While many people are currently working from home because of Covid-19, it seems an important time to remember the value of socialisation face to face in the physical workplace and how that relates to organisational performance. In this blog, I talk about how my PhD research into workplace effectiveness in hospital settings, led me to identify a spatial design index (SCI) that predicts care outcomes. Whilst effectiveness of hospital settings is of course also an area of interest in the current crisis, my research was conducted back in 2018, pre Covid-19, and what I want to talk about here is how SCI might now be applicable to evaluating the effectiveness of physical offices. The blog is in two parts: part 1 describes what SCI is and how it has been shown to predict performance. Part 2 is about applying SCI in co-working spaces as a means of evaluating effectiveness in delivering on desired outcomes for occupiers.

The key metrics that organisations use to measure space usually focus on efficiency. For example, the distances between main functional areas in a hospital ward and the frequency of traversing those travel paths are important for healthcare organisations because the shorter the walking distance the more time is spent with patients. These metrics have been applied to different ward typologies to define what is most efficient however studies failed to relate the ward layout to any outcome variables e.g. quality of care.

Hospital wards are environments where healthcare providers are often on the move, they use different kinds of spaces during a working day and visibility is key for chance encounters and impromptu interactions that help with information exchange and socialisation. In order to try to quantify all these spatial factors, I developed a new single measure called Spaces for Communication Index (SCI) that measures space effectiveness in the form of maximising communication opportunities which are expected to be crucial for good outcomes and can be applied to dynamic environments. My aim was that if I could show a link between SCI and performance outcomes, then this would help designers to evaluate design schemes of hospital layouts according to their impact on quality of care.

Initially, we I used six NHS wards to develop SCI where detailed information about movement and communication patterns of 102 healthcare workers was collected to define frequencies of travel between key areas. I found four major links between three key areas in total:

• ‘patient beds – patient beds’ which constituted 37% of total travel links
• ‘patient beds – nursing station’ which constituted 29%
• ‘patient beds – medicine room’ which accounted for 17%
• ‘nursing station – medicine room’ which accounted for 5%.

Next, I wanted to understand the spatial properties of these four major links. Every space walked through was assessed according to how spacious and large it is, and therefore what viewshed, it provides to encounter others. To study those viewsheds, the measure of connectivity was used which gauges the number of spaces immediately connected to a space of origin. Connectivity denotes the size of a viewshed from a specific vantage point.

I developed SCI by using two critical variables: the average connectivity of the paths that link key areas, and the frequency a path must be traversed. To define the second variable, I used the percentage of total journeys accounted for by each journey type derived from observations (so for example 37% for patient bed to patient bed) as a factor to multiply the average connectivity levels of each of the four major links. The four values were then summed up and to account for the ward unit size, and divided by the number of patient beds in each ward.

The SCI index was calculated for 31 NHS hospital wards and the figures were related to their care quality ratings using statistical analysis where hospitals were rated by the Care Quality Commission on a 4-point scale ranging from ‘outstanding’ to ‘inadequate’. Results from the analysis showed that SCI can significantly predict which wards provided better healthcare quality (p = 0.005).

This means that SCI can be used as a tool to assess layouts and anticipate levels of care quality. The higher the index, the better the quality of care. In terms of design, these results identified the importance of spaces with larger viewsheds that care providers pass through. The larger the area in size, the better, as more opportunities for interactions are provided.

SCI is an objective method that can be used for comparing design options of different hospital wards and decide how to best allocate functional spaces in a layout. The new tool compares layouts for their effectiveness in maximising communication opportunities and thus providing better organisational outcomes such as care quality. Increasing the dataset would help to increase the confidence in using SCI in this way, but my analysis provides a great start and basis for further application. SCI earned the 2019 RIBA award for research and more academically inclined readers can read my full paper here.

Next week I will show how this message can be applied to co-working spaces, so stay tuned!