patient-generated data

Archive for patient-generated data

Voice: The most natural user interface for healthcare

There’s so much promise, and such a natural fit for voice in healthcare that unlike electronic medical records, we should not have to mandate its use. If anything, right now we are being limited by the lack of HIPAA controls rather than end-user demand. If the sessions at the recent Voice Summit, which was focused broadly on voice tech, and the upcoming Voice of Healthcare and Voice Summit at Connected Health conferences are any indication, there are many natural use cases, and a lot of pent-up demand.

With so many concerns about documentation and screens getting between patients and physicians, and the ability to deliver empathy, and to rapidly learn from interactions using natural language processing, and artificial intelligence, voice seems a natural fit and unobtrusive interface that could leapfrog traditional interfaces.

The Healthcare track at Voice Summit showed some of this promise, but also pointed out that we are still early days. Many solutions are pilots or prototypes, and I had the distinct impression that some of today’s HIPAA workarounds would not stand up to a detailed audit. Despite Alexa’s sponsorship of the conference, Google’s strong presence, and both companies push into all things healthcare, both were mum on whether or when their consumer voice devices might be HIPAA compliant. Regardless, healthcare organizations and technology vendors alike are charging forward on new scenarios for healthcare, and you can see by the diversity that if even a few of these end up being the “killer app” it’s a big opportunity.

Patient Care

Rooming: Waiting for a physician to see you in an exam room is often a wasted opportunity. A voice interface in the clinic room, could help further pinpoint why a patient is having a visit or educate pre and post visit on medical issues. Or simply having a voice assistant capture the questions that a patient has during a visit might go a long way to improving the visit.

Inpatient stay: The combination of voice assistants, wifi, and tablets could completely replace expensive and proprietary systems for inpatient patient engagement. We’re already seeing use cases for anonymous interactions with voice devices to order food, check the time, or find out the time of the next physician visit.

Long-term care: Alzheimers and dementia care are cited as the poster child for the benefits of voice in long-term care facilities. Unlike human caregivers, voice assistants never get tired of answering the same questions repeatedly. There are so many times you don’t want Saturday Night Live to predict the future, but with this one they got it right.

Patient Engagement

If we define patient engagement as interactions outside the clinic, then the opportunities today fall into three main categories triage (or eventually diagnosis), education, and self-management.

Triage Skills: Today we see some basic triage skills from organizations like Mayo Clinic, and Boston Children’s Hospital where you can check some basic first aid, or ask common questions about children’s health. While there are approximately 1,000 healthcare skills, most likely there will be a few winners or “go-to” experiences here from leading healthcare organization or trusted publishers like WebMD. (Interestingly, the presenter from WebMD was one of the more skeptical on voice experiences for patients at the Voice Summit, possibly because of the complexity of the information they present through text, video, and images on the Web.)

Health Education: Chunking content into manageable bites is currently being touted as the best practice for education material through voice. However, this is an area where the interactivity that’s possible through voice will be necessary for stickiness. If you think about the best podcasts, they use different techniques to both engage you and also impart knowledge: interviewing, verbatim quotes, sound effects, interjections, and expository material. To get engaging and sticky health education content, publishers will have to think about how to test for knowledge, advance explanations, and interact with the end-users. Since we can only remember 5 things at a time, simply chunking content is not going to be enough to make the delivery of health education through voice stick.

Reminders and Interactive Health Tasks: As we’ve seen from our testing, where voice interfaces may have the most impact for patients is in helping them complete health tasks for example, in medication adherence, simple surveys, or check-ins and reminders of basic information. Given that the voice interface is a natural in the home, checking in with a voice assistance on when to take medication, or tracking meals is an easy way to engage with a care plan. As well, cloud-based interactive voice response systems could call patients with reminders and check-ins.

Clinical Notes

Conquering the pain of charting is possibly the closest term opportunity for voice in healthcare. With every increasing workloads, and the need to capture information digitally for both care and reimbursement, the EMR has been blamed for physician burnout and lack of job satisfaction. Microsoft recently partnered with UPMC to use their Cortana voice assistant to transcribe clinical notes during a patient/provider interaction. Others attacking this space include SayKara, Robin, and incumbent, Nuance Communications. With HIPAA compliance, it’s hard not to imagine Amazon and Google looking at it as well.

Hands-free lookup

Voice really shines as an interface when your hands are not free, like driving, dentistry, or when you need to keep your hands clean. Voice is a natural in settings where touching a screen or device can cause contamination or distraction. Simplifeye is tackling this in dentistry to improve charting, and lookup of x-rays, and we expect this to infiltrate all aspects of healthcare.

You may have seen a recent article on why Alexa is not ready for healthcare primetime. With all of these great examples it’s hard to believe it. It turns out that the criticisms in this article basically highlight the current limitations of voice overall (except for HIPAA compliance of course). However, some of the challenges of discovery, context, and navigation, are why we at Wellpepper believe in not just voice, but a “Voice And” future where voice is a key interface that is helped or helps others like screens or even augmented reality. Voice is powerful, “Voice And” will be even better.

Posted in: Behavior Change, chronic disease, HIPAA, patient engagement, Patient Satisfaction, patient-generated data, 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) →

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) →

Are Women Better Surgeons? Patient-Generated Data Knows The Answer

As empowerers of patients and collectors of patient-generated data, we’re pretty bullish on the ability for this data to show insights. We fully admit to being biased, and view things through a lens of the patient experience and outcomes, which is why we had some ideas about a recent study that showed female surgeons had better outcomes than male surgeons.

The study, conducted on data from Ontario, Canada, was a retrospective population analysis of patients of male and female surgeons looking at rates of complications, readmissions, and death. The results of the study showed that patients of female surgeons had a small but statistically significant decrease in 30-day mortality and similar surgical outcomes.

Does this mean that women are technically better surgeons? Probably not. However, there is one sentence that stands out to a possible reason that patients of female surgeons had better outcomes.

A retrospective analysis showed no difference in outcomes by surgeon sex in patients who had emergency surgery, where patients do not usually choose their surgeon.

This would lead us to believe that there is something about the relationship between the patient and the provider that is resulting in better outcomes. We have seen this at Wellpepper, while we haven’t broken our aggregate data down by gender lines, we have seen that within the same clinic, intervention, and patient population, we see significant differences in patient engagement and outcomes between patients being seen by different providers.

Some healthcare professionals are better than others at motivating patients, and the relationship between provider and patient is key for adherence to care plans which improve outcomes. By tracking patient outcomes and adherence by provider, using patient-generated data, we are able to see insights that go beyond what a retroactive study from EMR data can show.

While our treatment plans, and continued analysis of patient outcomes against those treatment plans go much further than simply amplifying the patient-provider relationship, for example with adaptive reminders, manageable and actionable building blocks, and instant feedback, never underestimate the power of the human connection in healthcare.

Posted in: Adherence, Behavior Change, big data, Clinical Research, patient-generated data

Leave a Comment (0) →

What Motivates You, May Not Motivate Me

At Wellpepper our goal is to empower people to be able to follow their care plans and possibly change their behavior, so we think a lot about how to motivate people. Early on when working with Terry Ellis, Director of the Boston University Center for Neurorehabilitation, wanted to make sure that our messages to patients that may struggle with adherence were positive. She works with people who have Parkinson’s disease, and stressed that while they may improve symptoms they would not “get better.”

Last week I had a similar conversation with an endocrinologist about diabetes care plans. People with chronic diseases are often overwhelmed and may take a defeatist attitude to their health. Feedback and tools need to be non-judgmental and encouraging. Ideas like “compliance” and “adherence” may not be the way to look at it. Sometimes the approach should be “something is better than nothing.” And humans, not just algorithms need to decide what “good” is.

Am I good or great?

Here’s an example, non-healthcare related of algorithmic evaluation gone wrong. Rather than applauding me for being in the top tier of energy efficient homes, the City of Seattle, says I’m merely “good.” There’s no context on my “excellent” neighbors, for example are they in a newly built home compared to my 112 year old one, and no suggestions on what I might want to do to become “excellent. (Is it the 30-year old fridge?) I’m left with a feeling of hopelessness, rather than a resolve to try to get rid of that extra 2KW. Also, what does that even mean? Is 2KW a big deal?

Now imagine you’re struggling with a chronic disease. You’ve done your best, but a poorly tuned algorithm says you’re merely good, not excellent. Well, maybe what you’ve done is your excellent. This is why we enable people to set their own goals and track progress against them, and why care plans need to be personalized for each patient. It’s also why we don’t publish stats on overall adherence. Adherence for me might be 3 out of 5 days. For someone else it might be 7 days a week. It might depend on the care plan or the person.

As part of every care plan in Wellpepper, patients can set their own goals. Sometimes clinicians worry about the patient’s ability to do this. These are not functional goals, they represent what’s important to patients, like family time or events, enjoying life, and so on. We did an analysis of thousands of these patient-entered goals, and determined that it’s possible to track progress against these goals, so we rolled out a new feature that enables patients to do this.

Patient progress against patient-defined goal

Success should be defined by the patient, and outcome goals by clinicians. Motivation and measures need to be appropriate to what the patient is being treated for and their abilities. Personalization, customization, and a patient-centered approach can achieve this. To learn more, get in touch.

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

Leave a Comment (0) →

Alexa Voice Challenge for Type 2 Diabetes: Evolving An Idea

For the past couple of months some of our Wellpepper team, with some additional help from a couple of post-docs from University of Washington, have been working hard on a novel integrated device, mobile, and voice care plan to help people newly diagnosed with type 2 diabetes as part of our entry in the Alexa Diabetes Challenge.

Team Sugarpod

This challenge offered a great opportunity to evolve our thinking in the power of integrating experiences directly into a person’s day using the right technology for the setting. It also provided the opportunity to go from idea to prototype in a rapid timeframe.

Our solution featured an integrated mobile and voice care plan, and a unique device: a voice powered scale that scans for diabetic foot ulcers, a leading cause of amputation, hospitalization, and increased mortality, and is estimated to cost the health system up to $9B per year.

During the challenge, we had access to amazing resources, including a 2-day bootcamp held at Amazon headquarters during which we heard from experts in voice, behavior change, caring for people with type 2 diabetes, and a focus group with people who have type 2 diabetes. We also had 1:1 sessions with various experts who had seen our entry and helped us think through the challenges of developing it. After the bootcamp, we were assigned a mentor, an experienced pharmacist and diabetes educator, who was available for any questions. Experts from the bootcamp also held office hours where we explored topics like

Early Prototype Voice Powered Scale & Scanner

how to help coach people in what they can do with an Alexa skill, and how to build trust with a device that takes pictures in your bathroom.

As we evolved our solution, we were fortunate to have support from Dr Wellesley Chapman, medical director of Kaiser Permanente Washington’s Innovation Group. We were able to install the device in a Diabetes and Wound Clinic. We used this to train our image classifier to look for foot ulcers, and compare results to human detection, and also to test the voice service. We used an anonymous voice service as Alexa and the Lex services are not currently HIPAA-eligible.

We gathered feedback from diabetes educators, clinicians at KP Washington, and across the country, and from people with Type 2 diabetes. While not everyone wanted to use all aspects of the solution, they all felt that the various components: voice, mobile, and device offered a lot of support and value. As well, we determined that there is an opportunity for a voice-powered scale and scanner in the clinic which could aid in early detection and streamline productivity. Voice interactions in the clinic are a natural fit.

Judges and Competitors: Alexa Diabetes Challenge

The great thing about a challenge is the constraints provided to do something really great in a short period of time. We’re so proud of the Sugarpod team, and also incredibly impressed with the other entries in this competition ranging from a focus on supporting the mental health challenges faced by people newly diagnosed with Type 2 diabetes to a specific protocol for diet and nutrition, to solutions that helped manage all aspects of care. We enjoyed meeting our fellow competitors at the bootcamp and the final, and wish we had met in a situation where we could collaborate with them. We also appreciated the thoughtful feedback and questions from the judges, and would definitely have a lot to gain from deeper discussions with them on the topic.

Stay tuned for more on our learnings through this challenge and our experiences with voice.

Posted in: Healthcare Disruption, Healthcare Technology, Healthcare transformation, M-health, Managing Chronic Disease, Outcomes, patient engagement, patient-generated data

Leave a Comment (0) →

Introducing Sugarpod by Wellpepper, a comprehensive diabetes care plan

We’re both honored and excited to be one of five finalists in the Alexa Diabetes Challenge. We’re honored to be in such great company, and excited about the novel device our team is building. You may wonder how a team of software folks ends up with an entry with a hardware component. We did too, until we thought more about the convergence happening in technology.

We were early fans of the power of voice, and we previewed a prototype of Alexa integration with Wellpepper digital treatment plans for total joint replacement at HIMSS in February 2017. Voice is a great interface for people who are mobility or vision challenged, and the design of Amazon Echo makes it an unobtrusive home device. While a mobile treatment plan is always with you, the Amazon Echo is central in the home. At one point, we thought television would be the next logical screen to support patients with their home treatment plans, but it seems like the Echo Show is going to be more powerful and still quite accessible to a large number of people.

Since our platform supports all types of patient interventions, including diabetes, this challenge was a natural fit for our team, which is made up of Wellpepper staff and Dr Soma Mandal, who joined us this spring for a rotation from the University of Georgia. However, when we brainstormed 20 possible ideas for the challenge (admittedly over beer at Fremont Brewing), the two that rose to the top involved hardware solutions in addition to voice interactions with a treatment plan. And that’s how we found ourselves with Sugarpod by Wellpepper which includes a comprehensive diabetes care plan for someone newly diagnosed, and a novel Alexa-enabled device to check for foot problems, a common complication of diabetes mellitus.

Currently in healthcare, there are some big efforts to connect device data to the EMR. While we think device data is extremely interesting, connecting it directly to the EMR is missing a key component: what’s actually happening with the patient. Having real-time device data without real-time patient experience as well, is only solving one piece of the puzzle. Patients don’t think about the devices to manage their health – whether glucometer, blood pressure monitor, or foot scanner – separately from their entire care plan. In fact, looking at both together, and understanding the interplay between their actions, and the readings from these devices, is key for patient self-management.

And that’s how we found ourselves, a mostly SaaS company, entering a challenge with a device. It’s not the first time we’ve thought about how to better integrate devices with our care plans, but is the first time we’ve gone as far as prototyping one ourselves, which got us wondering which way the market will go. It doesn’t make sense for every device to have their own corresponding app. That app is not integrated with the physician’s instructions or the rest of the patient’s care plan. It may not be feasible for every interactive treatment plan to integrate with every device, so are vertically integrated solutions the future? If you look at the bets that Google and Apple are making in this space, you might say yes. It will be fascinating to see where this Alexa challenge takes Amazon, and us too.

We’ve got a lot of work cut out for us before the final pitch on September 25th in New York. If you’re interested in our progress, subscribe to our Wellpepper newsletter, and we’ll have a few updates. If you’re interested in this overall hardware and software solution for Type 2 diabetes care, either for deploying in your organization or bringing a new device to market, please get in touch.

Read more about the process, the pitch, and how we developed the solution:

Ready When You Are: Voice Interfaces for Patient Engagement

Alexa Voice Challenge for Type 2 Diabetes: Evolving a Solution

 

Posted in: Behavior Change, chronic disease, Healthcare Disruption, Healthcare Technology, Healthcare transformation, M-health, Managing Chronic Disease, patient-generated data

Leave a Comment (1) →

In Defense of Patient-Generated Data

There’s a lot of activity going on with large technology companies and others trying to get access to EMR data to mine it for insights. They’re using machine learning and artificial intelligence to crawl notes and diagnosis to try to find patterns that may predict disease. At the same time, equal amounts of energy are being spent figuring out how to get data from the myriad of medical and consumer devices into the EMR, considered the system of record.

There are a few flaws in this plan:

  • A significant amount of data in the EMR is copied and pasted. While it may be true that physicians and especially specialists see the same problems repeatedly, it’s also true that lack of specificity and even mistakes are introduced by this practice.
  • As well, the same ICD-10 codes are reused. Doctors admit to reusing codes that they know will be reimbursed. While they are not mis-diagnosing patients, this is another area where there is a lack of specificity. Search for “frequently used ICD-10 codes”, you’ll find a myriad of cheat sheets listing the most common codes for primary care and specialties.
  • Historically clinical research, on which recommendations and standard ranges are created, has been lacking in ethnic and sometimes gender diversity, which means that a patient whose tests are within standard range may have a different experience because that patient is different than the archetype on which the standard is based.
  • Data without context is meaningless, which is physicians initially balked about having device data in the EMR. Understanding how much a healthy person is active is interesting but you don’t need FitBit data for that, there are other indicators like BMI and resting heart rate. Understanding how much someone recovering from knee surgery is interesting, but only if you understand other things about that person’s situation and care.

There’s a pretty simple and often overlooked solution to this problem: get data and information directly from the patient. This data, of a patient’s own experience, will often answer the questions of why a patient is or isn’t getting better. It’s one thing to look at data points and see whether a patient is in or out of accepted ranges. It’s another to consider how the patient feels and what he or she is doing that may improve or exacerbate a condition. In ignoring the patient experience, decisions are being made with only some of the data. In Kleiner-Perkin’s State of the Internet Report, Mary Meeker estimates that the EMR collects a mere 26 data points per year on each patient. That’s not enough to make decisions about a single patient, let alone expect that AI will auto-magically find insights.

We’ve seen the value of patient engagement in our own research and data collected, for example in identifying side effects that are predictors of post-surgical readmission. If you’re interested, in these insights, we publish them through our newsletter.  In interviewing patients and providers, we’ve heard so many examples where physicians were puzzled between the patient’s experience in-clinic or in-patient versus at home. One pulmonary specialist we met told us he had a COPD patient who was not responding to medication. The obvious solution was to change the medication. The not-so-obvious solution was to ask the patient to demonstrate how he was using his inhaler. He was spraying it in the air and walking through the mist, which was how a discharge nurse had shown him how to use the inhaler.

By providing patients with useable and personalized instructions and then tracking the patient experience in following instructions and managing their health, you can close the loop. Combining this information with device data and physician observations and diagnosis, will provide the insight that we can use to scale and personalize care.

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

Leave a Comment (0) →
Google+