While this dot provides overview, building from Donath and Zhang et al.‟s work [2][4], additional detail is also provided.  The individual care or condition metrics that the central circle is derived from are arranged around one side of the circle along with the caregiver involved.  The state of each metric (green, yellow, red) is indicated around the opposite side.  Curved lines join the metrics, on one side, to the states, on the other.  The very process of creating these data visualizations has the additional benefit of fostering collaboration between caregivers, as it is integral to the cyclical relationship between a resident‟s condition and care. Condition, which is recorded by all caregivers, prompts the creation of new care goals, which are delivered by multiple caregivers and ultimately influence back upon the condition. As per Donath, the circular layout also spatializes the social relationship between the resident and his or her circle of care. It assists the user to build a mental map of the information structure, displaying who is participating on a given care goal, each caregiver‟s unique treatments, and those caregiver‟s recorded votes for those treatments. Lastly, longitudinal data analysis and review is afforded in this setup by a mechanism by which a caregiver can scrub back and forwards in time in any visualization to understand change over time for a care or condition metric. Deeper analysis of the data than is possible through the visualizations or time scrubbing mechanics is facilitated by an analysis function to graphically compare two or more datasets (condition metric, care goal/treatment). In the analysis function, a user can change visualization mechanisms, switching between a straightforward line graph or a novel bubble history view that allows for simultaneous visualization of the historical change in votes and the volume of voting activity.  These analyses are created by navigating to each data set‟s visualization and selecting the analysis button. This provides the caregiver with the option to add that visualization‟s data to the analysis currently underway or to create a new analysis starting with that visualization‟s data. These analysis results and parameters can be saved for future reference.  4    (a) (b) Figure 2(a): Bubble Analysis (b) Sketch observation. Additional information is displayed on each app screen alongside the main visualization, such as observations recorded in any media that relate to it and the caregiver who recorded it.  This information currently cannot be analyzed or visualized, yet it serves as a compliment to the structured observations produced by voting results. It also serves to facilitate communication between caregivers by readily displaying the activities and contributions by other caregivers. Such communication is enabled by an inter-application messaging system that combines both explicit and implicit messages into one unified messaging hub. In it direct messages between caregivers are collected in the messages page alongside invitations to collaborate on care goals for residents, which are automatically (implicitly) generated when a new care goal is created and caregivers are invited to it.   Conclusion Although a work-in-progress, CCM illustrates and presents methods to collect and analyze informal and qualitative health care data while simultaneously fostering an environment of collaboration around care and condition in long-term healthcare environments. Future steps include the development of a high-fidelity prototype for testing and evaluation with healthcare workers regarding issues such as „voter fatigue‟, vote weighting, which condition metrics to use in what context, usability and navigation.i  References [1] J. Belden, R. Grayson and J. Barnes. Defining and Testing EMR Usability: Principles and Proposed Methods of EMR Usability Evaluation and Rating.  Healthcare Information and Management Systems Society (HIMSS), 2009. [2] J. Donath and F. Viegas.  The Chat Circles Series: Explorations in designing abstract graphical communication interfaces.  ACM, 2002. [3] J. Yi, Y. Kang, J. Stasko, and J. Jacko, Understanding and characterizing insights: How Do People Gain Insights Using Information Visualization. Proc. Beyond Time And Errors: Novel Evaluation Methods For Visualization 2008. [4] Z. Zhang, B. Wang, F. Ahmed, I. Ramakrishnan, R. Zhao, A. Viccellio and K. Mueller. The Five W‟s for Information Visualization with Application to Healthcare Informatics. In IEEE Trans Vis Comput Graph, 2013. [5] D. Norman. The Design of Everyday Things. NYC: Basic Books. 1988.                                                    i We thank our funder MITACS Canada/Boeing and our colleagues at CAIS, UFSCar, University of British Columbia and Dalhousie University. 

Fostering Insight and Collaboration in Long-Term Healthcare through Collection and Visualization of Qualitative Healthcare Data

OCAD University Open Research Repository
Resea rch  Labs,  Resea rch  Progra m s
2014  
Foste ring  Insight  and  Collabora t ion  
in  Long- Term  Healthca r e  through  
Collection  and  Visualization  of  
Qualita tive  Healthca r e  Data
Diamond,  Sara  and  Pridha m,  Hudson  and  Stevens,  Anne  and  
Szigeti,  Steve  and  Arunach ala n ,  Bhuvanesw a r i  
Sug g e s t e d  citat io n:  
Diamond,  Sara  and  Pridham,  Hudson  and  Stevens,  Anne  and  Szigeti,  Steve  
and  Arunachalan ,  Bhuvanesw a r i  (2014)  Foste ring  Insight  and  Collabora t ion  
in  Long- Term  Healthca r e  through  Collection  and  Visualization  of  Qualita tive  
Healthca r e  Data.  In:  MobileHCI  2014:  Workshop  on  Designing  the  Future  of  
Mobile  Healthca r e  Suppor t ,  23  Septem b e r  2014,  Toronto  Canada .  
(Unpublished)  Available  at  http://open re s e a r c h .oca d u .ca/ id/epr in t /45/
Open  Research  is  a publicly  accessible,  curated  repository  for  the  preservation  and  
disse mination  of  scholarly  and  creative  output  of  the  OCAD  Universi ty  com mu ni t y .  
Material  in  Open  Research  is  open  access  and  made  available  via  the  consen t  of  the  
author  and/or  rights  holder  on  a non- exclusive  basis.  
  
1 
 
Fostering Insight and Collaboration in 
Long-Term Healthcare through 
Collection and Visualization of 
Qualitative Healthcare Data 
 
Abstract 
The Care and Condition Monitor (CCM) is a tablet-
based, networked visual analytics tool for collecting, 
structuring and analyzing informal and qualitative 
healthcare data. Building off research into application 
usability and best practices for communicating complex 
information, CCM illustrates how visual analytics tools 
coupled with social communication within teams of 
caregivers enables capturing of longitudinal informal 
data that would otherwise go unrecorded. 
This expanded scope of information can support 
medical decision making by making it possible to 
analyze informal and qualitative health care data, 
creating a multi-dimensional holistic picture of a 
person‟s health care and condition over time. 
Author Keywords 
Health informatics, informal data, long term care, 
mobile platforms, qualitative data, visual analytics.  
ACM Classification Keywords 
B.4.1 Data Communication Devices [Mobile Device]; 
H.5.2 User Interfaces [Prototyping]; J.3 Medical 
information Systems [Health]; C opyright is  held by the author/owner(s ). 
Mobile HCI 2014 Sept   23 – 26, Toronto, Ontario, CanadaA C M 
XXX-X-XXXX-XXXX-X/XX/XX. 
 
 
Hudson Pridham 
OCAD University 
100 McCaul St. 
Toronto, ON, M5T 1W1, CA 
hp12pk@student.ocadu.ca 
 
Anne Stevens 
OCAD University 
100 McCaul St. 
Toronto, ON, M5T 1W1, CA 
astevens@faculty.ocadu.ca 
 
Steve Szigeti 
OCAD University 
100 McCaul St. 
Toronto, ON, M5T 1W1, CA 
sszigeti@ocadu.ca 
 
Sara Diamond 
OCAD University 
100 McCaul St. 
Toronto, ON, M5T 1W1, CA 
sdiamond@ocadu.ca 
 
Bhuvaneswari Arunchalan 
OCAD University 
100 McCaul St. 
Toronto, ON, M5T 1W1, CA 
barunachalan@ocadu.ca 
 
 
 
2 
 
Introduction 
The Care and Condition Monitor (CCM) is a tablet based 
visual analytics tool that uses a simple voting strategy 
to collect and structure informal and qualitative 
healthcare data, presenting it in a circular visualization 
format to represent the data. Belden et al. established 
the following usability design principles relevant to 
healthcare delivery: (a) minimize caregiver cognitive 
load, (b) design for UI simplicity, naturalness and 
consistency, (c) make color meaningful, and (d) 
preserve context [1]. These principles guided the 
development of the application, ensuring it presents 
information in a simple and intuitive manner to 
caregivers: in the CCM, visualizations of data are 
prominently displayed in order to prompt user analysis, 
and visual and input complexity is reduced by only 
displaying relevant menus and functions to the task at 
hand, providing cues as to when a particular action is 
available. 
CCM was tailored to support the existing practices of 
caregivers at the partner facility, Clemente Ferreira 
CAIS hospital in Lins, Brazil, a long term care 
residential institution for people with a range of 
congenital mental and physical conditions. In CAIS, like 
many care facilities, in addition to traditional medical 
forms and notebooks, caregivers rely on informal 
communication practices, such as conversations at shift 
change, to analyze the full picture of a resident‟s 
status. The design of the application and research 
focused on capturing and translating informal practices 
into a system of qualitative data analysis that supports 
the caregivers‟ culture and practices, reinforces CAIS ‟s 
sense of community, improves collaboration across 
professional categories, and improves overall 
understanding of care and condition data. The last two 
points, care and condition, were identified through the 
research of the CCM team as components of resident 
well-being which were necessary to record, visualize, 
and foster collaboration around. Condition is measured 
by five metrics (general health, morale, cognition, 
mobility and form) that were derived from CAIS‟s 
records and are standardized for all residents.  Care, on 
the other hand, is individualized and organized around 
care goals tailored to the needs of each resident and 
translated into action that are described as treatments.  
The Prototype 
Clearly communicating recorded condition and care 
data became the core of the application design effort. 
The main component of this, the visualization/analysis 
system for the application, was guided by the work of 
Yi, et al. On insight formation Yi, et al. speculate that, 
“an insight does not appear suddenly “out of the blue,” 
but instead there are paths or procedures leading to 
the insight” [3]. Two procedures Yi, et al. identifie bear 
specific relevance: Provide Overview and Adjust. 
Through Provide Overview users understand the larger 
body of relevant data. The Adjust process facilitates 
that exploration. This is achieved by changing 
perspective on the data through adjustments to the 
level of abstraction and data range selected. [3]. 
Taking these processes of insight generation into 
account, combined with Belden et al.‟s principles, the 
core structure of the application was designed to 
provide a clear overview of data wherever possible. For 
example, by coupling the core navigation system for 
the app with the data visualization the user is rewarded 
for navigating through the application‟s various 
screens. She or he is always presented with a new 
unique visualization on each screen. Navigating down 
into the app from a user‟s profile into a particular 
treatment acts to filter the data range being viewed in 
the accompanying visualization, switching from an 
overview visualization of the patients care/condition to 
only visualizing that one treatment. This system 
provides the user with a clear conceptual model of how 
the data relates and assists in making more informed 
judgments. As Don Norman notes in his seminal book, 
 
3 
 
The Design of Everyday Things, “conceptual models 
allow us to predict the effect of our actions” [5].  
 
 (a) (b) 
Figure 1(a) & (b): Care and condition visualizations. 
The visualizations of caregivers‟ votes for treatments 
and condition metrics use a simple input system 
consisting of three possible states: green for positive, 
yellow for neutral, and red for negative. These are then 
aggregated and mapped along the green/yellow/red 
spectrum in the large coloured dot in the centre of the 
display.  While this dot provides overview, building 
from Donath and Zhang et al.‟s work [2][4], additional 
detail is also provided.  The individual care or condition 
metrics that the central circle is derived from are 
arranged around one side of the circle along with the 
caregiver involved.  The state of each metric (green, 
yellow, red) is indicated around the opposite side.  
Curved lines join the metrics, on one side, to the 
states, on the other.  
The very process of creating these data visualizations 
has the additional benefit of fostering collaboration 
between caregivers, as it is integral to the cyclical 
relationship between a resident‟s condition and care. 
Condition, which is recorded by all caregivers, prompts 
the creation of new care goals, which are delivered by 
multiple caregivers and ultimately influence back upon 
the condition. As per Donath, the circular layout also 
spatializes the social relationship between the resident 
and his or her circle of care. It assists the user to build 
a mental map of the information structure, displaying 
who is participating on a given care goal, each 
caregiver‟s unique treatments, and those caregiver‟s 
recorded votes for those treatments. 
Lastly, longitudinal data analysis and review is afforded 
in this setup by a mechanism by which a caregiver can 
scrub back and forwards in time in any visualization to 
understand change over time for a care or condition 
metric. 
Deeper analysis of the data than is possible through the 
visualizations or time scrubbing mechanics is facilitated 
by an analysis function to graphically compare two or 
more datasets (condition metric, care goal/treatment). 
In the analysis function, a user can change visualization 
mechanisms, switching between a straightforward line 
graph or a novel bubble history view that allows for 
simultaneous visualization of the historical change in 
votes and the volume of voting activity.  These 
analyses are created by navigating to each data set‟s 
visualization and selecting the analysis button. This 
provides the caregiver with the option to add that 
visualization‟s data to the analysis currently underway 
or to create a new analysis starting with that 
visualization‟s data. These analysis results and 
parameters can be saved for future reference. 
 
4 
 
 
 (a) (b) 
Figure 2(a): Bubble Analysis (b) Sketch observation. 
Additional information is displayed on each app screen 
alongside the main visualization, such as observations 
recorded in any media that relate to it and the 
caregiver who recorded it.  This information currently 
cannot be analyzed or visualized, yet it serves as a 
compliment to the structured observations produced by 
voting results. It also serves to facilitate communication 
between caregivers by readily displaying the activities 
and contributions by other caregivers. Such 
communication is enabled by an inter-application 
messaging system that combines both explicit and 
implicit messages into one unified messaging hub. In it 
direct messages between caregivers are collected in the 
messages page alongside invitations to collaborate on 
care goals for residents, which are automatically 
(implicitly) generated when a new care goal is created 
and caregivers are invited to it. 
 
 
Conclusion 
Although a work-in-progress, CCM illustrates and 
presents methods to collect and analyze informal and 
qualitative health care data while simultaneously 
fostering an environment of collaboration around care 
and condition in long-term healthcare environments. 
Future steps include the development of a high-fidelity 
prototype for testing and evaluation with healthcare 
workers regarding issues such as „voter fatigue‟, vote 
weighting, which condition metrics to use in what 
context, usability and navigation.i  
References 
[1] J. Belden, R. Grayson and J. Barnes. Defining and 
Testing EMR Usability: Principles and Proposed Methods 
of EMR Usability Evaluation and Rating.  Healthcare 
Information and Management Systems Society 
(HIMSS), 2009. 
[2] J. Donath and F. Viegas.  The Chat Circles Series: 
Explorations in designing abstract graphical 
communication interfaces.  ACM, 2002. 
[3] J. Yi, Y. Kang, J. Stasko, and J. Jacko, 
Understanding and characterizing insights: How Do 
People Gain Insights Using Information Visualization. 
Proc. Beyond Time And Errors: Novel Evaluation 
Methods For Visualization 2008. 
[4] Z. Zhang, B. Wang, F. Ahmed, I. Ramakrishnan, R. 
Zhao, A. Viccellio and K. Mueller. The Five W‟s for 
Information Visualization with Application to Healthcare 
Informatics. In IEEE Trans Vis Comput Graph, 2013. 
[5] D. Norman. The Design of Everyday Things. NYC: 
Basic Books. 1988.  
 
                                                 
i We thank our funder MITACS Canada/Boeing and our 
colleagues at CAIS, UFSCar, University of British 
Columbia and Dalhousie University. 


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Fostering Insight and Collaboration in Long-Term Healthcare through Collection and Visualization of Qualitative Healthcare Data

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