Creating an impactful design can be subjective. Personal preference may lead one person to favor a particular design technique, while another person may prefer a completely different approach. And while there will always be variances in personal preference, new trends frequently emerge in the world of art and design. Often, these trends come as a reaction to or even rejection of previous trends.

Even in a crowded and competitive market, I've noticed that designers who strive to stand out often go with a more simplistic approach in an effort to connect with the user. The same is true in web design. The current trend, resulting from increasing complexity in web design, is to take a minimalist approach to create more meaningful engagement. Across all platforms and industries, designers leverage minimalism as a way to streamline their designs and focus the user’s attention on what is most important.

What Is Minimalist Design?

Like all other trends, minimalism was influenced by other trends in design, such as the geometric abstractions associated with the Bauhaus movement and Zen philosophy in Japanese culture. Minimalism focuses on simplicity, expressing only those details that are most essential in a design. It is often used as a way to showcase a subject’s true form, accentuating its inherent elegance. In web design, this means stripping away anything that would draw the user away from the focus of the design. This strategy pushes designers to prioritize the various elements in their designs and eliminate anything that is not necessary, simplifying the user interface (UI).

Characteristics Of Minimalist Design

Minimalist design is best characterized by the phrase “Less is more.” Negative space is often used to great effect in web design, creating strikingly focused imagery. This lack of content can make a design feel simple and clean, while still being sophisticated. When coupled with dramatic photography, illustrations or typography, the message of the design becomes exceptionally clear to the user.

High contrast is also common in minimalist designs, drawing attention through its boldness. Designers may also choose to use a limited color scheme, sometimes deciding on a monochrome scheme. By limiting the use of color, they can clearly dictate the focal point of the design.

Impact Of Minimalism In Web Design

Web designs can be elevated with a minimalist approach by simplifying the overall interface for the user. Because designers have to establish the most important elements, each and every item they include in the design must have a purpose. If a piece of content does not support user tasks in some way, it must be eliminated. This can greatly simplify the user’s interaction on the site. When applied appropriately, minimalism can help designers achieve this simplicity while also enticing the user to explore the website.

Another benefit of minimalist web designs is that the lack of complexity in the content can lead to faster loading times and better screen compatibility, which improves the user experience (UX).

An Alternate Viewpoint

As a response to this minimalist trend in web design, some designers are shifting in the opposite direction toward maximalism. In contrast to minimalism, maximalist designs are characterized by a “More is more” approach that embraces vibrancy and overabundance. Bright colors, bold fonts, patterned backgrounds and complex animations are just some of the various features used in maximalist designs, both print and digital. To make these excessive elements successful, designers must use maximalism deliberately and strategically rather than haphazardly. When done right, maximalism can be a great way to make an impact.

For certain brands, a minimalist approach may not be in alignment with who they are. A brand wanting to convey a sense of bold liveliness may find that a minimalist design fails to showcase its energetic personality. It is therefore incredibly important to understand brand identity so that whatever design approach is used makes sense in relation to the brand as a whole.

Creativity and ingenuity will continue to evolve and uncover new trends in design. Minimalism has been used in web design to create compelling communication with its acutely focused messaging. Its powerful influence stirs the imagination and is sure to inspire future designers.

In a exact article published in the Science Advances Journal, researchers developed a one-step method to engineer multifunctional M1 phenotype macrophages from specialized membrane-fusogenic liposomes for effective adoptive cell therapy (ACT) against solid tumors.

Specifically, they fused an anti-CD47 (aCD47)–modified lipid shell of anti-phagocytosis-blocking repolarization-resistant membrane-fusogenic liposome (ARMFUL) into the M1 macrophage membrane surface, which simultaneously delivered colony-stimulating factor 1 (CSF-1) receptor inhibitor BLZ945-loaded core into the cytosol.

By blocking CD47, the ARMFUL boosted the macrophage's phagocytosis against the tumor, which enhanced ACT.

Study: Anti–phagocytosis-blocking repolarization-resistant membrane-fusogenic liposome (ARMFUL) for adoptive cell immunotherapy. Image Credit: Gorodenkoff/Shutterstock.com

Background

ACT is a well-recognized cancer treatment involving ex vivo engineering of immune effector cells, including natural killer (NK) cells, T-cells, macrophages, etc., and their reinfusion to identify and eliminate tumor cells.

There have been immense advancements in customizing ACTs; however, these technologies fail against many types of hematologic malignancies (solid tumors) due to their poor efficacy. Perhaps multiple immunological barriers in solid tumors resist the back-fused effector cells.

A pragmatic solution could be endowing effector cells with multiple functionalities by engineering two or more cellular targets. The other challenge, however, is the different spatial distributions of cellular targets in the cell.

Moreover, step-by-step engineering of multifunctional effector cells adds cost and complexity, further hindering their clinical translation. 

An unmet need exists for enhanced ACT against tumors using refined synchronous cell engineering of multiple targets at varying subcellular levels within a single process.

Since membrane-fusogenic liposomes mimic the natural membrane fusion process, the researchers hypothesized that these could be an optimal tool for refined cellular engineering for ACT enhancement. 

Cell engineering is a conglomerate of diverse bioengineering methodologies that intervene in intercellular/extracellular molecular targets of gene editing, metabolism, drug/protein regulation, and other important functionality endowment processes. 

About the study 

In the present study, researchers used a three-step process to construct ARMFUL. Next, they constructed bifunctional ARMFUL/M1 macrophages. Further, they evaluated the functionalities of ARMFUL/M1 in vitro. They first tested the anti-M2 polarization capacity of ARMFUL/M1 macrophages.  

Next, the team evaluated the phagocytosis ability of engineered ARMFUL/M1 macrophages using fluorescence-activated cell sorting (FACS).

They monitored the phagocytosis process with a live-cell dynamic imaging and analysis system. Furthermore, the researchers tested ARMFUL/M1 macrophages in vivo in a B16F10 melanoma-bearing mouse model. 

Results and conclusions

Compared to their non-engineered counterparts, ARMFUL/M1 resisted M2 polarization. Ribonucleic acid (RNA) sequencing analysis of ARMFUL/M1 with/without interleukin-4 (IL-4) treatment resisted M2 polarization by inhibiting colony-stimulating factor 1 receptor (CSF1R).

A gene ontology (GO) enrichment analysis also confirmed that they had enhanced immunological functions.

As expected, ARMFUL/M1 macrophages also showed enhanced tumor phagocytosis ability. Besides excellent anti-M2 polarization and enhanced tumor phagocytosis ability, ARMFUL/M1 macrophages exhibited antigen-presenting capacity (APC), which activates the adaptive arm of immunity against tumors.

In vivo, ARMFUL/M1 retained their M1 phenotype in solid tumors and repolarized aboriginal protumorigenic tumor-associated macrophages (TAMs) toward the M1 phenotype to effectively remodel the tumor microenvironment. Subsequently, they activated the immunity of systemic T cells to inhibit distant tumor progression.

According to the authors, the study developed a membrane fusion–mediated cell engineering technique, which is the first to utilize the membrane fusion effect for cell engineering to construct multifunctional effector cells. 

They used ARMFUL, specialized membrane-fusogenic liposomes, to engineer M1 phenotype macrophages with multiple functionalities in a single step for enhancing ACT against solid tumors. 

At the same time, ARMFUL simultaneously transported the core payload and a modified lipid shell to the cytosol and cell membrane, respectively.

This spatial distribution refined the efficiency of two engineering drugs, thereby improving the tumor phagocytic ability of macrophages and their anti-M2 polarization capacity.

This makes this approach unique from all existing sole-target engineered cellular therapeutics aimed at one active site target for remolding. 

All macrophages armed with ARMFUL had higher antitumor efficacy. With chimeric antigen receptors cell therapy (CAR), they could have many more biomedical applications and be highly clinically useful.

In the future, ARMFUL could be adapted to accommodate other combinations of cell engineering reagents. It would facilitate the development of different types of adaptive effector cells with diverse functionalities, which might turn ARMFUL into a universal platform to personalize cell behaviors/functions for the improved antitumor effect of ACTs. 

Likewise, ARMFUL could help remold a larger set of immune effector cells used in ACT, e.g., NK and stem cells. In other words, it is a versatile toolbox for designing personalized and effective cell-based immunotherapies for cancer treatment.

Design of experiments enables engineers to demonstrate or understand a process while providing information required for achieving regulatory compliance.

A valuable method for predicting process variability, design of experiments (DOE) allows medical device engineers to validate their processes in order to Strengthen product quality. On February 12 from 10:45 to 11:15 a.m., Robert Launsby, president of Launsby Consulting, will present a workshop at MD&M West exploring the advantages of the DOE approach. In the following guest blog, he explains the advantages of the DOE strategy over other experimental methods and highlights the ability of this method to predict whether a process is likely to meet engineering specifications.

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Formally developed in the early 1900s, experimental design is now more than 100 years old. But only during the last 15 years has it been applied worldwide by masses of engineers, scientists, and technicians. The primary reasons for this shift is the availability of powerful computers, the prevalence of easy-to-use software, and the accessibility of teaching approaches that provide a practical and pragmatic understanding of experimental design.

In a graduate course on statistical experimental design many decades ago, my classmates and I learned to invert matrices, transform matrices, crunch lots of numbers, and generate ANOVA and regression tables using HP and TI calculators. We became skillful in crunching the numbers--I got A's in the class--but had no understanding of how to tackle a typical industrial problem using design of experiments tools. Fortunately, many of the classes taught today in industry are much more practical and applications oriented.

Experimental design involves systematic and controlled changes to the input parameters in a process in order to mathematically estimate the impact of the process's key output parameters. Each input parameter under consideration is varied at two or more setpoints (or levels). Based on the number of variables involved in the design, the experimenter uses software to exploit and evaluate a balanced family of trials. Relationships between input factors and output variables (responses) can be readily visualized using simple graphs. And by applying proper experimental design approaches, statistically and practically significant data can also be assessed.

The DOE approach is not the only strategy available for conducting design experiments. Another method, known as one-factor-at-a-time experimentation, relies on an easier to understand set of tests and offers "pick the winner" analysis. Especially before the advent of the computer/software revolution in the last few decades, one-factor-at-a-time experimentation was a popular technique for conducting experiments. Nevertheless, this technique has several weaknesses. It cannot detect interactions (synergism) between factors and responses; it becomes convoluted if two or more response outputs must be traded off; it cannot mathematically determine the individual contribution of each input factor to changes in the response; and it cannot perform simple graphical analysis using such aids as main effects plots, interaction plots, contour plots, and Pareto charts showing factor effects for response.

Yet another experimental method can be dubbed "run a bunch of tests based on what we think will provide useful results." However, this method tends to be much less efficient than DOE, does not lend itself to simple analysis approaches, and does not allow for precision in estimating input factor effects.

Superior to other experimental methods, design of experiments enables us to demonstrate or further a fundamental understanding of a process while providing key information required to achieve regulatory compliance. Focusing on drug and biological products, an FDA Guidance Document titled "Process Validation: General Principles and Practices" (January 2011) discusses design of experiments as a valuable tool for understanding and characterizing processes. Hopefully, the language in this document will eventually become part of the guidance for medical device process validation as well. Used during process characterization or as part of the operational qualification phase of process validation--in other words, before the formal process validation stage--design of experiments helps us to understand the key input variables and the optimal setpoint for each input variable.

Many DOE practitioners ask, how much input variation can we allow while ensuring that the process delivers response variation that meets engineering specifications? To ascertain the expected amount of variation in a response given known variation in key input variables, a properly designed experiment can generate a mathematical model that can then be used to perform sensitivity analysis using simulation tools such as Monte Carlo analysis. As part of characterization or the operational qualification phase of process validation, such an analysis allows us to predict whether the process has the potential to meet engineering specifications during the performance qualification stage and during ongoing manufacturing.
 

Today the design of things that involve human interaction, such as programs, product delivery, and services, is more art than science. Here is how it typically works: We use our creativity to brainstorm a few big ideas, experts decide which one they like, and then investors bet on the winner, often with billions of dollars at stake.

This way of design thinking should be replaced by a superior method that can enable us to innovate with more success and less risk. Specifically, we can use scientific insights to generate new ideas and then systematically test and iterate on them to arrive at one that works.

Advances in two academic fields afford this opportunity. The first is behavioral science, which gives us empirical insights into how people interact with their environment and each other under different conditions. Behavioral science encompasses decades of research from various fields, including psychology, marketing, neuroscience, and, most recently, behavioral economics. For example, studies reveal that shorter deadlines lead to greater responsiveness than longer ones,¹ that too much choice leads people to choose nothing,² and many more observations, often counterintuitive, about how people react to specific elements of their context.

The second academic field is impact evaluation. Economists have used randomized controlled trials (RCTs) and other experimental methods to measure the impact of programs and policies. Such impact evaluations are becoming more and more common in the social sector and in government. These methods allow us to test whether an innovation actually achieves the outcomes that the designer sought.

Taking a scientific approach also solves another common problem: Sometimes we do not even realize that there is something in need of rigorous, thoughtful design. When we look carefully, the success of most of what we design for people depends as much, if not more, on the human interaction as on the physical product. For example, the first iPhone offered essentially the same functions (phone, calendar, address book, etc.) as a BlackBerry, but it totally changed the experience of using those functions.

In the social and public sectors, programs and services are made up largely of human interactions. And yet anything involving human interaction can be designed more scientifically, and more successfully, when behavioral science and impact evaluation are applied. For instance, a vaccine is a technological product, but how and when parents get their children vaccinated, and how they are reminded to do so, is as much a part of the innovation as the vaccine itself. Poorly designed interactions make products less successful and can also underlie serious social problems.³

By putting behavioral science and impact evaluation together—a methodology we call behavioral design—we can design more like engineers than like artists. We can use behavioral science to develop ideas that are much more likely to work than those relying entirely on intuition. And we can rigorously test those ideas to determine which ones truly work. Following the model of engineering and scientific progress, we can build on prior success to make enormous advances that, under previous approaches, would not be possible.

A Better Methodology

At ideas42, the behavioral science innovation lab I co-lead, we encounter many different approaches to innovation among our partners. I have also spent considerable time comparing notes with experts in design thinking, attending design workshops, and reading about design methodologies. The typical approaches for innovation range from quickly brainstorming some ideas in a boardroom to using some version of human-centered design (HCD). Fundamentally, all of these approaches aim to generate “big ideas” that appeal to the intuition of a few decision makers considered experts in the area where the idea is to be implemented.

HCD appears to be the methodology of choice for a significant, and growing, number of organizations. The most advanced version begins with defining the problem or design mandate, and then conducts qualitative research with potential users and proceeds through a series of structured exercises to promote creative thinking. The design team may also test some crude prototypes to get feedback along the way. This approach is called “human-centered” because it focuses on users’ and other stakeholders’ needs and preferences.

In the qualitative research phase, designers use ethnographic techniques such as qualitative interviewing and observation. They not only interview potential users but also may talk to others, such as program administrators and front-line staff involved in delivering a program or product. In the design phase, HCD employs several techniques to enhance creativity (which remain useful in the next-generation behavioral design methodology as well). Finally, HCD ends with trying a few prototypes with a handful of potential users. Some ethnographic research methods are incorporated into HCD, but on the whole the approach is still much closer to an art than a science.

It is time to build on HCD with a better method. Let us begin our investigation by comparing how engineers invent new technology. Two features stand out. First, engineers rely on a rich set of insights from science to develop new ideas. Every invention builds on countless previous attempts. For example, the Wright brothers are credited with inventing the airplane, but the key parts of their design leaned on previous inventions. The wing was based on science that went back to 1738, when Daniel Bernoulli discovered his principle about the relationship between pressure and the speed with which a fluid is moving. The engine design was borrowed from automotive engines invented more than 25 years earlier. They were able to test model wings in a wind tunnel thanks to Frank H. Wenham, who had invented that critical apparatus 30 years before that, in 1871.⁴

Second, contrary to popular belief, inventions do not come simply from a single flash of insight, but rather from painstaking refinement in small steps. Sir James Dyson, the famous vacuum cleaner tycoon, went through 5,126 failed iterations of his new wind tunnel design to separate dirt from air before he landed on the right one.⁵ Inventors sometimes iterate only on particular components before working on the complete invention. For example, the Wright brothers tested some 200 wing designs in a wind tunnel before settling on the right one.

Why do engineers work so differently from those of us who are designing for human interactions? Until recently, we did not have a sufficiently large body of scientific insights that describes how humans interact with their environment, and each other, under different conditions. True, the field of user-experience design offers some insights, but it is very new and is still restricted to certain elements of digital interactions such as Web-page layout and font size. Direct marketers within for-profit businesses have experimented with letters and phone scripts for years, but those findings also cover a very narrow set of interactions and are often not public.

The second engineering feature—experimenting and iterating—is also hard to replicate, because measuring whether something “works” in this case is more complex than simply turning on a piece of technology and playing with it. We must first clearly define what outcomes we want from the design, devise a way to measure them, and finally run a test that reliably tells us whether our design is achieving them.

More Rigorous Testing of Ideas

The problem with HCD and similar approaches to innovation is that they depend too much on intuition. Research has repeatedly shown that our intuitions about human beings are often wrong. Take the commonsensical idea that penalties always help prevent people from engaging in bad behaviors; this notion may have intuitive appeal, but it has proven false. For example, in a study of Israeli day-care centers that sanctioned parents for being late to pick up their children, researchers found that penalties made parents even more likely to be late.⁶ This is because they viewed the penalty as a cheap price for the option to be late, versus feeling bound by a social obligation to be timely.

Not only do the social and behavioral sciences provide us better starting points, but it also enables us to prototype and test ideas more readily, because we can measure if they are working using impact evaluation methods as well as lab testing procedures from experimental psychology. We can then iterate and Strengthen on the idea until we have a solution ready for implementation.

The behavioral design methodology incorporates HCD’s fundamental approach of being human centered and thoughtful, but adds scientific insights and iterative testing to advance HCD in three significant ways. First, it applies observations about people from experimental academic research. HCD’s reliance solely on self-reported and intuitive insights presents a risk, since so much human behavior is unconscious and not transparent. Also, psychology research shows that people’s self-perception is biased in several ways.⁷ When we do supplement academic insights with qualitative research, we can use behavioral science to make the latter less vulnerable to bias. For example, we can get more unvarnished answers by asking subjects what their peers typically do rather than what they themselves do. When asked about themselves, subjects may be embarrassed to admit to certain behaviors or may feel compelled to provide what they assume the interviewer thinks is the “right” answer.

Second, behavioral design can enhance HCD in the design phase. The behavioral science literature can contribute ideas for solutions based on previously tested interventions. As behavioral design becomes more widely used, more and more data will become available on what designs work and under what conditions. In filtering ideas, we can use behavioral science to anticipate which solutions are likely to suffer from behavioral problems such as low adoption by participants or misperception of choices.

Third, this new approach improves upon HCD by adding more rigorous testing. Many HCD practitioners do test their ideas in prototype with users. While helpful, and part of behavioral design as well, quick user testing cannot tell us whether a solution works. Behavioral design leverages experimental methods to go much further without necessarily adding considerable cost or delay.

Using this approach, we test whether something works—whether it triggers a desired behavioral result—rather than whether the subject thinks something works. We can also test a single component of more complex designs, such as whether a particular piece of information included on a Web page makes a difference, in a lab setting with subjects from our target audience. This is analogous to aeronautical engineers testing wing designs in wind tunnels. By testing and iterating in the field, we do not need to bet on an untested big idea but instead can systematically develop one that we know works. Testing is also what makes it possible, in the design phase, to build on previous successful ideas.

ideas42’s work includes many examples of using behavioral design to invent solutions to tough social problems. For example, we recently worked with Arizona State University (ASU) to encourage more eligible students to apply for a special federal work-study program called SEED. In fall 2014, before we started working with ASU, only 11 percent of eligible students were applying for SEED jobs, leaving nearly $700,000 in financial aid funds unused. ASU wanted our help to increase this proportion.

Diagnosing the problem through a behavioral lens, and interviewing students and staff, we learned that students mistakenly believed that SEED jobs were menial and low-wage. Some thought that a work-study job would interfere with their education rather than complement it. Others intended to apply but missed the deadline or failed even to open the e-mail announcing the program. We designed a series of 12 e-mails to attempt to mitigate all of these barriers. The e-mails dispelled the misperceptions about workstudy jobs by stating the correct facts. They made the deadline more salient by reminding students how many dollars of aid they stood to lose. Behavioral research shows that losses loom larger than gains, so the loss framing promised to be more impactful than telling students how much they stood to gain. The e-mails asked students to make a specific plan for when they would complete the work-study job application to reduce the chance that they would forget or procrastinate past the deadline. These behaviorally informed e-mails were compared against a control group of 12 e-mails that contained only basic information about how to apply to the SEED program.

With the redesigned e-mails, which ASU has now adopted, 28 percent more students applied for jobs, and the number of total applications increased by 56 percent. As we were sending 12 e-mails, we used the opportunity to test 12 different subject lines to try to maximize the number of students who opened the e-mail. In five out of the 12 cases, the rate of opening increased by 50 percent or more, relative to a typical subject line. A subject line that increased the open rate from 37 percent to 64 percent made students feel special: “You have something other freshmen don’t.” The control in this case was commonly used language to remind the recipient of impending deadlines: “Apply now! SEED jobs close Thursday.”

The Behavioral Design Methodology

Efforts like this one may sound like nothing more than trial and error, but a systematic and scientific process underlies them that tracks the success of engineering or medicine more closely than HCD. It begins with defining a clear problem, diagnosing it, designing solutions, testing and refining the effectiveness of those ideas, and then scaling the solutions.⁸ It also starts from a body of knowledge from behavioral science, rather than intuition and guesswork, so that the solutions tried are more likely to succeed.

Let us take a closer look at these steps:

1. Define. The first step is to define the problem carefully to ensure that no assumptions for causes or solutions are implied and that the desired outcome is clear. For example, organizations we serve commonly ask: “How do we help our clients understand the value of our program?” In this formulation, the ultimate outcome is not explicitly defined, and there is an assumption that the best way to secure the outcome is the program (or product) in question. Say the relevant program is a financial education workshop. In this case, we do not know what behaviors the workshop is trying to encourage and whether classroom education is the best solution. We must define the problem only in terms of what behaviors we are trying to encourage (or discourage), such as getting people to save more.

2. Diagnose. This intensive phase generates hypotheses for behavioral reasons why the problem may be occurring. To identify potential behavioral hurdles, this approach draws insights from the behavioral science literature and what we know about the particular situation. For example, in the ASU work-study project, we hypothesized that many students intended to apply but failed to follow through because they procrastinated past the deadline or simply forgot it. Both are common behavioral underpinnings for such an intention-action gap.

After generating some initial hypotheses, the next step is to conduct qualitative research and data analysis to probe which behavioral barriers may be most prevalent and what features of the context may be triggering them. Here, “context” refers to any element of the physical environment, and any and all experiences that the consumer or program’s beneficiary is undergoing, even her physical or mental state in the moment.

Qualitative research usually includes observation, mystery shopping (purchasing a product or experiencing a program incognito to study it firsthand), and in-depth interviews. Unlike typical qualitative research that asks many “why” questions, the behavioral approach focuses on “how” questions, since people’s post-hoc perceptions of why they did something are likely to be inaccurate.

3. Design. Having filtered down and prioritized the list of possible behavioral barriers via the diagnosis phase, we can generate ideas for solutions. Here many of the structured creativity techniques of HCD prove useful. When possible, it is best to test a few ideas rather than to guess which solution seems best. Solutions also change during their journey from the whiteboard to the field, as numerous operational, financial, legal, and other constraints invariably crop up. Such adaptations are critical to making them scalable.

4. Test. We can then test our ideas using RCTs, in which we compare outcomes for a randomly selected treatment group vis-à-vis those for a control group that receives no treatment or the usual treatment. Although RCTs in academic research are often ambitious, multiyear undertakings, we can run much shorter trials to secure results. An RCT run for academic purposes may need to measure several long-term and indirect outcomes from a treatment. Such measurement typically requires extensive surveys that add time and cost. For iterating on a design, by contrast, we may only measure proximate indicators for the outcomes we are seeking. These are usually available from administrative data (such as response to an e-mail campaign), so we can measure them within days or weeks rather than years. We measure long-term outcomes as a final check only after we have settled on a final solution.

When RCTs are impossible to run even for early indicators, solutions can be tested that approximate experimental designs. A more detailed description of these other methods is outside the scope of this article but is available through the academic literature on program evaluation and experimental design.

If the solution is complex, we first test a crude prototype with a small demo of users to refine the design.⁹ We can also test components of the design in a lab first, in the way that engineers test wing designs in a wind tunnel. For example, if we are designing a new product and want to refine how we communicate features to potential users, we can test different versions in a lab to measure which one is easiest to understand.

5. Scale. Strictly speaking, innovation could end at testing. However, scaling is often not straightforward, so it is included in the methodology. This step also has parallels with engineering physical products, in that designing how affordably to manufacture a working prototype is, in itself, an invention challenge. Sometimes engineers must design entirely new machines just for large-scale manufacturing.

Scaling could first involve lowering the cost of delivering the solution without compromising its quality. On the surface, this step would be a matter of process optimization and technology, but as behavioral solutions are highly dependent on the details of delivery, we must design such optimization with a knowledge of behavioral principles. For example, some solutions rely on building a trusted relationship between frontline staff and customers, so we would not be able to achieve a cost reduction by digitizing that interface. The second part of scaling is encouraging adoption of an idea among providers and individuals, which itself could benefit from a scientific, experimental process of innovation.

A Closer Look at the Methodology

To be fair, it is sometimes impossible to go through the full, in-depth behavioral design process. But even in these cases, an abridged version drawing on scientific insights rather than creativity alone is always feasible. Notice that the define, diagnose, and design stages of the behavioral design process apply the scientific method in two ways: They draw on insights from the scientific literature to develop hypotheses, and they collect data to refine those hypotheses as much as possible. The first of these steps can be accomplished even in a few hours by a behavioral designer with sufficient expertise. The second component of data collection and analysis takes more time but can be shortened while still preserving a scientific foundation for the diagnosis and design. Field testing with a large demo can be the most time-consuming, but lab tests can be completed within days if time is constrained.

Two sorts of hurdles typically confront the full behavioral design process: lack of time and difficulty measuring outcomes. In our experience, time constraints are rarely generated by the problem being addressed. More often, they have to do with the challenges of complex organizations, such as budget cycles, limited windows to make changes to programs or policies, or impatience among the leadership. If organizations begin to allocate budgets for innovation, these artificial time constraints will disappear.

To better understand working under a time constraint, consider ideas42’s work with South Africa’s Western Cape to reduce road deaths during the region’s alcohol-fueled annual holiday period. The provincial government had a small budget left in the current year for a marketing campaign and only a few weeks until the holiday season began. The ideas42 team had to design a simple solution fast; there was no time to set up an RCT with a region-wide marketing campaign. The team instead used an abridged version of the first three stages to design a solution grounded in behavioral science. Quick diagnosis revealed that people were not thinking about safe driving any more than usual during the holidays, despite the higher risk from drunk driving. To make safe driving more salient, ideas42 designed a lottery in which car owners were automatically registered to win but would lose their chance if they were caught for any traffic violations. That design used two behavioral principles coming out of Prospect Theory,¹⁰ which tells us that people tend to overestimate small probabilities when they have something to gain, and that losses feel about twice as bad as the equivalent gain feels good.

Applying the first principle, we used a lottery, a small chance of winning big, rather than a small incentive given to everyone. Using the second, we gave people a lottery ticket and then threatened to take it away. Since an RCT was not feasible, we measured results by comparing road fatalities in the treatment period with road fatalities in the same month of the previous year; this showed a 40 percent reduction in road fatalities. There were no known changes in enforcement or any other policies. While ideas42 was not able to continue to collect data in subsequent years, because its contract ended, the program saw success in subsequent years as well, according to our contacts in government.

Adopting Behavioral Design

If you were convinced of behavioral design’s value and wanted to take the leap, how would you do it? There are resources available, and many more are still in the works. Behavioral insights are not yet readily available in one place for practitioners to access, but are instead spread out over a vast literature spanning many academic disciplines, including psychology, economics, neuroscience, marketing, political science, and law. Results from applications of behavioral science are even more distributed because many are self-published by institutions such as think tanks, impact evaluation firms, and innovation consultancies.

To mitigate this problem, ideas42, in partnership with major universities and institutions that practice behavioral design in some form, is building an easily searchable Web-based resource as well as a blog that will make it possible to find ready-to-use behavioral insights in one place. In the meantime, some of these organizations, including ideas42, also offer classes that teach elements of behavioral design as well as some key insights from behavioral science that practitioners would need in order to do behavioral design. As the practice of behavioral design is adopted more widely, and its use generates more insights, it will become more powerful. Like technology, it will be able to continue to build on previous discoveries.

Organizations and funders would also do well to adopt the behavioral design approach in their thinking more generally. Whenever someone proposes a new approach for innovation, people scour the methodology for the secret sauce that will transform them into creative geniuses. In this case, the methodology applications of behavioral science, in themselves, do have a lot to offer. But even more potential lies in changing organizational cultures and funding models to support a scientific, evidence-based approach to designing interventions. Here are three suggestions about how organizations can adopt behavior design:

Fund a process (and people good at it), not ideas. | Today’s model for funding innovation typically begins with a solution, not a problem. Funders look to finance the testing or scaling up of a new big idea, which by definition means there is no room for scientifically analyzing the problem and then, after testing, developing a solution. Funders should reject this approach and instead begin with the problem and finance a process, and people they deem competent, to crack that problem scientifically. To follow this path, funders must also become comfortable with larger investments in innovation. The behavioral design approach costs a lot more than whiteboards, sticky notes, and flip charts—the typical HCD tools—but the investment is worth it.

Embrace failure. | In a world where ideas are judged on expert opinion and outcomes are not carefully measured, solutions have no way of failing once they leave the sticky-note phase and get implemented. In a new world where ideas must demonstrably work to be successful, failure is built into the process, and the lessons learned from these failures are critical to that process. In fact, the failure rate can serve as a measure of the innovation team’s competence and their bonafide progress. To be really innovative, a certain amount of risk and courting failure is necessary. Adopting a process that includes failures can be hard to accept for many organizations, and for the managers within those organizations who do not want their careers to stall; but as in engineering and science, this is the only way to advance.

Rethink competitions. | The first XPRIZE for building a reusable spacecraft rekindled the excitement for competitions, which have now become common even outside the technology industry. However, competitions to invent new technology are fundamentally different: With a spacecraft, it is relatively easy to pick the winner by test-flying each entry. In the social sector, by contrast, competitions have judging panels that decide which idea wins. This represents a big-idea approach that fails to motivate people to generate and test ideas until they find one that demonstrably works well, rather than one that impresses judges. Staged competitions could work much better by following a behavioral-design approach. The first round could focus on identifying, or even putting together, the teams with the best mix of experience and knowledge in behavioral design and in the domain of the competition. Subsequent rounds could fund a few teams to develop their ideas iteratively. The teams whose solutions achieved some threshold of impact in a field test would win. Innovation charity Nesta’s Challenge Prize Centre has been using a similar approach successfully, as has the Robin Hood Foundation, with the help of ideas42.

Revolutionizing how we innovate presents a huge opportunity for improving existing programs, products, and policies. There is already sufficient scientific research and techniques to begin making the change, and we are learning more about how to better devise things for human interactions every day. The more we use a scientific approach to innovate, and construct platforms to capture findings, the more science we will have to build on. This immense promise of progress depends on changing organizational cultures and funding models. Funders can and must start to bet not on the right “big ideas” but on the right process for solving challenges and on the people who are experts in that process. They must also not just expect failures, but embrace them as the tried and true means for achieving innovation.

Read more stories by Piyush Tantia.

A Brooklyn, NY company is introducing the first highly engineered helical compression spring made from plastic composites, with an eye on medical, electronics and other demanding markets.

"Plastic springs can never fully match the strength of metal springs, but we wanted to develop a composite spring that would approach the strength of metal and still offer corrosion resistance and other properties engineers are looking for," says Subramanya Naglapura, global product manager for Lee Spring. Those other properties include imperviousness to magnetic fields, retention of properties at elevated temperatures, light weight, and low thermal and electrical conductivity, as well as recyclability.

"We worked very closely with Sabic Innovative Plastics (formerly known as GE Plastics) to develop the right material, and we chose Ultem^(R) resin, a very high-strength plastic," he says. Ultem polyetherimide performs in continuous use to 340F (170C) and meets the other requirements established by Lee Spring. It's designed to perform under load with minimum side thrust.

Development of the first highly engineered composite springs ever produced required significant testing and tool development efforts.

Shear Modulus

"Published materials' properties focus on mechanical characteristics such as impact strength, but there isn't data available on what we are most interested in - shear modulus," says Naglapura. Sabic Innovative Plastics conducted significant testing on the materials' properties required for use as a spring, while Lee Spring tested actual springs at its labs.

A contract injection molder did flow analysis to develop data on orientation of the glass fibers in the spring. Initial production quantities will be met from a single-cavity tool, but multi-cavitation tooling will be built as demand grows, says Naglapura. Examples of target applications in the medical field are syringes and fluid delivery devices.

Development of a mechanical design was challenging. "The relatively weak strength of plastic materials, as compared to steel, means that traditional spring designs with round cross-sections would not provide sufficient load for most applications" says Naglapura. Furthermore, injection molding is a difficult process for creating a helical shape due to undercuts that impede withdrawal of a multi-piece rigid molding with squared ends. Surface finish can also be affected by knit lines where melt flows meet in a mold cavity.

The Lee Spring design has a nearly rectangular cross section to maximize the amount of active material used as opposed to the more common round wire spring design. The exact cross section is a slight trapezoid to facilitate manufacturability.

"The design element that proved difficult yet critical is the configuration of the ends to provide for minimal side thrust and maximum flat load bearing surface without creating stress points or increasing the solid height while again accounting for manufacturability," says Naglapura.

Due to difficulties with both design and manufacturing processes, there are very few springs made from plastic available in the market today.

Helical compression springs are widely used because of their simple configuration and high functionality.

They are cylindrical in shape with an outside diameter allowing fit inside a cylindrical bore and an inside diameter that fits over a round rod for secure positioning. They generally have ends that are machined perpendicular (square) to the cylindrical axis so forces applied parallel to the cylindrical axis are reacted to with a minimal amount of side thrust.

The LeeP^(TM) Plastic Composite Springs catalog items are designed to fit in standard bore sizes from 0.375 to 1 inch with free lengths from 0.375 to 1.250 inch. Different sizes can be stacked and/or nested to get varied lengths and spring rates.

Squaring the ends also reduces the overall length of the spring in its fully compressed state (referred to as "solid height") so they consume a minimal amount of space.

Helical compression springs are used in medical devices, sensitive instrumentation, fluid power control valves and aerospace equipment.

Another goal with the composite spring is to Strengthen the recyclability of the entire mechanism.

"Many products employing the use of helical compression springs are made of environmentally friendly materials and are generally recyclable except for one small but critical component - the spring," says Naglapura. "Before being able to recycle these products, users must incur the inconvenience and expense of disassembling and separating the metal spring from the other recyclable components."

Valves or devices made from plastics in certain chemical families may be fully recyclable if the spring is made with a compatible polymer.

LeeP^(TM) Plastic Composite Springs are available in the Lee Spring catalog as stock items with six strength levels, including different levels of glass reinforcement. Strength rises as the level of glass reinforcement increases. Lee Spring may also develop custom springs made from other polymers and fillers to meet specific customer requirements, Naglapura says.

For more information:

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An MD&DI September 1999 Column

RISK-BASED COMPLIANCE

Minimizing the risks associated with a new medical product is, perhaps, the manufacturer's primary responsibility. Indeed, medical device regulatory authorities worldwide are moving away from a reliance on compliance with standards alone toward product acceptance criteria that are based on the level of risk that the device poses to the human body.

In the European Union, for example, the essential requirements of the Medical Devices Directive (MDD 93/42/EEC) state that: "Devices must be designed and manufactured in such a way that, when used under the conditions and for the purposes intended, they will not compromise the clinical condition or the safety of patients, or the safety and health of users or, where applicable, other persons, provided that any risks which may be associated with their use constitute acceptable risks when weighed against the benefits to the patient and are compatible with a high level of protection of health and safety." Manufacturers intent on developing safe, innovative, and effective products that will be marketable globally must therefore adopt a risk analysis strategy that spans every stage of product development.

After discussing the differences between standards-based and risk-based focuses, this article outlines the principles underlying the risk-based approach to medical product design, as well as the steps involved in performing a risk analysis.

STANDARDS VERSUS RISK ANALYSIS

Among the factors driving the move from a standards-based approach to ensuring product safety to one that focuses on risk-based analysis is the speed of technological innovation. While many relevant international standards have been developed over the past two decades, particularly for electromechanical devices, these documents are based on the then-current state of the art and can never keep pace with technological advances. In addition, regulation based on standards alone tends to stifle innovation, to the detriment of the public health, because designers must spend their time on looking for ways to comply with the standards, rather than on developing new technologies that may lead to inherently safer products.

Furthermore, even when standards exist to cover all of a product's design elements, compliance with multiple standards carries no ensure that the product will be free of risk. Simply demonstrating compliance with standards is not sufficient to ensure product safety. There are many occasions in which a product has met a standard, but presents risks even when operated for its intended use. For example, an electric wheelchair's intended environment, as defined by the standard, is indoors. Once an electric wheelchair enters an open-air environment, it has the potential to be subjected to stray radio frequency, which may impact its normal operation.

In the EU, many designers of medical electrical equipment have attempted to circumvent the limitations of a standards-based approach by applying clause 3.4 of IEC 60601-1:1988, Medical Electrical Equipment, Part 1: General Requirements for Safety, which permits design solutions that don't comply with the letter of the standard, provided the product's safety level meets the spirit of clause 3.1, which states: "Equipment shall, when transported, stored, installed, operated in Normal Use, and maintained according to the instructions of the manufacturer, cause no safety hazard which could reasonably be foreseen and which is not concerned with its intended application, in normal condition and in single fault condition." Trying to apply clause 3.4, however, opens the manufacturer up to challenges from regulatory agencies that may dispute the rationale given for an alternative design solution.

How do the standards-based and risk-based approaches differ? When using a risk-based approach to design, a manufacturer assumes that risks exist, identifies them, attempts to eliminate them, and tests the product to technical standards to verify that particular risks (e.g., fire, electric shock, mechanical hazards, biocompatibility, or accuracy) are minimal. This is opposite to the way many companies use technical standards today, in which the product is tested to determine that the standards are met in full and risk analysis is used as a supplementary procedure.

The following example illustrates the difference between the two approaches. The manufacturer of a manually propelled wheelchair who is pursuing a standards-based strategy would research the applicable standards and then conduct testing to demonstrate compliance with those requirements. If the manufacturer were committed to a risk-based analysis approach, however, the research effort would address a two-part question: what are the applicable product standards for this wheelchair, and what hazards could be associated with its use? The manufacturer would then list all materials, parts, and assemblies used to produce the wheelchair and identify the possible failure modes related to each item.

The results of this exercise would be the identification of many different scenarios in which the user or patient could be harmed, some of which could even cause fatalities. Steps would then be taken to eliminate or mitigate these possibilities. When the standards-only approach is used, the scenarios leading to potential fatalities might be overlooked. If so, the plaintiff in a later product liability suit would be able to maintain that the manufacturer had not exercised due diligence because the product failure was foreseeable.

Figure 1. Routes to achieving market entry in the European Union.

Manufacturers adopting a risk-based strategy can expect the number of procedures that will be necessary to meet the requirements for entry into the global marketplace to increase, but the procedures themselves should become simpler (Figure 1). One of the major changes—and advantages—of risk-based compliance is that the manufacturer's design processes will become more visible to the regulatory agencies while proprietary information will be guarded from would-be competitors. Risk-based approaches tend to be procedures. These procedures become part of a quality system, which is audited by regulatory agencies such as FDA or notified bodies.

GETTING STARTED

Companies that are reorienting their approach to ensuring product safety to a risk-based strategy need to understand its underlying principles, which are listed below.

• Eliminate or reduce risks as far as possible. Any remaining undesirable side effect must constitute an acceptable risk when weighed against the performance intended.
• Protect devices, patients, and operators against risks resulting from the influence of environmental conditions that are reasonably foreseeable (e.g., electromagnetic fields).
• Where appropriate, integrate protective measures such as alarms and interlocks into the design to mitigate risks that cannot be eliminated.
• Inform users of the residual risks associated with failures of the protective measures.
• Clearly define the product's intended use and reflect that use in the labeling and other instructions. Where necessary and appropriate, include details relating to the population that the device is intended to treat, the indications for use (i.e., the condition the product is intended to treat), any contraindications (i.e., under what circumstances the product cannot be used safely and which patients it should not be used to treat—pregnant women, for example, or those with certain additional health problems), and restrictions on use (i.e., under what conditions special clinical controls must be exercised).
• Clearly and unambiguously document the process of determining what risks were considered to be "reasonably foreseeable." Seek outside assistance, if needed, from clinical experts.
• Wherever possible, use technical consensus standards to verify that the identified risks have been minimized during the design phase.

Logistics: Manpower and Timing. The fundamental task of risk analysis is to identify foreseeable hazards and tabulate them against their probability and severity, which will provide a value for criticality. Critical and high-level risks can then be addressed and eliminated.

Adopting this approach affects two key operational logistics—who is involved in the process and when it takes place. In terms of manpower, the company needs to dedicate sufficient personnel with the necessary skills to perform the risk analysis. Compliance engineers must understand risk identification, control, and management, while designers may need to focus their attention on ergonomics, safety, and functionality in a new way.

As for timing, the risk analysis process should begin as early as possible in the product development cycle. The earlier that hazards are identified and addressed, the greater the chances for successfully minimizing risk.

Establishing a Risk Analysis Team. The risk management process should be directed by a risk analysis team consisting of the following members.

• A risk analysis manager with responsibility for ensuring that the analysis is performed in a structured and purposeful manner. Ideally, the manager should be very familiar with the tools used to perform a risk analysis and have a general understanding of the device being analyzed, as well as a working knowledge of the relevant technical standards.
• A project manager with responsibility for ensuring that any necessary complementary studies are performed and that preventive measures and other design changes are implemented.
• A designer whose task is to describe the functioning of the device to the group prior to the risk analysis of each function block.
• One or more independent designers who are familiar with the product type in technical respects, but have not been involved in developing the design specifications. The independent designers are included on the team to provide advice on design improvements and changes.
• A clinical specialist who is very familiar with the use of the type of device being designed. Physicians with relevant experience in the clinical application also should be consulted in order to address specific or special questions on an ongoing basis.

Marketing personnel may also be included on the team to convey user needs, and quality and customer service staff may be consulted regarding the complaint history of similar devices. As with all working teams, meetings should be recorded, including specific recommendations and actions assigned.

Defining the Project. Once the team is in place, the analysis process begins by defining the object to be analyzed, its intended purpose, and the types of risks to be analyzed. (The discussion that follows is limited to the analysis of risks to the patient or operator.) All of the possible risks associated with, or related to, the product or any of its parts are then analyzed.

Among the factors that should be considered at this early stage of the process are areas where the device interfaces with the human body and with other equipment or modules. For noninvasive and nonimplantable parts of the system, the operating environment must be fully defined, including temperature, humidity, barometric pressure, dust or contaminant ingress, electromagnetic compatibility, and voltage supply. Another important factor, which is often overlooked, is what's known as the transport and storage influence. These are cases in which prolonged exposure to certain environments could have an adverse effect on the product or its accessories.

RISK ANALYSIS TOOLS

Each of the three major risk analysis tools focuses on a different aspect of risk, but they are all used to achieve the same end result—a device with risks that are as low as reasonably possible (ALARP). It is the responsibility of the risk analysis team to identify which method or methods are most appropriate. Obviously, if a product is simple (a tongue depressor, for example) the level of risk analysis can be lower than that for a complex device. There is no right or wrong analytical method. However, it is often safer to analyze the same device using various tools to minimize the chance of something being overlooked. Whichever methods are used, the results become the basis for efforts to mitigate the probability and severity of device-related risks.

FMECA versus FMEA. Failure mode effects and criticality analysis (FMECA) is a more involved process than failure mode and effects analysis (FMEA), which is widely used today in industry as a "what if" process. The difference is that FMECA attaches a level of criticality to the failure modes so that redesign efforts can be prioritized and assigned to appropriate personnel in a consistent manner.

Figure 2. Example of a failure mode effects and criticality analysis form.

FMECA begins by breaking down the product into individual function blocks using a tree structure. One way to do this is to create a block diagram of the product, or of the processes associated with the production of the device, or both. The breakdown is documented, and an FMECA form (Figure 2) is then filled in for each of the function blocks. For integration into the analysis, software is broken down into software blocks, which are regarded as function blocks. During the early phases of a product development project, the software breakdown may be crude, since individual components of the software may not have been developed or clearly defined. In addition to columns for identifying the subsystem (i.e., function block) and its function, the FMECA form contains the following columns:

• Failure mode: How might the subsystem fail? Possible failure modes include component failure (e.g., open circuit, short circuit, clock signal loss, voltage rail loss, etc.), handling failure, design failure, body tissue reactions indicating nonbiocompatibility, component aging or wear, environmental impacts, and incorrect use or reuse (in the case of single-use sterile parts).
• Effect/Consequence: What will happen when the failure mode occurs? The scenario should be described, including the presence and effects of indicators or alarms. For example, if an operator is alerted that the failure mode exists, would there be time to intervene before the patient is injured?
• Probability: How likely is this failure mode to occur?
• Consequence: What effect will the failure have on the patient, operator, or product (e.g., electric shock, fire, overdose, underdose, none, etc.)?
• Comments: What specific additional information regarding the above items is important to note? Preventive measures should be documented, and relevant standards cited. Possible additional measures that will be investigated to lessen the severity or probability of the failure can also be documented.

As part of the failure mode analysis, the criticality of the failure modes is calculated in terms of severity and probability. The severity level (se) is a measure of the possible consequences of a failure and can be expressed using a numerical scale. For example, death can be rated 10 and a malfunction that has no direct effect on the operator or patient could be given a 1, with various levels of discomfort and injuries ranked between them. The scale should not be so large that it is difficult to distinguish one level from another or so narrow that it becomes insensitive to differences between effects. The probability level (pr) is the estimated likelihood of a failure occurring within a time period or under certain circumstances. It also can be expressed on a scale of 1 to 10 and is usually based on data from previous models or similar products for which failures and their causes have been documented. Criticality (cr) is calculated by multiplying the severity by the probability: cr = se X pr. Risks with a high criticality value require an in-depth consideration of design solutions, as do those with high severity and probability levels alone.

Fault Tree Analysis. Using the FMECA process, the risk analysis team can determine the effects of single subsystem failures; however, the effects of combined failures are difficult to identify. Fault tree analysis (FTA) is used to examine such linkages. Based on the possible harm a product can cause, FTA begins by stating how a patient or operator is affected. The effect may be death or serious injury (something that the patient or operator is unlikely to recover from), moderately serious injury (something, although painful or extremely uncomfortable or debilitating, that the patient or operator will recover from eventually), or negligible injury (something that the patient or operator will recover from in a very short time). More levels can be added to make the analysis more sensitive.

Figure 3. Example of a fault tree analysis for a simple circuit. The three events linked by the AND gate must all occur to trigger the potentially fatal effect.

The next step is to establish what combination of events would lead to the various effects. The risk analysis team graphically depicts situations and failures that jointly or individually cause injury using logical operators such as AND and OR gates (Figure 3). An AND gate indicates that several events must happen simultaneously in order for the failure to occur further up in the tree, whereas an OR gate indicates that one or several events can occur independently of one another. Naturally, AND gates are preferable.

The number of simultaneous events that need to occur in an AND junction can assist in calculating probability. The finished tree will show where the risks are and which base events must interact in order for each risk to arise. The FTA for a simple circuit shown in the figure indicates which three events must happen simultaneously for the patient or operator to receive a fatal shock.

Action Error Analysis. Another risk measurement tool, known as action error analysis (AEA), analyzes interactions between humans and machine. Its purpose is to provide answers about what happens if an operator does something right but at the wrong time, or does the wrong thing at the right time, or does nothing at all. An AEA can be used to analyze product start-up procedures and to discover deficiencies in instructions for performing multiple device connections to patients (e.g., a closed-loop system that monitors and controls the delivery of medical gases to a patient).

Figure 4. Example of an action error analysis event tree.

Figure 4 provides an example of an event tree used in AEA. The top event is A on the far right. The AND gate indicates that in order for it to occur, events C and B must occur simultaneously. However, for event C to occur, either or both B and D on the lower level must occur. The symbol at the entry to event D indicates that this event corresponds to the lowest level of the tree; the symbol at the entry to event B indicates that the events that cause B to occur are found on another tree. The out arrow at the bottom of the box indicates that event B is found in other places on the same tree, in which case Boolean algebra is used to derive the minimum set.

CONCLUSION

There's no question that adopting a risk-based approach to compliance will bring changes to the medical device industry. From a logistical standpoint, manufacturers will need to assemble risk analysis teams who understand the principles and procedures of a risk-based compliance strategy. Product development schedules will need to be altered so risk analysis can be performed as early as possible in the design process, enabling designers to make modifications that will eliminate or mitigate risks.

As manufacturers are busy adjusting to the risk-based concept within their own operations, they can also expect more national and international regulatory bodies to insist on evidence that adequate risk analysis has been performed. The Medical Devices Directive already requires risk analysis for devices marketed in 15 countries in Europe. As harmonization continues, this concept will undoubtedly spread and medical products increasingly will be judged against the criterion of their freedom from risk.

BIBLIOGRAPHY

Analysis Techniques for System Reliability—Procedures for Failure Mode and Effects Analysis (FMEA), IEC 60812. Geneva: International Electrotechnical Commission (IEC), 1985.

Dependability Management—Part 3: Application Guide—Section 9: Risk Analysis of Technology Systems, IEC 60300-3-9. Geneva: IEC, 1995.

Fault Tree Analysis (FTA), IEC 61025. Geneva: IEC, 1990.

Medical Devices—Risk Analysis, EN 1441. Brussels: European Committee for Standardization, 1998.

Medical Devices—Risk Management, Part 1: Application of Risk Analysis, ISO/DIS 14971-1. Geneva: International Organization for Standardization (draft).

Medical Electrical Equipment, Part 1: General Requirements for Safety, Section 4: Collateral Standards: Safety Requirements for Programmable Electronic Medical Systems, IEC 60601-1-4. Geneva: IEC, 1993.

Don Sherratt is the medical stream director for Intertek Testing Services (Boxborough, MA) and was responsible for setting up Notified Body 0359 in the UK.

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