DAta Collection & Analysis

Our methods of data collection and analysis below, have included a literature review, google review scan, conversations with friends, family and colleagues and a patient interview.

Data Collection

A mind map diagram listing all literature review articles in Phase A. Articles are colour coded into categories: trade (14 total), scholarly and peer-reviewed (12 total), government (3 total), advocacy (12 total), and popular press (8 total).
Phase A

Literature Review Phase A

Our literature review covered a range of topics including wayfinding, environment design, patient journeys, tactile surfaces, biophilia, surface design, multisensory design, and more.

A mind map diagram listing all literature review articles in Phase B & C. Articles are colour coded into categories: trade (5 total), scholarly and peer-reviewed (3 total), government (2 total), advocacy (6 total), and popular press (3 total).
Phase B,C

Literature Review Phase B, C

Researching in this way helped reveal how multifaceted the hospital environment is. There is a lot to consider when designing an inclusive indoor public space.

Other Collection Methods

Conversations with friends, family, and colleagues

Speaking with family and friends who have first-hand hospital experience helped validate our initial findings.

Google review scan

We conducted a scan of Google reviews, focusing on larger hospitals in Toronto which had less than a 3-star overall rating.


Using Google’s OutScraper tool, we extracted over 300 relevant Google reviews which provided a direct source of knowledge of patients’ pain points when visiting hospitals.

Looking at the Google reviews for hospitals was an excellent source of qualitative, public data. Rather than guessing what people’s pain points were when visiting hospitals, the Google reviews allowed us to get direct feedback from patients about their good and bad experiences at hospitals.

Participant Interview

We conducted a semi-structured interview with a participant who has had multiple experiences with long-term recovery in hospitals.

Data Analysis & Models

We conducted a sentiment analysis of the Google reviews data using the MonkeyLearn analysis tool. We then analyzed the data further by tagging the reviews based on specific themes ranging from poor care from hospital staff, to long wait times, and way-finding issues.

Pie chart showing google reviews sorted based on common themes. Privacy 7.1%, Environment 7.1%, Wait times 23.8%, Poor care & service 61.9%
Google reviews sorted on common themes
A conceptual model showing the level of connection importance between spaces in the hospital. There is a strong connection between long term-care spaces and lab spaces, a somewhat strong connection between pharmacy spaces and inpatient rooms, and a not very critical connection between housekeeping spaces and doctor's offices.
Phase C - Spacial Relationship Model

Spatial relationships conceptual model

We conceptualized our findings by developing a relationship map of the hospital layout. The map shows the interaction of spaces within the hospital environment and highlights the importance of the connection between in-patient rooms and areas such as nurse stations, doctor offices and courtyards. Hospital spaces are frequently travelled by patients, doctors and nurses and intersect frequently. Because needs at the hospital change as you move from one room to the next, planning should be done depending on the room type, addressing the needs for equipment and relationships at the room level.

A conceptual model showing hospital patient room functionality. The model includes 1. Users, such as patients and doctors 2. Elements, such as furniture and technology 3. Ambient environment, such as noise, lighting and odour 4. Optimization, such as privacy, safety and social support 5. Procedures, such as recovery and check-up 6. Concerns, such as infection control and occupancy.
Phase C - Functionality Model

Functionality conceptual model

We further conceptualized our findings by mapping out hospital patient room functionality. The patient room is a large and complicated area because its design and functionality directly affect not only patients and families, but hospital staff and administration as well. The model focuses on multi-patient rooms with two or more beds and considers elements that provide comfort, the ambient environment and optimizing patient well-being.

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