Tech

Tech

The Patient Expert: Healthcare’s Untapped Workforce

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One of my favorite patient advocates consultants–that’s Kym Martin (far right) on a panel I ran at Health 2.0–has a new job at one of the most interesting patient consultant companies. Here’s her story!–Matthew Holt

Let me ask you two questions.

On a scale of 1 to 10, how would you rate the quality of the “real-world” patient insights your team gathers to inform your mission-critical, life-altering work?

Are you clear on the needs, trends, and challenges facing the patients you’re trying to serve?

Why Listen to Me?

For the past four years, I’ve listened to hundreds of healthcare leaders discuss patient issues from their perspective as clinicians, technologists, researchers, academics and administrators.

While I’m grateful to these leaders for working feverishly on my behalf as a patient, I question the completeness of their patient view.

The reason shouldn’t come as a surprise. Patients are too often left out of the conversations about the services and products designed to improve their care.

Hey, Machine Learning

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Hey Machine Learning,

I heard what Forbes said about your “setback” at MD Anderson.  I also heard rumors going around HIMSS that maybe it’s “too soon” for you to be in healthcare. At first I thought, “serves you right.” There was so much hype that I could barely recognize you.

Then I realized that, in a way, we’re all to blame. The journalists, vendors, researchers, and data scientists – all of us that tried to make you popular in healthcare. I guess things just sort of got out of hand.

You have to believe me when I say we meant well. We wanted people to see how special you really are. And the whole “30+ years of clinical research and thousands of published studies” wasn’t working. Apparently, evidence is only cool with your research buddies.

So you got a makeover. The cool kids in marketing gave you new nicknames. People started rumors about all these crazy things you were up to. Suddenly, after years of being invisible Machine Learning was the talk of the town. Did you hear, Machine Learning is now going by Artificial Intelligence!  Artificial Intelligence will cure cancer! I heard Big Data will replace doctors! Do you mean Machine Learning?  I don’t know but I heard Cognitive Computing just created the latest fashion craze!

Really, it was all just too much for any one set of methods to live up to.

But that doesn’t change who you are and what you’re capable of. Yes, Queries and Dashboards are more popular. But you don’t get caught up constantly dwelling on the past like they do. And sure, Traditional Statistics have prestige. But we both know they can be a bit myopic at times. And Risk Scores…don’t even get me started on Risk Scores.

You are different.  And that’s a good thing.

I personally have seen you consider millions of different data points – even free text notes – to spot falls in hospitals, prevent admissions of elderly patients, and route people with serious mental illness to appropriate care sooner. You don’t need to be a doctor. Because you can make doctors better at doctoring.

A Great Leap Forward (Or Backward) For the National Health IT Agenda?

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At HIMSS, I listened carefully to payers, providers, patients, developers, and researchers. Below is a distillation of what I heard from thousands of stakeholders.

It is not partisan and does not criticize the work of any person in industry, government or academia. It reflects the lessons learned from the past 20 years of healthcare IT implementation and policymaking. Knowing where we are now and where we want to be, here are 10 guiding principles.

1. Stop designing health IT by regulation

Through its certification program, ONC directs the specific features, functionality, and design of electronic health records. As a result, technology developers devote the majority of their development resources to fulfilling government requirements instead of innovating to meet market and clinician demands. The certification program has established a culture of compliance in an industry ready for data-driven innovations. ONC’s role in the health IT industry made sense eight years ago when IT adoption in healthcare lagged considerably behind all other sectors, but today the certification program impedes a functioning market and must be reformed.

Imagine Ransomware, For Your Body

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Wired has an article up, “Medical Devices are the Next Security Nightmare.”  It’s all about how vulnerable almost all of these implantable devices and hospital telemetry devices are, with old, unpatchable operating systems, open ports, all that.

Let’s just think about this. Imagine someone hacking your implanted defib or insulin pump.

Wait. No need. Imagine just getting an email telling that they have hacked into it. They have the keys to your body’s engine. And they want something in return for not turning it off.

“Give us your credit card and bank account information — all of it. Now. Or we will start screwing up your body, a little bit or a lot, whenever we feel like it, dumping all the insulin into the bloodstream at once. Or just giving you a heart attack. You have until 5pm EST.

What the IBM Watson – MD Anderson Split Means. And What It Doesn’t Mean.

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Last week’s news that MD Anderson Cancer Center has pulled the plug on its two year partnership with IBM-Watson led many critics to wonder out loud if the machine-learning revolution is in trouble, and if Big Data could be about to become the latest tech industry buzzword to die a well-deserved death. It’s a little more complicated than that, argues HealthCatalyst’s Dale Sanders in this can’t-miss presentation. The problems with the MD Anderson-Watson partnership probably say more about the “Big Data Industry” and the goings-on at IBM as they do about the technology. Still, there are important lessons we can learn from the episode.

A Million Jobs in Healthcare’s Future

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“The Future is Here. It’s Just Not Evenly Distributed.”

It’s true.

Science fiction writer William Gibson said that right. We simply have to look around enough – now – to find out what the future holds.

The future may never be evenly distributed. But it’s surely becoming the present faster.

What would you do when…

Here are a series of what-would-you-do-when questions to think about. Each of these are a reality today, somewhere.

There’s more medical data than insight

Kaiser Permanente presently manages 30 petabytes of data. Images. Lab tests. EHRs. Patient data. Billing. Registries. Clinical trials. Sooner than later, most medical devices (big and small) will become smart. They will have an IP address like a Fitbit and send data over the cloud.

What would happen when medical data expands to exabytes, zettabytes, and may be even a yottabyte (10^24)?

What it means for jobs: Expect a boom in data-related opportunities. Data scientists. Visualization gurus. Statisticians. Mathematicians who can build predictive models. Anyone who can spot wisdom from information.

Genetic programming becomes the new software gig

People interested in programming are well-suited to become biologists of tomorrow because ATGC (the genomic alphabet) can now be tinkered digitally using tools like CRISPR.

[Read: A programming language for living cells]

If you are a developer, you could join a bio hackerspace or create your own. Explore how programming can make foul-smelling E.coli develop the fragrance of bananas.

Population Health Isn’t Working Out Quite the Way They Said It Would. What’s Going On?

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I hate shots.  Every year when flu season rolls around, I think, “what’s in it for me?” The answer is, “it isn’t for me. It’s for the herd.” I am young and healthy enough that I am unlikely to die of the flu but I have children, older people and vulnerable patients I care about it, so I get a flu shot every year.

This is true population health. I get a flu shot for the benefit of others. Population health has been extended to a much larger set of activities that have no communal benefit. One patient with diabetes doesn’t benefit from another getting a foot exam. (Mammograms, colonoscopies, no communal benefit. STD screening, on the other hand, fits in the category of true population health.)

This distinction matters. Here’s why:

  1. People are keenly aware of being told to do things that aren’t for their personal benefit.
  2. People reject recommendations that don’t match their health needs.
  3. People are much more likely to follow recommendations from people they trust.  Points 1 & 2 above undermine trust.

Lively discussion with my fellow panelists at upcoming HIMSS17 panel on consumer engagement highlighted my own misgivings about the absence of the patient’s individuality and voice in population health efforts. We all want better health in the population, but are we going about it in the right way?

Population health puts people into categories by conditions (diabetes, hypertension, depression), age, lab results and medical billing data. These categories presume their own importance. When in fact, psychosocial, behavioral and environmental factors determine individual health far more.  Patient goals, preferences and barriers to care tell us what stands between that patient and better health. Without this data, population health efforts are undermined.

A Measure of Insight on MACRA

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Featured Presentation: http://bit.ly/2lhvpjM

A 2016 study by Researchers at Weill Cornell Medical College and the Medical Group Management Association found that physicians and their staff spend between 6 and 12 hours per week processing and reporting quality metrics to the government – at a cost of $15.4 billion a year.

As a recent Health Catalyst MACRA survey confirms, that burden is expected to significantly worsen in 2017 and beyond as physicians struggle to report quality metrics for the Medicare Access & CHIP Reauthorization Act (MACRA) – the federal law that changes the way Medicare pays doctors. Commercial health insurers are expected to follow the government’s lead with similar programs of their own. In complex organizations, successfully achieving performance targets and submitting accurately for MACRA incentives will require integrating multiple measures across financial, regulatory and quality departments.

Data For Improving Healthcare vs Data For Exasperating Healthcare Workers

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The phrase “healthcare data” either strikes fear and loathing, or provides understanding and resolve in the minds of administration, clinicians, and nurses everywhere. Which emotion it brings out depends on how the data will be used. Data employed as a weapon for purposes of accountability generates fear. Data used as a teaching instrument for learning inspires trust and confidence.

Not all data for accountability is bad. Data used for prescriptive analytics within a security framework, for example, is necessary to reduce or eliminate fraud and abuse. And data for improvement isn’t without its own faults, such as the tendency to perfect it to the point of inefficiency. But the general culture of collecting data to hold people accountable is counterproductive, while collecting data for learning leads to continuous improvement.

This isn’t a matter of eliminating what some may consider to be bad metrics. It’s a matter of shifting the focus away from using metrics for accountability and toward using them for learning so your hospital can start to collect data for improving healthcare.

Digital Health, Health Reform & the Underserved – Where Will 2017 Lead?

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by LYGEIA RICCIARDI

In these first days of the Trump Administration, there is a great deal of uncertainty, but it’s clear that healthcare will remain in the spotlight. Repealing and replacing “Obamacare” is still at the top of the Republican party’s—and President Trump’s—agenda.

Congress and Trump have already taken steps to repeal the Affordable Care Act (ACA), though a replacement for it has yet to be articulated. Trump promises “insurance for everybody” in a form that is “much less expensive and much better,” but has yet to reveal details about how to meet his goals.

While changes in healthcare policy will have ramifications for all Americans, members of underserved populations are likely to be disproportionately impacted because they are statistically less healthy  and are also the least likely to have health insurance coverage. Parts of the ACA address Medicaid, which provides health insurance to 70 million people—by definition among the poorest Americans. Nine million whites make up the largest racial group of people who have gained coverage as a direct result of the ACA, but significant numbers of minorities, including 3 million African Americans and 4 million Hispanics, have also gained coverage. The ACA also helps LGBT Americans by forbidding discrimination due to gender or sexual orientation, and by enabling same-sex families to apply for joint healthcare coverage. According to a report issued by the nonpartisan Congressional Budget Office on January 17th, if the ACA were to be rolled back without a replacement, 18 million people would lose health insurance in the first year. There would also be significant restrictions in reproductive health services for women.