Tag Archives: Agrawal

On Air Traffic Control and Health Costs

Explosion of data volumes. Interoperability of systems. Large servers in the sky that can analyze enormous amounts of data, compute complex algorithms in real time and communicate in microseconds. Mobile communication through devices that patients, providers and staff all carry all the time.

What does this all mean for hospital operations?

Based on our work with dozens of hospitals and conversations with 100-plus others, we think the near future of hospital operations is quite exciting. Call it what you will—“Hospital 2.0,” “No Waiting Rooms,” “Hospital Operations Center”—the basic building blocks to enable the future of hospital operations are already here.

Today, two major shifts are putting pressure on hospitals to rethink how they deliver care: (a) increased demand for care from the Affordable Care Act and the growing number of people with chronic illnesses and (b) the move toward value-based care.

See Also: 5 Trends in Health IT

These shifts have big implications across the board but, most importantly, in operations. Hospitals are under constant pressure to do more with less. Every day, they face an operational paradox: Scarce resources are both overbooked and underutilized within the same day. This leads to several undesirable outcomes: long patient waiting times, overworked staff, millions of dollars of unnecessary operational costs and an insatiable appetite for expanding existing facilities or constructing entirely new ones. For specialty services like chemotherapy, it could take days or weeks for a new patient to be given a slot, yet the typical infusion chair is occupied less than 60% of the time between 7 a.m. and 7 p.m. The same is true of operating rooms; study after study shows that hospitals don’t utilize their resources optimally.

Historically, process improvement efforts in hospitals worked with small, historical snapshots of data from which the core operational issues were identified. From this, strategies were developed, implementation plans were executed and disciplines for continuous improvement were established. This was the best approach when all that was available were rear-view mirror data snapshots and Microsoft Excel as the analytic engine of choice. Today, there’s a lot more data to learn from. On average, health systems produce as much as two terabytes of data per patient every year. Combined with the explosion of smart devices, computational power in the cloud and the growing pervasiveness of data science and machine learning algorithms, an entirely different realm of operational optimization has suddenly become possible. It is similar to the realization that, decades ago, general surgeons did the best they could with the insight they gleaned from grainy X-ray images. Today, armed with high-resolution MRI/PET images and fiber-optic cameras, the same surgeons can execute surgeries an order of magnitude more complex than those they could have imagined being able to do when they were surgical residents a few decades ago.

Consider the following scenarios on how predictive analytics is already optimizing patient pathways within hospitals:
  • Hospitals are working on optimizing access to treatments such as chemotherapy. By looking at historical demand patterns and operational constraints, sophisticated forecasting algorithms can predict the daily volume and mix of patient volume and can orchestrate appointment slots so there are no “gaps” between treatments. This radically improves chair utilization, lowers patient waiting times and reduces the overall cost of operations. Doing this without sophisticated data science is hard — for example, just arranging the order in which 70 patients can be slotted for their treatments in a 35-chair infusion center is a number exceeding 10^100, as this analysis shows. Trying to solve this problem with pen, paper or Excel is a pointless exercise.
  • Operating rooms are key resources within the hospital. Study after study shows that the OR utilization at most large hospitals is, at best, 50-60%. In most hospitals, operating rooms are allocated to surgeons using “blocks.” (For simplicity, the blocks are often either half-day or full-day blocks.) Even the most prolific and productive surgeons often don’t fully utilize the blocks they are given, and the process for reallocating blocks on a monthly basis—or even for last-minute block swaps—is cumbersome and manual. Using data science and machine learning, hospitals can monitor utilization, identify pockets for improvement, automatically reallocate underutilized blocks and improve overall operating room utilization. A three to five point improvement in block utilization is worth $2 million per year for a surgical suite with just four operating rooms.
  • In-patient bed capacity is a constraining bottleneck in most hospitals, yet virtually every hospital solves this problem with an arithmetic-based “huddle” approach that reviews the patient census from the overnight stay in each unit, adds known incoming patients, subtracts known discharges and then decides if the unit is flirting with the limits of its available capacity. This cycle repeats itself, often several times a day, with a planning horizon of the day at hand. On the other hand, Google completes the search bar while we are typing because it has analyzed millions of search terms similar to the one you are entering, and it automatically presents the four or five highest probability queries you intend to submit. Imagine looking at each overnight patient, finding the 1,000 patients over the last two years who entered the hospital with a similar diagnostic or procedure code and then reviewing their “flight path” through the hospital (i.e., number of days spent in each of the units prior to discharge). Then, an aggregate probabilistic assessment of the likely occupancy of each unit could be developed. Not only would it provide a better answer for today, it would help anticipate the evolving unit capacity situation over the next five to seven days, thereby leading to smarter operational decisions on transfers, elective surgery rescheduling, etc.
  • A similar machine-learning approach can help orchestrate patient flows at clinics, labs, the pharmacy and any unit within the hospital network that struggles with the operational paradox of being overbooked and underutilized at the same time.

An interesting metaphor for the future of hospital operations is how airport operations, air traffic control and sophisticated scheduling have transformed air travel for passengers. They, too, have enormous complexity and the mission-critical requirement of passenger safety in the face of challenging external conditions.

Three direct parallels:

  • For a single flight to transport passengers safely from point A to point B, it requires the “above the wing” services (boarding, food, crew) and “below the wing” services (baggage, fuel, tire check, other inspections) to come together seamlessly. Similarly, to perform even a routine surgery, services like labs, pharmacy, the clinician, the surgeon and the supporting team all need to come together to be able to safely and successfully treat the patient.
  • Every day, at any busy airport, tens of thousands of passengers  navigate their personal journey across connecting flights while relying on “invisible supporting services” such as bag transfers and re-bookings in the case of delays, weather systems, etc. Similarly, on any given day in a busy hospital, thousands of patients navigate their personal journey across a continuum of care while relying on the supporting services of labs, pharmacy, etc. to be timely and accurate.
  • The volume of airline passengers has grown from a few thousand to a few million per day, and airports and airlines have been forced to do “more with less.” Similarly, the Affordable Care Act and a growing and aging population combined with the increased incidence of chronic disease will require hospitals to do “more with less.”

The aviation industry has diligently invested in the required technology, systems and processes to monitor, measure, collaborate and orchestrate. Similarly, hospitals are beginning to invest in the technology, systems and processes to maximize patient access at each “node” and to streamline the linkages across nodes.

Just as the advent of air traffic control and fine-grained scheduling transformed airports like JFK from handling only a few hundred flights each day in the 1960s to managing thousands of takeoffs and landings a day within the same airspace, modern technologies and predictive analytics will lead to the creation of a similar air-traffic-control capability for hospitals. Assets like the OR, inpatient beds, clinics, infusion chairs and MRI machines will be far better utilized throughout the day. Many more patients will be treated within the same facilities, and they will need to wait far less between the “legs of their flight” across the continuum of care.

This post was written by Mohan Giridharadas, the CEO of LeanTaaS. 

Dear Founders: Are You Listening?

Since my last post, “Distribution is 80% of your problem,” I have had the opportunity to speak in-depth with several terrific start-up founders about some of the incredible things they are doing and why things are not going so well. Several of their stories remind me of another big lesson I have learned over the years: We entrepreneurs often mistake “listening” as “waiting to talk,” until it’s too late.

A Little Knowledge (About Your Users) Is a Dangerous Thing

All the stories have a similar theme: We launched our product, and we got 10,000-plus users (or 100-plus small paying customers) using unscalable ways. Now, we are not sure of what to do next.

One founder I communicated with had talked to hundreds of her paying users and managed to convince herself that her market was women who want to make sure their kids don’t get too much unsupervised screen time. We talked to the company’s users and discovered that, in fact, the core group that loved the app were working women who want to keep track of their kids and know they are safe after school. Whenever this start-up had spoken to its user, it heard the answer it wanted to hear, not what the users were saying. The lesson learned here was about waiting to tell users what they “should” be doing with the app.

Another app — one that got to 20,000 users quickly with a small amount of seed money — found, once we dug deep, that fewer than 150 of their users were active weekly. The start-up had no idea who these 150 users were or what, specifically, they were doing with the product. After 20 user interviews, we discovered the start-up’s core use case was far from what the company thought it was and that the product was too hard to use. For far too long, the start-up was convinced its technology would change the world, especially because 20,000 users seemed to be using the product.

A third, B2B-focused start-up I recently spent time with has more than 100 paying users but has stalled growth and usage numbers. When I asked the company to tell me who its users were and what pain point it was solving, I kept getting back a laundry list of features and user personas instead. When the company dug deeper and spoke to users, it found that, of the 27 features, users are using two and that no one had discovered the three the company thinks are the real killer benefits. We realized the company’s model needs to shift away from “my users are using the wrong features and should have discovered the ‘right ones.'” As a start-up, you don’t get to tell users what scenarios and which features they should use your product for; consumers will tell you by using whatever they find useful.

Apple May Not Need to Talk to Users, But the Rest of Us Do

As a founder, you start with a hypothesis. You have all these incredible suppositions on how you will change the world with your product. You may think you can get away with: “My users do not know what they are doing. I will tell them what they should do. It works for Apple (or so goes the myth) so it will work for me — let’s just ignore users.” Believe me, those kinds of companies are black swans. For the rest of us, our users matter—who they are, what they use our products for and what they ignore.

This is for two basic reasons:

  1. Product/Market Fit: Unless we know and understand our users (or potential users), our incoming hypothesis of the value our product provides is literally that —a hypothesis. Sure, some people may not get it, and some may just dismiss it. But without a group of people who buy into the value we hypothesize that we can provide and who agree to become ecstatic users of our product, we probably did not have a real hypothesis to begin with, just a supposition that is wrong.
  2. Go-to-market: The more detail we can find out about users, the more we can figure out how to go after them in a tight, focused way. Going after moms who want to limit unsupervised screen time is very different from attracting busy working moms who really want to know where their kids are after school. The two are different products, have different features and have a different go-to-market.

One potential red herring during the early days comes when you manage to attract a chunk of users quickly. You can easily get deluded by the numbers — they’re like inventory, they hide a lot of problems. You convince yourself that what you’re doing can’t be wrong if 20,000 users think you’re right. The fact is that these 20,000 people do not think you are right ;  you somehow managed to “get” them, and they experimented with your product hoping to find something of use. 200 of those users might think you are onto something, but you don’t know who those 200 are. If you understood what those 200 really like about your product, you might be able to find the next 20,000 users who are really right for you.

What to Avoid When You Do Decide to Talk to Users

  1. Don’t defend what you have built and try to convince them you are right;
  2. Don’t keep coming back to your vision and what will come later or focus on product features they should be using;
  3. Don’t make a sales pitch about your company and yourself, make it about them and their real reaction to your product—even if it means you have to throw everything away and start over again.

If you do not do these things, you have not really listened to your users—you have just waited for your turn to talk and convinced yourself you understand your users.

A FRAMEWORK FOR WHEN TO LISTEN TO USERS–AND HOW 

Here’s a framework I have developed over the years about when and how to listen to users:

The First 500 Users

Those first 500 users are the most important people in your journey. You need to do more than just talk to them, you need to build a solid relationship with them — they are the foundation of your product.

In my previous start-up, a career marketplace, I personally introduced my early adopters to friendly hiring managers at many companies and helped them land a job. A lot of those early customers are now my Facebook friends. Some of them even became our ambassadors and had equity in the company.

Those first users add immense value. They  validate your hypothesis, refine your ideas, recruit more users and test new features, on top of a whole lot more. And they are also very forgiving to defects, crashes, bad user experience (UX), everything.

I used to schedule as many phone calls with them as I could. In every conversation, I would first show what we were working on (in detail) and get their feedback. I would then open up  and  ask about what they were doing with the product, why they chose it over others, how they found it added value, what related issues they had that we could help with, among other questions. I logged every conversation.

Listening Is Hard to Do—For Founders in Particular

Most of the time when we think we’re listening, we are actually just waiting for our turn to talk. Here are three reasons why:

  1. We are always busy talking — to ourselves. Even when we are obviously talking to someone else, we are also internally talking to ourselves. So listening genuinely — muting your internal conversation and giving someone your full attention — is hard.
  2. For founders, listening genuinely is harder. Most entrepreneurs have their product, features, ideas and vision so deeply ingrained that, when they talk to users, entrepreneurs are always defending things they find users having problems with . (“But you didn’t see the profile page; the settings let you change this,” “There are so many cool things you can do, didn’t you see this feature?,” “We’ll get to that in Version 3,” “Wait, no, you don’t understand, that’s where the puck is going,” etc.)
  3. It is not easy for people to articulate what they are thinking. To really understand what users are saying, you have to read between the lines. Even if you lead with your world view, you really have to listen to users’ views carefully — both what is said and what is not.

Talking to users requires real effort . Be aware of that and start focusing on your first 500 users. Treat your early adopters with special respect — make them feel special and take care of them beyond just the product.

Beyond the First 500 Users

Moving forward with your customer base requires using other techniques (in addition to real conversations) that are still important. One such tactic is talking through the product,  provoking conversations with product experiments.

An example of this would be radically changing your on-boarding — drop everything and get them in — for a small set of users and seeing what happens. Remove a feature you think is not useful and wait for users to complain. Removing things temporarily is the best way to test if they are really valuable.

It also helps to create ancillary products  ( quick prototypes )  to test value outside your core product. As you learn more about your users, you will start to see more value propositions, some that align with your vision and some that don’t.

Until you are truly convinced you have product-market fit, do not be shy about running small experiments on the side to keep testing different ideas. Use conversations to create hypotheses, and experiment quickly.

Another technique is to always ask, “What else would you want this product to do for you?” in every support email. My start-up once introduced a critical defect in our iPhone app that led to hundreds of support emails. Adding that one question uncovered several hundred feature requests, including a lot we had not thought about.

Talking to users as you scale is more than just about having conversations. Lead with a hypothesis, measure, iterate, run side experiments continuously to test.

Dear founder, do not wait to talk to your users until it’s too late.

And when you do, listen. Don’t just wait to talk.

The World Owes Me Nothing

I am fortunate to live amid incredibly smart, driven, hard-working people who care about making an impact. Sometimes, some of them trust me enough to come to me for business and career advice.

Before every such meeting, I try hard to set aside my beliefs and biases and just listen. For me, it takes genuine effort to actually listen and remember that listening to someone isn’t really the same thing as just waiting to talk. I do my best not to make someone clearly in pain feel good with the formulaic “10 steps to happiness” psychobabble.

The problem usually starts with a clear symptom : “I hate my boss,” “I don’t have faith in my CEO,” “I deserve more equity,” “I need a bigger title,” etc. Having been in their shoes as an employee, a manager, a CEO, I’ve dealt with many of these feelings myself, so I can often relate to where people are coming from. I suppose that’s the real value of talking to someone—it helps separate problems from symptoms, and knowing the problem is half the solution.

A lot of times, what I discover in these conversations—once we talk through what’s going on and dig deeper into the situation—is that these surface emotions are just really reflections of the real problem, which is larger, more corrosive and harder to admit.

Entitlement.

The problem is we all feel entitled to something. Entitlement is a subtle and implicit belief that we deserve things, that the world owes us something.

The truth, something we all know, is that the world owes us nothing. However, it is hard to remember that at the right time, when you are feeling entitled.

I am not suggesting that having expectations, desires and sometimes taking things for granted is unnatural or even bad. I am saying that if you stop for a minute and zoom out, you’ll start to realize that a lot of your pain goes away if you stop feeling entitled and that dealing with the reality of your situation becomes a heck of a lot easier.

So the next time you are feeling upset about something, try it . Zoom out and tell yourself, “The world owes me nothing,” and see what happens.

When I do it mindfully, I can tell you I feel a sudden emptiness, followed by a delightful lightness. Sure, it may only last for a minute, but that little lull puts things in perspective, replacing the heaviness of “I deserve better” with “I am grateful for what I have. There will always be more I want. It will never be enough, but it will all be OK.”

Try this for a week: Every morning, tell yourself , “The world owes me nothing.” See if it subconsciously affects your thoughts, alters your tone and orchestrates your actions throughout the day. Note how that sets you up for a simple but powerful call of duty, to be useful to people around you—your family, friends, co-workers, customers, investors, neighbors, strangers, everyone! Be grateful for the many, many things you have.

We begin life with a cry. In the end, the only thing that matters is how many people cry when we die. Or maybe that, too, is an entitlement.

Originally published on Medium

1 Myth, 2 Truths, 5 Hot Trends in Health IT

There is a myth out there that healthcare providers are unwilling to adopt new technology. It’s just not true. In the last few months, I have spoken to dozens of healthcare leaders at hospitals both small and large, and I am amazed at their willingness to understand and adopt technology.

Pretty much every hospital CEO, COO, CMIO or CIO I talk to believes two things:

With growing demand, rising costs and constrained supply, healthcare is facing a crisis unless providers figure out how to “do more with less.”

Technology is a key enabler. There is technology out there to help save more lives, deliver better care, reduce costs and achieve a healthier America. If a technology solution solves a real problem and has a clearly articulated return on investment (ROI), healthcare isn’t that different from any other industry, and the healthcare industry is willing to adopt that technology.

Given my conversations, here are the five biggest IT trends I see in healthcare:

1. Consumerization of the electronic health record (EHR). Love it or hate it, the EHR sits at the center of innovation. Since the passage of the HITECH Act in 2009—a $30 billion effort to transform healthcare delivery through the widespread use of EHRs—the “next generation” EHR is becoming a reality driven by three factors:

  • Providers feeling the pressure to find innovative ways to cut costs and bring more efficiency to healthcare delivery
  • The explosion of “machine-generated” healthcare data from mobile apps, wearables and sensors
  • The “operating terminal” shifting from a desktop to a smartphone/tablet, forcing providers to reimagine how patient care data is produced and consumed

The “next generation” EHR will be built around physicians’ workflows and will make it easier for them to produce and consume data. It will, of course, need to have proper controls in place to make sure data can only be accessed by the right people to ensure privacy and safety. I expect more organizations will adopt the “app store” model Kaiser pioneered so that developers can innovate on their open platform.

2. Interoperability— Lack of system interoperability has made it very hard for providers to adopt new technologies such as data mining, machine learning, image recognition, the Internet of Things and mobile. This is changing fast because:

  • HHS’s mandate for interoperability in all EHRs by 2024 means patient data can be shared across systems to enable better care at lower cost.
  • HITECH incentives and the mandate to move 50% of Medicare payments from fee-for-service to value-based alternatives by 2018 imply care coordination. Interoperability will become imperative.
  • Project Argonaut, an industry-wide effort to create a modern API and data/services sharing between the EHR and other systems using HL7 FHIR, has already made impressive progress.
  • More than 60% of the proposed Stage 3 meaningful use rules require interoperability, up from 33% in Stage 2.

3. Mobile— With more than 50% of patients using their smartphone to monitor health and more than 50% of physicians using (or wanting to use) their smartphone to monitor patient health, and with seamless data sharing on its way, the way care is delivered will truly change.

Telemedicine is showing significant gains in delivering primary care. We will continue to see more adoption of mobile-enabled services for ambulatory and specialty care in 2016 and beyond for three reasons:

  • Mobile provides “situational awareness” to all stakeholders so they can know what’s going on with a patient in an instant and can move the right resources quickly with the push of a button.
  • Mobile-enabled services radically reduce communication overhead, especially when you’re dealing with multiple situations at the same time with urgency and communication is key.
  • The services can significantly improve the patient experience and reduce operating costs. Studies have shown that remote monitoring and mobile post-discharge care can significantly reduce readmissions and unnecessary admissions.

The key hurdle here is regulatory compliance. For example, auto-dialing 9-1-1 if a phone detects a heart attack can be dangerous if not properly done. As with the EHR, mobile services have to be designed around physician workflows and must comply with regulations.

4. Big data— Healthcare has been slower than verticals such as retail to adopt big data technologies, mainly because the ROI has not been very clear to date. With more wins on both the clinical and operational sides, that’s clearly changing. Of all the technology capabilities, big data can have the greatest near-term impact on the clinical and operational sides for providers, and it will be one of the biggest trends in 2016 and beyond. Successful companies providing big data solutions will do three things right:

  • Clean up data as needed: There’s lots of data, but it’s not easy to access it, and isn’t not quite primed “or clean” for analysis. There’s only so much you can see, and you spend a lot of time cleansing before you can do any meaningful analysis.
  • Meaningful results: It’s not always hard to build predictive analytic models, but they have to translate to results that enable evidence-based decision-making.
  • Deliver ROI: There are a lot of products out there that produce 1% to 2% gains; that doesn’t necessarily justify the investment.

5. Internet of Things— While hospitals have been a bit slow in adopting IoT, three key trends will shape faster adoption:

  • Innovation in hardware components (smaller, faster CPUs at lower cost) will create cheaper, more advanced medical devices, such as a WiFi-enabled blood pressure monitor connected to the EHR for smoother patient care coordination.
  • General-purpose sensors are maturing and becoming more reliable for enterprise use.
  • Devices are becoming smart, but making them all work together is painful. It’s good to have bed sensors that talk to the nursing station, and they will become part of a top level “platform” within the hospital. More sensors also mean more data, and providers will create a “back-end platform” to collect, process and route it to the right place at the right time to can create “holistic” value propositions.

With increased regulatory and financial support, we’re on our way to making healthcare what it should be: smarter, cheaper and more effective. Providers want to do whatever it takes to cut costs and improve patient access and experience, so there are no real barriers.

Innovate and prosper!

Why Millennials Are the Best Workers

It has become fashionable to trash Millennials. They lack a strong work ethic, have no grit, aren’t respectful or patient and definitely don’t understand corporate culture. The trashing fits with how people romanticize the 1950s as the golden age of American culture, when everything was just somehow better.

I don’t know whether Gen X is just irritated that they’re getting older or whether people are forming their opinions solely based on Buzzfeed, but I think the stereotype is wrong - dead wrong. In fact, I will go out on a limb and state that Millennials may actually be the best generation of workers we’ve ever seen.

And I say this having hired hundreds of new college grads – and seasoned professionals – over the past 20 years. Here’s why:

1. They’re too big for their britches.

Today’s young job seekers have grown up with a startup mentality. The value of embracing failure has been etched into their psyche by entrepreneurs and tech titans like Steve Jobs and Elon Musk. So, unlike past generations, they are not necessarily looking for stability. They don’t dream of landing a job at GM or IBM. They approach positions with the understanding that they may have to put in 110% to succeed, even with the near certainty that their employer won’t be around five years from now.

Put that in contrast to the stigma of entitlement attached to Millennials. It’s true that many baby boomer parents have raised them with a perspective of possibility. They’ve been encouraged to follow their dreams and passions. And from watching Mark Zuckerberg or President Obama, they’ve learned first-hand that it’s not just dogma; anything really is possible.

So where some see entitlement, I see greater authenticity and audacity.

Millennials will shoot for the stars – and if they fall down, they’ll get right back up and try a different way.

2. They just don’t communicate the way you do.

If you’ve watched “Mad Men,” you’ve seen the fast-paced advertising world struggle to become more connected with innovations like… the speaker phone. Fast forward to today, where first-time job seekers not only understand and embrace collaborative technologies but don’t know anything different.

While many offices struggle to get their workforce to embrace services like Yammer or Basecamp, Millennials have been doing those things for years. They’ve been learning with social classroom tools and chatting on Facebook, Twitter and Instagram every waking hour. As a result, they actually conceive of communication in a one-to-many paradigm, which is a huge plus for companies that are spread out globally and interact primarily in a virtual environment.

3. They expect things to happen instantly.

I don’t know anyone over the age of 50 who doesn’t complain about how fast the world is moving these days. However, in the case of job performance, that’s a very very good thing. Think about it. Thirty years ago, everything took a lot more time. The data you needed to make critical business decisions was delivered weeks later by a mail truck. Someone had to physically be sitting in a predetermined location at the right time for you to call on the phone.

Our expectations for accomplishing tasks were, naturally, based on the resources and structures we had in place. Simply put, we moved much slower. And, God bless them, there are many professionals out there who still work the same way.

Not Millennial workers. With the pace of news, communication and responsiveness nearly instant, that’s how they approach work. They know nothing else. Plus, they have the necessary tools to support them. Give a Millennial employee a research assignment on your competitors, and you’ll get the project back in 24 hours. Twenty years ago, the same project might have taken a month. One piece of advice: Just make sure you attach a deadline to the assignment.

4. They expect too much.

Studies show that young job seekers today are passionate about how their jobs affect the world. In fact, they value job fulfillment over monetary reward. Many balk at the traditional model of doing charitable good only when you have reached a certain level of economic wealth or solely in your free time. They want to reach financial well-being and achieve social good simultaneously .

What does that mean for employers? I would hope it could open the doors to two things. First, we have the ability to retain skilled and valuable Millennial workers by creating environments where social impact is lauded. That will reduce employee turnover and save companies thousands of dollars each year in recruiting, hiring and lost productivity.

More important, Millennials are a driving force toward significant, scalable and lasting social change that will benefit everyone, whether it’s about the environment, socioeconomic diversity or just a healthier work-life balance. In case you’ve forgotten, the U.S. ranks the worst among all modern economies in vacation time and pay.

5. They think differently from you.

Millennials are the most diverse generation in U.S. history. Minorities, roughly a third of the U.S. population today, are expected to become the majority by 2042. So Millennials don’t just embrace diversity on the job; they expect it.

From race and religion to gender and sexuality, they’ve come of age with a greater comfort of multiplicity of all kinds. They’ve entered adulthood with an African-American president and been the catalyst for many states legalizing same-sex marriage. Female leaders like Hillary Clinton and Sheryl Sandberg have shaped their views on gender equality.

Imagine how that translates in the workplace. The payoffs touch every single area of a business by opening the doors to increased creativity, agility and productivity, new attitudes and language skills, a more global understanding, new solutions to difficult problems, stronger customer and community loyalty and improved employee recruitment and retention.

6. They are obsessed with technology.

Today even the industries that historically have been slow to innovate are finally adopting a web- and mobile-first philosophy. Century-old brick-and-mortar stores are fighting to keep Amazon at bay; healthcare finds itself transformed by the Affordable Care Act. Job seekers with coding and programming skills from Java to Ruby to SQL are desperately needed at all types of companies right now. Big data analytics, video game design, app development, software architecture – the list goes on and on for highly sought Millennial workers with tech expertise. But the issue isn’t just about the hard skills they bring.

If you’ve spent any time with a child lately, you’ve probably noticed that they can master an iPad within minutes. It’s mind-blowing – and a little frightening – to imagine how future generations of consumers will interact with technology.

Millennial workers are the bridge to that future, through social media, mobile, the cloud and other real-time technologies that haven’t even been invented yet. They are graduating with both academic skills and innate behavioral skills that companies will need to engage with customers in much more meaningful (and profitable) ways.

It’s the way Millennials think about technology, and their relationship with it, that is changing everything. So, having Millennial employees on staff to advise on your customer relations strategy or spearhead innovative new mobile and social media programs is invaluable for any business of any size, place or industry.