Tag Archives: healthcare

Sometimes Ancient Wisdom Needs to be Left Behind

Sometimes Ancient Wisdom Needs to be Left Behind

GUEST POST from Greg Satell

I recently visited Panama and learned the incredible story of how the indigenous Emberá people there helped to teach jungle survival skills to Apollo mission astronauts. It is a fascinating combining and contrast of ancient wisdom and modern technology, equipping the first men to go to the moon with insights from both realms.

Humans tend to have a natural reverence for old wisdom that is probably woven into our DNA. It stands to reason that people more willing to stick with the tried and true might have a survival advantage over those who were more reckless. Ideas that stand the test of time are, by definition, the ones that worked well enough to be passed on.

Paradoxically, to move forward we need to abandon old ideas. It was only by discarding ancient wisdoms that we were able to create the modern world. In much the same way, to move forward now we’ll need to debunk ideas that qualify as expertise today. As in most things, our past can help serve as a guide. Here are three old ideas we managed to transcend.

1. Euclid’s Geometry

The basic geometry we learn in grade school, also known as Euclidean geometry, is rooted in axioms observed from the physical world, such as the principle that two parallel lines never intersect. For thousands of years mathematicians built proofs based on those axioms to create new knowledge, such as how to calculate the height of an object. Without these insights, our ability to shape the physical world would be negligible.

In the 19th century, however, men like Gauss, Lobachevsky, Bolyai and Riemann started to build new forms of non-Euclidean geometry based on curved spaces. These were, of course, completely theoretical and of no use in daily life. The universe, as we experience it, doesn’t curve in any appreciable way, which is why police ask us to walk a straight line if they think we’ve been drinking.

But when Einstein started to think about how gravity functioned, he began to suspect that the universe did, in fact, curve over large distances. To make his theory of general relativity work he had to discard the old geometrical thinking and embrace new mathematical concepts. Without those critical tools, he would have been hopelessly stuck.

Much like the astronauts in the Apollo program, we now live in a strange mix of old and new. To travel to Panama, for example, I personally moved through linear space and the old Euclidean axioms worked perfectly well. However, to navigate, I had to use GPS, which must take into account curved spaces for Einstein’s equations to correctly calculate distances between the GPS satellites and points on earth.

2. Aristotle’s Logic

In terms of longevity and impact, only Aristotle’s logic rivals Euclid’s geometry. At the core of Aristotle’s system is the syllogism, which is made up of propositions that consist of two terms (a subject and a predicate). If the propositions in the syllogism are true, then the argument has to be true. This basic notion that conclusions follow premises imbues logical statements with a mathematical rigor.

Yet much like with geometry, scholars began to suspect that there might be something amiss. At first, they noticed minor flaws that had to do with a strange paradox in set theory which arose with sets that are members of themselves. For example, if the barber who shaves everyone in town who doesn’t shave themselves, then who shaves the barber?

At first, these seemed like strange anomalies, minor exceptions to rules that could be easily explained away. Still, the more scholars tried to close the gaps, the more problems appeared, leading to a foundational crisis. It would only be resolved when a young logician named Kurt Gödel published his theorems that proved logic, at least as we knew it, is hopelessly broken.

In a strange twist, another young mathematician, Alan Turing, built on Gödel’s work to create an imaginary machine that would make digital computers possible. In other words, in order for Silicon Valley engineers to code to create logical worlds online, they need to use machines built on the premise that perfectly logical systems are inherently unworkable.

Of course, as I write this, I am straddling both universes, trying to put build logical sentences on those very same machines.

3. The Miasma Theory of Disease

Before the germ theory of disease took hold in medicine, the miasma theory, the notion that bad air caused disease, was predominant. Again, from a practical perspective this made perfect sense. Harmful pathogens tend to thrive in environments with decaying organic matter that gives off bad smells. So avoiding those areas would promote better health.

Once again, this basic paradigm would begin to break down with a series of incidents. First, a young doctor named Ignaz Semmelweis showed that doctors could prevent infections by washing their hands, which suggested that something besides air carried disease. Later John Snow was able to trace the source of a cholera epidemic to a single water pump.

Perhaps not surprisingly, these were initially explained away. Semmelweis failed to format his data properly and was less than an effective advocate for his work. John Snow’s work was statistical, based on correlation rather than causality. A prominent statistician William Farr, who supported the miasma theory, argued for an alternative explanation.

Still, as doubts grew, more scientists looked for answers. The work of Robert Koch, Joseph Lister and Louis Pasteur led to the germ theory. Later, Alexander Fleming, Howard Florey and Ernst Chain would pioneer the development of antibiotics in the 1940s. That would open the floodgates and money poured into research, creating modern medicine.

Today, we have gone far beyond the germ theory of disease and even lay people understand that disease has myriad causes, including bacteria, viruses and other pathogens, as well as genetic diseases and those caused by strange misfolded proteins known as prions.

To Create The Future, We Need To Break Free Of The Past

If you were a person of sophistication and education in the 19th century, your world view was based on certain axiomatic truths, such as parallel lines never cross, logical propositions are either true or false and “bad airs” made people sick. For the most part, these ideas would have served you well for the challenges you faced in daily life.

Even more importantly, your understanding of these concepts would signal your inclusion and acceptance into a particular tribe, which would confer prestige and status. If you were an architect or engineer, you needed to understand Euclid’s geometric axions. Aristotle’s rules of logic were essential to every educated profession. Medical doctors were expected to master the nuances of the miasma theory.

To stray from established orthodoxies carries great risk, even now. It is no accident that those who were able to bring about new paradigms, such as Einstein, Turing and John Snow, came from outside the establishment. More recently, people like Benoit Mandelbrot, Jim Allison and Katalin Karikó had to overcome fierce resistance to bring new ways of thinking to finance, cancer immunotherapy and mRNA vaccines respectively.

Today, it’s becoming increasingly clear we need to break with the past. In just over a decade, we’ve been through a crippling financial crisis, a global pandemic, deadly terrorist attacks, and the biggest conflict in Europe since World War II. We need to confront climate change and a growing mental health crisis. Yet it is also clear that we can’t just raze the global order to the ground and start all over again.

So what do we leave in the past and what do we bring with us into the future? Which new lessons do we need to learn and which old ones do we need to unlearn? Perhaps most importantly, what do we need to create anew and what can we rediscover in the ancient?

Throughout history, we have learned that the answer lies not in merely speculating about ideas, but in finding real solutions to problems we face.

— Article courtesy of the Digital Tonto blog
— Image credit: 1 of 950+ FREE quote slides from http://misterinnovation.com

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Why Explainable AI is the Key to Our Future

The Unseen Imperative

Why Explainable AI is the Key to Our Future

GUEST POST from Art Inteligencia

We’re in the midst of an AI revolution, a tidal wave of innovation that promises to redefine industries and transform our lives. We’ve seen algorithms drive cars, diagnose diseases, and manage our finances. But as these “black box” systems become more powerful and more pervasive, a critical question arises: can we truly trust them? The answer, for many, is a hesitant ‘maybe,’ and that hesitation is a massive brake on progress. The key to unlocking AI’s true, transformative potential isn’t just more data or faster chips. It’s Explainable AI (XAI).

XAI is not a futuristic buzzword; it’s the indispensable framework for today’s AI-driven world. It’s the set of tools and methodologies that peel back the layers of a complex algorithm, making its decisions understandable to humans. Without XAI, our reliance on AI is little more than a leap of faith. We must transition from trusting AI because it’s effective, to trusting it because we understand why and how it’s effective. This is the fundamental shift from a blind tool to an accountable partner.

This is more than a technical problem; it’s a strategic business imperative. XAI provides the foundation for the four pillars of responsible AI that will differentiate the market leaders of tomorrow:

  • Transparency: Moving beyond “what” the AI decided to “how” it arrived at that decision. This sheds light on the model’s logic and reasoning.
  • Fairness & Bias Detection: Actively identifying and mitigating hidden biases in the data or algorithm itself. This ensures that AI systems make equitable decisions that don’t discriminate against specific groups.
  • Accountability: Empowering humans to understand and take responsibility for AI-driven outcomes. When things go wrong, we can trace the decision back to its source and correct it.
  • Trust: Earning the confidence of users, stakeholders, and regulators. Trust is the currency of the future, and XAI is the engine that generates it.

For any organization aiming to deploy AI in high-stakes fields like healthcare, finance, or justice, XAI isn’t a nice-to-have—it’s a non-negotiable requirement. The competitive advantage will go to the companies that don’t just build powerful AI, but build trustworthy AI.

Case Study 1: Empowering Doctors with Transparent Diagnostics

Consider a team of data scientists who develop a highly accurate deep learning model to detect early-stage cancer from medical scans. The model’s accuracy is impressive, but it operates as a “black box.” Doctors are understandably hesitant to stake a patient’s life on a recommendation they can’t understand. The company then integrates an XAI framework. Now, when the model flags a potential malignancy, it doesn’t just give a diagnosis. It provides a visual heat map highlighting the specific regions of the scan that led to its conclusion, along with a confidence score. It also presents a list of similar, previously diagnosed cases from its training data, providing concrete evidence to support its claim. This explainable output transforms the AI from an un-auditable oracle into a valuable, trusted second opinion. The doctors, now empowered with understanding, can use their expertise to validate the AI’s findings, leading to faster, more confident diagnoses and, most importantly, better patient outcomes.

Case Study 2: Proving Fairness in Financial Services

A major financial institution implements an AI-powered system to automate its loan approval process. The system is incredibly efficient, but its lack of transparency triggers concerns from regulators and consumer advocacy groups. Are its decisions fair, or is the algorithm subtly discriminating against certain demographic groups? Without XAI, the bank would be in a difficult position to defend its practices. By implementing an XAI framework, the company can now generate a clear, human-readable report for every single loan decision. If an application is denied, the report lists the specific, justifiable factors that contributed to the outcome—e.g., “debt-to-income ratio is outside of policy guidelines” or “credit history shows a high number of recent inquiries.” Crucially, it can also definitively prove that the decision was not based on protected characteristics like race or gender. This transparency not only helps the bank comply with fair lending laws but also builds critical trust with its customers, turning a potential liability into a significant source of competitive advantage.

The Architects of Trust: XAI Market Leaders and Startups to Watch

In the rapidly evolving world of Explainable AI (XAI), the market is being defined by a mix of established technology giants and innovative, agile startups. Major players like Google, Microsoft, and IBM are leading the way, integrating XAI tools directly into their cloud and AI platforms like Azure Machine Learning and IBM Watson. These companies are setting the industry standard by making explainability a core feature of their enterprise-level solutions. They are often joined by other large firms such as FICO and SAS Institute, which have long histories in data analytics and are now applying their expertise to ensure transparency in high-stakes areas like credit scoring and risk management. Meanwhile, a number of dynamic startups are pushing the boundaries of XAI. Companies like H2O.ai and Fiddler AI are gaining significant traction with platforms dedicated to providing model monitoring, bias detection, and interpretability for machine learning models. Another startup to watch is Arthur AI, which focuses on providing a centralized platform for AI performance monitoring to ensure that models remain fair and accurate over time. These emerging innovators are crucial for democratizing XAI, making sophisticated tools accessible to a wider range of organizations and ensuring that the future of AI is built on a foundation of trust and accountability.

The Road Ahead: A Call to Action

The future of AI is not about building more powerful black boxes. It’s about building smarter, more transparent, and more trustworthy partners. This is not a task for data scientists alone; it’s a strategic imperative for every business leader, every product manager, and every innovator. The companies that bake XAI into their processes from the ground up will be the ones that successfully navigate the coming waves of regulation and consumer skepticism. They will be the ones that win the trust of their customers and employees. They will be the ones that truly unlock the full, transformative power of AI. Are you ready to lead that charge?

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credit: Gemini

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Why Most Corporate Innovation Programs Fail

(And How To Make Them Succeed)

Why Most Corporate Innovation Programs Fail

GUEST POST from Greg Satell

Today, everybody needs to innovate. So it shouldn’t be surprising that corporate innovation programs have become wildly popular. There is an inherent tradeoff between innovation and the type of optimization that operational executives excel at. Creating a separate unit to address innovation just makes intuitive sense.

Yet corporate innovation programs often fail and it’s not hard to see why. Unlike other business functions, like marketing or finance, in a healthy organization everybody takes pride in their ability to innovate. Setting up a separate innovation unit can often seem like an affront to those who work hard to innovate in operational units.

Make no mistake, a corporate innovation program is no panacea. It doesn’t replace the need to innovate every day. Yet a well designed program can augment those efforts, take the business in new directions and create real value. The key to a successful innovation program is to develop a clear purpose built on a shared purpose that can solve important problems.

A Good Innovation Program Extends, It Doesn’t Replace

It’s no secret that Alphabet is one of the most powerful companies in the world. Nevertheless, it has a vulnerability that is often overlooked. Much like Xerox and Kodak decades ago, it’s highly dependent on a single revenue stream. In 2018, 86% of its revenues came from advertising, mostly from its Google search business.

It is with this in mind that the company created its X division. Because the unit was set up to pursue opportunities outside of its core search business, it didn’t encounter significant resistance. In fact, the X division is widely seen as an extension of what made Alphabet so successful in the first place.

Another important aspect is that the X division provides a platform to incubate internal projects. For example, Google Brain started out as a “20% time project.” As it progressed and needed more resources, it was moved to the X division, where it was scaled up further. Eventually, it returned to the mothership and today is an integral part of the core business.

Notice how the vision of the X division was never to replace innovation efforts in the core business, but to extend them. That’s been a big part of its success and has led to exciting new business like Waymo autonomous vehicles and the Verily healthcare division.

Focus On Commonality, Not Difference

All too often, innovation programs thrive on difference. They are designed to put together a band of mavericks and disruptors who think differently than the rest of the organization. That may be great for instilling a strong esprit de corps among those involved with the innovation program, but it’s likely to alienate others.

As I explain in Cascades, any change effort must be built on shared purpose and shared values. That’s how you build trust and form the basis for effective collaboration between the innovation program and the rest of the organization. Without those bonds of trust, any innovation effort is bound to fail.

You can see how that works in Alphabet’s X division. It is not seen as fundamentally different from the core Google business, but rather as channeling the company’s strengths in new directions. The business opportunities it pursues may be different, but the core values are the same.

The key question to ask is why you need a corporate innovation program in the first place. If the answer is that you don’t feel your organization is innovative enough, then you need to address that problem first. A well designed innovation program can’t be a band-aid for larger issues within the core business.

Executive Sponsorship Isn’t Enough

Clearly, no corporate innovation program can be successful without strong executive sponsorship. Commitment has to come from the top. Yet just as clearly, executive sponsorship isn’t enough. Unless you can build support among key stakeholders inside and outside the organization, support from the top is bound to erode.

For example, when Eric Haller started Datalabs at Experian, he designed it to be focused on customers, rather than ideas developed internally. “We regularly sit down with our clients and try and figure out what’s causing them agita,” he told me, “because we know that solving problems is what opens up enormous business opportunities for us.”

Because the Datalabs units works directly with customers to solve problems that are important to them, it has strong support from a key stakeholder group. Another important aspect at Datalabs is that once a project gets beyond the prototype stage it goes to one of the operational units within the company to be scaled up into a real business. Over the past five years businesses originated at Datalabs have added over $100 million in new revenues.

Perhaps most importantly, Haller is acutely aware how innovation programs can cause resentment, so he works hard to reduce tensions through building collaborations around the organization. Datalabs is not where “innovation happens” at Experian. Rather it serves to augment and expand capabilities that were already there.

Don’t Look For Ideas, Identify Meaningful Problems

Perhaps most importantly, an innovation program should not be seen as a place to generate ideas. The truth is that ideas can come from anywhere. So designating one particular program in which ideas are supposed to happen will not only alienate the rest of the organization, it is also likely to overlook important ideas generated elsewhere.

The truth is that innovation isn’t about ideas. It’s about solving problems. In researching my book, Mapping Innovation, I came across dozens of stories from every conceivable industry and field and it always started with someone who came across a problem they wanted to solve. Sometimes, it happened by chance, but in most cases I found that great innovators were actively looking for problems that interested them.

If you look at successful innovation programs like Alphabet’s X division and Experian’s Datalabs, the fundamental activity is exploration. X division explores domains outside of search, while Datalabs explores problems that its customers need solved. Once you identify a meaningful problem, the ideas will come.

That’s the real potential of innovation programs. They provide a space to explore areas that don’t fit with the current business, but may play an important role in its future. A good innovation program doesn’t replace capabilities in the core organization, but leverages them to create new opportunities.

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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Preparing for Multiple Futures, Not Just One

Scenario Planning

Preparing for Multiple Futures, Not Just One

GUEST POST from Art Inteligencia

From my perspective here in the United States, but working with organizations around the world as they navigate an increasingly uncertain world, I’ve observed a common pitfall: a tendency to plan for a single, most likely future. While forecasting and strategic planning are essential, relying on a singular prediction can leave businesses vulnerable to unexpected disruptions and missed opportunities. The antidote to this rigidity lies in the power of scenario planning – a human-centered approach to exploring multiple plausible futures, allowing us to develop more robust strategies and foster a mindset of adaptability and innovation.

Scenario planning is not about predicting the future with certainty; that is an exercise in futility. Instead, it’s about envisioning a range of potential futures, each based on a different set of key uncertainties and driving forces. By developing these diverse scenarios, we can stress-test our current strategies, identify potential risks and opportunities that we might otherwise overlook, and ultimately become more resilient and agile in the face of change. It’s a crucial tool for fostering strategic foresight and building organizations that are prepared to thrive, no matter what the future holds. This proactive approach moves us beyond reactive problem-solving to a position of informed anticipation and proactive adaptation.

A human-centered approach to scenario planning emphasizes the importance of diverse perspectives and collaborative engagement. It involves bringing together individuals from different departments, backgrounds, and levels within an organization to contribute their unique insights and challenge assumptions. This collaborative process fosters a deeper understanding of the complexities of the future and builds collective ownership of the resulting strategies. Key steps in effective scenario planning include:

  • Identifying Key Uncertainties: Brainstorming the critical factors that could significantly impact the organization’s future, such as technological advancements, economic shifts, regulatory changes, and social trends.
  • Developing Plausible Scenarios: Grouping these uncertainties into coherent and contrasting narratives of potential futures. Each scenario should be distinct and internally consistent.
  • Analyzing the Implications: Evaluating how each scenario would impact the organization’s current strategies, operations, and goals. Identifying potential risks and opportunities within each future.
  • Developing Adaptive Strategies: Formulating actions and strategies that would be effective across a range of different scenarios, enhancing the organization’s resilience and flexibility.
  • Monitoring and Adapting: Continuously tracking the key indicators that might signal which scenario is unfolding and being prepared to adjust strategies accordingly.

Case Study 1: Shell’s Pioneering Work in Scenario Planning

The Challenge: Navigating the Volatile and Unpredictable Energy Sector

In the 1970s, Shell, a major global energy company, faced significant uncertainty due to oil crises and geopolitical instability. Traditional forecasting methods proved inadequate in such a turbulent environment. Recognizing the limitations of predicting a single future, Shell’s leadership embraced scenario planning as a strategic tool to better understand and prepare for a range of possibilities.

The Scenario Planning Process:

Shell developed several distinct scenarios, such as “The Crisis of the Seven Sisters” (focusing on the power of OPEC) and “The Slowdown” (exploring a period of economic stagnation). These scenarios were not predictions but rather carefully crafted narratives based on different assumptions about key drivers like oil prices, political events, and technological developments. By rigorously analyzing the implications of each scenario, Shell was able to identify potential vulnerabilities and develop more robust strategies. For example, their early scenario planning helped them anticipate the oil shocks of the 1970s and respond more effectively than many of their competitors. They also developed a deeper understanding of the long-term trends shaping the energy landscape.

The Impact and Key Insight:

Shell’s long-standing commitment to scenario planning has been credited with helping the company navigate numerous periods of uncertainty and maintain a strong strategic position in the energy sector. Their ability to think systematically about multiple futures has fostered a culture of adaptability and foresight. Shell’s experience demonstrates that scenario planning is not just a theoretical exercise but a practical tool that can provide a significant competitive advantage in industries characterized by volatility and complexity. The key insight is that by exploring a range of plausible futures, organizations can develop more resilient strategies and avoid being blindsided by unexpected events.

Key Insight: Scenario planning enables organizations to anticipate and prepare for a range of potential futures, enhancing their resilience and strategic agility in volatile environments.

Case Study 2: A Regional Hospital System Preparing for Healthcare Transformation

The Challenge: Adapting to Evolving Healthcare Regulations, Technologies, and Patient Needs

A regional hospital system in the Pacific Northwest faced a complex and rapidly changing healthcare landscape. Uncertainties included potential shifts in government regulations, the emergence of new medical technologies, evolving patient expectations for convenience and personalized care, and the increasing pressure to control costs. Relying solely on traditional strategic planning, which often projects current trends into the future, felt insufficient to prepare for such a dynamic environment.

The Scenario Planning Process:

The hospital system engaged a diverse group of stakeholders, including physicians, nurses, administrators, and even patient representatives, in a scenario planning workshop. They identified key uncertainties such as the pace of technological adoption (e.g., telehealth, AI in diagnostics), the future of healthcare funding models, and shifts in patient demographics and preferences. This led to the development of several distinct scenarios, such as “Tech-Driven Care” (emphasizing remote monitoring and AI-assisted diagnostics), “Value-Based Healthcare” (focusing on outcomes and cost efficiency), and “Patient-Centric Ecosystems” (highlighting integrated and personalized care experiences). For each scenario, the team analyzed the potential impact on their service delivery models, infrastructure needs, workforce planning, and financial sustainability. They then brainstormed adaptive strategies that would allow them to succeed in multiple future contexts, such as investing in digital health infrastructure, developing flexible care delivery models, and fostering partnerships with community health organizations.

The Impact and Key Insight:

The scenario planning process helped the hospital system develop a more nuanced understanding of the challenges and opportunities ahead. It fostered a shared sense of awareness and urgency among stakeholders and led to the identification of strategic priorities that were robust across multiple potential futures. For example, they accelerated their investments in telehealth capabilities and began exploring value-based care models, recognizing that these were likely trends regardless of the specific regulatory or technological landscape that ultimately unfolded. The key insight from this case is that scenario planning, when approached collaboratively and with a focus on human needs and potential disruptions, can empower organizations to make more informed strategic decisions and build greater resilience in the face of uncertainty.

Key Insight: Collaborative scenario planning, involving diverse stakeholders, can help organizations in complex and uncertain sectors like healthcare develop robust strategies that are adaptable to a range of potential futures.

Embracing a Future-Ready Mindset

Across the globe, the pace of change is only accelerating. Organizations that cling to a single view of the future risk being caught unprepared. Scenario planning offers a powerful, human-centered approach to navigate this uncertainty. By embracing the possibility of multiple futures and proactively developing adaptive strategies, we can move beyond simply reacting to events and instead shape a more resilient and innovative future for our organizations and the communities we serve. It’s about cultivating a future-ready mindset – one that embraces uncertainty, fosters collaboration, and prepares us not for one future, but for many.

Extra Extra: Futurology is not fortune telling. Futurists use a scientific approach to create their deliverables, but a methodology and tools like those in FutureHacking™ can empower anyone to engage in futurology themselves.

Image credit: Pexels

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Top 10 Human-Centered Change & Innovation Articles of June 2022

Top 10 Human-Centered Change & Innovation Articles of June 2022Drum roll please…

At the beginning of each month we will profile the ten articles from the previous month that generated the most traffic to Human-Centered Change & Innovation. We also publish a weekly Top 5 as part of our FREE email newsletter. Did your favorite make the cut?

But enough delay, here are June’s ten most popular innovation posts:

  1. An Innovation Action Plan for the New CTO — by Steve Blank
  2. The Lost Tribe of Medicine — by Arlen Meyers, M.D.
  3. What Can Leaders Do to Have More Innovative Teams? — by Diana Porumboiu
  4. Transformation Insights — by Bruce Fairley
  5. Selling To Generation Z – This is What They Want — by Shep Hyken
  6. It is Easier to Change People than to Change People — by Annette Franz
  7. Leading a Culture of Innovation from Any Seat — by Patricia Salamone
  8. Harnessing the Dragons of your Imagination for Innovation — by Braden Kelley
  9. Successful Asynchronous Collaboration — by Douglas Ferguson
  10. Four Reasons the Big Quit Exists — by Braden Kelley

BONUS – Here are five more strong articles published in May:

If you’re not familiar with Human-Centered Change & Innovation, we publish 4-7 new articles every week built around innovation and transformation insights from our roster of contributing authors and ad hoc submissions from community members. Get the articles right in your Facebook, Twitter or Linkedin feeds too!

Have something to contribute?

Human-Centered Change & Innovation is open to contributions from any and all innovation and transformation professionals out there (practitioners, professors, researchers, consultants, authors, etc.) who have valuable human-centered change and innovation insights to share with everyone for the greater good. If you’d like to contribute, please contact me.

P.S. Here are our Top 40 Innovation Bloggers lists from the last two years:

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The 3 Student Entrepreneur Personas

The 3 Student Entrepreneur Personas

GUEST POST from Arlen Meyers, M.D.

Healthcare professional schools, healthcare innovation and entrepreneurship education, and training programs are growing. However, one question is should they be required or elective?

The medical student persona has changed in the past several years. Seeing around corners is always hard. However, to go to where the puck will be is a useful step when planning strategy and tactics to meet the needs of customers segments. Here are some ways to help build your parabolic mirror view of what’s next.

If you have a product or service and are planning not just for the now, but the next and new, then painting a picture of your customer archetype or persona is a key tool.

Do you know who your dream customer is?

There are three steps for understanding your dream customer:

  1. Consider the big issues they are facing – look wider and investigate global issues, such as hunger, environmental sustainability or education.
  2. Identify the industry trends that are affecting them – technology, big data, cyber security, etc.
  3. Describe your customer avatar/archetype/persona now – make a collage including their goals and values, demographics, their pain points and challenges.

Here are the various sickcare innovation and entrepreneurship student segments.

That said, the argument for mandatory is that all students should be exposed to core concepts, like design thinking, much like rotating through core clinical rotations, if nothing else, to get exposure to potential career choices. It might even make them better doctors and possibly help with burnout.

The argument for elective is that all students won’t have the same interests and it would be a waste of time and resources leading the laggards to water knowing you can’t make them drink.

One way to sort potential students is to understand the entrepreneurship education customer segments and their 3 core personas.

The Convinced and Confident know entrepreneurship should be part of their career pathway. In fact, many of them have had entrepreneurial life experiences prior to medical school.

The Curious but Clueless don’t know what they don’t know but are willing to learn more. Many have never held a job in their life. Some might be willing, but unable to develop an entrepreneurial mindset. Others discover their innerpreneur, and move on.

The Could Care Less are unwilling and unable to give it a try. Their attitude is , “I went to medical school to take care of patients, not take care of business”. What they don’t realize is that if you don’t take care of business, you have no business taking care of patients.

Here is what I learned teaching sickcare innovation and entrepreneurship to 1st year medical students.

Here is what I learned teaching sickcare innovation and entrepreneurship to a cohort of xMBA/HA students.

If you are part of creating or teaching these programs, you will eventually have to sort the wheat from the chaff. If you are a leaderpreneur, your job will depend on doing so.

Image Credit: Pixabay

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How Collaborative Innovation is Revolutionizing the Healthcare Sector

How Collaborative Innovation is Revolutionizing the Healthcare Sector

GUEST POST from Chateau G Pato

In the rapidly evolving healthcare industry, traditional models of innovation are no longer sufficient. Today, the integration of technology, data-driven insights, and collaborative approaches is redefining the future of healthcare. In this thought leadership article, we will explore the powerful impact of collaborative innovation within the healthcare sector, highlighting two compelling case studies that demonstrate its transformative potential. Let’s dive in!

Case Study 1: Open Innovation Platforms in Drug Discovery

In the quest for new treatments, pharmaceutical companies are increasingly turning to collaborative innovation models. One remarkable example is the Open Innovation Drug Discovery (OIDD) project by Eli Lilly. Instead of relying solely on internal expertise, Lilly embraced external collaboration, opening up its early-stage research projects to the global scientific community. Through a secure online portal, scientists from diverse backgrounds and organizations could contribute their ideas and expertise, leading to accelerated scientific breakthroughs.

The OIDD project not only tapped into a vast pool of collective intelligence but also fostered a collaborative ecosystem that transcended organizational boundaries. By collaborating openly, Lilly expanded their research network, leading to a 30% increase in the number of partnerships and a substantial reduction in drug development costs. This collaborative innovation model benefited not only Lilly but also the broader healthcare community, as it democratized access to cutting-edge research and improved patient outcomes.

Case Study 2: Healthcare Hackathons for Accelerated Innovation

Hackathons, traditionally associated with the technology sector, are increasingly finding their place in healthcare innovation. These intensive collaborative events bring together diverse teams comprising clinicians, engineers, designers, and entrepreneurs to tackle pressing healthcare challenges. By leveraging their collective skills and viewpoints, these teams work relentlessly over a short period, converging new ideas into viable solutions.

One striking example is the MIT Hacking Medicine initiative, which has revolutionized the healthcare hackathon landscape. Through their hackathons, MIT has successfully addressed a wide range of healthcare problems, such as telemedicine, patient monitoring, and personalized medicine. Participants with different backgrounds join forces, benefiting from interdisciplinary collaboration and ultimately creating groundbreaking solutions. These innovations have the potential to transform patient care, improve healthcare access, and enhance operational efficiency across the sector.

To delve deeper into the transformative power of collaborative innovation within the healthcare industry, we recommend reading the in-depth article, Lead Innovation, Don’t Manage It by Arlen Meyers. This article offers a comprehensive exploration of collaborative innovation and its role in reshaping healthcare delivery.

Conclusion

As the healthcare sector advances, collaborative innovation is emerging as a powerful force for positive change. The case studies discussed above, along with numerous others, clearly demonstrate the significant impact that collaborative approaches can have on accelerating breakthroughs, fostering cross-disciplinary collaborations, and enhancing patient outcomes. By embracing collaborative innovation, healthcare organizations can tap into a broader collective intelligence, tackle complex challenges, and revolutionize the delivery of care. The future of healthcare lies in collaborative ecosystems, empowered by technology and driven by a shared vision of improving health and well-being.

SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.

Image credit: Pexels

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How Blockchain Technology is Transforming Healthcare Data Security

How Blockchain Technology is Transforming Healthcare Data Security

GUEST POST from Chateau G Pato

In an era where data breaches and cyber threats have become prevalent, the importance of safeguarding sensitive healthcare information cannot be overstated. Traditional methods of data security often fall short in providing the necessary measures to protect patient records and mitigate potential risks. However, with the advent of blockchain technology, a revolutionary solution has emerged that holds the potential to transform healthcare data security. Blockchain offers a decentralized and immutable system that enhances privacy, interoperability, and transparency. In this article, we explore how blockchain technology is revolutionizing healthcare data security through two compelling case studies.

Case Study 1: MedRec’s Improved Patient Data Sharing

MedRec, a groundbreaking blockchain-powered platform developed by researchers at the Massachusetts Institute of Technology (MIT), is reshaping the landscape of healthcare data security. MedRec aims to provide patients with ownership and control over their medical records while enabling seamless data sharing across healthcare providers.

The traditional process of sharing medical records often involves numerous intermediaries and lacks transparency, leading to potential data inaccuracies and privacy breaches. MedRec solves these challenges by employing blockchain’s cryptographic principles to guarantee patient privacy, data integrity, and interoperability.

Through MedRec, patients have unique cryptographic identities allowing them to control access to their medical records. Immutable timestamps on the blockchain ensure permanent records of patients’ consent to share data, keeping the process transparent and secure. Healthcare providers can access patient information under the explicit permission of the patient, reducing the risk of unauthorized access.

By leveraging blockchain, MedRec presents a resilient solution that grants patients control over their data while ensuring efficient and secure information sharing across the healthcare ecosystem.

Case Study 2: Chronicled’s Drug Authentication and Supply Chain Management

Counterfeit drugs pose a significant threat to patient safety and can lead to severe health implications. Blockchain technology, however, is disrupting the pharmaceutical industry’s drug authentication and supply chain management systems, mitigating such risks. Chronicled, a blockchain-based platform, enables end-to-end traceability and verification of pharmaceutical products, streamlining the supply chain process and ensuring patient safety.

By utilizing unique product identifiers, Chronicled creates immutable records of each step in the supply chain, including manufacturing, packaging, distribution, and dispensing. Any entity involved in the drug supply chain can access pertinent information, ensuring transparency and facilitating accountability. This decentralized approach significantly reduces the risk of counterfeit drugs entering the market, leading to enhanced patient trust and safety.

Furthermore, the real-time visibility provided by the blockchain allows quick detection and removal of compromised products from the market, reducing potential harm to patients. Overall, Chronicled’s implementation of blockchain technology enhances drug authentication, secures the supply chain, and protects patients from the harmful effects of counterfeit medications.

Conclusion:

Blockchain technology has the potential to revolutionize healthcare data security by offering robust solutions that ensure patient privacy, data integrity, and greater transparency. The case studies of MedRec and Chronicled highlight the transformative impact of blockchain in addressing significant challenges within the healthcare sector.

As blockchain continues to evolve, its deployment in healthcare creates a unique opportunity to establish a secure and efficient ecosystem for the handling of sensitive medical information. By leveraging the advantages of decentralized systems, cryptographic principles, and immutable records, the technology aims to protect patient data, prevent unauthorized access, and eliminate counterfeit drugs. The successful implementation of blockchain in healthcare data security promises a more secure and trustworthy future for healthcare systems worldwide.

SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.

Image credit: Pixabay

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The Role of Artificial Intelligence in Revolutionizing Healthcare Delivery

The Role of Artificial Intelligence in Revolutionizing Healthcare Delivery

GUEST POST from Chateau G Pato

In recent years, the integration of artificial intelligence (AI) in healthcare has gained significant momentum, reshaping the way medical services are delivered. As AI technologies continue to advance, they have the potential to revolutionize healthcare systems, improve patient outcomes, and enhance overall efficiency. This thought leadership article explores two compelling case studies that exemplify the transformative power of AI in healthcare and shed light on its immense potential for the future.

Case Study 1: AI-Powered Diagnostic Assistance

One crucial aspect of healthcare delivery that AI has transformed is the process of medical diagnosis. Historically, clinicians used their expertise and assessments to identify diseases, often relying on time-consuming procedures and subjective interpretations. Today, AI-based diagnostic assistance systems have emerged as valuable tools to aid healthcare professionals in prompt and accurate diagnoses.

One striking example is the application of AI in radiology. A research team at Stanford University developed an AI algorithm named CheXNet, which was trained on vast amounts of medical data to identify abnormalities in chest X-rays. In a study conducted, CheXNet was found to outperform radiologists in identifying pneumonia from X-ray images. Integrating this AI tool into clinical practice can significantly reduce diagnostic errors, accelerate diagnosis timelines, and enhance patient care by assisting practitioners in identifying potential risks and initiating appropriate treatment promptly.

Case Study 2: AI-Driven Predictive Analytics

Another domain where AI has showcased its prowess in revolutionizing healthcare delivery is through predictive analytics. The ability to anticipate healthcare trends, predict disease outbreaks, and identify high-risk patients considerably enhances the efficiency of healthcare systems and enables proactive interventions.

For instance, the University of Chicago Medical Center implemented an AI-powered predictive system that accurately identified patients at high risk of developing sepsis—the leading cause of mortality among hospitalized patients. By analyzing vast amounts of patient data, such as vital signs, laboratory results, and clinical notes, the AI system generated early warnings, allowing healthcare providers to intervene promptly. This proactive approach resulted in a significant reduction in sepsis-related mortality rates, demonstrating the tremendous potential of AI in saving lives and minimizing adverse outcomes.

The Future Trajectory of AI in Healthcare:

These case studies provide valuable insights into the transformative impact of AI in healthcare delivery. However, the true potential of AI is yet to be fully realized. Several barriers, such as data privacy concerns, ethical considerations, and implementation challenges, need to be addressed to unlock AI’s full capabilities and further revolutionize healthcare delivery.

In the future, AI is anticipated to support personalized medicine by tailoring treatments to individual patients, improving medication adherence through intelligent reminders and monitoring, and facilitating seamless integration of electronic health records across healthcare providers. Moreover, AI-driven robotics and telemedicine solutions can enhance accessibility to quality healthcare, bridging geographical gaps and providing care to underserved populations.

Conclusion

Artificial intelligence has emerged as a powerful tool that holds immense promise for revolutionizing healthcare delivery. By augmenting diagnostic accuracy, facilitating proactive interventions, and enhancing overall efficiency, AI has the potential to transform healthcare systems to benefit both patients and providers. While challenges exist, it is crucial for stakeholders to collaborate, invest in research, and navigate ethical considerations to leverage AI’s transformative capabilities fully. With continued advancements and responsible integration, AI can truly revolutionize healthcare delivery, ensuring a healthier future for all.

SPECIAL BONUS: The very best change planners use a visual, collaborative approach to create their deliverables. A methodology and tools like those in Change Planning Toolkit™ can empower anyone to become great change planners themselves.

Image credit: Pexels

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Mask of the Road Warrior – The Xupermask

Xupermask on WILL.I.AM

WILL.I.AM and Honeywell have collaborated to bring the Xupermask to market.

What is the Xupermask?

It’s probably easiest to describe the Xupermask as equal parts: health & safety equipment, personal electronics, and fashion statement.

At its heart the Xupermask is a human-centered design intended to empower the user to feel both safe AND productive. It addresses the following set of user needs that are mostly unmet by traditional mask options:

1. Fits well to the face so escaping air doesn’t fog up your glasses
2. Fit also better prevents unsafe air from entering
3. Fans improve the ease of respiration
4. HEPA filters improve air quality
5. Built-in microphone for easier and safer phone calls
6. Built-in Bluetooth noise cancelling headphones for phone and entertainment

For me, the Xupermask seems like overkill for many day to day situations.

But, when I think about getting on public transport every day or flying on a commercial airline cross-country or across an ocean, the idea of having a Xupermask to wear becomes quite appealing.

And for those of us in the western United States, this could come in quite handy during forest fire season – just saying.

What do you think about the Xupermask?

Innovation or not?

Image credit: Xupermask


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