The Design Economy primer: how design is revolutionising health, business, cities and government

Curated on Posted on by Stefaan Verhulst

James Pallister at the Design Council: “In the four sections that follow, we offer a guide to the design economy in the twenty-first century – a flavour of the critical issues, leading companies, research institutes and designers in:

1. Health

A growing awareness of the social impact of design has led to an increasing number of designers working in health and well-being.​​

2. Business

Global corporations, following in the tracks of Apple, Philips and IBM, are building design studios and seeking Chief Design Officers to join their boards and orchestrate the transition from marketing-led to design-led businesses.

3. Cities

With a rapidly increasing proportion of the population living in cities, design is being used to tackle the implications of this demographic shift in areas like housing and infrastructure.​

4. Government

In the UK, Europe and the US, designers can now be found close to the seat of government, employing design to improve public services and policies.

With design expanding into these important and largely uncharted areas, we urgently need to begin asking informed questions about design and its practical and ethical territory.

John Mathers, Chief Executive of the Design Council, asks us to pause for a moment to consider, “How has design, which many still associate largely with style and consumerism, come to be something one might look to for solutions to the most complex and challenging problems facing humanity today – problems requiring not just local fixes using clever design objects, but solutions that reimagine systems themselves? Are we, at this point, really even still talking about the same discipline?”

The questions, perhaps, boil down to one: ‘What should design do?’ …(More)”

 

Health Big Data in the Commercial Context

Curated on Posted on by Stefaan Verhulst

CDT Press Release: “This paper is the third in a series of three, each of which explores health big data in a different context. The first — on health big data in the government context — is available here, and the second — on health big data in the clinical context — is available here.

Consumers are increasingly using mobile phone apps and wearable devices to generate and share data on health and wellness. They are using personal health record tools to access and copy health records and move them to third party platforms. They are sharing health information on social networking sites. They leave digital health footprints when they conduct online searches for health information. The health data created, accessed, and shared by consumers using these and many other tools can range from detailed clinical information, such as downloads from an implantable device and details about medication regimens, to data about weight, caloric intake, and exercise logged with a smart phone app.

These developments offer a wealth of opportunities for health care and personal wellness. However, privacy questions arise due to the volume and sensitivity of health data generated by consumer-focused apps, devices, and platforms, including the potential analytics uses that can be made of such data.

Many of the privacy issues that face traditional health care entities in the big data era also apply to app developers, wearable device manufacturers, and other entities not part of the traditional health care ecosystem. These include questions of data minimization, retention, and secondary use. Notice and consent pose challenges, especially given the limits of presenting notices on mobile device screens, and the fact that consumer devices may be bought and used without consultation with a health care professional. Security is a critical issue as well.

However, the privacy and security provisions of the Heath Insurance Portability and Accountability Act (HIPAA) do not apply to most app developers, device manufacturers or others in the consumer health space. This has benefits to innovation, as innovators would otherwise have to struggle with the complicated HIPAA rules. However, the current vacuum also leaves innovators without clear guidance on how to appropriately and effectively protect consumers’ health data. Given the promise of health apps, consumer devices, and consumer-facing services, and given the sensitivity of the data that they collect and share, it is important to provide such guidance….

As the source of privacy guidelines, we look to the framework provided by the Fair Information Practice Principles (FIPPs) and explore how it could be applied in an age of big data to patient-generated data. The FIPPs have influenced to varying degrees most modern data privacy regimes. While some have questioned the continued validity of the FIPPs in the current era of mass data collection and analysis, we consider here how the flexibility and rigor of the FIPPs provide an organizing framework for responsible data governance, promoting innovation, efficiency, and knowledge production while also protecting privacy. Rather than proposing an entirely new framework for big data, which could be years in the making at best, using the FIPPs would seem the best approach in promoting responsible big data practices. Applying the FIPPs could also help synchronize practices between the traditional health sector and emerging consumer products….(More)”

What makes some federal agencies better than others at innovation

Curated on Posted on by Stefaan Verhulst

Tom Fox at the Washington Post: “Given the complexity and difficulty of the challenges that government leaders face, encouraging innovation among their workers can pay dividends. Government-wide employee survey data, however, suggest that much more needs to be done to foster this type of culture at many federal organizations.

According to that data, nearly 90 percent of federal employees are looking for ways to be more innovative and effective, but only 54 percent feel encouraged by their leaders to come up with new ways of doing work. To make matters worse, fewer than a third say they believe creativity and innovation are rewarded in their agencies.

It’s worth pausing to examine what sets apart those agencies that do. They tend to have developed innovative cultures by providing forums for employees to share and test new ideas, by encouraging responsible risk-taking, and by occasionally bringing in outside talent for rotational assignments to infuse new thinking into the workplace.

The Department of Health and Human Services (HHS) is one example of an agency working at this. In 2010 it created the Idea Lab, with the goal to “remove barriers HHS employees face and promote better ways of working in government.”

It launched an awards program as part of Idea Lab called HHS Innovates to identify promising, new ideas likely to improve effectiveness. And to directly support implementing these ideas, the lab launched HHS Ignites, which provides teams with seed funding of $5,000 and a three-month timeframe to work on approved action plans. When the agency needs a shot of outside inspiration, it has its Entrepreneurs-in-Residence program, which enlists experts from the private and nonprofit sectors to join HHS for one or two years to develop new approaches and improve practices….

While the HHS Idea Lab program is a good concept, it’s the agency’s implementation that distinguishes it from other government efforts. Federal leaders elsewhere would be wise to borrow a few of their tactics.

As a starting point, federal leaders should issue a clear call for innovation that demands a measurable result. Too often, leaders ask for changes without any specificity as to the result they are looking to achieve. If you want your employees to be more innovative, you need to set a concrete, data-driven goal — whether that’s to reduce process steps or process times, improve customer satisfaction or reduce costs.

Secondly, you should help your employees take their ideas to implementation by playing equal parts cheerleader and drill sergeant. That is, you need to boost their confidence while at the same time pushing them to develop concrete action plans, experiments and measurements to show their ideas deliver results….(More)”

Overcoming Barriers to Data Sharing in Public Health: A Global Perspective

Curated on Posted on by Stefaan Verhulst

Chatham House Paper by Michael Edelstein and Dr Jussi Sane: “Political, economic and legal obstacles to data sharing in public health will be the most challenging to overcome.

  • The interaction between barriers to data sharing in public health is complex, and single solutions to single barriers are unlikely to be successful. Political, economic and legal obstacles will be the most challenging to overcome.
  • Public health data sharing occurs extensively as a collection of subregional and regional surveillance networks. These existing networks have often arisen as a consequence of a specific local public health crisis, and should be integrated into any global framework.
  • Data sharing in public health is successful when a perceived need is addressed, and the social, political and cultural context is taken into account.
  • A global data sharing legal framework is unlikely to be successful. A global data governance or ethical framework, supplemented by local memoranda of understanding that take into account the local context, is more likely to succeed.
  • The International Health Regulations (IHR) should be considered as an infrastructure for data sharing. However, their lack of enforcement mechanism, lack of minimum data sets, lack of capacity assessment mechanism, and potential impact on trade and travel following data sharing need to be addressed.
  • Optimal data sharing does not equate with open access for public health data….(More)”

 

The big medical data miss: challenges in establishing an open medical resource

Curated on Posted on by Stefaan Verhulst

Eric J. Topol in Nature: ” I call for an international open medical resource to provide a database for every individual’s genomic, metabolomic, microbiomic, epigenomic and clinical information. This resource is needed in order to facilitate genetic diagnoses and transform medical care.

“We are each, in effect, one-person clinical trials”

Laurie Becklund was a noted journalist who died in February 2015 at age 66 from breast cancer. Soon thereafter, the Los Angeles Times published her op-ed entitled “As I lay dying” (Ref. 1). She lamented, “We are each, in effect, one-person clinical trials. Yet the knowledge generated from those trials will die with us because there is no comprehensive database of metastatic breast cancer patients, their characteristics and what treatments did and didn’t help them”. She went on to assert that, in the era of big data, the lack of such a resource is “criminal”, and she is absolutely right….

Around the same time of this important op-ed, the MIT Technology Review published their issue entitled “10 Breakthrough Technologies 2015” and on the list was the “Internet of DNA” (Ref. 2). While we are often reminded that the world we live in is becoming the “Internet of Things”, I have not seen this terminology applied to DNA before. The article on the “Internet of DNA” decried, “the unfolding calamity in genomics is that a great deal of life-saving information, though already collected, is inaccessible”. It called for a global network of millions of genomes and cited theMatchmaker Exchange as a frontrunner. For this international initiative, a growing number of research and clinical teams have come together to pool and exchange phenotypic and genotypic data for individual patients with rare disorders, in order to share this information and assist in the molecular diagnosis of individuals with rare diseases….

an Internet of DNA — or what I have referred to as a massive, open, online medicine resource (MOOM) — would help to quickly identify the genetic cause of the disorder4 and, in the process of doing so, precious guidance for prevention, if necessary, would become available for such families who are currently left in the lurch as to their risk of suddenly dying.

So why aren’t such MOOMs being assembled? ….

There has also been much discussion related to privacy concerns that patients might be unwilling to participate in a massive medical information resource. However, multiple global consumer surveys have shown that more than 80% of individuals are ready to share their medical data provided that they are anonymized and their privacy maximally assured4. Indeed, just 24 hours into Apple’s ResearchKit initiative, a smartphone-based medical research programme, there were tens of thousand of patients with Parkinson disease, asthma or heart disease who had signed on. Some individuals are even willing to be “open source” — that is, to make their genetic and clinical data fully available with free access online, without any assurance of privacy. This willingness is seen by the participants in the recently launched Open Humans initiative. Along with the Personal Genome Project, Go Viral and American Gut have joined in this initiative. Still, studies suggest that most individuals would only agree to be medical research participants if their identities would not be attainable. Unfortunately, to date, little has been done to protect individual medical privacy, for which there are both promising new data protection technological approaches4 and the need for additional governmental legislation.

This leaves us with perhaps the major obstacle that is holding back the development of MOOMs — researchers. Even with big, team science research projects culling together hundreds of investigators and institutions throughout the world, such as the Global Alliance for Genomics and Health (GA4GH), the data obtained clinically are just as Laurie Becklund asserted in her op-ed — “one-person clinical trials” (Ref. 1). While undertaking the construction of a MOOM is a huge endeavour, there is little motivation for researchers to take on this task, as this currently offers no academic credit and has no funding source. But the transformative potential of MOOMs to improve medical care is extraordinary. Rather than having the knowledge die with each of us, the time has come to take down the walls of academic medical centres and health-care systems around the world, and create a global knowledge medical resource that leverages each individual’s information to help one another…(More)”

A New Source of Data for Public Health Surveillance: Facebook Likes

Curated on Posted on by Stefaan Verhulst

Paper by Steven Gittelman et al in the Journal of Medical Internet Research: “The development of the Internet and the explosion of social media have provided many new opportunities for health surveillance. The use of the Internet for personal health and participatory health research has exploded, largely due to the availability of online resources and health care information technology applications [1–8]. These online developments, plus a demand for more timely, widely available, and cost-effective data, have led to new ways epidemiological data are collected, such as digital disease surveillance and Internet surveys [8–25]. Over the past 2 decades, Internet technology has been used to identify disease outbreaks, track the spread of infectious disease, monitor self-care practices among those with chronic conditions, and to assess, respond, and evaluate natural and artificial disasters at a population level [6,8,11,12,14,15,17,22,26–28]. Use of these modern communication tools for public health surveillance has proven to be less costly and more timely than traditional population surveillance modes (eg, mail surveys, telephone surveys, and face-to-face household surveys).

The Internet has spawned several sources of big data, such as Facebook [29], Twitter [30], Instagram [31], Tumblr [32], Google [33], and Amazon [34]. These online communication channels and market places provide a wealth of passively collected data that may be mined for purposes of public health, such as sociodemographic characteristics, lifestyle behaviors, and social and cultural constructs. Moreover, researchers have demonstrated that these digital data sources can be used to predict otherwise unavailable information, such as sociodemographic characteristics among anonymous Internet users [35–38]. For example, Goel et al [36] found no difference by demographic characteristics in the usage of social media and email. However, the frequency with which individuals accessed the Web for news, health care, and research was a predictor of gender, race/ethnicity, and educational attainment, potentially providing useful targeting information based on ethnicity and income [36]. Integrating these big data sources into the practice of public health surveillance is vital to move the field of epidemiology into the 21st century as called for in the 2012 US “Big Data Research and Development Initiative” [19,39].

Understanding how big data can be used to predict lifestyle behavior and health-related data is a step toward the use of these electronic data sources for epidemiologic needs…(More)”

What, Exactly, Do You Want?

Curated on Posted on by Stefaan Verhulst

Cass Sunstein at the New York Times: “Suppose that you value freedom of choice. Are you committed to the mere opportunity to choose, or will you also insist that people actually exercise that opportunity? Is it enough if the government, or a private institution, gives people the option of going their own way? Or is it particularly important to get people to say precisely what they want? In coming decades, these seemingly abstract questions will grow in importance, because they will decide central features of our lives.

Here’s an example. Until last month, all 50 states had a simple policy for voter registration: If you want to become a voter, you have the opportunity to register. Oregon is now the first state to adopt a radically different approach: If the relevant state officials know that you live in Oregon and are 18 or older, you’re automatically registered as a voter. If you don’t want to be one, you have the opportunity to opt out.

We could easily imagine a third approach. A state might decide that if you want some kind of benefit — say, a driver’s license — you have to say whether you want to register to vote. Under this approach, the state would require you to make an active choice about whether to be a voter. You would have to indicate your desires explicitly.

In countless contexts, the government, or some private institution, must decide among three possible approaches: Give people the opportunity to opt in; give people the opportunity to opt out; or require people to make some kind of active choice. For example, an employer may say that employees will be enrolled in a pension plan only if they opt in. Alternatively, it may automatically enroll employees in a pension plan (while allowing them the opportunity to opt out). Or it may instead tell employees that they can’t start work unless they say whether they want to participate in a pension plan.

You may think that while the decision raises philosophical puzzles, the stakes are small. If so, you would be wrong; the decision can have huge consequences. By itself, the opportunity to choose is not all that matters, because many people will not exercise that opportunity. Inertia has tremendous force, and people tend to procrastinate. If a state or a private company switches from a system of opt-out to one of opt-in, or vice versa, it can have major effects on people’s lives.

For example, Oregon expects that its new policy will produce up to 300,000 new registered voters. In 2004, Congress authorized the Department of Agriculture to allow states and localities to automatically enroll eligible poor children in school meal programs, rather than requiring their parents to sign them up. As a result, millions of such children now have access to school meals. In many nations, including the United States, Britain and Denmark, automatic enrollment in pension plans has significantly increased the number of employees who participate in pension plans. The Affordable Care Act builds on this practice with a provision that will require large employers to enroll employees automatically in health insurance plans.

In light of findings of this kind (and there are many more), a lot of people have argued that people would be much better off if many institutions switched, today or tomorrow, from “opt in” designs to “opt out.” Often they’re right; “opt out” can be a lot better. But from the standpoint of both welfare and personal freedom, opt out raises problems of its own, precisely because it does not involve an actual exercise of the power to choose….(More)

21st-Century Public Servants: Using Prizes and Challenges to Spur Innovation

Curated on Posted on by Stefaan Verhulst

Jenn Gustetic at the Open Government Initiative Blog: “Thousands of Federal employees across the government are using a variety of modern tools and techniques to deliver services more effectively and efficiently, and to solve problems that relate to the missions of their Agencies. These 21st-century public servants are accomplishing meaningful results by applying new tools and techniques to their programs and projects, such as prizes and challenges, citizen science and crowdsourcing, open data, and human-centered design.

Prizes and challenges have been a particularly popular tool at Federal agencies. With 397 prizes and challenges posted on challenge.gov since September 2010, there are hundreds of examples of the many different ways these tools can be designed for a variety of goals. For example:

  • NASA’s Mars Balance Mass Challenge: When NASA’s Curiosity rover pummeled through the Martian atmosphere and came to rest on the surface of Mars in 2012, about 300 kilograms of solid tungsten mass had to be jettisoned to ensure the spacecraft was in a safe orientation for landing. In an effort to seek creative concepts for small science and technology payloads that could potentially replace a portion of such jettisoned mass on future missions, NASA released the Mars Balance Mass Challenge. In only two months, over 200 concepts were submitted by over 2,100 individuals from 43 different countries for NASA to review. Proposed concepts ranged from small drones and 3D printers to radiation detectors and pre-positioning supplies for future human missions to the planet’s surface. NASA awarded the $20,000 prize to Ted Ground of Rising Star, Texas for his idea to use the jettisoned payload to investigate the Mars atmosphere in a way similar to how NASA uses sounding rockets to study Earth’s atmosphere. This was the first time Ted worked with NASA, and NASA was impressed by the novelty and elegance of his proposal: a proposal that NASA likely would not have received through a traditional contract or grant because individuals, as opposed to organizations, are generally not eligible to participate in those types of competitions.
  • National Institutes of Health (NIH) Breast Cancer Startup Challenge (BCSC): The primary goals of the BCSC were to accelerate the process of bringing emerging breast cancer technologies to market, and to stimulate the creation of start-up businesses around nine federally conceived and owned inventions, and one invention from an Avon Foundation for Women portfolio grantee.  While NIH has the capacity to enable collaborative research or to license technology to existing businesses, many technologies are at an early stage and are ideally suited for licensing by startup companies to further develop them into commercial products. This challenge established 11 new startups that have the potential to create new jobs and help promising NIH cancer inventions support the fight against breast cancer. The BCSC turned the traditional business plan competition model on its head to create a new channel to license inventions by crowdsourcing talent to create new startups.

These two examples of challenges are very different, in terms of their purpose and the process used to design and implement them. The success they have demonstrated shouldn’t be taken for granted. It takes access to resources (both information and people), mentoring, and practical experience to both understand how to identify opportunities for innovation tools, like prizes and challenges, to use them to achieve a desired outcome….

Last month, the Challenge.gov program at the General Services Administration (GSA), the Office of Personnel Management (OPM)’s Innovation Lab, the White House Office of Science and Technology Policy (OSTP), and a core team of Federal leaders in the prize-practitioner community began collaborating with the Federal Community of Practice for Challenges and Prizes to develop the other half of the open innovation toolkit, the prizes and challenges toolkit. In developing this toolkit, OSTP and GSA are thinking not only about the information and process resources that would be helpful to empower 21st-century public servants using these tools, but also how we help connect these people to one another to add another meaningful layer to the learning environment…..

Creating an inventory of skills and knowledge across the 600-person (and growing!) Federal community of practice in prizes and challenges will likely be an important resource in support of a useful toolkit. Prize design and implementation can involve tricky questions, such as:

  • Do I have the authority to conduct a prize or challenge?
  • How should I approach problem definition and prize design?
  • Can agencies own solutions that come out of challenges?
  • How should I engage the public in developing a prize concept or rules?
  • What types of incentives work best to motivate participation in challenges?
  • What legal requirements apply to my prize competition?
  • Can non-Federal employees be included as judges for my prizes?
  • How objective do the judging criteria need to be?
  • Can I partner to conduct a challenge? What’s the right agreement to use in a partnership?
  • Who can win prize money and who is eligible to compete? …(More)

Data Science and Ebola

Curated on Posted on by Stefaan Verhulst

Inaugural Lecture by Aske Plaat on the acceptance of the position of professor of Data Science at the Universiteit Leiden: “…Today, everybody and everything produces data. People produce large amounts of data in social networks and in commercial transactions. Medical, corporate, and government databases continue to grow. Ten years ago there were a billion Internet users. Now there are more than three billion, most of whom are mobile.1 Sensors continue to get cheaper and are increasingly connected, creating an Internet of Things. The next three billion users of the Internet will not all be human, and will generate a large amount of data. In every discipline, large, diverse, and rich data sets are emerging, from astrophysics, to the life sciences, to medicine, to the behavioral sciences, to finance and commerce, to the humanities and to the arts. In every discipline people want to organize, analyze, optimize and understand their data to answer questions and to deepen insights. The availability of so much data and the ability to interpret it are changing the way the world operates. The number of sciences using this approach is increasing. The science that is transforming this ocean of data into a sea of knowledge is called data science. In many sciences the impact on the research methodology is profound—some even call it a paradigm shift.

…I will address the question of why there is so much interest in data. I will answer this question by discussing one of the most visible recent challenges to public health of the moment, the 2014 Ebola outbreak in West Africa…(More)”

User Experience is a Social Justice Issue

Curated on Posted on by Stefaan Verhulst

Sumana Harihareswara at code4lib: “…Before I worked in open source, I worked in customer service. I saw first-hand how design flaws (in architecture, signage, and websites) could frustrate and drive away customers and make more work for me. Every time I participated in an open source project — AltLaw, GNOME, MediaWiki, and more — I’ve brought that experience with me. I found it particularly striking that small changes on Wikipedia could cause large changes in user behavior, as I discuss in this essay, which is adapted from my keynote speech.
This issue goes beyond software, as I explain with the healthcare and banking examples. The spark that caused me to write the speech was reading Professor Lisa J. Servon’s piece in The Atlantic about the usability of storefront check cashing services; I saw a pattern where poor user experience repels people from crucial and empowering services, and decided, in a flash of anger and inspiration, to write “User Experience is a Human Rights Issue.”…

The Last Mile Problem

The largest hurdles we as technologists face are choosing to make the right things in the first place and choosing to make them usable. In the 1990’s, telecommunications companies laid down a lot of fiber to connect big hubs to one another, but often it took years to connect those hubs to the actual houses and schools and shops and offices, because it was expensive, or because companies were not creative enough to do it well. This is called the “last mile problem,” and I think usability has a similar problem. We have to be creative and disciplined enough to actually provide services in a way that people can use them.
When we’re building services for people, we often have a lot more practice seeing things from the computer’s point of view or from the data’s point of view than from another person’s point of view. In tech, we understand how to build arteries better than we understand how to build capillaries. Personally, I think capillaries are more interesting than arteries. Maybe it’s just personal temperament, but I like all the little surprising details of how people end up experiencing the ripple effects of big new systems, and how users actually interact with the user interface of a service, especially ones that we don’t really think of as having a user interface. Like taxes, or healthcare, or hotels. All these big systems end in little capillaries, where people exchange information or get healed or get whatever they need. And when those capillaries aren’t working correctly, then those people just don’t get what they need. The hubs are connected to each other, but people aren’t connected to the hubs.
Over and over, in lots of different fields, we see that bad usability makes a huge difference. When choosing between two services, people will make very different choices, depending on which service actually seems designed around the user’s needs….(More)”