Healthcare Technology

Archive for Healthcare Technology

Digital Transformation in Pharma: Digital Pharma West

Like the rest of the healthcare industry, the pharma industry is also grappling with lots of data, disconnects from end-users, and shifting to a digital-first experience while grappling with ongoing regulatory and privacy challenges. Actually it’s pretty much what every industry is grappling with, so the good news is that no one is getting left behind in this digital revolution.

In pharma though, the division between commercial and R&D creates both delays and lags in implementing new technology and the regulatory challenges cause specific issues in communication with both providers and patients.

Last week, I was invited to speak at Digital Pharma West about our work in voice-enabling care plans for people with Type 2 diabetes, and also how our participation in the Alexa Diabetes Challenge enabled us to engage with pharma. It was my first ‘pharma-only’ conference, so it was interesting to contrast with the provider and healthcare IT world.

If you think that there are a lot of constituents who care about digital health in provider organizations, pharma rivals that. For example, there was a discussion about the value of patient-facing digital tools in clinical trials. While everyone agreed there could be real value in both efficiencies of collecting data, and engaging patients and keeping them enrolled in trials, a couple of real barriers came up.

First the question of the impact of the digital tools on the trial. Would they create an intended impact on the outcomes, for example a placebo effect? Depending on how the “usual care condition” is delivered in a control group, it might not even be possible to use digital tools in both cohorts, which could definitely impact outcomes.

Another challenge with digital technology in randomized control trials is that technology and interfaces can change much faster than drug clinical trials. Considering that elapsed time between Phase 1 and Phase 3 trials can be years, also consider that the technology that accompanies the drug could change dramatically during that period. Even technology companies that are not “moving fast and breaking things” may do hundreds of updates in that period.

Another challenge is that technology may advance or come on the market after the initial IRB is approved, and while the technology may be a perfect fit for the study, principle investigators are hesitant to mess with study design after IRB approval.

Interestingly, while in the patient-provider world the number of channels of communication are increasing significantly with mobile, texting, web, and voice options, the number of touch points in pharma is decreasing. Pharma’s touchpoints with providers are decreasing 10% per year. While some may say that this is good due to past overreach, it does make it difficult to reach one of their constituents.

At the same time, regulations on approved content for both providers and patients means that when content has had regulatory approval, like what you might find in brochures, on websites, and in commercials, the easiest thing to do is reuse this content. However, new delivery channels like chatbots and voice don’t lend themselves well to static marketing or information content. The costs of developing new experiences may be high but the costs of delivering content that is not context or end-user aware can be even higher.

At the same time, these real-time interactive experiences create new risks and responsibilities for adverse event reporting for organizations. Interestingly, as we talk with pharma companies about delivering interactive content through the new Wellpepper Marketplace, these concerns surface, and yet at the same time, when we ask the difference between a patient calling a 1-800 line with a problem and texting with a problem there doesn’t seem to be a difference. The only possible difference is a potential increase in adverse event reporting due to ease of reporting, which could cause problems in the short term, but in the long term seems both inevitable and like a win. Many of the discussions and sessions at the conference were about social media listening programs for both patient and provider feedback, so there is definitely a desire to get and make sense of more information.

Like everyone in healthcare, digital pharma also seems to be at an inflection point, and creativity thinking about audiences, channels, and how to meet people where they are and when you need them is key.

Posted in: Adherence, Clinical Research, Data Protection, Health Regulations, Healthcare Disruption, Healthcare Policy, Healthcare Research, Healthcare Social Media, Healthcare Technology, HIPAA, M-health, Outcomes, pharma, Voice

Leave a Comment (0) →

The Challenge of Challenges: Determining When To Participate

There’s an explosion of innovation in healthcare and with that comes a plethora of incubators, accelerators, pitches, challenges, prizes, awards, and competitions. Trying to sort through which ones are worth paying attention to can be a full-time job. At Wellpepper we’ve tried to be selective about which ones we enter. A recent post by Sara Holoubeck, CEO and founder of Luminary Labs about the outcomes of challenges got me thinking about the cost/benefit analysis of entering challenges. Both costs and benefits come in hard and soft varieties.

If you want to be scientific, you can assign a score to each of the costs and the benefits, and use it to decide whether to throw your hat in the ring. (For the purposes of this blog post, we’ll use the term “challenge” to refer broadly to all of these opportunities.)

Costs

  • Time: How many hours will your team need to put into this challenge? How much of your team needs to be involved?
  • Focus: Does the focus on this challenge distract your team from core customer or revenue priorities?
  • Financial: Is there an entry fee to participate? What other costs, like travel, may you need to incur to deliver on the challenge?
  • Strategy: Is this challenge aligned with your
  • IP: Do you have to give up intellectual property rights as part of this challenge? Do you have to give away any confidential information that you are not yet ready to share publically?

Benefits

  • Financial: Is there prize money? Does it cover your expected costs? Could you actually profit from entering? If winner receives funding who decides the terms? Is this an organization that would be beneficial to have on your cap table?
  • Focus: Does this challenge provide the team with a forcing function to deliver innovation in an area that is aligned with your overall strategy?
  • Innovation: Does this challenge take your team in stretch direction or enable you to demonstrate a direction on your roadmap that you may otherwise not immediately approach due to market issues?
  • Publicity: Where will the winner be announced? Is there a PR strategy for the entire process or just the winner? Does it help your organization to be aligned with the content or sponsors of this challenge?
  • Introductions: Who will this challenge help you meet that can further your business goals?

It’s up to you to consider the cost/benefit analysis. Both may not have to be high, but when they are the opportunity can be high if you have the ability to put in the effort. You may also consider your chances of winning if it’s defined as a competition, and whether there is any drawback to losing, or if just participating provides enough benefit.

Here are a few examples from our own history that may help illustrate the tradeoffs.

Low cost/medium benefit

We entered a local pitch event for a national organization. The effort to pitch was minimal: we had case studies and examples that fit the thesis directly. The event was nearby and there was no cost to enter. The pitch was short. We won this pitch and got some local awareness and leads. However, when we were offered to go to the national conference and pitch for an even shorter period in a showcase heHIMSS Venture+ Winnersld simultaneously with other conference activities and with no actual competition, we declined as the cost/benefit was not there.

Medium cost/medium benefit

Each year HIMSS has a venture competition at the annual conference. We won this event in 2015, and received PR as well as in-kind benefits at HIMSS conferences including booth space. The effort to prepare was medium: any startup should be prepared for an onstage venture pitch, and the audience was exactly right. As a follow on from this event we’ve been involved in panels showcasing our progress.

High cost/migh benefit

Both the Mayo Clinic ThinkBIG challenge, and the Alexa Diabetes Challenge had a relatively high effort and opportunity cost to participate and high rewards, but both were aligned with directions our company had already embarked on, and both resulted in deeper connections for us with the sponsoring organizations, positive press, validation of our company and solution, and financial support.

In the case of the Mayo Clinic ThinkBIG challenge, we received investment on our convertible note for winning, and the challenge afforded us introductions to important clinical and IT contacts at Mayo Clinic. We were also able to showcase our solution to other potential customers live at the annual Transform event.

Our team put in a tremendous effort on our winning entry for the Alexa Diabetes Challenge but the pay-off was worth it in a number of ways. Certainly the prize money and publicity was welcome, but more importantly, we have created new IP and also come to a whole new understanding of how people can move through their daily lives with technology to support them in managing chronic conditions.

Both of these challenges have afforded us ongoing opportunities for engagement and awareness as a result our participation, and our positive outcomes.

One thing to note, none of these challenges I mention had an entry fee. Sometimes nominal entry fees are used to deter casual entries, but for the most part if a challenge is seeking to fund itself by charging the startups to participate, it’s the wrong model.

While you don’t have to be this explicit when making your decisions about entering a challenge, consideration of the costs and opportunity cost of either participating or not, can help you sort through the ever increasing number of grand challenges.

Posted in: Healthcare Disruption, Healthcare Technology, Healthcare transformation, Uncategorized, Voice

Leave a Comment (0) →

Dispatches from the Canadian E-Health Conference: The same but different

Bear statue in VancouverThe annual Canadian E-Health Conference was held in Vancouver, BC last week. I had the opportunity to speak about the work we’re doing at Wellpepper in applying machine learning to patient-generated data, and in particular the insights we’ve found from analyzing patient messages, and then applying a machine-learned classifier to alert clinicians when a patient message might indicate an adverse event. Our goal with the application of machine-learning to patient generated data is to help to scale care. Clinicians don’t need to be alerted every time a patient sends a message; however, we don’t want them to miss out if something is really important. If you’d like to learn more about our approach, get in touch.

My session was part of a broader session focused on ‘newer’ technologies like machine-learning and blockchain, and some of the other presenters and topics definitely highlighted key differences between the US and Canadian systems.

Aside from the obvious difference of Canada having universal healthcare, there were subtle differences at this conference as well. While the same words were used, for the most part: interoperability, usability, big data, and of course blockchain and AI, the applications were different and often the approach.

Interoperability: Universal doesn’t mean one

Each province has their own system, and they are not able to share data across provinces. Unlike the UK which has a universal patient identifier, your health records in Canada are specific to the province you live in. As well, apparently data location for health records is sometimes not just required to be in Canada, but in the actual province where you reside and receive care. As for interoperability, last we heard, British Columbia was doing a broad roll out of Cerner while large systems in Alberta were heading towards EPIC, so Canada may see the same interoperability challenges we see here if people move between provinces.

Privacy: The government is okay, the US is not

What’s interesting is as a US company, is that whenever we talk to health systems in Canada they bring up this requirement, but as soon as you mention that the PIPEDA requirements enable patients and consumers to give an okay for out of Canada data location they agree that it’s possible. Regardless, everyone would rather see the data in Canada.

What was possibly the most striking example of a difference in privacy was from one of my co-presenters in the future technologies session, who presented on a study of homeless people’s acceptance of iris scanning for identification. 190 out of 200 people asked were willing to have their irises scanned as a means of identification. This identification would help them access social services, and healthcare in particular. The presenter, Cheryl Forchuk from the Lawson Health Research Institute said that the people who participated didn’t like to carry wallets as it was a theft target, that they associated fingerprinting with the criminal justice system, and that facial identification was often inaccurate due to changes that diet and other street conditions can make. When I tweeted the 95% acceptance rate stat there were a few incredulous responses, but at the same time, when you understand some of the justifications, it makes sense. Plus, in general Canadians have a favorable view of the government. The presenter did note that a few people thought the iris scan would also be a free eye exam, so there may have been some confusion about the purpose. Regardless, I’m not sure this type of identification would play out the same way in the US.

Reimbursement: It happens, just don’t talk about it

The word you didn’t hear very much was reimbursement or when you did, from a US speaker the audience looked a bit uncomfortable. The funny thing is though, that physicians have billing codes in Canada as well. It’s just that they are less concerned about maximizing billing versus being paid for the treatment provided and sometimes even dissuading people from over-using the system. Budgets were discussed though, and the sad truth that money is not always smartly applied in the system, and in a budget-based system, saving money may decrease someone’s future budget.

Blockchain: It’s not about currency

Probably the biggest difference with respect to Blockchain was the application, and that it was being touted by an academic researcher not a vendor. Edward Brown, PhD from Memorial University suggested that Blockchain (but not ethereum based as it’s too expensive) would be a good way to determine consent to a patient’s record. In many US conferences this is also a topic, but the most common application is on sharing payer coverage information. Not surprisingly this example didn’t come up at all. If you consider that even though it is a distributed ledger, a wide scale rollout of Blockchain capabilities for either identification or access might be more likely to come from a system with a single payer. (That said, remember that Canada does not have a single payer, each province has its own system, even if there is federal funding for healthcare.)

“E” HR

Physician use of portalFor many of the session the “E” in e-health stood for EHR, which while also true in the US, the rollout of wide scale EHRs is still not as advanced. Cerner and EPIC in particular have only just started to make inroads in Canada, where the a telecommunications company is actually the largest EHR vendor. In one session I attended, the presenter had done analysis of physician usage of a portal that provided access to patient labs and records, but they had not rolled out, what he was calling a “transactional” EHR system. Physicians mostly accessed patient history and labs, and felt that if the portal had prescribing information it would be perfect. Interesting to see this level of access and usage, but the claim that they didn’t have an EHR. What was also interesting about this study is that it was conducted by a physician within a health system rather than an academic researcher. It seemed like there was more appetite and funding for this type of work within systems themselves.

Other Voices: Patients!

Patients on the mainstageDuring the interlude between the presentations and judging for the well-attended Hacking Health finals, and on the main stage, presenters interviewed two advocate patients. While they said this was the first time they’d done it, both patients had been at the conference for years. So while the mainstage was new, patient presence was not, and patient advocate and blogger Annette McKinnon pushed attendees to go further when seeking out engaged patients. Noting that retirees are more likely to have the time to participate in events she asked that they make sure to seek out opinions for more than 60 year old white women.

There was also an entire track dedicated to First Nations Healthcare. Think of the First Nations Health authority as a VA for the indigenous people of Canada, which incorporates cultural differences and traditional practices of the First Nations people. The track started and concluded with an Elder song and prayer.

Manels

Speaking of diversity, I didn’t witness any manels.

Best Quote

 

Posted in: big data, Clinical Research, Health Regulations, Healthcare Disruption, Healthcare Research, Healthcare Technology, Healthcare transformation, Interoperability, M-health, patient-generated data

Leave a Comment (0) →

Healthcare Transformation: Emulating Disney Is Not A Bad Idea

Last week, I had the privilege of speaking to a group of CMIOs about disruption and consumerism in healthcare. We had a lively discussion, with the two main takeaways being that having a broad digital strategy is key, and also that healthcare really needs to find its own way to delivering the things consumers want. While looking to other industries for inspiration is a good way to think about change, blindly implementing strategies without thinking about how to adapt them for your own industry is not a good path.

We started off the discussion with this quiz from Elizabeth Rosenthal, former physician and health editor of the New York Times, and author of An American Sickness. Try it for yourself: it’s fun to try to figure out which is the hospital and which is the luxury hotel. (The CMIOs got 8/12 correct. Can you beat them?)

This prompted a debate about how much environment matters to healing, and why hospitals have no “back office.” Having a calming environment can definitely promote healing, however, it wasn’t clear from some of the images presented in the quiz whether healing or luxury was the goal.

Adopting ideas from other industries without fully understanding their priorities and understand how they might differ from your goals. For example, people may complain about the Disneyfication of healthcare, and point to managing to the HCHAPS survey as driving this and other evils. However, did you know that Disney’s #1 corporate value is safety? Adopting safety as a number one organizational value in healthcare would be completely relevant and appropriate. What has happened with these hotel-like experiences is adopting the surface of what Disney stands for without understanding the core goals and objectives of the experience or of the patient, or even of what Disney is trying to achieve.

Recently I received this in the mail from UnitedHealthcare.

Much has been written about the power of hand-written notes, however, usually within business situations and often from a senior manager to a junior manager. This, however, is not a good use of a handwritten note. It’s so many kinds of wrong, and bordering on creepy, especially since I had just gone for my annual physical.

The pressure to deliver better service, and better outcomes is not going to decrease in healthcare. However, it’s easy to avoid these types of pitfalls by considering what people are really looking for. This might not be the same for all patients, but we think this sets up a good framework to approach consumerization.

In addition to thinking about how your offerings, outreach, and engagement with patients fulfills these needs, going a step further, you could try to think about which one of these is most important to each individual patient, and that’s really the crux of delivering a great patient or consumer experience.

Posted in: Healthcare Technology, Healthcare transformation, Meaningful Use, Outcomes, patient engagement, Patient Satisfaction

Leave a Comment (0) →

Wellpepper now an Amazon Partner Network Advanced Technology Partner

Wellpepper is pleased to announce that we are now an AWS Advanced Technology partner!

When we started Wellpepper in 2012, we evaluated a list of hosting options. We looked at availability and durability guarantees, the breadth of service offerings, how deeply the provider was investing in their cloud offerings, and their expertise and compliance with healthcare requirements.

AWS was clearly at the head of the pack in their cloud investment, and had the most believable availability and durability guarantees. Over the last 5 years, this has proven true – AWS has been a rock solid platform for us. But what’s really been incredible is to watch how fast AWS has broadened their service offerings (many new useful platform-as-a-service tools), and pulled many of these under the HIPAA-eligible service umbrella.

Our software architecture has evolved over time. We have always relied heavily on EC2 instances and S3 for bulk object storage, and we still do. We have also started using services like Lambda for some of the newer parts of our platform. We also rely heavily on AWS services like CloudWatch for monitoring and logging, CloudTrail for auditing, and CodeDeploy to deploy services automatically. We did a little video about our architecture with the AWS Startups team last year if you want to know more.

As Advanced Tier partners, we’re looking forward to delivering the Wellpepper patient engagement platform through the AWS marketplace, in addition to selling directly.

Posted in: Healthcare Technology

Leave a Comment (0) →

HIMSS 2018: We’re having a party in your house!

From the opening keynote of HIMSS 2018, you could tell things were going to be different. Unlike last year, where actors touted the marvels of flash drives and backup storage, this year kicked off with singers from The Voice. Not sure how to interpret their music choices, though, I’m sure Leonard Cohen never envisioned his anthem Hallelujah pumping up 45,000 healthcare IT experts.

Keynote speaker Eric Schmidt executive chairman of Alphabet, admonished the crowd to get to the cloud, any cloud, even Google Cloud’s competitors. He also described a scenario with an assistant named Liz, listening in on a doctor/patient visit and transcribing notes. Ironically, this exact scenario was announced by Microsoft the week before. I’ve witnessed shifts to digital and cloud before in other industries, and it does take a village, so Eric calling on the power of the technology and being rather vendor agnostic is a good sign. That said, there were a few things in his talk that might have ruffled his audience. First, where were the partners? In the utopia of voice and cloud for healthcare that Schmidt described the only partner referenced was Augmedix, poster child for Google Glass, and absolutely no healthcare system examples. Which makes sense, as when asked by HIMSS president emeritus, Steven Lieber for his parting words to the crowd, Schmidt said:

“You’re late to the party.”

Which is an interesting comment at as he was a guest keynote speaker at a healthcare IT event and representing big tech, so you could interpret this to mean:

“You’re late to the party (that we’re throwing in your house).”

As the keynote emptied in a mass stream to the tradeshow floor, I eavesdropped on a number of conversations, and many people weren’t too happy about the message: “they (aka tech) don’t understand how complicated our lives are.” It’s an interesting conundrum, because Google et al have solved some pretty complicated problems making sense of what we’re all looking for online, a problem of completely unstructured data, and yet, as recent Facebook incidents show, there can be a lack of respect for people’s data and privacy that is crucial for any type of healthcare deployment in big tech.

The tradeshow floor itself showed a lot of new entrants, including booths from Lyft and Uber, who previously had only partnered with companies like Circulation for medical transportation, and a much larger Google Cloud and Amazon Web Services presence than the previous year. Microsoft and IBM have been at the healthcare party for a long time, and have settled in.

Big tech is indeed at the party. Who else is at the party? Purveyors of security and in particular block-chain crypto were definitely there. We saw APIs hanging around the punch bowl, this time invited by the new Blue Button 2.0 initiative, unlike previous years as the date of big tech.

Who wasn’t at the party? Patients. On the one hand, we’ve found that the digital patient experience and patient engagement is now mainstream, and our research partner Tamara Deangelis from Boston University Center for Neurorehabilitation was awesome talking about patient/provider messaging at the patient engagement summit. At the broader HIMSS conference, it seemed only vendors were representing patients. Most of the patient invitations must have gotten lost in the mail.

One CIO I talked to suggested that there was a different feeling at HIMSS this year and that this is the year we’ll look back and see that things really changed for healthcare IT. We’ve seen an acceleration of the shift to the cloud for new patient-facing applications, and a rapid realization of a need for an overall patient digital strategy. All heartening, especially since it will take everyone at the party to accomplish this transformation, debutantes and charming hosts alike.

Until next year’s party, cheers!

(Footnote: The actual Google Cloud party had a long line immediately, so some people heeded Schmidt’s words about not being late for the fantastic view of the Bellagio fountains, poke bowls, and open bar. The party was predominantly male, which hopefully isn’t part of the vision. Of course, it was at the same time as the Women in Healthcare IT event, which I heard was awesome. Perhaps a poor party choice on my part.)

Posted in: Healthcare Technology, Healthcare transformation, HIMSS, Interoperability

Leave a Comment (0) →

HIMSS 2018…See you there!

HIMSS17 in Orlando was a great conference for Wellpepper. We’re looking forward to HIMSS18 in Las Vegas even more!

We have a long list of sessions to attend and booths to visit, but below are some places you’re guaranteed to find us:

Monday, March 5th

  • Hear from Tami Deangelis on how our research partners at Boston University engaged patients outside the clinic and improved outcomes using Wellpepper care plans. She is speaking at the “Remote Patient Messaging for Adherence and Engagement” session from 4:05pm-4:25pm at the Patient Engagement & Experience Summit

Tuesday, March 6th

  • Hall G, Innovation Zone: Booth 9900-78 from 9am-6pm
  • CTO, Mike Van Snellenberg will be demonstrating our voice-powered scale and foot scanner, and integrated diabetes care plan at the Industry Showcase at BHI & BSN 2018 https://bhi-bsn.embs.org/2018/industry-showcase/

Wednesday, March 7th

  • Hall G, Innovation Zone: Booth 9900-78 from 9am-6pm
  • CEO, Anne Weiler, will be sharing the Wellpepper Vision and Mission at HIMSS VentureConnect http://www.himssconference.org/education/specialty-programs/venture-connect
  • CEO, Anne Weiler, will be joining other industry leaders to continue the conversation with CMS toward inclusion of patient engagement and outcomes tracking in the MIPS Improvement Activity for provider reimbursement

Thursday, March 8th

  • Hall G, Innovation Zone: Booth 9900-78 from 9am-4:30pm

We can’t wait to connect with friends, partners, colleagues and industry leaders to continue the journey towards an amazing patient experience. Hope to see you there!

Posted in: Healthcare Disruption, Healthcare Technology, M-health, Outcomes, patient engagement, Uncategorized

Leave a Comment (0) →

Supporting Patient Motivation

What motivates people to improve their health and stay on the right track over time?

This question is on the mind of every practitioner, whether it’s a physician sending someone home with a wound care plan, a nutritionist giving dietary advice to help manage diabetes or a physical therapist providing exercises to get a frozen shoulder moving again. They’re thinking: “Will the patient do it?” To a great extent, the answer to this question determines how successful their treatment plans will be.

Some of this blog’s most popular posts have explored the issue of motivation because it is a major underpinning of patient engagement technology – will the patient use, and stick with, the technology that in turn helps them adhere to their care plans?

The subject of motivation usually starts with a discussion about goal-setting. This process, at least in the medical context, typically begins when the practitioner sets goals for the patient and provides a care plan that tells the patient what they need to do in order to get there. Some practitioners feel this should be motivation enough for a patient. In reality, they know it’s not.

So what is motivation? A great deal of research has gone into the subject, particularly with regard to behavior change. It is most often described as being either extrinsic (outside the individual) or intrinsic (inside the individual). With extrinsic motivation, we engage in a behavior or activity either to gain some sort of external reward or avoid a negative consequence. With intrinsic motivation, we engage in something because we find it personally fun or rewarding.

While these are the two areas most often discussed, there are other, deeper dimensions to motivation, including fear-based and development-based motivators – and these can be either extrinsic or intrinsic. Understanding the interplay among these different forms of motivation is an important element in successful health coaching and in the creation of successful, supportive technologies that assist people in reaching their health goals.

Fear-based motivation comes in two basic flavors: deficiency-based and threat-based. Deficiency-based motivations come from the sense you are lacking in some way. These can have an external, socio-cultural source (just watch any personal care product advertisement: you smell bad, your hair is the wrong color and your teeth aren’t nearly white enough) or an intrinsic source (e.g. internal pressure “shoulds,” self-imposed discipline or overcoming the deficiency of lost health). Threat-based motivations tap into fear at a deeper level. In the world of medicine, this might be a medical incident that serves as a wake-up call, and the threat of disability or death propels a person to make serious lifestyle changes.

Development-based motivation tends to come from the desire for personal growth or self-actualization. It can also be externally sourced (e.g. from positive peer health norms or positive environmental conditions like smoke-free public spaces) or intrinsic – from the satisfaction, pleasure or joy we derive from doing something.

Research has shown that while fear can be a great motivator for getting people started on something, the positive, development-based motivators tend to be more powerful in keeping people engaged and active in behavior change over the longer term.

I believe one of the reasons the Wellpepper patient engagement platform is so successful at driving patient engagement with care plans (70% engagement compared to an average of 20% engagement with portals) is because the Wellpepper team understands this complex motivation dynamic very well and they have incorporated some of the most successful elements from it into their platform. They call it the “3rd approach” and here’s why I think it works.

Wellpepper takes a very obvious extrinsic motivator – the practitioner’s care plan – and turns it into an application that incorporates both intrinsic and extrinsic development-based motivators that keep people engaged over time. There are many layers we could explore here, but we’ll start with a few of the big ones.

Setting aspirational goals: In addition to the functional goals set by the practitioner, Wellpepper provides the ability for patients to set their own personally meaningful, aspirational goals that can support and reinforce their motivation to heal. For example, someone recovering from a total joint replacement operation might set a future vision of wanting to hike to their favorite fishing spot with a grandchild. They can use Wellpepper to set interim goals that lead them toward that vision and can rate their own progress on a Likert scale.

Research in positive psychology has shown that this kind of personal vision and goal setting is highly successful at sustaining motivation over time. In this case the patient is more likely to complete their prescribed exercises because it leads them toward goals that are personally meaningful about their own healing and about doing something special with someone they love.

Personalized experience: Wellpepper also provides a personalized experience for the patient. Using the same joint replacement example, instead of getting a piece of paper with a series of exercise diagrams or a generic video, the practitioner can record the patient doing their own exercises. Seeing yourself, and hearing the personal comments of the physician or physical therapist as you do it, is not only easier to follow, it feels personal. And, as you begin to improve, when you watch yourself then and now, seeing your own progress can be very satisfying (a powerful development-based motivator).

Adaptive notification: Wellpepper’s patented adaptive notification system means the patient doesn’t get the same generic reminder every day – it changes the notification based on the patient’s progress and level of engagement, keeping the extrinsic motivator relevant, fresh and focused on personal development.

Tracking progress: By enabling people to track progress on their goals and sharing that information with their practitioners, patients tap into positive, extrinsic motivation. Also tracking progress on personal, aspirational goals helps people feel a greater sense of accomplishment and direction over their own developmental outcomes.

While motivation for any one individual can be elusive, the way Wellpepper weaves together the positive extrinsic and intrinsic development-based motivators may be the key to its success in helping patients stay motivated and helping practitioners answer the age-old question: “Will the patient do it?”

If they’re using Wellpepper, chances are, they will.

Jennifer Allen Newton is Wellpepper’s PR lead, and also a Functional Medicine Certified Health Coach. 

Posted in: Adherence, Healthcare motivation, Healthcare Technology, Healthcare transformation, patient engagement, Physical Therapy

Leave a Comment (0) →

Alexa, Get Well Soon

The unofficial winner of the Super Bowl ad race this year was “Alexa Loses Her Voice”, an ad that shows celebrities subbing for Alexa when she (anthropomorphic being that she is, comes down with a cold). Both USA Today and YouTube are calling it the most watched ad.

Alexa, who won USA TODAY’s 30th Ad Meter?

“Well, um – me.”

Jeff Bezos looks skeptical that his team can replace Alexa as he should be, since their solution of Gordon Ramsay, CardiB, Rebel Wilson, and Anthony Hopkins is both extremely expensive, (Wellppper CTO Mike Van Snellenberg did the math), and breaks the key trust relationship that people have with Alexa.

Voice is a natural interface, and empathy can be quickly established by the types of utterances and engagement. By default, Alexa apologizes when she doesn’t understand something and it feels genuine. Compare that to Gordon Ramsay insulting his poor hapless user—all the guy wants is a bit of help making some comfort food. What he gets is abuse.

Or, the woman who wants Alexa’s help while she’s in her boudoir presumably getting ready for a date with her love. Instead, Anthony Hopkins insinuates that something horrific has happened to her beau possibly involving a pet peacock.

Cardi B insults a young man’s interest in Mars. Let’s hope she has not squashed his spirit of discovery and his desire to ask questions.

Since this is an all-ages blog, we won’t even mention the response Rebel Wilson gives from her bubble bath to the poor gentleman who asked Alexa to set the mood for a party. He and everyone at his party were fully traumatized.

We get it, Alexa is just better at delivering what people are asking for than real people. Especially real people with attitude like these celebrities.

As we found in our research with people with type 2 diabetes, Alexa has a natural ability that these celebrity Alexa impersonators do not. You can see it in this feedback we received from real people trying to manage Type 2 diabetes.

  • “Voice gives the feeling someone cares. Nudges you in the right direction”
  • “Instructions and voice were very calm, and clear, and easy to understand”

Voice is a natural fit to deliver empathy and care. However, since each one of these people is expecting Alexa, and has no visual indicator that anything has changed, the negative experiences will reflect on Alexa and she’ll have to win back their trust.

While the implied message of the ad spot is that Alexa does a better job of delivering on your needs than any of these celebrity experts we’re still feeling a bit traumatized by the abuse they hurled. For the sequel to this commercial, we’d expect to see Jeff firing the team that replaced Alexa with celebrities, and Alexa as a therapist working through the trust issues that her replacements created. She can do it. We believe in her.

Posted in: Behavior Change, Healthcare Disruption, Healthcare Technology, Voice

Leave a Comment (0) →

Your Cupcakes Are Not My Goals

This year Google Maps tried out a short-lived motivational technique of showing how many cupcakes you would burn off or ostensibly could eat if you chose to walk to your destination. Not surprisingly this backfired, and they quickly retracted the feature. The reasons ranged from users expressing feelings of shame for not walking, to those with eating disorders saying it would encourage more obsessive behavior. Beyond that, many questioned how Google was even calculating both caloric expenditure and the actual calories in the cupcakes.

Regardless of the myriad of criticisms the experiment illustrated a key point: motivation and goal setting is best left to the individual, and understanding someone’s personal context is extremely important if you want to help them set goals.

One of our most read blog posts of 2017 was a 2015 post on whether setting SMART or MEANINGFUL goals was most effective for patients. I’m not sure why this bubbled to the top this year but the post provides an overview of two thoughtful frameworks for helping patients set goals.

At Wellpepper, we’d like to propose a third methodology: let people figure out what’s important to them. This year we expanded a capability we’ve had since V.1 that enables patients to set their own goals. This is a free-form, 140 character text box where patients write about what’s important to them. Over the years, we’ve had some clinicians express concern about whether patients could set their own goals. Functional goals are best left to the experts, but these are life goals, things that are important to people and why they are even bothering to use this app which helps them through healthcare activities to manage chronic diseases or recover from acute events.

Since we already knew that setting patient-generated goals is motivating, we also got to wondering whether you could track progress in a generic way based on patient-generated goals. After analyzing thousands of patient-generated goals, we figured out that asking a question about the patient’s perception progress on a Likert scale would work, and so this year we expanded the patient goal task type to include tracking.

It looks like this.

In case you’re skeptical that this works, here are a few examples of patient-generated goals.

Spend more time with family.

Get outside more frequently.

Walk more.

Be ready for vacation.

Now ask the question. See, it’s entirely possible for patients to set their own goals, unaided, and track progress against those goals. We’re pretty excited about the possibilities of this for improving motivation, and also for further analysis of patient adherence and outcomes. If you’d like to know more, or see a demo, we’d love to hear from you.

Posted in: Behavior Change, Healthcare motivation, Healthcare Technology, Healthcare transformation, patient engagement, patient-generated data

Leave a Comment (0) →

May You Live In Interesting Times: Wellpepper’s Most Interesting Blog Posts of 2017

Who would have predicted 2017? As soon as the election results were in, we knew there would be trouble for the Affordable Care Act no one could have predicted the path through repeal with no replacement to claw backs in a tax bill that no one has read. It’s been a crazy ride in healthcare and otherwise. As we look ahead to 2018, we’ve found that a good place to start is by looking back at what was popular in 2017.

Looking back over the past year’s top blog posts, we also believe trends that started in 2017, but will even stronger in 2018. These four themes bubbled up to the top in our most-read blog posts of 2017:

Shift to the cloud

We’ve noticed a much wider spread acceptance of cloud technologies in healthcare, and the big cloud platform vendors have definitely taken an interest in the space. Wellpepper CTO Mike Van Snellenberg’s comprehensive primer on using AWS with HIPAA protected data was one of our most read posts. Since he wrote it, even more AWS services have become HIPAA-eligible.

Using AWS with HIPAA-Protected Data – A Practical Primer

Consumerization of healthcare

Consumer expectations for efficient online interactions have been driven by high-deductible plans and an expectation from consumer technology and industries like retail and banking that customer service should be personalized, interactive, and real-time. These two posts about the consumerization of healthcare were among the most popular.

The Disneyfication or Consumerization of Healthcare

Consumerization Is Not A Bad Word

Value of patient-generated data

In 2017 we saw a real acceptance of patient-generated data. Our customers started asking about putting certain data in the EMR, and our analysis of the data we collect showed interesting trends in patient adherence and predictors of readmission. This was reflected in the large readership of these two blog posts focused on the clinical and business value of collecting and analyzing patient-generated data.

In Defense of Patient-Generated Data

Realizing Value In Patient Engagement

Power of voice technology

Voice technology definitely had a moment this year. Okay Google, and Alexa were asked to play music, turn on lights, and more importantly questions about healthcare. As winners of the Alexa Diabetes Challenge, we saw the power of voice firsthand when testing voice with people newly diagnosed with Type 2 diabetes. The emotional connection to voice is stronger than mobile, and it’s such a natural interaction in people-powered healthcare. Our blog posts on the Alexa Diabetes Challenge, and developing a voice solution were definitely in the top 10 most read.

Introducing Sugarpod by Wellpepper, a comprehensive diabetes care plan

Building a Voice Experience for People with Type 2 Diabetes

Ready When You Are: Voice Interfaces for Patient Engagement

Since these themes are still evolving we think 2018 will present a shift from investigation to action, from consideration to deployment and possibly insights. Machine-learning and AI will probably remain high in the hype cycle, and certainly the trends of horizontal and vertical healthcare mergers will continue. We also expect a big move from one of the large technology companies who have all been increasing their focus in healthcare, which in turn will accelerate the shift to a consumer-focus in healthcare.

There’s a saying “may you live in interesting times.” We expect 2018 to be at least as interesting as 2017. Onwards!

Note: There was one additional post that hit the most popular list. Interestingly, it was a post from 2014 on whether SMART or MEANINGFUL goals are better for patients. We’re not sure why it resurfaced, but based on analysis we’ve done of patient-directed goals, we think there’s a third approach.

Posted in: Behavior Change, Healthcare Disruption, Healthcare motivation, Healthcare Research, Healthcare Technology, Healthcare transformation, HIPAA, patient engagement, patient-generated data, Voice

Leave a Comment (0) →

4 Reasons Why the Future of Health IT is Serverless (AWS re:Invent 2017 wrap-up)

The big theme at AWS re:Invent 2017 was serverless computing. Whether deploying microservices in containers using ECS, Kubernetes, or Fargate, or building systems using Lambda that connect to serverless relational databases like Serverless Aurora or DynamoDB, Amazon is rapidly moving to remove “undifferentiated heavy lifting” common to building and deploying software applications.

Healthcare has historically been slow to move to the cloud. Some of this stemmed from spotty HIPAA eligibility, and from a desire of health systems not to be the first to break new ground. Today, however, many of the barriers have been cleared away: serverless technologies like Lambda and ECS are already on Amazon’s HIPAA-eligible services list with many more likely to come in the future.

There are many benefits to serverless architectures, including faster time to market, lower operating costs, and lower complexity. Here are 4 compelling reasons why serverless systems are uniquely positioned to thrive in healthcare:

Improved Security

The HIPAA security rule contains a number of requirements for server security. You’d be hard pressed to find a list of security recommendations that doesn’t start with patching your servers. Indeed, over the last year unpatched servers have led to several major security incidents and breaches. There are many (poor) reasons why people don’t patch. Failure to patch machines promptly is a significant risk vector.

With serverless systems, this risk vector goes away.

https://www.csoonline.com/article/3075830/data-protection/zero-days-arent-the-problem-patches-are.html

In actuality, the risk is not entirely removed; instead you’re selling it to Amazon. Underneath serverless technologies, there are still servers running operating systems. However, the bet that you’re making is that Amazon has this down to a science across their millions of servers in a way that other IT departments can’t match.

 

Governance and Compliance

HIPAA mandates a set of administrative controls that govern things like access control and auditability. This is another area that is already baked deeply into serverless architectures.

AWS contains a strong policy-driven identity and access framework in AWS IAM. This is a core component of serverless architectures to control access at every step in the architecture. Applying the ‘least privilege’ principle with IAM roles naturally limits the “blast radius” if a service does become compromised. And because policies are all held in one place, it’s easier to see and control which accounts have access to what.

Auditability and robust logging go hand-in-hand, and if serverless architectures do anything, they generate a ton of log data. Each service, from AWS Gateway routing request to VPC delivering network traffic, to Lambda services handling requests, to S3 getting and setting bulk data is heavily logged, with most logs aggregating into either S3 or CloudWatch Logs. Several of the re:Invent sessions this year explored novel ways to report on this data using tools like ElasticSearch (note: the AWS-managed ElasticSearch Service is not yet on the HIPAA eligible list), and even automatically detect anomalous usage patterns using Kinesis Analytics.

Finally, AWS Artifact organizes all of the compliance documentation for Amazon’s part of the shared-responsibility model, including things like your AWS Business Associate Addendum (BAA), and access to SOC2 audits.

All of this stuff is just baked in, and there’s hardly any work needed to make use of it.

 

Availability and Scalability

While the security and encryption parts of HIPAA get most of the attention, it also contains provisions for ensuring availability, business continuity, and emergency mode operations.

Capacity and availability is something that used to be hard to plan in the days of individual server instances. A well-designed serverless architecture, by contrast, encourages robust-by-design implementations that can scale based on actual usage. Deploying across multiple data centers (AZs) is the default. Deploying across multiple regions is easy. This once again removes a common source of error and failure and gives solution builders tools to build “internet scale” systems that deliver three, four, or more 9’s of availability.

And in the unlikely event that there is an outage, backup and restore is also easy. Relational (Aurora) databases automatically perform backups, and backup/restore support for the DynamoDB document database was announced at re:Invent.

 

Increased Interoperability

Healthcare data has often been locked into data silos inside EMRs and other proprietary systems-of-record. Additionally, the quantity of data has meant that health systems need to undertake massive data consolidation and data warehousing projects to begin to recognize the value stored in this data.

At the same time, in recent years, there has been an explosion in patient-generated data. Vast quantities of activity tracking data, medication adherence records, blood glucose measurements, and patient reported outcome data (to name a few examples) sits collected but underused and uncorrelated.

In modern serverless architectures, patient data from inside and outside the four walls of the clinic can be easily collected and stored in large-scale data lakes like S3 where it can be easily aggregated, cleaned, transformed, queried, and reported on. HIPAA regulations are easily fulfilled, with HIPAA-compliant encryption at no additional cost just a button-click away (or sometimes a few buttons if you want to manage your own encryption keys). Control over who can access and use this data are returned to governance groups and clinicians based on business requirements and policy rather than obscure formats, closed databases, and network firewalls.

 

Wrap Up

At Wellpepper, we help healthcare providers deploy interactive care plans to their patients, so we take our data security and compliance responsibilities seriously. We were an early adopter of the AWS cloud back when EC2 and S3 were the only services available under the HIPAA umbrella, but things have changed! Following AWS’ announcement earlier this year that Lambda is now HIPAA-elegible, we’ve been looking more seriously at serverless system design, and we like what we see.

This is the future that anyone building solutions in healthcare IT should be excited about.

 

Relevant Content from AWS re:Invent 2017

Adopting Microservices in Healthcare: Building a Compliant DevOps Pipeline on Amazon ECS

What’s new in AWS Serverless 

Simplifying Healthcare Data Management on AWS 

Building a Secure and Healthcare-Compliant Platform for Adopting a Cloud-First Strategy using AWS 

American Heart Association: Finding Cures to Heart Disease Through the Power of Technology 

 

 

Posted in: Adherence, Data Protection, Healthcare Technology, Interoperability

Leave a Comment (1) →

Healthcare + A.I. Northwest

The Xconomy Healthcare + A.I. Northwest Conference at Cambia Grove featured speakers and panels discussing the opportunities and prospects for applying machine learning and artificial intelligence to find solutions for health care. The consensus was that we are no longer held back by a lack of technological understanding and ability. A.I. and M.L. models can be learned at a large scale by harnessing the power of the cloud and advances in data science. According to the panelists, today’s challenges to incorporating A.I. into healthcare include abundant, but inadequate data and resistance from health systems and providers.

Many researchers have found insufficient data to be an unexpected challenge. As keynote speaker Peter Lee of Microsoft Research pointed out, the more data we have, the better our machine learned models can be. He used an analogy to a speech identifier trained on multiple languages such that the model predicted English better after learning French to illustrate that improvements can be made with large sets of unstructured data. Unfortunately, because we are not capturing enough of the right kind of data for researchers, much patient data is getting lost in the “health data funnel” due to PHI and quality concerns. Lee called for more data sharing and data transparency at every level.

Physician researchers on multiple panels were concerned about a lack of suitable data. Soheil Meshinchi, a pediatric oncologist from Fred Hutchinson Cancer Research Center, is engaged in collecting data specific to children. He discussed his research on Acute Myeloid Leukemia on the panel titled, ‘Will A.I. Help Discover and Personalize the Next Breakthrough Therapy?’. While there is a large body of research on AML in adults, he has found that the disease behaves much differently at a genomic level in children. He also expressed distrust in some published research because studies are rarely reproduced and often a researcher who presents results contrary to existing research faces headwinds at journals who are reticent to publish “negative data”. His focus at this point is gathering as much data as he can.

Matthew Thompson, a physician researcher at the University of Washington School of Medicine, argued on the “Innovations for the Over-Worked Physician” panel that technology has made patient interaction demonstrably worse, but that these problems can and should be solved innovatively with artificial intelligence. His specific complaints include both inputting and extracting data from health system EHRs, as well as an overall glut of raw patient data, often generated by the patient himself, and far too much published research for clinicians to digest.

Both keynote speakers, Microsoft’s Lee and Oren Etzioni of the Allen Institute for Artificial Intelligence, referenced the large numbers of research papers published every year. According to Etzioni, the number of scientific papers published has doubled every nine years since World War II. Lee referenced a statistic that 4000 studies on precision cancer treatments are published each year. They are both relying on innovative machine reading techniques to analyze and categorize research papers to make them more available to physicians (and other scientists). Dr. Etzioni’s team has developed SemanticScholar.org to combat the common challenges facing those who look for research papers. He aims to reduce the number of citations they must follow while also identifying the most relevant and up-to-date research available. One of the advantages of taking this approach to patient data

is that scientific texts have no PHI concerns. Lee’s team is marrying patient data and machine reading to match potential research subjects with appropriate NIH studies.

Dr. Thompson was concerned that too much data is presented to the medical staff and very few of the “predictive rules” used by ER personnel are both ‘accurate and safe’. When reviewing patient outcomes and observations to predict the severity of an infection, he found that patients or their caregivers would provide ample information, but often clinicians would disregard certain details as noise because they were atypical symptoms. The amount of data that providers have to observe for a patient is massive, but machine learned models may be utilized to distill that data into the most relevant and actionable signals.

Before data is gathered and interpreted, it must be collected. Like Dr. Thompson, Harjinder Sandhu of Saykara sees ponderous, physician-driven data entry via EHR as significant barrier to efficient data collection. Sandhu notes that healthcare is the only industry where the highest-paid teammember is performing this onerous task and his company is using artificial intelligence to ease that burden on the physician.

Once patient data has been aggregated and processed into models, the challenge is getting the information in front of providers. This requires buy-in from the health system, physician, and, occasionally, the patient and his caregivers. Mary Haggard of Providence Health and Services spoke on the “Tech Entrepreneurs Journey into Healthcare” panel and stated that the biggest problem for entrepreneurs is defining the correct problem to solve. During the “Investment Perspective” panel, Matt Holman of Echo Health Ventures recommended tech startups emphasize an understanding of the context of the problem within a health system.

One of the most important and difficult hurdles for health technology companies is working into clinical workflow. Mike McSherry from Xealth has found that physician champions who know how they want to use technology help with integrating into a health system or physicians group. Lynn McGrath of Eigen Healthcare believes physicians want their data to be defined, quick to assess, condensed, and actionable, while Shelly Fitz points out that providers are not used to all the data they are receiving and they don’t yet know how to use it all. These are all issues that can and will be solved as healthcare technology continues to become more intelligent.

As Wellpepper’s CTO, Mike Van Snellenberg pointed out, health systems and doctors are resistant to “shiny new things”, for good reason. When approaching a health system, in addition to engaging the administration, clinicians need to understand why the machine learned model is recommending a given course of treatment. After integration, patients will also want to understand why a given course of treatment is being recommended. Applying artificial intelligence solutions to medicine must take into account the human element, as well.

The exciting possibilities of artificial intelligence and machine learning are hindered more by human constraints in health systems and data collection than by available technology. “Patients are throwing off all kinds of data when they’re not in the clinic,” according to our CTO. Wellpepper’s tools for capturing patient-generated data provide a pathway for providers to access actionable analysis.

Posted in: Healthcare Disruption, Healthcare Technology, patient engagement

Leave a Comment (0) →

Boston University Center for Neurorehabilitation: A Novel Mobile Intervention For People With Parkinson’s Disease

In 2013, when we were a brand new m-health company, we had the good fortune to meet Terry Ellis, PhD, Director of the Center for Neurorehabilitation at Boston University. Dr. Ellis was an early investigator in the value of digital interventions, and saw an opportunity to partner with Wellpepper so that her team could focus on the new care models, and Wellpepper could focus on the technology. The first building blocks in the Wellpepper platform aligned closely with outpatient rehabilitation, and Dr Ellis and team wanted to prove that people who had Parkinson disease could improve strength and mobility without costly in-person visits. At Wellpepper, we also had an interest in proving that mobile health can improve outcomes, and also that those 50 plus could use mobile technology.

Persons with Parkinson Disease (PD) have been described as 29% less active than older adults without PD, and see a 12% decline in mobility for each year after their first diagnosis with the disease. In-person interventions with physical therapists can help, but in the usual care condition, a person has one in-person assessment at The Center for Neurorehabilitation, and may not be seen again for 6 months to a year, during which time there was a decline in mobility. Dr Ellis and team were looking for a way to prove out a novel intervention that could improve outcomes for these patients.

Patient Experience

This video does a great job of showing the patient experience, both with the clinician and while using the application at home.

User Journey from Wellpepper on Vimeo.

Outcomes

While Dr. Ellis and team are still analyzing additional data, and will be submitting to a peer-reviewed journal, and are exploring expanded studies on the topic, we can share some very promising results.

  • This study revealed that using mobile health technology to remotely monitor and adapt exercise programs between bouts of care in persons with Parkinson disease was feasible and acceptable.
  • On average, subjects engaged with the app every week for 85% (+/- 20%) of the weeks with an 87% satisfaction rating.
  • Significant improvements in physical activity, walking and balance measures were observed over 12 months.
  • People who showed lower exercise self-efficacy at the beginning of the study saw the greatest gains.

Technology

  • This technology used the Wellpepper platform, clinic application for iPad, and patient application for iOS. Requirements were for ease of use for both clinicans and patients. Features include the ability to record custom video of patients doing their exercises, for patients to record results, and for patients and providers to message securely with each other.
  • Fitbit was used for patients to track non-exercise activity, and this was the first integration of a consumer exercise tracker with the Wellpepper platform.
  • The entire Wellpepper platform is built on Amazon Web Services, in a HIPAA secure manner, which was a requirement for the study. No data was stored on mobile devices and all personal health information was encrypted in transit and at rest.
  • The Boston University team required a monthly data extract of all patient-generated data for their analysis purposes.
  • Post study, we were able to analyze anonymized patient-provider messages using a machine learned message classifier, and have presented this data at digital health conferences.

The positive preliminary results of this study, lead to a larger study with seniors at risk of falls, lead by principal investigator Jonathan Bean, MD from Harvard Medical School. Details of this intervention are available here. While Dr Bean is also in the process of submitting to a peer-reviewed journal, his assessment is that outcomes exceeded clinically significant measures.

We are looking forward to sharing more about the results of both of these studies when they are publicly available in peer-reviewed journals. If you are a researcher who would like to know more, contact us and we may be able to put you in touch with the study leads.

Posted in: Clinical Research, Exercise Physiology, Healthcare Technology, Healthcare transformation, M-health

Leave a Comment (0) →

Wellpepper Wins $125K Grand Prize in Alexa Diabetes Challenge

NEW YORK: Today, the Challenge judges awarded Wellpepper the $125,000 grand prize in the Alexa Diabetes Challenge. Wellpepper is the team behind Sugarpod, a concept for a multimodal diabetes care plan solution using voice interactions.

The multi-stage Challenge is sponsored by Merck & Co., Inc., Kenilworth, New Jersey, U.S.A., supported by Amazon Web Services (AWS), and powered by Luminary Labs. In April, the competition launched with an open call for concepts that demonstrate the future potential of voice technologies and supporting Amazon Web Services to improve the experience of those who have been newly diagnosed with type 2 diabetes.

“Technology advances are creating digital health opportunities to improve support for people managing life with a chronic disease,” said Tony Alvarez, president, Primary Care Business Line and Customer Strategy at Merck & Co., Inc. “One purpose of the Alexa Diabetes Challenge was to identify new ways to use the technology already present in a patient’s daily routine. The winner of the Challenge did just that.”

Sugarpod is a concept for an interactive diabetes care plan solution that provides tailored tasks based on patient preferences. It delivers patient experiences via SMS, email, web, and a native mobile application – and one day, through voice interfaces as well. Since much of diabetes management occurs in the home, the Wellpepper team recognized that integrating voice was the natural next step to make the platform more convenient where patients are using it most. During the Challenge, Wellpepper also prototyped an Alexa-enabled scale and foot scanner that alerts patients about potential foot problems, a common diabetes complication.

“Sugarpod helps newly diagnosed people with type 2 diabetes integrate new information and routines into the fabric of their daily lives to self-manage, connect to care, and avoid complications. The Challenge showed us the appeal of voice solutions for patients and clinical value of early detection with home-based solutions,” said Anne Weiler, co-founder and CEO of Wellpepper.

The Challenge received 96 submissions from a variety of innovators, including research institutions, software companies, startups, and healthcare providers. The panel of judges, independent from Merck, narrowed the field down to Wellpepper and four other finalists, who each received $25,000 and $10,000 in AWS promotional credits and advanced to the Virtual Accelerator. During this phase of the competition, the finalists received expert mentorship as they iterated their solutions in preparation for Demo Day. At Demo Day on September 25, 2017, the five finalists presented their solutions to the judges and a live audience of industry leaders at the AWS Pop-up Loft in New York to compete for the grand prize.

“The Alexa Diabetes Challenge has been a great experiment to re-think what a consumer, patient, and caregiver experience could be like and how voice can become a frictionless interface for these interactions. We can imagine a future where technological innovations, like those provided by Amazon and AWS, are supporting those who need them most,” said Oxana Pickeral, Global Segment Leader in Healthcare and Life Sciences at Amazon Web Services.

Learn more at alexadiabeteschallenge.com and follow the Challenge at @ADchallenge.                                                                   

###

Contact: Emily Hallquist

(425) 785-4531 or emily@luminary-labs.com

Posted in: Healthcare Technology, Healthcare transformation, patient engagement, Press Release

Leave a Comment (0) →

Building a Voice Experience for People with Type 2 Diabetes

Recently we were finalists in the Merck-sponsored Alexa Diabetes Challenge, where we built a voice-powered interactive care plan, complemented by a voice-enabled IOT scale and diabetic foot scanner. The scanner uses the existing routine of weighing-in to scan the person’s feet for foot ulcers, a serious but usually preventable complication of Diabetes.

We blogged about our experience testing the device in clinic here

We blogged about feedback from patients here

This was a fun and productive challenge for our team, so we wanted to share some of our lessons learned from implementing the voice interface. This may be of interest to developers working on similar problems.

Voice Experience Design

As a team, we sat down and brainstormed a long list of things we thought a person with diabetes might want to ask, whether they were interacting with the scale and scanner device, or a standalone Amazon Echo. We tried not to be constrained by things we knew our system could do. This was a long list that we then categorized into intents and prioritized.

  • ~60% of the utterances we knew we’d be able to handle well (“My blood sugar is 85.”, “Send a message to my care team”, “Is it ok to drink soda?”)
  • ~20% of the utterances we couldn’t handle fully, but could reasonably redirect (“How many calories are in 8oz of chicken and a half cup of rice?”)
  • ~20% of the utterances we didn’t think we’d be able to get to, but were interesting for our backlog (“I feel like smoking.”)

After some “wizard of oz” testing of our planned voice interactions, we decided that we needed to support both quick-hit interactions, where a user quickly records their blood sugar or weight for example, and guided interactions where we guide the patient through a few tasks on their care plan. The guided interactions were particularly important for our voice-powered scale and foot scanner so that we could harness an existing habit (weighing oneself) and capture additional information at the same time. This allows the interaction to fit seamlessly into someone’s day.

 

Challenge 1: We wanted to integrate the speech hardware into our scale / foot scanner device using the Alexa Voice Service, rather than using an off-the-shelf Echo device.

The Alexa Voice Service is a client SDK and a set of interface standards for how to build Echo-like capabilities into other hardware products. We decided early on to prototype our device around a Raspberry Pi 3 board to have sufficient processing power to:

  • Handle voice interactions (including wake-word detection)
  • Drive the sensors (camera array, thermal imaging, load sensors)
  • Run an image classifier on the device
  • Drive on-device illumination to assist the imaging devices
  • Securely perform network operations both for device control and for sending images to our cloud service

Raspberry Pi in Sugarpod

The device needed built-in illumination in order to capture usable photos of peoples feet to look for ulcers and abnormalities. Since the device needed built-in illumination to perform imaging, one of our team members came up with the idea of dual-purposing the LED lighting as a speech status indicator. In the same way that the Amazon Echo uses blinking cyan and blue to show status on the LED ring, our entire scale bed could do this.

As we started prototyping with basic audio microphones and speakers, we quickly discovered how important the audio-preprocessing system is in our application. In our testing there were many cases of poor transcriptions or unrecognizable utterances, especially when the user was standing any distance from the device. Our physical chassis designs put the microphone height around 2’ from the ground, which is far from the average user’s mouth, and also in real-world deployments would be in echo-filled bathrooms. Clearly, we needed to use a proper far-field mic array. We considered using a mic array dev kit, which we decided was too expensive and added too much complexity for the challenge. We also spent a couple hours investigating whether we could hack an Echo Dot to use it’s audio hardware.

Eventually we decided that it would make the most sense to stick with an off-the-shelf Echo for our prototype. Thus, in addition to being a foot scanner and connected scale, the device is also the world’s most elegant long-armed Echo Dot holder! It was easy to physically include the Echo Dot into our design. We figured out where the speaker was, and adjusted our 3D models to include sound holes. Since the mic array and cue lights are on top, we made sure that this part of the device remained exposed.

We will be revisiting the voice hardware design as we look at moving the prototype towards commercial viability.

 

Challenge 2: We wanted both quick-hit and guided interactions to use the same handlers for clean code organization but hit some speed bumps enabling intent-to-intent handoff

Our guided workflows are comprised of stacks and queues that hand off between various handlers in our skill, but we found this hard to do when we moved to Alexa Skill Builder. The Alexa Skill Builder (currently in beta) enables skill developers to customize the speech model for each intent and provide better support for common multi-turn interactions like filling slots and verifying intents. This was a big improvement, but also forced us rework some things.

For example, we wanted the same blood sugar handler to run whether you initiated a conversation with “Alexa, tell Sugarpod my blood sugar is 85”, or if the handler was invoked as part of a guided workflow where Alexa asks “You haven’t told me your blood sugar for today. Have you measured it recently?”

We tried a number of ways to have our guided workflow handlers switch intents, but this didn’t seem to be possible to do with the Alexa API. As a workaround we ended up allowing all of our handlers to run in the context of both the quick-hit entry point intent (like BloodSugarTaskIntent) as well as in guided workflows (like RunTasksIntent), and then expanded the guided workflow intent slots to include the union set of all slots needed for any handler that might run in that workflow.

Another challenge was that we wanted to use the standard AMAZON.YesIntent or AMAZON.NoIntent in our skill, however the Alexa Skill Builder does not allow this, presumably because it needs to reserve these intents for slot and intent confirmation. Our workaround for this was to use a fictitious slot (we called ours “ConfirmationSlot”) in basically all of our intents which could be “confirmed”, every time we wanted to ask for Yes/No values. We factored this into a helper library that is used throughout our skill codebase.

if (confirmationSlot.confirmSlotStatus === 'CONFIRMED') {
  confirmationSlot.confirmationStatus = "NONE";
  handler.emitWithState("AMAZON.YesIntent");
  return true;
} else if (confirmationSlot.confirmSlotStatus === 'DENIED') {
  confirmationSlot.confirmationStatus = "NONE";
  handler.emitWithState("AMAZON.NoIntent");
  return true;
}

Challenge 3: The Voice Kit speech recognizer did not always reliably recognize complicated and often-mispronounced pharmaceutical names

One of our intents allows the user to report medication usage, by saying something like “Alexa, tell Sugarpod I took my Metformin.” People are not always able to pronounce drug names clearly, so we wanted to allow for mispronunciation. For the Alexa Diabetes Challenge, we curated a list of the ~200 most common medications associated with diabetes and its frequent co-morbidities, including over-the-counter and prescription medication. These were bound to a custom slot type.

When we tested this, however, we found that Alexa’s speech recognizer sometimes struggled to identify medication from our list. This was especially true when the speaker mispronounced the name of the medication (understandable with an utterance like “Alexa, tell Sugarpod I took my Thiazolidinedion”). We observed this even with reasonably good pronunciation. We particularly liked “Mad foreman” as a transcription when one of us asked about “metformin.” Complex pronunciations are a well-known problem in medical and other specialized vocabularies, so this is a real-world problem that we wanted to invest some time in.

Ideally, we would have been able to take an empirical approach and collect a set of common pronunciations (and mispronunciations) of the medications and train a new model. It may additionally be interesting to use disfluencies such as hesitation and repetition in the recorded utterance as features in a model. However, this is not something that is currently possible using Alexa Skills Kit.

Our fallback was to use some basic algorithmic methods to try to find better matches. We had reasonable success with simple fuzzy matching schemes like Soundex and NYIIS, which gave us a good improvement over the raw Alexa ASR results. We also started to evaluate whether an edit-distance approach would work better (for example, comparing phonetic representations of the search term against the corpus of expected pharmaceutical names using a Levenshtein edit distance), but we eventually decided that a Fuzzy Soundex match was sufficient for the purposes of the challenge (Fuzzy Soundex: David Holmes & M. Catherine McCabe http://ieeexplore.ieee.org/document/1000354/).

Even though our current implementation provides good performance, this remains an area for further investigation, particularly as we continue to work on larger lists of pharmaceuticals associated with other disease or intervention types.

 

Conclusion

This challenge helped us stretch our thinking about the voice experience, and gave us the opportunity to solve some important problems along the way. The work we’ve done is beneficial not just for Diabetes care plans, but also for all of our other care plans too.

While the Alexa voice pipeline is not yet a HIPAA-eligible service, we’re looking forward to being able to use our voice experience with patients as soon as it is!

Posted in: Healthcare Technology, Voice

Leave a Comment (0) →

Ready When You Are: Voice Interfaces for Patient Engagement

We started experimenting with voice as a patient interface early this year, and showed a solution with a voice-enabled total-joint care plan to a select group of customers and partners at HIMSS 2017. Recently we were finalists in the Merck-sponsored Alexa Diabetes Challenge, where we built a voice-enabled IOT scale and diabetic foot scanner, and also a voice-powered interactive care plan.

Over the course of the challenge we tested the voice experience with people with Type 2 diabetes. We also installed the scale and scanner in a clinic, and we found that clinicians also wanted to engage with voice. Voice is a natural in the clinical setting: there’s no screen to get in the way of interactions, and people are used to answering questions. Voice is also great in the home.

However, voice isn’t always the best interface which is why we think multimodal care plans including voice, text, mobile, and web can deliver a more comprehensive solution. Since it’s easier for someone to overhear a conversation than look at your smartphone or even computer screen, mobile or web are often better interfaces depending on the person’s location (for example taking public transit), or the task they need to do (for example, reporting status of a bowel movement). We do think that voice has many great healthcare applications, and benefits for certain interactions and populations.

In our testing, we found that both patients and providers really enjoyed the voice interactions and wanted to continue the conversation. They felt very natural, and people used language that they would use with a human. For example, when asked to let the voice-powered scale know when he was ready to have his foot scan, one person responded with:

“Ready when you are.”

This natural user interface presents challenges for developers. It’s hard to model all the possible responses and utterances that a person would use. Our application, would answer to ready, sure, yes, and okay, but the “when you are” caused her some confusion.

Possibly the most important facet of voice is the connection people have with voice is extremely strong, and unlike mobile voice is not yet associated with the need to follow up, check email, or other alerts. (Notifications on voice devices could change this.)

“Voice gives the feeling someone cares. Nudges you in the right direction”

Creating a persona for voice is important, and relying on the personas created by the experts like the Alexa team, is probably the best way for beginners to start.

“Instructions and voice were very calm, and clear, and easy to understand”

Calm is the operative word here. Visual user interfaces can be described as clean, but calm is definitely a personification of the experience.

Voice is often seen as a more ubiquitous experience, possibly because using fewer words, and constantly checking for the correct meaning are best practices, for example “You want me to buy two tickets for Aladdin at 7:00 pm. Is this correct?” We often hear pushback on mobile apps for seniors, but haven’t heard the same for voice. However, during our testing, a senior who was hard-of-hearing told us she couldn’t understand Alexa, and thought that she talked too quickly. While developers can put pauses to set the speed of prompts and responses in conversation, this would mean that the same speed would have used for all users of the skill, which might be too slow for some or two fast for others. Rather than needing to build different skills based on hearing and comprehension speed it would be great if end-users could define this setting so that we can build usable interfaces for everyone.

While this was our first foray into testing voice with care plans, we see a lot of potential to drive a more emotional connection with the care plan, and to better integrate into someone’s day.

People need to manage interactions throughout their day, and integrating into the best experience based on what they need to do and where they are provides a great opportunity to do that, whether that’s voice, SMS, email, web, or mobile. While these consumer voice applications are not yet HIPAA-compliant, like our tester patient said we’ll be “ready when you are.”

Posted in: Behavior Change, Healthcare Technology, Healthcare transformation, patient engagement

Leave a Comment (0) →
Page 1 of 8 12345...»
Google+