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Myth Busting and Data Gathering At Parks Connected Health

While Verona was abuzz with news of EPIC making patient-records available for research (and possibly without patient consent), a smaller group in San Diego was busy busting myths about seniors, remote monitoring, AI, and patient-generated data at Parks Connected Health Conference.

I had the pleasure of speaking about the positive clinical outcomes we’ve seen using Wellpepper for remote monitoring, in our case providing care plans that enable patients to self-manage, and letting care givers know when people need more help, on a panel with representatives from ResMed, Reflexion Health, AT&T, and Rapid Response Monitoring Systems. While a truly entertaining panel has some disagreement, we were largely in agreement that remote care is here to stay, value-based models support and enable it, and patient-generated data provides valuable insights. While the approach may have been different across companies from connected devices, to avatar coaches for home-based physical therapy, to our patient-focused interactive care plans, we all saw similar successes with a patient or consumer-centric approach.

It was refreshing to hear examples from AARP and United Healthcare recognizing the importance of walking, falls prevention, and gait speed in senior’s health. Our study with Dr. Jonathan Bean showed clinical improvements with gait speed and balance through a digital intervention based on the Live Long Walk Strong program. More programs based on prevention and activity for seniors rather than sensors that detect falls after the fact are needed, and it’s great to see such powerful and prominent organizations advocating for that as well.

Parks and Associates is a research-led analyst firm, so each panel started with results of market research they’d completed, and also real-time audience polls of key issues or drivers in connected health. The audience and most speakers were bullish on technology as an enabler and amplifier for humans, whether that’s enabling clinicians to see more patients, enabling caregivers to stay in touch with their charges, or enabling consumers or patients themselves to self-manage. Technology, and in particular machine-learning and AI were not seen as the be-all and end-all, but as ingredients to a successful human-led strategy. (With the exception of a keynote by CirrusMD who advocated for people-backed triage and staying away from chatbots and AI.)

With CMS announcing goals for 50% of reimbursement to be value-based, reimbursement was less of a topic at this conference than in previous years. However, the complexity and fragmentation of healthcare is still a challenge, whether that’s in care settings, the payer/provider divide, or consumer versus medical grade monitoring devices. Usability is key, with many speakers talking about the difficulty of setting up and managing devices, even the best designed consumer devices. And while the focus was on seniors, it seems that everyone has struggled with packaging, networking, and connectivity.

In addition to the “AI will replace humans” myth, my other favorite myth to be busted at this conference was the idea that sensors and sensor data alone will solve all the problems. During one panel an audience member referred to sensor data as superior to patient observations. (Actually referring to it as “the worst” type of data.) Thankfully both MDs on the panel he was addressing said “the best thing you can do is listen to what your patient is telling you.”

We couldn’t agree more.

Connected Health, Wellpepper results

Posted in: Adherence, Aging, Healthcare Policy, Healthcare Technology, machine learning, patient engagement, patient-generated data, Physical Therapy

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Healthcare Summer Reading: Invisible Women

As artificial intelligence and machine-learning are increasingly touted as the solution for everything from shopping to healthcare, the need to better understand the data that goes into these solutions increases. We’ve written before about how trying to solve patient problems using only the EMR data delivers only half the equation because it leaves out everything that happened to the patient outside the clinic, and the patient’s own experience.

Machine Learning in Healthcare: How To Avoid GIGO

Self-Driving Healthcare

However, it turns out that this data may only deliver ¼ or less of the solution when it comes to women’s health, as the fact-packed “Invisible Women: Data Bias In a World Designed For Men” points out.

While Invisible Women tackles politics, healthcare, manufacturing, career, and finances, so many of the problem areas where key decisions are made without enough data result in population health and healthcare problems, even decisions about automobile manufacturing, snow shoveling, and portable cookstoves.

In example after example from every industry, the “normal” male is used as the standard, and women are seen as aberrations, resulting in health, safety, and finance inequality. One size fits all is actually one size fits average male.

If you need to make a case, either business or healthcare related, for the need for diversity of thinking, and for having the data for evidence-based decision making, this book will help you. You’ll also realize that in evidence-based decision making in healthcare, the data is missing for 51% of the population.

Examples include:

  • Crash test dummies that don’t approximate women’s bodies so that women are 17% more likely to die in a car crash.
  • Drug testing that does not require evaluation of outcomes by gender. (The UK does not require any gender evaluation for randomized control trials so researchers are advised to look at studies from other countries to ensure gender inclusion.)
  • Drug dosages that are not adjusted for size or hormones
  • Health trackers that underestimate women’s activities and don’t include menstruation tracking
  • Increased risk of hip fracture by making female solders match an arbitrary male gate length
  • Public transportation safety issues
  • Greater risk of women being misdiagnosed for heart failure because symptoms present differently
  • Portable cookstoves intended to decrease indoor pollution but aren’t used because they need constant tending, and mean that women can’t get other chores done.

The list goes on. Practically every paragraph in this book has a practical example where getting the right data, either qualitative or quantitative would have resulted in better quality of life for women (and everyone really.)

The good news is that this implicit bias that normal can be overcome with a contentious approach to collecting data and feedback, and a rigor of examining the data and outcomes by gender. Interestingly, when reading this book, I realized that this should be another way that we evaluate our Wellpepper care plans. We currently mostly segment our data analysis by age because there has been previous skepticism about older patients ability to use technology. Now I’m thinking that gender differences in care plan outcomes might be a really interesting source of insight. What might we learn about recovery? We know women experience pain and medication differently. Are their gender based clinical insights in our outcome data as well?

While Invisible Women is probably not beach reading, it’s still highly recommended book to add to your healthcare and data reading list.

Posted in: big data, Clinical Research, Healthcare Research, Healthcare transformation, machine learning, patient-generated data

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Self-Driving Healthcare

It’s 2019 and your car can drive itself most of the way to your doctor’s office, but once there, you will be handed a clipboard with a paper form asking you for your name, date of birth, and insurance information. Then you will wait to be seen by a doctor, who will spend your visit facing a screen transcribing your spoken complaint into the EMR, and then ask you where you’d like your lab results faxed.

How can it be that technology is making such huge strides in some areas of our lives, while others are seemingly stuck in the last century? When will healthcare have its self-driving moment?

The Promise of Self-Driving Cars

Self-driving cars are a great example of how computer science has been applied to solve difficult real-world problems. It was less than 15 years ago that the computer science community celebrated the first autonomous vehicles successfully completing a 130 mi course in under 10 hours as part of the DARPA grand challenge. Most of the heavily-funded university research teams that entered used traditional programming techniques. The winner of this competition, Stanford University, was characterized by its use of machine learning to train a vehicle by example, rather than writing the if-then-else code by hand.

Since this time, machine learning generally, and deep neural networks in particular, have proven to be unreasonably effective in solving problems with huge and highly complex inputs like image recognition, sensor integration and traffic prediction, among others. Companies like Waymo, Volvo, Uber, and Tesla have been pouring money into the autonomous vehicle space and making rapid progress. Many cars sold today come with some level of assisted driving like lane holding and collision prevention, and Tesla vehicles even come with a “Full Self Driving” option.

Machine Learning in Healthcare

So, what about healthcare? People’s health is a highly complex function of genetics, medicine, diet, exercise, and a number of other lifestyle factors. In the same way you make thousands of little steering corrections to stay in a lane, you make thousands of choices each day that impact your susceptibility to disease, quality of life, and longevity to name a few. Can the same toolset that can help cars drive themselves help us build good predictive models for health and healthcare?

There have certainly been efforts. Including some high profile failures. One big limitation is the data. On the one hand, healthcare is awash in data. Some claim it won’t even fit in the cloud (spoiler: it will). Much of the data in healthcare today is locked up in EMR systems. Once you’ve liberated it from the EMR, the next problem is that it’s not a great input for machine learning algorithms. A recent study in JAMA focused on applications of ML in healthcare found that EMR data had big data quality issues, and that models learned on one EMR’s dataset were not transferrable to another EMR’s dataset, severely limiting the portability of models. Imagine trying to build a self-driving car with partial and incompatible maps from each city and you’ll start to understand the problem with using EMR data to train ML models.

All The Wrong Data

But more important than this: even if the EMR data was clean and consistent, a big piece of the puzzle is missing: the data about the person when they’re not in the doctor’s office. We know that a person’s health is influenced largely by their lifestyle, diet, and genetics. But we largely don’t have good datasets for this yet.

You can’t build a self-driving car no matter how much many fluid level measurements and shop records you have: “I don’t know why that one crashed, its oil was changed just 4 weeks ago. And with a fresh air filter too!”  You also can’t build meaningful healthcare ML models with today’s EMR-bound datasets. Sure, there will be some progress in constrained problems like billing optimization, workflow, and diagnostics (particularly around imaging), but big “change the world” progress will fundamentally require a better dataset.

There are efforts to begin collecting parts of this dataset, with projects like Verily’s Project Baseline, and the recently-failed Arivale. Baseline, and others like it will take years or decades to come to fruition as they track how decisions made today impact a person many years down the line.

On a more modest scale, at Wellpepper we believe that driving high-quality and patient-centered interactions outside the clinic is a major key to unlocking improved health outcomes. This required us to start by building a communication channel between patients and their care providers to help patients follow their care plans. Using Wellpepper, providers can assign care plans, and patients can follow along at home and keep track of health measures, track symptoms, and send messages. Collecting this data in a structured way opens the door to understanding and improving these interactions over time.

For example, using regression analysis we were able to determine certain patterns in post-surgical side-effects that indicate a 3x risk of readmissions. And more recently we trained a machine learned classifier for unstructured patient messages that can help urgent messages get triaged faster. And this is just scratching the surface. Since this kind of patient-centric data from outside the clinic is new, we expect that there is a large greenfield of discovery as we collect more data in more patient care scenarios.

Better patient-centric data combined with state of the art machine learning algorithms hold huge promise in healthcare. But we need to invest in collecting the right patient-centric datasets, rather than relying on the data that happens to be lying around in the EMR.

 

Posted in: Healthcare Disruption, Healthcare Technology, Healthcare transformation, machine learning, patient-generated data

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