Soon, remote work will just be work, you’re going to have to level up as a manager.

I just learned of a whole new role in companies, Head of Remote. No one knows what it means. Sure sounds fancy. Remote work has been around my entire career, but it seems to have reached an inflection point.

There’s still lots of arbitrage in the system, hiring remote remains a competitive advantage. Job seekers view it as a significant perk. Anecdotally, opening a job on AngelList 4xs the number of candidates. And of course there’s the compensation arbitrage, you can get the best talent in Birmingham, pay them above local market rates and still come way under the standards in talent hubs like Bay Area and NYC.

This arbitrage won’t last forever. Eventually, more companies will adopt a remote work program.

So, as a people leader, are you going to be ready?

There’s a shift. Traditionally a people manager is focused on managing people. In a remote setting, you need to focus on managing processes and leading people. That’s a good modus operandi generally, but with a distributed team, it’s a must. Remote allows individual contributors to rock and roll. You need to set them up, define the goals, and get out of their way.

Here’s my simple approach to leading remote teams. Just enough process, individual growth, market vision. Let’s break that down.

Just enough process. I don’t want to manage anyone’s day-to-day, but you can't let people fend entirely for themselves. You have to provide tools and processes that help get the job done. Minimalism is good here, but it’s important to define goals and provide tactical methods.

Individual growth. Encourage and provide personal growth, and it’s really about learning. Let people learn new technical skills, encourage conference attendance and presentations, let them take on more responsibility and ownership. You get the idea. The people you want on your team are the ones that want to learn and grow, make that a core value.

Market vision. Finally, there should be a grand plan, a strategy, a vision. That should be rooted in your understanding of your specific market or customers. Why is your team doing what they’re doing? What’s the end goal? Do you understand your business and the market? Share that knowledge transparently with your team.

Just enough process. Individual growth. Market vision.

It sounds simple, but it takes plenty of work and learning of your own.

The best part is not only will you get to attract the best talent, but you’ll also retain it.

Google Health and Ascension, what's all the hoopla about?

Extra! Extra! Read all about it.

Google Health gets 50 million patient records full of tasty data. It’s over folks. Minority Report style predictions coming your way. Just imagine it, Google starts showing you ads for sugar-free candy and weight watchers, Google’s ad-targeting algorithms have welcomed you to pre-diabetes.

I’ve been trying to make heads and tails of this story.

You throw Google into the mix and it immediately makes for a good headline. Replace Google with Optum, HealthCatalyst or 3M and this never makes the front-page. My first instinct was to just ignore the hoopla but as the days went on, I couldn’t resist.

The story started with a ‘leak’’ from a Google whistle-blower (GWB). Eventually, they shared their concerns and motivations with us directly:

“Two simple questions kept hounding me: did patients know about the transfer of their data to the tech giant? Should they be informed and given a chance to opt-in or out?”

Respectively, they answered yes and no. And here lies the crux of it. The GWB is concerned about patients not knowing that this project is happening. But the reality is HIPAA allows these sort of partnerships without patient consent and it happens all the time. When data is used for ‘Health Care Operations’ patient consent is not required. 

Per the HHS:

“Health care operations are certain administrative, financial, legal, and quality improvement activities of a covered entity that are necessary to run its business and to support the core functions of treatment and payment.”

The bottom line, the provision on health care operations is fairly broad and easily allows for the Google and Ascension data sharing. Google is acting as a partner to help Ascension with their operations, all kosher. Google doesn’t get to own any of the data themselves. 

Now if the whistle-blower alleged that Google was taking this data and somehow siphoning some of it off for their own nefarious purposes. That would be illegal and sensational. But they don’t assert this beyond saying that it could happen if we are not careful. To me, really the GWB should be upset with HIPAA and it’s allowing healthcare providers to use PHI data for the business side of healthcare. And it is most certainly a business, lest some of us forget.

HIPAA too soft?

This made me think, is HIPAA too soft? Imagine the alternative - patient consent for every business need. That can quickly become quite stifling for innovation. Those familiar with the IRB and other research protocols, applying the same standards to operational improvements, I believe that would be deathly crippling for an already change-resistant system.

Another note, for some things you certainly can use de-identified data but for things like combining disparate data sources (i.e. what Google is helping Ascension with across its many systems), you literally need all the identifiers to create the full patient record.)

Ascension’s response by their EVP of Strategy and Innovation hits back on some of the clickbaity items. To summarize, hey we didn’t keep it a secret, it’s all kosher under the rules, data is safe and secure, and we are trying to fix a huge interoperability problem not send you targeted ads. 

Fair enough. Don’t be evil and all that.

For Google, the experience for their engineers of working with all sorts of healthcare data at scale is priceless. Can one of them be inspired by some ad-hoc analysis to create a new pre-diabetes ad targeting algorithm? Yep. But can they take actual patient data to create such an algorithm? Nope. If anyone has some good logging and auditing in place, to prevent data cowboys from breaking the rules. I bet it’s Google. Random data analysts at Ascension working with some legacy EHR, how’s the logging and auditing on that system? I’d guess, not as robust.

My takeaway/TLDR:

Google analyzing and storing 50 million patient records without patient knowledge can bring in a lot of clicks to your article. But beyond that, this is nothing new or that exciting. Players like Optum, HealthCatalyst and Blue Cross Blue Shield are crunching many millions of records. Maybe the HIPAA ‘healthcare operations’ provision is too broad but the alternative seems worse. I for one am excited that big tech is trying to drag healthcare into a more efficient digital future. But then again, I’ve always been a tech optimist and Silicon Valley fan.

Google vs. Apple vs. Amazon vs. Microsoft. Who will actually disrupt healthcare? 

I am taking bets at pavel@datapavel.com 

Don't call it Data Governance

Idea: What if we stop calling it Data Governance? 

Data Governance elicits feelings of boredom and numbness in my brain. Governance rhymes with Compliance. When is the last time you got excited about Compliance? Yeah, I didn’t think so. 

What is Data Governance? Let’s start with what it is not. It is not the tech side of things. We got our cloud infrastructure, raw data sources, transformation pipelines, specific data models, and dashboards. We got wrangled data sets for machine learning; we got regression and deep learning models. There’s a lot of SQL, Python, R code moving all the 0s and 1s around. That’s the tech side. 

The compliment to the tech side is the context. It’s the subject matter, the meaning, the business logic, the why, the how. 

Imagine we are trying to calculate the lifetime value (LTV) of a healthcare system patient. I know, I am crazy to apply a standard marketing metric to healthcare, indulge me. We might have the best data scientists east of the Mississippi; they can build models in their sleep. But our brilliant developers have no idea about the ins and out of healthcare patient revenue. Spoiler alert, it’s loaded with complexity.

They don’t know that some patients' LTV is based on their Medicare Advantage risk-adjusted capitated payments (fee-for-performance). For those patients, we get revenue based on membership, not on services provided. Then other patients just come in when they need a flu shot. The healthcare system gets paid every time they visit. (fee-for-service). And then there’re denials and write-offs to factor in, healthcare revenue cycle is a beast. 

To get to our patients’ LTV, we need to understand all these subtleties and carefully define the metric calculation for different patient tranches. We need to work together with the people that know the little details inside and out. We need to write it down; we don’t want anyone else starting from scratch (templates, business glossary). We need to check that our business definition matches our code (data validation, data integrity). We need someone on the business side to be our partner; they’ll help us validate, they’ll tell us what’s working and what’s useless, they’ll answer our questions, even the stupid ones (data stewards). We need a way to keep track of all the code, data models, reports, and dashboards that are related to this metric. (data lineage, data dictionary). 

We need to understand, organize, and keep track of the context that sits on top of our technology. This is data governance. But when I describe it above, it doesn’t sound dull or scary. It’s all the other stuff that around your code that makes what we are doing valuable to the business. It’s the fun stuff - it’s where the impact happens. 

So what if we stopped calling it Data Governance and started calling it Data Context instead. My eyes would glaze over less.

#datacontext by #datapavel

Digital twins, what starts with a wind turbine, ends with a digital Pavel?

I love sci-fi kind of stuff, so when my buddy mentioned digital twins on our first podcast episode, my ears perked up. 

What are digital twins? In essence, they are digital copies of real-world objects; they are computer simulations, lots of code, algorithms, and data all meshed together. 

Wikipedia offers a more elegant definition: ‘A digital twin is a digital replica of a living or non-living physical entity. By bridging the physical and the virtual world, data is transmitted seamlessly, allowing the virtual entity to exist simultaneously with the physical entity.’ 

Simulations have existed for a long time. Lots of us, myself included, have taken a simulation class. My class project was simulating the checkout lines at a Duane Reade in New York, ohh, the excitement!

So what’s different now, what’s with all the buzz?

Various technologies have matured and teamed up to make digital twins so robust of a simulation that they are pretty good digital copies of real objects.

The most significant factor is the so-called Internet of Things. We now have lots and lots of sensors and can collect and process data in real-time from all sorts of equipment, from the space shuttle engine to your smart fridge. (You don’t have a smart fridge? What are you living in 1990?) 

See, a digital twin is not just some code written by humans; it’s taking in real-time data from the physical object and adjusting the digital twin to match. 

Ok, lots of sensor data is coming in, but you still need a way to make sense of it. Here come our favorites: AI and machine learning algorithms can take in all that data and magically (mathematically) create a ‘living’ virtual model. 

IoT sensor data, machine learning, cloud computing all come together to make this happen.

Today, the applications are mostly for large industrial equipment. GE is using the framework to improve wind farm operations by building a full digital wind farm. NASA is using it to test next-generation space-craft, testing it before building it. 

That’s the jelly in this donut; you can build a whole production line out of digital twins and then experiment without actually doing any of the expensive physical testings.

Can this concept be applied to living things? To humans like you and me?

side note - am I human or am I data?

Can you imagine a digital copy of yourself in your EMR, updated continuously based on your real-time data: calories, steps, sleep, medications, real-time biometric data like heart rate and blood pressure, etc.…

If you have enough data to build a virtual copy, can we test a drug on a person without testing it on the actual person?

Can we simulate based on an individual’s genetic code and their gut microbiome? Is this the future of personalized medicine? I am getting excited.

I think we are still quite some time away from perfect digital copies of our bodies, but I can see it happening in the next 20 years. One thing for sure, we are going to need to store and process all that data. That means more opportunity for big tech and more opportunity for anyone that likes to work with data.

Data data data everywhere, with no signs of slowing down. 

When is lunchtime in a virtual company?

There’s something romantic about a factory whistle that stops all work. I just imagine a factory and its assembly lines buzzing with movement, everyone working together like bees in a beehive.

And then, WhphhhaaaeeeuuueeeeeeooooooooooooooOoooooooooOooooOoooooooooooeeeeeeeeeeeeeeeeeooooooooooooo. All work stops.

It’s lunchtime, yaba daba doo.

One high school summer, I was working for a direct-mail company stuffing envelopes — shoutout Letter Perfect (in business since 1978). Not quite an assembly line but there were dependencies, one person would stuff the envelopes, another would seal them with the letter sealing machine. You don’t lick 1000s of envelopes, that’s crazy talk.

For efficiency, we all took lunch at the same time. I got to know folks I otherwise would have never met. The work, in a real space, doing physical things made us take a break together, and it helped build a culture of support for each other.

Fast forward to my first role out of college, going through the new hire IT program at Deutsche Bank. Wall Street, baby. There were a bunch of us recent college grads starting at the same time. A couple of us became friends, and we’d frequently all go for lunch together. We were all in the same building but working for different divisions. Of course, the work was all IT/knowledge work, no physical effort required, no factory floor. The work itself didn’t mean we all had to take a break at the same time. It was just fun to grab lunch together. Sometimes we’d eat in the cafeteria; sometimes we’d run out for lunch. We'd always have a few laughs. 

What happens to lunch if you work for a virtual company?

Virtual work brings with itself a different level of independence. I wrote about this before; you’re in control of your time; no one is tracking your every minute. At Bluetree, our team and clients spanned at least three different time zones. For me, on the west coast, the hot zone was 8a-2pm. The overlap hours. There were days when those overlap hours were booked end to end. But when you can wheel yourself from your desk to your fridge without leaving your chair, lunch is at your fingerprints, awesome right?

Well, yes and no. Without other teammates around you, there are no physical cues, no one getting up to leave the office for the salad bar or to warm up their chicken masala, which generates a delicious curry fragrance to remind you to take a break. 

In the past 4.5 years, I can count on two hands, the number of times I left my house for lunch. Postmates and leftovers dominated my lunch menu. 

So yes, working remotely in a virtual company, you can easily skip lunch, snack on some nuts and bury yourself in code and emails.

Don’t worry, its not all gloom and doom. 

Remember, you’re working remotely, which means your office and your schedule are your own doing (within reason). I started to book an hour on my calendar for a lunchtime workout: a quick yoga class, some weight lifting, or a bouncy jog. Sure, I sometimes had to push through and go meeting to meeting but then at 2 pm, a quick workout, shower, and it’s like your brain rebooted — new processing power. I wasn’t overly consistent, and I know others who booked their lunchtime every day and took that time regularly. But for me, a little chaos worked best.

I’ve seen folks propose virtual lunches. Just get together with a few teammates and eat on webcams. I tried it once, but it always felt a little hokey to me, perhaps that would diminish with more reps. 

What’s the insight here? 

There’s no foreman to tell us to take lunch in today’s knowledge economy, especially so if you’re a remote employee. Breaks during the workday are great for productivity. Sometimes when working remotely, we can get into a state of flow and work non-stop, and sometimes that’s the right move, I’ve certainly done it and felt great when finishing a piece of code or sales proposal. 

There’s no one right way to do lunch when working remotely. You should experiment. Try a workout for lunch (my personal favorite). Try blocking lunch every day (even during your main overlap hours) and making something in your kitchen. Try meeting a local friend. Experiment with your schedule and your day. That’s the fun part about being remote. 

Did you have lunch today? Did you leave your remote office?

Oh, snap, its about time for me to forage my refrigerator as well — Mnyam mnyam mnyam. 

A fridge full of food!

The Basics of Machine Learning for Business

Machine Learning is sexy, it’s a buzzword, but it’s also changing businesses across all industries in a very real and rapid way. It feels like voodoo even to me, a trained engineer, maybe it’s all the hype and my proclivity for science fiction.

It’s not voodoo, let’s break it down.

Go back in time and show your iPhone to someone in the 5th century, they’ll think you got some voodoo too.

At the core of Machine Learning (ML) are so-called models. ML models are functions. You know like a f(x) = 5x + 10. Only they get super complex with lots of parameters, not just a lonely x-variable.

In essence, ML is a bunch of math algorithms running on lots of data with the purpose of building a model, aka figuring out all the parameters of a complex function.  

No magic, this is just math. Math can be scary, but good ol’ Pavel will protect you, don’t fret.

We’ve been using ML models or functions for three things, usually to predict things:

Zoltar does not actually use Machine Learning, he’s fun though.

• Regression

  • I’ve got a bunch of data; I want to fit a curve to it. f(x) = mx + b, find m and b

• Classification

  • Are these customers likely to churn? Is this an image of a dog or a muffin?

• Clustering

  • Segmenting populations, customers, arranging by category (search engine), discovering similar items

Basically, those are your three styles of models. You’ll pick an approach based on the specific business problem or question you’re trying to solve.

We can think about the overall machine learning lifecycle, via 3 stages:

These bears are cute. There’s three of them.

1.     Prepare data, get your cowboy gear on and do some wrangling.

2.     Feed the data into the math monster, build and train a model/function

3.     Deploy the model, feed live data into the function and do something with the result of that function (detect a fraudulent transaction and block it)

This is a cyclical and iterative process. Once deployed, we take the latest data and see if your targeted metrics are improving, feed more data in and create an even more precise model.

Why are AI and ML so HOT HOT HOT right now?

More data is being generated and captured. You need the data for machine learning, without data this does not exist. More data tends to produce more accurate models.

More compute at cheaper rates. With the cloud, we can spin up, 2000 GPUs (specialized processors) to train a model for a couple hours for a few bucks. Imagine having to build your own computing infrastructure instead, real estate lease and all.

We have free access to state of the art algorithms, tools and frameworks. Tensorflow, PyTorch, scikit-learn, you get the idea, the cutting edge ML algorithms are open-source.

Bottom line; don’t be afraid, it’s just some math. Most business will use ML either directly or through vendor-software to improve operations and sales. For lots of businesses, much of the data still lays there untapped. State of the art tools are freely available.

This article is completely inspired (borderline plagiarized) by Matt Winkler’s video, see the full thing here (scroll down a bit).

Hiring remote is a serious competitive advantage, why and how you can do it today.

I’ve worked fully remotely for the past 5 years and hybrid office-remote throughout my entire career. Hell, I even worked from my dorm room, a work-study gig as the web admin for the history department. So one could say I am a bit of an armchair expert in the field. 

Today, working from home a couple days a week has become the norm across many organizations. Yet, companies are still reluctant to go fully remote or just hire remote-based employees. I contend its an untapped competitive advantage that allows you to find and retain the best talent. And, I assume we all agree, talent matters. 

Why you should do it? 

1. It’s what the potential hires out there want. I’ve interviewed and hired a bunch of folks over the past three years for a healthcare IT consulting firm. Travel to client sites has always been part of the work. But the overwhelming preference of everyone has been to work remotely, and I’ve found people generally deliver better work when working from their home office then after flying cross country. 

2. Cost-of-living arbitrage leads to less cost and more profit. Done right, this is a win win. Great talent exists everywhere. Sure SF, NYC, London have become hubs. But there are also dedicated, smart and curious people all over the country. You can take advantage of the cost of living differences by hiring in lower-cost markets. Done right, you’re still beating the local rates, making your new employee happy while coming under the rates you’d end up paying for your market’s local talent. This arbitrage won’t last forever but might as well take advantage of it while it’s here. Of course, you’ll save on the cost of the physical space for your employees as well. 

3. Remote facilitates a culture of ownership. Look, it’s not a magic bullet. But I’ve found that remote when done right brings along a sense of independence and control. You take breaks, lunch, walks, play with your kids, vacuum the floor, whenever you want. No one is watching you. The inherent focus is on results. Results and independence lead directly to an ownership culture. When you feel like you own your work, you do your best. From the other angle, micro-managing remote employees is hard, easier to just trust and let them go out there and conquer. 

Ok, Pavel, it sounds interesting. Tell me how...

Starting a company from scratch? Take the plunge and hire remotely. There’s plenty of software from Slack to Google that will make it easy for you to collaborate. You’ll get your choice of the best talent on the market, not tying yourself to your local pool.

Hiring for an existing team? Having trouble finding the right hire locally? No time like the present. You likely have all the tools in place because your folks are already working remotely some of the time. Look for new hires that are result driven and independent. I’ll be honest, I think adding a remote employee to an existing team can be difficult. You already have all your non-virtual processes in place. You have to adjust, add dial-ins and start turning on video in your meetings. It can be rough for the first remote hire, hence, looking for someone who can tough it out with their result-oriented mindset. Now, to make this really work, you have to allow all of your current team members to go fully-remote. The new hire can’t be just some special case. It’ll be a transition but all that cash you save on your office lease can be used instead for a sunny beach-front company retreat. Hmm, that sounds pretty good. 

Tactically, you can just indicate on all your job posting platforms that you’re willing to hire remote or even better post for positions in the lower cost market that have the talent you’re looking for. 

In summary, people want to work remotely, you can find better talent at lower rates by playing the cost-of-living arbitrage game, remote working lets people control their time and focus on getting results, which is good for your business. 

An Open Love Letter to the Bluetree Analytics Team

Well look at us, in the past 3.5 years we built a sustainable business that’s delivering precious data and insights into all corners of the healthcare enterprise. It all feels rosy now but I remember the shaky start, as we dived into the deep end of the unknown. 

At first there was just two of us, figuring it out all out in real time. I remember the first client visit, I was nervous, could we pull of an analytics assessment? I spent countless hours going through the finished product. It turned out pretty good, I thought - hey - we can do this. 

We started hiring. I slowly came to realize the power of talent. If you hired for the right overarching characteristics, the specific certifications and resume details, didn't matter. Real talent evolved their skillset quickly to meet the demands of the market. 

We kept our reporting factory humming along and started playing with data integration. Long live claims and Caboodle. We figured out the best ways to do things and shared it across customers. We hired more awesome people, talented, hungry and kind.

That last one is important. Being honest, transparent and kind, trumps your SQL prowess every day. Y’all are kind people trying to do right by each other, never lose that. 

I remember writing my first big $500K+ proposal (we didn't win it) but we were able to take all our know-how and put it on paper in a compelling way. We were picking up steam. 

Today, it’s full steam ahead, a team of ~30 and growing. A virtual team that has maintained its innovative and help-each-other attitude. The culture we built, that's more important than the millions in revenue and profit (though those numbers should make you all proud too). I’d love to take the credit but it was all of you that did it not me. 

I can’t wait to see how you evolve, grow and CRUSH the Epic Analytics game. 

The last few weeks after I put in my notice, I’ve had flashes of - oh shit, this team is so awesome, I made a huge mistake. That’s just a testament to you all. I also had flashes of excitement as I open a new chapter for datapavel. Nothing like oscillating between sadness and excitement, but it’s normal. 

Good luck all, you’ll accomplish anything you put your mind to and I’ll be there to cheer you on.

Love,

Pavel

Pavel’s top 5 super ranking of rankings.

I present the most authoritative ranking of rankings every compiled. Ranked using very complex and proprietary algorithm.

1. US News World Report - how I chose my college, just went to the highest one I got into, and all my healthcare friends get excited about it

2. KLAS - best use of the color orange

3. Forbes 400 - move over bill gates, datapavel is coming to town

4. Michelin - hmm this is not a real ranking but the Michelin man is so lovable and food is yummy

5. Nielsen Ratings - if they don’t measure Netflix are they still relevant? 

That’s just a bit of fun inspired by the recent release of US News’s Rankings.

Do you value rankings or ratings? 

I live and die by Yelp stars and Rotten Tomatoes scores. I used to trust Amazon but that’s been hit or miss. 

Rankings are easily interpreted, I’ll take #1 please, but for some of them the methodology is so convoluted it’s basically a black box.

Ratings are more subjective, but typically if I see. 4-5 stars, I’m in. 

But even more insightful is seeing a distribution of all the scores. Though sometimes that can be a little hard to interpret without a decent stats background.

Anyways, I rank myself the #1 Pavel, five stars. You get five stars for reading this too. Five stars for everyone.

Do you value ratings or rankings or both? Are we too obsessed with both of these? How can healthcare better utilize rankings and ratings?

Follow me on Twitter for all the latest hilarity.

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• I feel sad for the DBA… https://t.co/FaKQSEwckO

  Jan 12, 2023, 8:14 AM

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3 easy rules to build an awesome work culture

Team Culture.

I have a confession to make. I’ve never really played team sports. The closest I got was doing Mock Trial in high school. I was a witness. But even that wasn’t that 'teamy'. I’ve always been more of a solo artist. An individual contributor.

It was in my work life that I started to really see why teams are important. First of all – as I painfully learned when starting my own business – if you must you can see what it was via the wayback machine – doing everything by yourself is impossible. No one is great at everything. I needed a team to succeed but at the time I didn’t really know it. That business never made a dollar.

In my first exposure to healthcare data, I joined a small team funded by a research grant. It had a visionary leader - a PI who was an MD, an application guru, a patient whisperer and myself the data person. Mixing it all together, we were able to improve patient care and outcomes at the local HIV clinic. The project was a success because it combined our individual talents in service of a meaningful and clear vision. 

Now after 3 years at Bluetree Analytics, I’ve built a team culture that’s in a word, awesome. Best part is I kind of did it by accident. But there are lessons in there for any people managers. Here they are. 

Pavel’s 3 Rules of Building a Company Culture

(in no specific order)

1.   Honesty and Transparency

I’ve been allergic to bullshit since I was a kid. I don’t like lying or being lied to. It’s so much easier to just be honest and transparent. As I built the team, I’ve always shared our financial performance, our challenges and successes widely. Some people love that level of detail and transparency, some people don’t care but everyone appreciates the no-spin zone. 

2.   Have your team’s best interest in mind. On an individual basis.

I’ve seen this advice in different form from lots of people. But in essence, just ask your people what they want and listen. From money, to more time off, to learning opportunities, you have to figure out what your individual team members want from their work. And its different for everybody. Then you can figure out a way to support them on the path to get there. Basically, just try to have their best interest in mind. And if you do, that will translate to better engagement, high quality output and a happy, collaborative team. For example, if someone wants a chance at becoming a manager, note that, put them on a path to get there. Well, when they eventually take that manager role, they are much more likely to do the same thing for their direct reports and the virtuous cycle of growth continues.

3.   Give trust immediately.

The best people don’t want to be micromanaged, they want to be trusted and supported. I hire the best, or at least I assume they’re the best until proven otherwise. So my default with every new hire is to trust them implicitly. I trust they will do great work for the client, help others on the team and ask for help when they need it. Trust is taken away when something happens to warrant that. This is the opposite of trust is earned.

In summary, don’t swindle, truly care for your team and give them your trust. If you do that, they will support each other, tell you exactly what they need to be productive and perform at the highest levels.

On Data Translators, the human kind

I posted an older article from HBR on data translators yesterday and I wanted to share some more of my thoughts on the subject.

I immigrated to the states when I was 10 years old. In my early teens, I spent a lot of time translating for my parents and family as they were still learning English. My young brain just soaked it up and I became bi-lingual almost magically.

As I reread this article from HBR, I started to think about my own translator experience. And as I did, it helped me understand what a data translator really does.

From the simplest question about a cable bill fee to the most complicated Medicare application form. My family and the system simply spoke different languages, like actually different languages, Russian vs. English. Even easy tasks were made difficult or impossible because of that. Someone had to translate. There’s another word for translator that perhaps is even more appropriate. Interpreter. It wasn’t just about translating every word, you had to get the essence of the meaning across, interpret. Perhaps we should be using the term Data Interpreter instead, but terminology is not the point.

The need for folks that can do that translation and interpretation in our current data-driven world is the crux of it.

We must admit to ourselves that the business and data people fundamentally speak different languages. But doesn’t mean that one or the other is less capable or talented, take the ego out of it. We need a way to bridge that language gap.

I think one of the main reasons for my personal success as an analytics developer and the success of our Bluetree Analytics team is our ability to become our own data translators. A lot of the time there’s no one dedicated to this task, so by necessity we learn the business and its processes at a near-native level. That allows us to build tools that actually generate the valuable insights. Of course, learning a new business at that level requires time. Time that’s not always there as the business has urgent analytics needs now.

I suggest, in healthcare and really every other business today, we need to invest in developing more folks that can bridge that language gap. Whether it’s taking the operational stakeholders that know your business well and increasing their data literacy or taking data gurus and teaching them all of the intricacies of the business. When doing the former, find people that are ‘good at excel’, that’s always a good signal for data savviness. This will take investment, in people and programs, but if done right, the insights this investment generates will pay for itself over and over.

Thoughts?

Can AI take our Healthcare IT jobs?

wA.I.ne (patent pending)

We had an all company get together last week. We did a fun little exercise, broke into small teams and attempted to design a company that will put Bluetree out of business. The main goal was to let folks from different teams that never see each other in person to work together for an hour. But there was a pretty dominant theme across the board.

Care to guess?

Yep, you got it, AI + Machine Learning + data. Ahh who doesn’t love a good buzzword salad.

How?

We do a ton of Epic build and workflow optimization. Can you imagine a world where the system optimizes itself? Learning from all the users and figuring out the best navigators and orders and all that fun stuff to display in a clinical setting.

What about a world where you simply talk to the EHR? Let’s call that virtual assistant wA.I.ne

Hey Waine, order a refill for Pavel’s Nexium. Or will you even need to say that as it listens in and automatically takes all the notes and files all the orders while the doc asks and listens to all your issues. The voice to text technology is there, the NLP not quite.  

What about all the analytics work? While our focus has been on data engineering and creating robust architecture for our clients. We still do a ton of work just generating reports and extracting data.

How far are we from a world where you can simply say –

Hey Waine, show me my denials for Aetna broken out by remark code.

Hey Waine, what docs aren’t meeting their visit goals, show me their templated time for the past 3 months.

Couple of takeaways.

I think voice is super powerful and a Siri/Alexa/Ok Google for healthcare is inevitable. An EHR that users can interact through voice entirely, both clinical and back office, will be awesome. And once it does that, why not put a little instance of it in your home. Feeling sick, just say Hey Waine, I have a cough and it’ll take it from there. I am not saying it’ll replace the doctor, but it’ll connect you with a live on call doc that will help diagnose.

We need lots of people working together to build the data foundation to make this possible and the likes of Amazon/Google/Facebook to throw their billions at it.

Am I living in a jetsons flying car world dream or is this going to be our inevitable reality? I honestly don’t know. My gut is this will happen but will it happen in my lifetime, your guess is as good as mine.

Regardless, I’d rather build the technology that disrupts then be the one it disrupts.

For now, while wA.I.ne is just a figment of my imagination, we’ll have to rely on the old school Wayne to help us get to a better healthcare IT world.

Thoughts?