Data-Driven Design

Designers usually get to know their users in order to design for them. This can be done for example by interviews, observations and context mapping. In all of these design methods, designers are trying to understand the user in their context and by their means. However, with data-driven design, the data that all users create together will become the basis for the designer.

Data-Driven Health Care. Image retrieved from MIT.

The amount of information in the world is growing with a rate of 60% each year (Donhorst, & Anfara (2010). But that does not mean that this information is directly available for the decision makers. An example of these are the translators of the IKNL (2018). This organisation is analysing most of the cancer patients in the Netherlands that are visiting a doctor in a hospital and provide data to hospitals and communicate via their website. Via these websites, patients are able to see for example what the quality of life is after a specific kind of treatment. When this data is made available for the patients, it leads to better share decision making, because they are better informed (Raghupathi, Raghupathi, 2014).

Although probabilities derived from large batches of data are often more reliable than human reasoning, patients will always tend to trust the human more. Therefore, in data-driven design in the Healthcare application, the balance should be found between data and a doctor as a middleman. Moreover, an average doctor has a short amount of time with their patient and they also need to use this time to process data. In order to collect more data, the most important design challenge is to find a way to collect more without interfering with the doctor-patient time. Additionally, Grossglauser & Saner state that the use of data will also contribute to detect health issues quicker and will automate processes where medical staff is needed less. (Grossglauser & Saner, 2014).

References and Interesting Links:

  • Donhost, M. J., & Anfara, V. A. (2010). Data-Driven Decision Making. Middle School Journal, 42(2), 56–63.
  • Grossglauser, M., & Saner, H. (2014). Data-driven healthcare: from patterns to actions. European journal of preventive cardiology, 21(2_suppl), 14-17.
  • IKNL. (2018). IKNL – Integraal Kankercentrum Nederland.
  • Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health information science and systems, 2(1), 3.

Topic Contributors: Laura Heikamp and Milou Mertens

Design for Autonomous Ageing

The aging population is growing in numbers and proportion in every country in the world. As of 2017, the amount of people over the age of 60 was 13% (25% in Europe), and the amount of people over the age of 80 was 1.85% (Ageing, UN).

Woman holding a Philips phone. Retrieved from Pixabay.

Both groups are increasing rapidly, the population over 60 will duplicate by 2050, while the population over 80 will triplicate. It means huge challenges on the horizon for key pillars of our society, such as markets, labour, services, housing, etc., in which design can play a big role in order to overcome them.

Designing for autonomous ageing is defined as “The freedom to determine one’s own actions”. Translated to the care context, the senior person is considered autonomous, and in charge (Tischa, J. M. et. al). Design choices are very important to achieve a safe and independent lifestyle for senior citizens. The Aging with Dignity New York City report highlights many opportunities, such as building additional bus shelters and benches to better serve the older population. But, what are the key points in order to create better products for the aging population?

Firstly, it is important to understand that ageing is a multidimensional process of change that conditions the physical, mental and social aspects of a person. It is a functional decline that affects mobility, sight and hearing, with the increase of multimorbidity, the co-occurrence of 2 or more chronic medical conditions in one person (Tischa, J. M. et. al). All that is further complicated by the  higher interpersonal variability.

Secondly, it is necessary to identify common causes that affect elderly in their daily interaction with products, such as burns or falls at home. In addition, it also helps to group them by their level of autonomy. Is the person dependent on someone else or is he or she completely independent? Does he or she live alone or with the partner?

Finally, once the target limitations and group are defined, human-centered design methods must be implemented, always including the stakeholders, which in this case usually are: doctors, nurses, caregivers, family members, etc. This design methodologies, together with inclusive design, help to navigate a difficult, multifaceted and ill-defined problem.

At the faculty of Industrial Design of TU Delft, research and multiple projects with the aim of improving the autonomy and quality of life of the older population are being elaborated. More information in the Design Innovation for Ageing.

References and Interesting Links:

  • Ageing, UN. Retrieved from
  • Aging with Dignity: A Blueprint for Serving NYC’s Growing Senior Population. (n.d.). Retrieved from
  • Tischa J. M. Van Der Cammen, Albayrak, A., Voûte, E., & Molenbroek, J. F. (2016, 11). New horizons in design for autonomous ageing. Age and Ageing. doi:10.1093/ageing/afw181

Topic Contributors: Kevin Mamaqi Kapllani

More information

Risk communication

In the domain of healthcare, risk communication is about presenting benefits and harms of different options, in situations in which people need information to make decisions.

Risk communication serves to inform the patient (and family), so that they understand the potential risks, supports them in making informed decisions regarding threats to health and safety, and encourages them to participate in minimising or preventing these risks (RISC Amsterdam, n.d.).

There are different complexities surrounding risk communication. Dutch legislation requires doctors to always inform patients about potential benefits and harms (WGBO, 2017). However, there are exceptions, such as very acute situations and situations in which there is strong evidence that an option has many benefits and no (or hardly any) harms. In the case of the latter, the doctor can direct and guide the patient towards the option he concludes to be best (paternalistic approach). However, in the case of uncertainty, the doctor will facilitate the patient’s decision by providing him with transparent information. The patient can then make a decision without further help of professionals (informed decision making), or with active guidance (shared decision making).

There is a plethora of tools that help communicate risks, such as online quiz De Risicotest (PreventieConsult, n.d.), infographicsand leaflets. Risk communication tools can convey information in different ways: via numbers (tables, statistics), visual display (pie charts, graphs, or visuals as in Figure 1), or verbal terms (using terms as ‘high risk’, ‘small chance’).

Figure 1: visual displaying benefits and risks of medicines for treating urgency incontinence in women (AHRQ, 2014).
Figure 1: visual displaying benefits and risks of medicines for treating urgency incontinence in women (AHRQ, 2014).

These risk communication strategies bring forth different difficulties for people to understand the information. The three main issues are the involvement of:

  • Numerical probability information, which many people find difficult to understand.
  • Abstract epidemiological information, which often lacks an intuitive meaning.
  • Unbalanced information, which is the overemphasis of benefits and the underemphasis of risks.

Designers can contribute greatly by bridging the gap between doctor and patient. Risk communication strategies should be created in which the designers’ skill is required to, for example, craft a user journey to determine when and how risks can be communicated in an understandable way. Empathy on the designer’s part is needed to understand the user’s context and determine whether informed or shared decision making should be supported. Naturally, knowledge regarding Medisign is necessary for the designer to be able to make these decisions.

References and Interesting Links:

  • PreventieConsult. (n.d.). Retrieved May 1, 2018, from
  • RISC Amsterdam. (n.d.). Retrieved May 1, 2018, from
  • The SHARE Approach-Communicating Numbers to Your Patients: A Reference Guide for Health Care Providers. (2014, July 25). Retrieved May 1, 2018, from
  • Wet op de geneeskundige behandelingsovereenkomst (WGBO). (2017, October 20). Retrieved May 1, 2018, from

Interesting Links:

  • Damman OC, Bogaerts NM, van den Haak MJ, Timmermans DR. How lay people understand and make sense of personalized disease risk information. Health Expect 2017; doi: 10.1111/hex.12538.
  • Damman OC, Bogaerts NMM, Van Dongen D, Timmermans DRM. Barriers in using cardiometabolic risk information among consumers with low health literacy. British Journal of
  • Health Psychology 2016; 21(1):135-56.
  • Galesic M, Garcia-Retamero R. Statistical numeracy for health: A cross-cultural  comparison with probabilistic national samples. Arch Intern Med 2010; 170:462-468.
  • Hofman, Del Mar; Patients’ expectations of the benefits and harms of treatments, screening and tests:  a systematic review. JAMA Intern Med 2015;175(2):274-286

Topic Contributors: Michael Soenthorn Speek