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Soliman-Junior, J., Tzortzopoulos, P., and Kagioglou, M. 2020. “Exploring Mistakeproofing in Healthcare
Design.” In: Tommelein, I.D. and Daniel, E. (eds.). Proc. 28th Annual Conference of the International
Group for Lean Construction (IGLC28), Berkeley, California, USA, doi.org/10.24928/2020/0034, online
at iglc.net.
People, Culture, and Change: Lean Tools and Performance Measurement 193
EXPLORING MISTAKEPROOFING IN
HEALTHCARE DESIGN
Joao Soliman-Junior1, Patricia Tzortzopoulos2, and Mike Kagioglou3
ABSTRACT
The process of verifying the compliance of design solutions to regulations is critical in
healthcare design due to the complexity of the requirements present in healthcare settings.
The majority of previews research concentrates on “mistake-searching”, assuming that
design will not be compliant and, as such, necessitates an approach focussed on finding
inconsistencies, rather than avoiding mistakes during the design process. This paper
reports findings of an ongoing research that follows the Design Science Research
approach, with the aim of exploring how existing technologies can support incorporating
mistakeproofing (poka yoke) into healthcare design, framed within the regulations
compliance process. A set of technological strategies was assessed according to
mistakeproofing principles. The analysis evidenced their characteristics, benefits,
limitations and examples of application. These technologies rely on the use of hybrid
approaches, providing assistance to designers across all design stages, which in turn
supports better decision-making and contributes towards improving value generation. A
theoretical framework was proposed based on the synergy between design support
systems, requirements subjectivity and jidoka. It highlights the importance of better
understanding and enhancing the relationship between human designers and different
technologies through automation.
KEYWORDS
Mistakeproofing (mistake-proofing, mistake proofing), poka yoke, healthcare design,
regulations, autonomation.
INTRODUCTION
Many sources of information support decision-making across the design process. These
are extensive sets of data (Kiviniemi and Fischer 2004), representing a basic framework
upon which solutions are created and assessed. Regulatory information from codes,
standards and other documents are responsible for a considerable part of this data (Macit
İlal and Günaydın 2017), introducing different types of constraints into the design process
(Von der Tann et al. 2018). Within healthcare design, this becomes even more critical due
to (i) the complexity of regulatory documents associated to this type of building,
describing their functional and technological aspects (Chellappa 2009); and (ii) the impact
1 PhD Student, School of Art, Design and Architecture, University of Huddersfield, Huddersfield, HD1
3DH, UK, Joao.SolimanJunior@hud.ac.uk, orcid.org/0000-0002-8089-8628
2 Professor, School of Art, Design and Architecture, University of Huddersfield, Huddersfield, HD1
3DH, UK, P.Tzortzopoulos@hud.ac.uk, orcid.org/0000-0002-8740-6753
3 Professor, School of Engineering, Western Sydney University, Penrith NSW 2751, Australia,
M.Kagioglou@westernsydney.edu.au, orcid.org/0000-0003-3521-1484
Exploring Mistakeproofing in Healthcare Design
194 Proceedings IGLC28, 6-12 July 2020, Berkeley, California, USA
of design quality on services delivered in these facilities, which, in turn, have a major
influence on health outcomes (Tzortzopoulos et al. 2005).
Assessing design proposals by verifying the fulfilment of requirements is an important
step in the design process, which provides an opportunity to improve design value (Fiksel
and Hayes-Roth 1993). Past research has extensively explored the use of automated rule
checking to support design assessment (Eastman et al. 2009; Nawari 2019). This was
done by framing their scope within the compliance process, considering different
regulatory frameworks (Macit İlal and Günaydın 2017; Nawari 2019). Most of these
initiatives emerged from a fragmented background, related to the proposition of different
sets and types of solutions to overcome limitations of manual-based compliance
approaches (Eastman et al. 2009; Macit İlal and Günaydın 2017).
Past research on automated rule checking indicate a move towards a digital and
technology-based assessment process (Soliman-Junior et al. 2019). Despite representing
an advancement, most efforts still emulate the common rationale of “mistake-searching”.
They start from the assumption that design proposals will not be compliant, and mistakes
(or non-compliances, in this context) need to be detected, flagged and informed to the
design teams, so they can react and modify the design accordingly (i.e. rework). This is
the main issue associated to such underlying reactive reasoning – finding inconsistencies
after they are made, rather than aiming to avoid design mistakes in the first place.
This paper presents a theoretical analysis exploring how existing technologies can
support incorporating mistakeproofing 4 (poka yoke) into healthcare design. The
assumption is that embedding mistakeproofing into design operations can contribute
towards shifting assessment to a more iterative, simultaneous and continuous design-
checking process, reducing rework. This contributes to improve design outputs in terms
of compliance to regulatory requirements and therefore acts as an assurance tool rather
than a testing tool. It is important to highlight that in this paper, mistakes are considered
to be any design non-compliance to the applicable regulatory framework. Thus, design
mistakes represent, in this context, a potential source of derogation and rework from both
the perspective of design teams and regulatory institutions in charge of assessing design
proposals.
DESIGN ASSESSMENT THROUGH HUMAN-TECHNOLOGY
PERSPECTIVE
There are two different understandings of how automated rule checking might support
the design compliance process (Hjelseth 2016). These are (i) compliance checking seen
as a separate process from the overall design, performed at determined times (usually
done by importing the building model into a rule-checking engine); and (ii) design
solution checking, usually performed in a continuous way, as a support and parallel
operation during the design process. Lee et al. (2019) concluded that the second
understanding supports design optimisation by correcting errors and exploring different
design alternatives. The main limitation of this study (Lee et al. 2019) was incorporating
information from subjective requirements, which still remains a challenge according to
the authors perspective.
In fact, the problematic rhetoric around using automation in design assessment and
requirements subjectivity is recognised by the literature as a major shortfall (Nawari 2012;
Dimyadi and Amor 2013; Lee et al. 2019). This is because human involvement is
4 Mistakeproofing is spelled in one word in this paper for consistency with other IGLC authors.
Joao Soliman-Junior, Patricia Tzortzopoulos, and Mike Kagioglou
People, Culture, and Change: Lean Tools and Performance Measurement 195
fundamental in dealing with the abstract and subjective elements, which are implicitly
embedded within regulatory documents (Nawari 2012; Solihin and Eastman 2016).
Technology helps human activities during design in different ways (Heumann and Davis
2019), as well as using different degrees of automation (Soliman-Junior et al. 2019). In
practice, this indicates a need for hybrid approaches, which support design assessment
through better exploration of human-technology interactions. Such approaches cope with
requirements subjectivity in such a way as to not hinder the creativity of human designers
during the design process – which is imperative to increase design value.
These approaches incorporate a well-known concept within the Lean community,
originated in manufacturing i.e. jidoka or autonomation (Shingo 1986), “automation with
a human touch” (Liker 2004). Autonomation is related to machines that were designed so
they could identify problems and promptly stop production. The aim was freeing people
to use their creativity and unique reasoning for essential operations, related to increasing
value (Liker and Meier 2006). As such, autonomation is fundamentally related to (i) built-
in quality; (ii) mistakeproofing; and (iii) assuring that humans are free to perform value-
adding work, i.e. people are in the centre of the system (Liker, 2004). These principles
are explored here, applied to design, by investigating different digital technologies and
their potential applications from an autonomation perspective.
MISTAKEPROOFING
Mistakeproofing is also known by the Lean community as poka yoke. It was originally
defined in manufacturing by Shingo (1986), and relates to the development and
application of creative devices that can effectively reduce the chance of making errors, so
waste can be eliminated (Liker 2004). According to McMahon (2016), mistakeproofing
relies on processes bounded by constraints, aiming to prevent incorrect operations. ASQ
(2019) complements the previous definition by stating that using automated techniques
to avoid errors or identifying errors promptly is at the centre of mistakeproofing. This is
fundamentally a paraphrase of Shingo’s own perception (Shingo 1986), who claimed the
importance of quickly finding mistakes is to avoid rework and other types of waste, due
to late detection of errors. The concept of mistakeproofing is based on six principles
(Shingo 1986; McMahon 2016; Tommelein 2019), which are presented in Table 1.
The creative and subjective nature of the design process is a fruitful setting for the
occurrence of inconsistencies and errors. These errors are unintentional and stem from
the idiosyncratic way humans think and undertake tasks (Tommelein 2019), associated to
cognitive error mechanisms (Stewart and Grout 2001). In design, the negative effects of
mistakes must be alleviated, so they do not compromise further decision-making and
reduce their impacts on the project.
Mistakeproofing has been an area of research poorly explored over the last decades,
due to the lack of systematic use and studies in the construction industry (Tommelein
2019). Studies related to its application focussed on building design are even more scarce.
Examples of using mistakeproofing in design have only been presented by Tommelein
(2008; 2019), amongst an extensive set of applications from different domains. There is
still no discussion on how mistakeproofing could be supported by technologies used in
the design process.
Even though mistakeproofing was defined within the manufacturing domain, its six
principles are also valid to the design of a product and its associated operations
(Tommelein 2019). In fact, these principles can be understood as categories of
mistakeproofing, organised by their effectiveness in reducing waste. They might support
Exploring Mistakeproofing in Healthcare Design
196 Proceedings IGLC28, 6-12 July 2020, Berkeley, California, USA
identifying how mistakeproofing can be incorporated to different operations across the
design process, considering the regulatory framework as a baseline.
Table 1: Principles of Mistakeproofing
(Shingo 1986; McMahon 2016; Tommelein 2019)
Principle Definition
Elimination
Removing the possibility of error by redesigning the product or
operation in such a way as the step of the operation or the part of the
product becomes unneeded.
Prevention Designing a product or operation in such a way as to prevent the
occurrence of any mistake at all.
Replacement Substitute an operation by another which is more reliable, with the aim
of improving repeatability and improving consistency.
Facilitation Making operations easier by using various techniques so they are less
error-prone.
Detection Identifying a mistake straightaway so it can be corrected before further
processing.
Mitigation Minimizing the effects and impact of errors.
RESEARCH METHOD
This paper reports a theoretical analysis based on findings from an ongoing research,
which is framed within the healthcare design and compliance context. This research
adopts the Design Science Research approach (DSR), which supports solving problems
that have both practical and theoretical relevance, in an effective and innovative way
(Lukka 2003). By using DSR, the research problem is understood while the artefact is
produced (Holmström et al. 2009). This process consists of iterative cycles of analysis,
understanding, development and refinement. Thus, findings presented by this paper do
not represent the artefact on their own, but constitute part of the reasoning behind this
evolving process.
The main sources of evidence in this paper are related to theoretical data from
literature review, by providing fundamental inputs on (i) automation and technology
applied to design; and (ii) clarifying concepts and principles related to autonomation and
mistakeproofing. Based on literature, different technological strategies were identified
and explored under the six principles of mistakeproofing. The analysis undertaken by this
study is also partially informed by empirical data which is not presented in the paper.
These correspond to a study developed in collaboration with an institution responsible for
Primary Healthcare buildings across England, and relate to: (i) analysis of regulations,
which supported understanding the content of information from requirements and how
mistakeproofing could be used under different strategies; and (ii) meetings and interviews,
which suggested how design support systems can naturally incorporate mistakeproofing,
through the perspective of different designers.
TECHNOLOGIES FOR DESIGN SUPPORT SYSTEMS BASED
ON MISTAKEPROOFING
The use of mistakeproofing can be explored in healthcare design by analysing how
existing technologies relate to its six principles. These also outline strategies to support
Joao Soliman-Junior, Patricia Tzortzopoulos, and Mike Kagioglou
People, Culture, and Change: Lean Tools and Performance Measurement 197
the development of design support systems. The analysis presented by Figure 1 represents
the key findings of this paper, which are discussed in this section. It is important to
acknowledge that some of these technologies already exist and are used in practice, while
others still demand some level of development to be further incorporated in such design
support systems, as indicated by the same figure.
Figure 1: Technologies for design support systems and their relationship to the
mistakeproofing principles
G
ENERATIVE
D
ESIGN
Generative design, also defined as algorithmic design, is related to “virtual geometric
processes that are highly numerically controlled and constrained parametrically” (Garber
2014, p. 125). In fact, the use of generative design, by applying Artificial Intelligence
(AI), has been explored since very early stages of digital design. There are two different
types of approaches related to using AI-based generative design: design optimisation and
design optioneering. Design optimisation is associated to genetic algorithms, and its use
aims to indicate the most suitable solution (Arora 1986), by solving problems with
multiple variables and disciplines (Gerber et al. 2012). Design optioneering, on the other
hand, takes advantage of such algorithms to generate multiple design alternatives (Gerber
et al. 2012), which can support further decision-making. While the first approach provides
the best possible solution, based on a pre-defined set of criteria; the second provides many
functional solutions, so designers can choose the most suitable one, according to their
own thoughts and criteria. These approaches could support incorporating mistakeproofing
by eliminating the possibility of error, while human designers were to solve the same
problems; e.g. sorting layout of floorplans based on constraint requirements of area and
spatial adjacencies. In both cases, the possibility of eliminating mistakes relies on whether
the set of rules and constraints from AI algorithms have been properly defined. Moreover,
the AI-based interfaces must be aligned with the designers’ workflow and their intuitive
reasoning (Heumann and Davis 2019).
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198 Proceedings IGLC28, 6-12 July 2020, Berkeley, California, USA
PARAMETRIC MODELLING
Parametric modelling supports defining a system by describing its parameters and
relationships (Kensek and Noble 2014). Its use can prevent the occurrence of errors by
applying constraints and object spatial boundaries, within object-based modelling tools.
Constraints are already used to support modelling operations in current software
applications, such as Autodesk Revit and Graphisoft Archicad. These are encoded within
software systems to force intent while designing (Kensek and Noble 2014). They reflect
relationships between objects according to real limitations e.g. a window object can only
be inserted if it is associated to a host object type wall. Object spatial boundaries are
related to the space required by objects in use. They correspond to their projection and
associated space for manoeuvre. This approach incorporates a specific type of
information from healthcare regulatory requirements, associated to free spaces around
specific objects, e.g. minimum free distances in front and on both sides of beds. The use
of parametric modelling could be further expanded, by modelling constraints and using
them as a way of embedding information from the regulatory framework into object
families and building models.
VISUAL PROGRAMMING LANGUAGE (VPL) TOOLS
Visual programming language (VPL) tools, such as Dynamo and Grasshopper, could be
used to replace part of the design process, typically done by humans under object-based
modelling interfaces. These programming languages use graphical elements to support
creating logic algorithms (Kensek and Noble 2014). One of the advantages of using VPL
approaches is the fact they can be maintained without much specialized support (Ghannad
et al. 2019). Moreover, they are user-centred, providing enough flexibility to be adapted
to different operations and designers (Preidel and Borrmann 2015, 2016). The use of VPL
tools in design support systems can be associated to prone to error activities related to
repetitive operations, e.g. including the same object many times in the building model
and adapting different elements within the building model, according to fixed parameters
and constraints.
WARNINGS AND ALERTS
Using warnings and alerts in object-based modelling interfaces could incorporate
mistakeproofing in the design process by: (i) facilitation – visually flagging potential
mistakes before they are made; or (ii) detection – promptly informing designers right
after any mistake is detected. While the first approach relies on warning signals, which
visually alert designers about potential mistakes, so they could react and proceed
straightaway with the design process; the second is related to identifying and informing
designers about any mistakes already made (Mathot et al. 2019). It is important to
acknowledge the dichotomy related to warnings and alerts on mistakeproofing, since they
could be either used before or after the occurrence of mistakes. They also could force a
complete stop in the design process, by introducing compulsory constraints in the
software algorithm, so errors could be corrected before advancing further – which is at
the core of mistakeproofing (Shingo 1986).
IMMERSIVE APPROACHES (VR/AR)
The use of immersive approaches through Virtual and Augmented Reality (VR/AR) is
not a novelty on their own in the AEC context, but they could be adopted differently
towards better incorporating mistakeproofing principles. Immersive approaches facilitate
Joao Soliman-Junior, Patricia Tzortzopoulos, and Mike Kagioglou
People, Culture, and Change: Lean Tools and Performance Measurement 199
designers’ work, but also support quick detection of mistakes. They rely on a concept
called manual-assist collaboration, which uses robots and digital tools to excel humans’
capabilities, under a continuous and integrated way (Johns et al. 2019). They enable
evidencing contextual information about the product and the associated processes. Thus,
they could provide an immersive environment, helping human designers to perform their
operations in all stages of design (Betti et al. 2019).
DESIGN ASSISTANT
A design assistant could facilitate the design process by supporting designer’s decision-
making. Within the context of design support systems, a design assistant does not exist
yet and should be envisioned as a constant guide. It could inform designers on their
decision-making, based on information from the regulatory framework. It should provide
the right information (what is needed) at the exact time designers need to use it (when is
needed), by suggesting where, how and why elements should be inserted across the design
process. Moreover, it could track decision-making by also recording where its evidence
or knowledge was needed and how they influenced and modified the design, promoting
continuous improvement. This type of approach demands interfaces between humans and
technologies to be excelled up to their current limits, since such integration relies on
maintaining designers’ workflows and cognitive processes (Heumann and Davis 2019).
The adoption of this strategy to object-based modelling tools, can, ultimately, provide a
platform for continuous learning from potential mistakes, improving the design process
by also informing designers about what could have gone wrong.
CURRENT AUTOMATED RULE CHECKING TECHNIQUES
Despite the advancements reported by literature over the last years, the use of automated
rule checking to support the design assessment is not widely observed in practice. There
are many limitations on existing software, mostly related to the way these were developed,
impacting their adaptability and flexibility to incorporate information from different
regulatory contexts (Eastman et al. 2009; Nawari 2019). In situations where automated
rule checking techniques are used, they relate to the lowest levels of mistakeproofing, in
which errors and inconsistencies have already happened. They detect and mitigate the
negative effects of mistakes in the design, depending on how their associated processes
and workflows are structured, as discussed by Hjelseth (2016): (i) if automated rule
checking is used at the end of the design stages, under an isolated and non-repetitive
approach, it relates to the mitigation principle; on the other hand, (ii) if automated rule
checking is incorporated to design at different stages, and performed under an iterative
and continuous way, it reflects the detection principle. In the first case, the effects of
mistakes can only be mitigated. Therefore, they impact on other design decisions,
potentially affecting the overall course of the associated project. Moreover, current uses
of automated rule checking demand building models to be exported from their native
object-oriented tool to other applications, under different file formats. This can eventually
result in and another error-prone situation, due missing information and conflicting files.
DISCUSSION
Considering the healthcare design context and the associated regulatory framework, most
of the strategies presented in the previous section could be introduced to operations
through hybrid approaches. They could further support reducing the occurrence of non-
compliances in later stages of the design process. In fact, the practical adoption of these
Exploring Mistakeproofing in Healthcare Design
200 Proceedings IGLC28, 6-12 July 2020, Berkeley, California, USA
technological strategies could reduce, and potentially eliminate derogations
5
from the
healthcare design process. They are one of the main causes of rework and clashes from a
design perspective; but also, are responsible for many delays, overspendings and
rejections from planning and funding organisations, under a project perspective.
The application of technology as discussed above, often represent an ideal situation.
There are many limitations associated to the way they could be incorporated to design
operations, which still demand further exploration in practice. Figure 2 presents an
overview of these technologies, examples of their application and their main limitations
identified in this study. In order to enable mistakeproofing, many of the proposed
strategies rely on the success of prior steps of the digital design process. This is associated
to precise definition of parameters, algorithms and constraints, based on information
associated to the regulatory framework.
Figure 2: Application and limitation of technologies
under a mistakeproofing perspective
From the analysis carried out in this paper, an important characteristic of applying
mistakeproofing in design was observed. In design, mistakeproofing strategies often do
not fit exclusively into one principle. This is due to the different ways the same technology
can be used, according to different factors such as context, workflow and the unique
reasoning of every designer. Moreover, it can be difficult to determine what is a mistake
in design and what is the relationship between mistakes and rework in this process.
Differently from production, design is based on iterative and complex operations. Thus,
much of the potential rework can actually be the source of emergent and creative solutions.
5
In the UK healthcare context, derogations are all design deviations from the standards, regulations and
other guidance documents. They emerge from circumstances where it may be impossible or impractical
to attain these requirements. Derogations are often presented and documented by a schedule, which
states the requirements being derogated, the reason why design is not compliant, the associated risks
and how their impact shall be mitigated.
Joao Soliman-Junior, Patricia Tzortzopoulos, and Mike Kagioglou
People, Culture, and Change: Lean Tools and Performance Measurement 201
This understanding suggests that mistakeproofing can assume different perspectives
within the production and design domains, as evidenced by this dichotomy.
Finally, it is also important to observe holistically how the strategies previously
explored embrace jidoka in design, at the same time they address key issues around
requirements subjectivity, from the automated rule checking research. Figure 3
demonstrates how the technological strategies (represented by design support systems)
can work, under a theoretical framework, as a convergent point between Lean and the
issue around requirements subjectivity. It can be understood as a synergetic element
between (i) the subjectivity embedded in the regulatory framework, potentially addressed
through hybrid solutions; and (ii) jidoka, through mistakeproofing. In both streams, this
synergy relies on the interaction between humans and technologies.
Figure 3: Relationship between requirements subjectivity and jidoka through
mistakeproofing, by using hybrid solutions in developing design support systems
CONCLUSIONS
This paper explored mistakeproofing within the design domain, under a theoretical
perspective. The aim of this study was to explore how existing technologies could support
incorporating mistakeproofing into healthcare design. This was done by investigating
relationships between different technological strategies and mistakeproofing principles to
support design operations. The main limitation of the analysis carried out in this paper is
that technologies were mostly assessed under a theoretical perspective. Thus, they have
not been applied and validated in design practice. These technologies have been
individually presented, identifying their main characteristics, benefits, limitations and
examples of application. By doing so, this paper demonstrated the potential feasibility of
using mistakeproofing within the design domain.
Findings presented in this paper also evidenced the need for hybrid approaches in
design. The use of technology should be understood as a means to provide continuous
advice and assistance across all stages and operations of the design process. A theoretical
framework emerged from the synergy observed between design support systems,
requirements subjectivity and jidoka (Figure 3). The interaction between people and
technologies is the foundation of this synergy, reflecting the basic definition of jidoka.
Future work should further investigate this framework and test the theoretical contribution
from each of the technologies in practice. Findings from this study suggest that exploring
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202 Proceedings IGLC28, 6-12 July 2020, Berkeley, California, USA
mistakeproofing in the design domain relies on better understanding the relationship
between human designers and technologies – and how they are enablers of one another.
ACKNOWLEDGMENTS
The authors would like to thank the Innovative Design Lab (IDL), University of
Huddersfield. Moreover, we would like to thank Community Health Partnerships (CHP)
collaborators, project and design team members, for their time, support and the
opportunity to help in this investigation.
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