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Archive for August, 2019

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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Study Results: The Rehabilitation Enhancing Aging Through Connected Health Prehabilitation Trial

In 2015, we announced a study with principal investigator Jonathan Bean, MD from Harvard Medical school to test his custom protocol for at-risk seniors using Wellpepper’s interactive care plan solution to deliver the intervention to patients, and for clinicians to monitor those patients. The intervention was based on a successful intervention that Dr. Bean delivered at Spaulding Rehab in Boston called “Live Long Walk Strong.” This intervention was aimed at improving strength and mobility in seniors to help prevent adverse events.  The project with Wellpepper, eventually called the REACH study, was to determine whether this type of intervention could be delivered remotely through a mobile interface, which would enable scaling the program to patients who weren’t able to attend in-person sessions (40% of the participants in the original Live Long Walk Strong Program deferred care due to travel requirements), and also decrease costs both for patients and providers. The REACH study used the following process. REACH study process

We’re pleased to report that the results of the study have now been published, with positive outcomes reported. This was designed as a quasi-experimental trial, where 75 participants were compared to a control group made up of a comparable sample of 100 people from the general population. Outcomes between groups were then compared, with clinically meaningful and statistically significant differences (as defined by P-values) observed in the study group.

Care Plan Intervention

Patients received a strength and conditioning program delivered first through in-person classes, and a mobile application, and then through the mobile application with remote messaging with a healthcare provider. During the last 4 months of the study, patients were left on their own and not monitored by a clinician. The study was designed to address not just physical health but incorporate aspects of motivational behavior change.

Motivational behavior change through an m-health intervention

Outcomes

  • Compared to the control group, participants in the program had a 73% decrease in emergency department visits during a 1-year period
  • Clinically meaningful improvements in mobility as recorded in the 6-minute walk test (+.8 meters/second) and Short Physical Performance Battery test (+.69 units)
  • 85% of patients were active at least twice per week
  • 89% rated application satisfaction at “good to excellent” and would recommend to a friend
  • 16-20 percentage point drop off in adherence during the last 4 unmonitored months

The REACH intervention shows positive outcomes in targeting functional decline and the avoidance of adverse event for older primary care adults. The potential benefits should be evaluated and confirmed on a larger scale. If your health system is managing a population that would benefit from an intervention like this, please be in touch.

More Information

If you are interested in deploying a solution in your organization based on the protocol used in this study, contact us.

Study Announcement Press Release

Study Methodology and Description

Published Study

Posted in: Healthcare Research, Outcomes, patient engagement, Patient Satisfaction, Physical Therapy, Prehabilitation, Research, Return on Investment

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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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