Category: Artificial Intelligence

The End of Work as We Know It: How an Increasingly Automated World Will Change Everything

12/16/2019

Career & Professional Development, Opinion

Updates, February 2021:
I encourage anyone interested in the future of workforce development and training to read this excellent article published in Forbes:
Human Capital Era Reality: The Skills Gap May Never Close

For other relevant readings on the future of work, see:
Future of Work: Insights for 2021 and Beyond (Milken Institute)
The Future of Work After COVID-19 (McKinsey Global Institute)
Preparing for the Future of Work (World Economic Forum)
World Economic Forum's Future of Jobs Report 2020

In a few short weeks we will be entering a new decade.

What will the 2020s hold? Technological advances continue to take shape at a blistering pace and will have effects on nearly all aspects of our lives. While most of these tech advances will be a clear benefit (improved healthcare, faster connectivity with 5G wireless, and greater access to online learning), others offer a potentially existential challenge to something most of us do for more than a third of our lives: work.

Work in the Age of AI
The nature of work is changing rapidly. Automation and artificial intelligence (AI) are allowing machines and computer programs to take on an extraordinary variety of tasks.

We think of this as a blue-collar issue with robots now doing most of the assembly at automobile plants across the world and autonomous vehicles coming to take truckers jobs soon. The truth is, though, very few jobs are safe from automation as many jobs are quite routine and skills-based.

Any job requiring technical skill alone may eventually be subsumed by advances in AI. This leads to the important question of how does one prepare for this sea change? How can you make yourself un-replaceable by a machine?

A 2017 McKinsey Global institute study concluded up to 800 million workers worldwide could lose their jobs to automation by 2030.

Workforce Transitions in a Time of Automation (McKinsey, Dec 2017)
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What Skills Will Be Valued in Future Work?
While it seems logical to assume that increases in AI would necessitate increased training in technical skills like computer programming, coding, engineering, or other scientific fields, the fact of the matter is that those technical skills themselves may be unsafe from the evolving capability of our machines.

Specifically, a report by the Brookings Institute where job descriptions were compared against AI-related-patents (see paper by Michael Webb at Stanford University for empirical details) found that white collar, data-intensive jobs in business, technology, engineering, and science are at greatest risk of being impacted by AI. The report quantified impact as having job duties that could be subsumed by coming AI advances. This doesn't mean AI would replace the job completely...in fact, AI may just supplement work in fields such as finance and scientific research. What is clear, though, is that only being good at a skill or technique that eventually can be automated is NOT a winning strategy.

"AI exposure will likely lower wages and lead to job replacement if human workers can no longer bring "extra value" that AI can't."
- Mark Muro, Senior Fellow & Lead Author of the Brookings report

Transferable, Human-Centered Skills are Irreplaceable (for now)
What will be in demand for the foreseeable future are transferable skills. These types of skills aren't technical but rather refer to intra- and inter-personal skills that we use to get our work done efficiently, by ourselves or with others.

LinkedIn found these 5 transferable skills to be most in demand in 2019 (based on job postings on the platform):
5. Time Management
4. Adaptability
3. Collaboration
2. Persuasion
1. Creativity

In addition, as more and more employees become tech-savvy, backgrounds that give employees perspective — historical, global, or otherwise — will become increasingly valuable. We will need to increasingly think about how our machines and interfaces are designed to interact with humans.

Currently, machines cannot think like human beings. The AI we have today is so-called "narrow AI,” performing objective functions using data-trained models. The AI from science fiction movies where machines have human-like intelligence, so-called "artificial general intelligence," is far from a reality and may never be achieved. So, we will need people to serve as the interpretational bridge between AI and society and back again...to insert humanity into our machines and their decisions.

Technical Training is NOT Enough:
The Role of Critical Thinking, Problem Solving, Data Synthesis, & Presentation Skills
There has been an increased emphasis and interest in STEM (Science, Technology, Engineering, & Mathematics) education to prepare the next generation for a new age of work. Colleges, particularly technical schools and community colleges, have invested heavily in career and technical education that seeks to broaden technical skills currently in demand (coding, software engineering, etc...). Ironically, though, being trained in the liberal arts and humanities may be more useful in the age of AI. In addition, there is data suggesting a surplus of STEM majors in some fields (see also this piece from The Atlantic).

It won't be so much our technical skills that differentiate us in the 21st Century economy. Rather, being able to think critically, synthesize information, present arguments persuasively, and work effectively with others will be increasingly valued.

Anyone working in the sciences knows that our knowledge and tools change fast. So, it may not be surprising to consider the skills we value now in coding, data manipulation, and analysis may not be what is needed 20 or even 10 years from now.

"It is not only what you know but how you learn that will set you apart."
- Thomas Friedman, author of The World is Flat: A Brief History of the Twenty-first Century

Graduate Students & Postdocs Have the Skills Needed for the 21st Century Economy
Having the skills to quickly dissect a problem, synthesize and interpret data, and clearly present insights and recommendations from your work are incredibly valuable. Luckily, if you have been pursuing a graduate degree or postdoctoral work, you have experience in all these areas. Each day you are working to troubleshoot problems, synthesize your data with what is already known, and form coherent narratives around your findings. You are uniquely poised to make sense of the flood of data that is and will be produced and stored from the countless scientific publications, device interactions, and web searches that occur each day.

Even the hot career field of data science is focused heavily on communicating insights from analyses. This skill may become even more critical as computers takeover the work of data preparation and analysis, leaving the humans to extract insights from the work.

Ph.D.-trained scientists are uniquely poised to be the data interpreters we will need in the 21st Century.

How to Hone & Leverage Your Transferable Skills Into Meaningful Work
A changing world demands individuals who can change with it. Flexibility and the ability the learn and adapt to new technology will be crucial. Scientists possess these and other transferable skills but need to more effectively hone and communicate them to potential employers.
In addition, current graduate students and postdocs must avoid becoming so focused in one domain or technique that they don't develop the transferable skills needed to succeed in the 21st Century economy.

How can current trainees practice and perfect their transferable skills?
Develop Teamwork & Leadership Skills
Taking leadership roles or volunteering in organizations that you are passionate about (including graduate student and postdoc associations) allows you to give back to your community while also honing your teamwork and leadership skills (for example, see). You can look to get involved with local or national organizations or local chapters of national organizations, potentially in research or career areas that interest you.

Volunteering within an organization while continuing in your current role will also force you to improve your time and project management skills as you seek to fulfill your many obligations. In addition, working with others in these organizations, especially those from different backgrounds than yours, will teach you the importance of effective communication skills (listening, synthesizing, presenting), leadership, and consensus building to achieving success.

Check out Volunteer Match for community volunteering opportunities.

Communication Skills
You should also seek out opportunities to practice and demonstrate your communication skills. Join Toastmaters to practice your public presentation skills or volunteer to deliver a talk to a general audience (local science cafes/clubs are a great venue). You can also start posting articles on the web to demonstrate your written communication skills (LinkedIn has a great self-publication feature). Showing you can speak to a general audience and not just academics is crucial. Your future coworkers and customers will have a variety of backgrounds and speaking to them in an accessible way will increase the rapport you can build with them.

Final Thoughts
While the future of work in the age of automation may seem scary, there is the real possibility that increased efficiencies will free us to do tasks that are mentally stimulating and personally rewarding. Instead of slaving over data we will be able to spend our time thinking about how it can be used to better the human condition. We will be able to spend more time interacting with one another instead of at our screens doing the menial tasks that often accompany knowledge-based work.

A more automated and efficient world will provide us more time to do what we enjoy and give back to others: to do meaningful work. By freeing us of routine, tedious tasks, AI also has the ability to unlock the vast untapped potential in so many of us...to allow us to pursue work and activities that are not motivated so much by their utility and profitability (as the machines will do much of that) as by their ability to bring happiness to our and others lives.
That is certainly a future worth aspiring towards.

Further Reading
AI & Work
Where Machines Could Replace Humans - and where they can't (yet)

What jobs are affected by AI? Better-paid, better-educated workers face the most exposure

Is Technology About to Decimate White-Collar Work?

When Will AI Exceed Human Performance? Evidence from AI Experts

In the Age of AI (PBS Frontline documentary)

Human Flourishing in the AI Age - We Need a New Story

Education & the 21st Century Labor Market
The Myth of the Science and Engineering Shortage

STEM Crisis of STEM Surplus? Yes & Yes

A Humanities Degree is Worth Much More Than You Realize

The Growing Importance of Social Skills in the Labor Market

You Can Do Anything: The Surprising Power of a "Useless" Liberal Arts Education (book)

The Future of Work
Meaningful Work: Viktor Frankl's Legacy for the 21st Century (book)

Life's Great Question: Discover How You Contribute to the World (book)

The Globotics Upheaval: Globalization, Robotics, & the Future of Work (book)

MIT Initiative on the Digital Economy

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Using Data Science and Artificial Intelligence to Improve Healthcare

9/30/2019

1 Comment

Data Analytics & Healthcare

This article originally appeared on the Health:Further blog on October 14, 2018.
It has been updated with current news and findings.

Data science is a buzzy term not only in the technology sector but in the wider culture as well. It has seeped into the common vernacular and promises increased insights and knowledge extracted from the vast quantity of data being generated every day.

The use of data science in healthcare is growing and artificial intelligence (AI) represents a huge business opportunity in the space. However, the potentially identifiable nature of health records and ethical concerns about how the data should be utilized and by whom makes working in this space a challenge.

The recent publication of work suggesting AI may be as good as clinician s in diagnosing disease further highlights the increased importance this technology will have in 21st Century healthcare.

I spoke with data scientists from 3 different healthcare companies on how their groups are using data to improve the quality and efficiency of healthcare.

axialHealthcare, Nashville, TN

Lindsey Clark, Ph.D., is Director of Data Science and Analytics at axialHealthcare in Nashville, TN. Since joining the company in 2015, Clark has watched axialHealthcare grow rapidly from 8 to more than 100 employees. Focusing on pain management and opioid care, axialHealthcare leverages medical, behavioral, and pharmacy claims data to drive improved patient care and financial savings for health insurers through technology-enabled capabilities.

In essence, axialHealthcare's goal is to understand which treatments are both effective and safe in the treatment of pain. Axial is also focused on determining if treatment approaches beyond potentially addictive opioids are viable for particular patients.

A big question at axialHealthcare is, “what does safe and effective pain management look like?” The answer seeks to ensure that 1) opioids are prescribed judiciously given their propensity for causing dependence and addiction and 2) that other pain reduction therapies are considered when warranted.

Clark emphasized the unique challenges of working with health information, including the critical need for data security and privacy as well as navigating the complex United States health system. The focus for most of U.S. healthcare is on what is reimbursable by either health insurers or Medicare/Medicaid (federal & state payers). Thus, every company trying to improve cost efficiency in healthcare must think about how their recommendations fit into the payers’ current reimbursement framework.

axialHealthcare has organized its Research and Development (R&D) group into data science and statistics/communication branches that communicate closely but have different functions. These teams work with the product team whose job is to think about the value they can extract from data insights and models to benefit customers. The R&D groups also provide support to the company’s clinical outreach team comprised of licensed clinical pharmacists and engagement specialists who work to change provider behavior and improve patient outcomes, which ultimately reduces costs on the healthcare system and protects insurers from spending on ineffective treatments.

axialHealthcare synthesizes a variety of data to improve patient care and drive savings for payers.

Most of the company’s data comes from insurance claims, but some is also gleaned from patient behavioral and electronic health record data. Although the team is always focused on new data-derived models of improved care and cost savings, it is critical for the data science team to align their projects with what the market needs.

“Insurers, our main clients, are very focused on short-term costs and so it’s often critical that our company frame the work in a way that indicates both short-term and long-term costs can be improved through data insights. Selling clients on the long-term cost savings can be difficult, especially if the short-term effects are increased costs to insurers,” according to Clark.

This point illustrates the challenge of providing solutions that are good for the business of healthcare and also for the health of patients.

Framing information in a way where payers can see the long-term savings generated from costly approaches in the short-term is critical to enacting meaningful, effective interventions.

Ultimately, the company hopes to collect its own data for two reasons: 1) access to data can be a challenge and 2) variables that may be of interest to the data science team aren’t always available in the data collected by a third party. Nevertheless, axialHealthcare’s current approach has proven effective for patients and payers.

Artificial Intelligence (AI) in Healthcare

At the 2018 Health:Further Festival held in Nashville, TN, I talked with two companies working to use artificial intelligence (AI) to improve healthcare.

Change Healthcare AI group, San Francisco, CA

Change Healthcare is the largest independent healthcare information technology company in the U.S., handling approximately 60% of medical claims. Alex Ermolaev is part of a growing AI team there. The goal of Change Healthcare’s AI group is to improve efficiency and add value on top of existing data management and analytics solutions.

See Alex's video on AI in Healthcare below.

Change Healthcare uses large, aggregated and de-identified claims, medical history, and treatment plan data from their databases to provide insights on how to increase the effectiveness of healthcare, particularly how to provide treatment that is more efficacious and economical. While Change has mostly claims data, Ermolaev said most of it is very extensive, often up to 400 pages per claim, so that meaning and insights can be extracted from the various doctor notes and other details from healthcare providers. Text from claims can be read and analyzed using natural language processing approaches to identify relevant information in the record.

Ermolaev, formerly at Nvidia, mentioned that most AI models can achieve very high accuracy (>95%) as long as the following 3 factors are available: 1) large amounts of data, 2) bigger/more complex models, 3) more computing power.

“The main limit to using AI in healthcare is the lack of large enough data sets,” according to Ermolaev. This shortage of data is not unexpected given the sensitivity of personal health information and the vast privacy protections in place. Thus, companies with access to the data have a great advantage when competing in the AI healthcare space.

As AI and predictive analytics grow in healthcare, Ermolaev believes we are moving from evidence-based healthcare, where treatment decisions are based on what’s been proven effective for the population in general, to intelligence-based care where a particular patient’s medical history informs more personalized treatment.

I was surprised to hear Ermolaev mention that genetic information (frequently promoted in academic circles as the key to precision medicine) is often not required to develop personalized insights. This highlights the fact that currently available medical history, behavioral, and symptom data is often adequate in creating dramatically more effective personalized treatment plans.

UPDATE:
Change Healthcare announced its Claims Life Cycle AI in February 2019, which seeks to reduce the number of denials for medical insurance claims processed by healthcare providers (see infographic).

Droice Labs, Brooklyn, NY

While at Health:Further, I also met with representatives from Droice Labs on Entrepreneur Alley at the 2018 Health:Further Festival, a showcase area where over 70 startup companies could meet with conference attendees.

Droice Labs brings the power of artificial intelligence to hospitals. The company’s technology provides personalized predictions of how a given treatment (e.g., a drug or a medical device) will perform for a given patient. This software solution is based on a combination of the latest medical research and learning algorithms, which together analyze how a treatment has performed on similar patients in the past by aggregating data from millions of patient records and treatment plans. This allows doctors to consider all of their options in real-time and choose the right treatment.

Droice labs uses a variety of patient datapoints to better understand individual differences in response to medical treatment.

Droice Labs has approximately 30 employees and works with providers, payers, pharma, and government clients to build applications that augment processes and improve workflows. The goal of this work is to improve the quality of care for patients while also decreasing the burden on physicians with the ultimate outcome of increasing the efficiency of the healthcare system.

The company has been around for just over 2.5 years. The founders of Droice have extensive backgrounds in technology and AI and take a “deep dive” approach to their projects, trying to understand the causation behind their insights and results. They then communicate these findings transparently to their clients.

The relatively small company has a very collaborative culture with employees from a variety of backgrounds—tech, healthcare (including clinicians), scientific research—that bring different but complementary perspectives to their work. “The company is structured to be very horizontal, an organizational setup that fosters the sharing of ideas among all individuals in the team,” according to Droice Labs Co-founder & CEO Mayur Saxena, Ph.D.

Speaking to M. Saxena and Co-founder & Chief Product Officer Harshit Saxena (no relation), it is clear Droice Labs has a growth-focused, startup-like culture with a hunger from employees to continue to innovate and do more. The company has a mission that appeals to young workers that want to work for a values-driven company. “One measure of success at Droice Labs is how many people we were able to impact by our work today,” says M. Saxena. “Do the insights we develop increase the well-being of an extra 1,000 people? How can we improve things to increase that impact?”

Both of the founders agree with a point mentioned on the main kickoff stage at the Health:Further Festival: healthcare touches everyone. When asked what got them into using their analytic skills in the healthcare space instead of a traditional technology company, M. Saxena emphasized the common human experience of healthcare: “my thought was, if I am going to consume it, I might as well work to improve it.” He went on to say that healthcare is full of great people that work tirelessly to improve human life and that the industry needs technology to enable healthcare workers to do their jobs more easily and effectively.

At companies like Droice Labs, Change Healthcare, and axialHealthcare, the approaches may differ but the goal is the same: to improve healthcare in the 21st Century through data and insights.

Read More
Artificial intelligence in healthcare: Past, present, and future
Artificial intelligence in healthcare
Applied data science in patient-centric healthcare

Additional Resources

Want to get into data science?
The Insight Data Science Fellowship program offers a fabulous training opportunity with demonstrated success in job placement afterwards.

NC State's Institute for Advanced Analytics offers a well-respected Master of Science in Analytics degree with excellent career outcomes for graduates.

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Reflections Blog

The End of Work as We Know It: How an Increasingly Automated World Will Change Everything

A 2017 McKinsey Global institute study concluded up to 800 million workers worldwide could lose their jobs to automation by 2030.

Using Data Science and Artificial Intelligence to Improve Healthcare

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