10 Tips: Designing with Lived Experience Participants

Recently, I led the redesign of a health service in response to Royal Commission findings. 😷

A key recommendation of the Commission was that the design of future services included ‘Lived Experience participants’ - that is, the services should be designed in consultation with the consumers, families, carers and supporters who use a system. 🌟They are, afterall, experts in what is currently working, what needs to change and how to best meet their needs. 

The project was extremely rewarding - we worked closely with people who benefit most from transformed services - but it was also challenging. The services needed to change, and so did the work-practices of those designing them.👷As with any ground-breaking work, we learned things we’d do differently next time.

Here are 10 important tips for designing with Lived Experience participants.

1. 🥇Take time to onboard Lived Experience participants, facilitators and designers. Each group needs to learn about the other, so ensure people have the emotional space and time to understand the context, appropriate use of language, engagement frameworks and the subject matter. It’ll save you time in the end.

2. Also allow the time to identify and onboard suitable participants. Ensure they are equipped with the skills necessary to meaningfully participate. If they aren’t, provide training.🧑‍🎓

3. Prior to kick off, set up briefing sessions for facilitators, scribes and designers, if possible including peer support workers and Lived Experience participants. Run a pre-mortem: what could go wrong and how do we avoid it?

4. Be cognizant of how power imbalances can make Lived Experience participants feel uncomfortable and unable to contribute on equal terms. 💔These feelings are usually amplified if they lack training and appropriate skills. (We decided to run separate sessions with policy experts and health practitioners for this reason.)

5. If workshops are remote, collaborative tools such as Miro are your friend! Not only do scribes and facilitators need to be able to use them competently, but any training you do with Lived Experience participants will be repaid - they’ll feel included and enabled to actively participate in design sessions. 🎨

6. Provide safe breakout rooms, virtual or in-person, with access to peer support for facilitators and participants. These may not be needed but just knowing they’re there provides psychological safety.

7. ⏳Build in time to debrief after discussing potentially triggering material. Facilitators, scribes and designers need this as much as Lived Experience participants. If the subject matter is particularly confronting, provide access to psychological support for those who need extra help. The aim of the process is to help people, not damage them or force them to end their participation.

8. Take it slowly. Design managers will need to check in with their team more often. Accommodate the need for facilitators and designers to have an extra day off now and then to decompress, especially after workshops. Reconsider placing designers and managers on challenging projects back-to-back. They might need time to process the experience.

9. After each workshop gather feedback 📋 from participants, facilitators and designers. Continue to refine engagement frameworks in response to the findings. Demonstrate how feedback is being used to refine the process.

10. Finally, provide feedback to Lived Experience participants on the impact of their contributions to the design of future services and products. Involving Lived Experience participants means that you value their input - don’t make them feel the process was tokenistic by failing to share outcomes. ✅

When executed sensitively, engagement with Lived Experience participants can create faster and more effective pathways to service transformation along with better user experiences, as vital frontline feedback is included during design, not as lengthy and costly revision to it. But doing so does require new skills, some extra preparation time, engagement frameworks, and a considered approach. Remember: “Nothing about us, without us, is for us”. 🙏

From vaccines to bonuses, if incentives don’t change long-term behaviour what’s the answer?

As governments grapple with COVID vaccine hesitancy, some have turned to lotteries to encourage communities to get a jab. From Quebec and Slovakia to California, cash, beer, holidays and cars have been given away to incentivise people to get vaccinated. Only last week, one lucky Australian won $1 million in a vax lottery backed by philanthropists and several corporations. But according to a recent study published in the JAMA Health Forum, vaccine lotteries had no significant effects on vaccine uptake in the US states that deployed them. This begs the question: do we really understand how to incentivise people?  

When and why don’t financial incentives work? 

In the case of the vax lotteries, researchers speculated that the incentives offered weren’t enough to counteract the mis-information about COVID vaccinations being spread on social media. Nor did they address the lack of trust in the institutions administering the shots. In other words, lotteries failed to address why people were hesitant about getting vaccinated. This failure to come to grips with the attitudes that underpin behaviour is common in incentive design - and it applies to that most common of all incentives, the bonus. 

Paying bonuses could waste your money

Economists have long promoted financial incentives to drive behaviour change in the belief that higher rewards lead to more effort and better performance. As a result, bonuses, recognition rewards and sales commissions are baked into the remuneration structures of most corporations. This is despite a growing body of research that shows incentives often don’t work and if they do compliant behaviours are mostly short lived. This is for the same reason vaccine lotteries don’t work: bonuses do nothing to change the underlying attitudes that drive behaviour. If you want long-term behaviour change, the research is conclusive - external rewards are largely ineffective.

Even when directed towards specific tasks, financial incentives are blunt instruments. Being paid to do a particular task can change how a person perceives that task, which in turn can negatively impact performance: if I need to be paid to do something then the task must be bad, or I must be ill-suited to the task. This effect has been found in dozens of studies across adults, children and genders. When people expect to be rewarded for completing a task, they perform worse than those who weren’t expecting to be paid. In some contexts, an incentive is worse than no incentive at all. Incentivising charitable acts is another example of this. 

Paying people to ‘do good’ is bad

Paying people to donate blood, argued Richard Titmuss, a psychologist working in the 1970s, was likely to result in fewer donations than if there was no incentive at all. Why? Because incentives are encoded with social messages about how an individual is perceived. If I need to be paid to donate blood then my self-image as a good person is undermined, which in turn negates my ‘intrinsic motivation’ to give blood. When financial incentives negatively impact self-perceptions, positive behaviour change is unlikely.

Negative effects of incentives on collaboration and risk-taking

Companies also need to consider the impact of incentives on collaboration and risk-taking. If you share a bonus bell curve with colleagues, the motivation to collaborate towards a universal goal is undermined because you’ve created internal rivalry rather than a common purpose. Furthermore, ‘employee of the month’ rewards make a winner out of one person and losers out of everyone else. Bonuses and rewards can undermine social cohesion, pit colleagues against each other and damage self-esteem. 

As for impacts on risk-taking, again incentives can work against desired outcomes. If your end of year bonus depends on success, you’re less likely to move away from the tried-and-tested methods and try something new. So, ironically, financial incentives intended to encourage innovation can have the opposite effect, at precisely the time innovation is a top priority for corporations.   

So, if financial incentives don’t work, what does? 

Research shows that goal setting and training have more long-term, positive effects on performance than incentives. Helping employees to set and track goals and then training them to achieve their objectives creates positive and empathetic workplaces and promotes long-term behaviour change. 

Pay is rarely ranked at the top of surveys of what matters to employees. As long as wages are competitive, above a certain threshold, money doesn’t matter. It’s time, therefore, to reconsider incentives and invest in what does matter to your people – support and training in a collaborative, non-competitive environment. 

Sources

Andorsky, N., Decoding the Why, New Degree Press, 2020

Ariely, D., Bracha, A., and Meier, S., 2009. "Doing Good or Doing Well? Image Motivation and Monetary Incentives in Behaving Prosocially," American Economic Review, American Economic Association, vol. 99(1), pages 544-555, March.

https://behavioralscientist.org/research-lead-dishonest-data-vaccine-lottery-results-practicing-multiple-sports-makes-perfect-a-history-of-behavioral-economics-and-more/

https://www.forbes.com/sites/jemimamcevoy/2021/10/15/lotteries-and-other-cash-prize-incentives-made-essentially-zero-difference-to-vaccination-rates-study-finds/?sh=35faa7e82e22

Gneezy, Uri, Stephan Meier, and Pedro Rey-Biel. 2011. "When and Why Incentives (Don't) Work to Modify Behavior." Journal of Economic Perspectives, 25 (4): 191-210

https://hbr.org/1993/09/why-incentive-plans-cannot-work

https://jamanetwork.com/journals/jama-health-forum/fullarticle/2785288

https://milliondollarvax.com/

Where did hotel quarantine go wrong? Service design might have an answer…

Since March 2020, Australia’s had 30 community Covid case that can be traced back to someone in hotel quarantine. Twenty of these have been in 2021 - so the situation is getting worse, not better. The initial leaks were largely traced back to contact through security guards and hotel staff, but more recently guests have been infected by other guests on the same floor, highlighting the airborne nature of the virus.

So what's gone wrong?

To a service designer, it seems like a classic case of not placing the customer at the heart of an experience and failing to consider end-to-end journeys.

The first thing to understand is that the guest in isolation is not the only ‘customer’ in the hotel quarantine ‘experience’.

Let’s think about the situation of the casual security guards as an example: how well did state governments understand their goals, motivations, needs and behaviours before deciding to use this workforce over the police or Australian Defence Force personnel? If they had, they might have foreseen that a casual security guard, lacking job security and proper training, could respond very differently to a crisis situation than a fully qualified professional.

Service design techniques, such as attribution of personas to key groups involved in the delivery and receipt of a service, could have shed light on their behavioural drivers and helped mitigate potential shortcomings.

In human-centred design (HCD), a ‘persona’ represents the shared behavioural drivers of a group of people. During the service design process, we create a persona for each key behavioural group based on qualitative, and to a lesser degree quantitative, research. Personas are sometimes created for specific roles as well. In this case, there might have been personas created for an untrained security guard, a positive COVID case in quarantine, a hotel cleaner and a health department official. Knowing the goals, motivations and needs of the various 'actors' involved in hotel quarantine helps pre-empt likely problems and define key roles and responsibilities: a 2020 report commissioned into the use of hotel quarantine found that unclear roles and responsibilities contributed to service failure.

A designer would then map each persona’s journey through hotel quarantine. Like the application of personas, ‘journey mapping’ is a key service design tool. It can be easy to concentrate on the central aspects of a customer experience, neglecting some of the details. Critically, journey mapping traces someone’s experience ‘end-to-end’ – that is, from start to finish.

Let’s take the arriving international traveller as our persona.

The traveller disembarks, collects luggage and moves through the airport. Then they take transportation from the airport to the hotel, check in, go into their room, go out for fresh air with a security guard in tow and meet healthcare providers. After 14 days of quarantine, they leave the hotel, get a taxi - you get the drift.

At each stage, a journey map details all the 'touchpoints' and actors involved in the delivery of a service, making visible the invisible dots that join disparate companies, government agencies, technologies, processes, data points and people. Mapping end-to-end journeys is important for a number of reasons.

Chief amongst them is the identification of risk. For example, mapping a security guard's end-to-end journey (which we would define as the workday, starting and ending at home) would have alerted officials to the fact that after interacting with highly contagious hotel guests, a guard might often go to a second job in a factory or warehouse to make ends meet. After that shift ended, a journey map would have then taken us to the high-density apartment block where our security guard lives within a large, extended family. Vulnerable points along a security guard's journey could have been identified before COVID found its way into the community, perhaps bringing into question the decision to use security guards with journeys such as this.

Importantly, because mapping end-to-end is a comprehensive process, it tends to bring together key decision-makers from different departments or organisations. This ensures a shared understanding of all the moving parts of a service, facilitating collective rather than siloed decision-making. Suddenly the ramifications of one agency’s decision become clear to all. Finally, prototyping and stress-testing a journey allow for course correction before funds are committed – a considerable economic benefit.

A thorough HCD process can take a little time, and is best done with a cool head – two things the pandemic has rendered in short supply. But as governments still struggle to provide safe quarantine services – and suffer the enormous financial consequences of this failure - a cost-effective approach that takes account of how people actually behave is long past due.

Sources

https://www.abc.net.au/news/2020-12-21/victoria-covid-19-hotel-quarantine-inquiry-final-report/13002956

 https://www.abc.net.au/news/2020-09-05/mistakes-in-shambolic-hotel-quarantine-program-laid-bare/12616682

 https://www.abc.net.au/news/2020-08-18/victoria-covid-crisis-traced-back-to-seven-travellers-inquiry/12568408

 https://www.designcouncil.org.uk/sites/default/files/asset/document/Design%20methods%20for%20developing%20services.pdf

https://www.interaction-design.org/literature/article/personas-why-and-how-you-should-use-them

https://www.designcouncil.org.uk/news-opinion/seven-tenets-human-centred-design

https://www.nngroup.com/articles/journey-mapping-101/#:~:text=Definition%3A%20A%20journey%20map%20is,order%20to%20create%20a%20narrative.

A Three-Factor Model for Exploring Trust in a Healthcare Bot

Abstract from LSE Dissertation (2020)

By 2030 the World Health Organisation estimates that the shortfall of healthcare workers globally will exceed 14 million. This shortfall comes at a time of rising healthcare costs, strained resources, increased demand and rapidly aging populations (New Health: A vision for sustainability, 2017; Spatharou et al.,2020). AI technologies are seen as a possible solution to growing healthcare problems, both as a way to reduce costs and deliver personalised healthcare services at scale. (Pereira & Diaz, 2019) Healthcare chatbots alone are expected to save the industry an estimated $3.6 billion globally per annum by 2022 (AI-Powered Chatbots to Drive Dramatic Cost Savings in Healthcare, 2017).

This study aimed to identify key factors that influence trust in chatbots to inform the design and marketing of healthcare agents to improve adoption and usage rates. Three categories of factors were used to explore trust in a healthcare bot: robot, human and environmental. Robot-factors included two levels of anthropomorphism. Human-factors included personality and demographic traits. Three different levels of information privacy disclosure, achieved by manipulating the type and complexity of a task as well as the type of data disclosed, explored associations between environmental-factors and trust.  A second dependent variable, likelihood to act on advice (LTA), was introduced to explore whether trust and LTA yielded similar results - both outcomes are important if the potential of healthcare bots is to be realised. Based on a literature review, a survey was distributed via the online platform, Prolific. Participants (N=602) were randomly assigned to one of 12 versions of a text-based chat interface, in which a bot-doctor discusses bone health with a patient.

Significant results were found for all three categories of factors, underscoring the need for a multi-dimensional approach. For example, if a bot-doctor was a digital human (a photo-realistic 3D avatar) a participant was less likely to act on its advice. But, the more human-like a bot-doctor was perceived to be the more likely a participant was to trust and act on its advice. The presence of the agreeable trait was associated with trust and LTA. Participants with spontaneous decision-making styles were also more likely to trust a bot-doctor. Finally, at higher levels of information privacy disclosure, compared to lower levels, participants were less likely to act on a bot-doctor’s advice. While this study yielded interesting insights, it also raised many unanswered questions and should be viewed as exploratory only.