Healthcare Technology

Archive for Healthcare Technology

The Known Citizen: A History of Privacy in Modern America

Although the “P” in HIPAA stands for portability, the question of privacy and data protection is a big topic in healthcare. While at the same time we need to protect all personal health information for patients, individual patients have the right to share that data how they wish. New legislation on data interoperability seeks to break down silos and data blocking to enable patients and providers to have access to data to improve care. With this as the current situation in 2019, those interested in privacy should not miss Sarah Igo’s excellent history of privacy and policy “The Known Citizen.” We recommend this book to all data and privacy nerds. While not focused on healthcare, it provides a great primer on the evolution of privacy and technology’s ability to outpace our understanding and desires both to be known and to be forgotten.

The book kicks off with the advent of photography and the debate at the time about whether people own their likeness. (At the time they didn’t, and people found their pictures on boxes in the grocery store.) It details the evolution of thought, law, and popular sentiment in privacy, including the first ideas that patients have a right to privacy, championed by nurse Dorothy Smith in 1969, and institutionalized in the Patient Bill of Rights in 1973. The premise is that while the loss of privacy is required in the doctor/patient relationship and to deliver care, this doesn’t mean that all aspects of privacy should be ignored. “Arranging for privacy”: curtains, confidentiality, (robes that close at the back?), can created a zone of privacy around the patient and help preserve the individual’s dignity. Smith felt that this was the duty of the nurse, although now we see it as the responsibility of everyone in healthcare from the receptionist to IT.

Healthcare privacy is also touched on in the social determinants of health, and whether people receiving public aid should have their entire lives under the microscope, and again, in the introduction of internal review boards and ethics committees for medical research to protect patient/subjects from harm, but also from disclosure of private information without their full cooperation or understanding of its use.

While you may know Betty Ford for her disclosure of addiction and subsequent support of treatment, she is also responsible for destigmatizing breast cancer and showing that open discussion, and especially by prominent figures can drive public health agendas. After Ford disclosed her breast cancer and mastectomy in the media, there was a noticeable uptick in mammograms, and over 5,000 calls of support to the White House.

While healthcare is a small part of this book, the learnings from society at large, and the race between technology, sentiment, and legislation have great lessons to apply in healthcare. And interestingly much of the discussion we are having today about being known, has been going on for over hundred years, and the
“big data” discussions for at least 50 years. Finally, this book has the added bonus of a really interesting bon mot for your next cocktail or mocktail party: the first reality TV show was broadcast in 1973, on PBS of all broadcasters!

Posted in: Behavior Change, big data, Clinical Research, Data Protection, Health Regulations, Healthcare Legislation, Healthcare Policy, Healthcare Research, Healthcare Technology

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HIMSS WA Innovation Summit

I had the opportunity to attend the Washington HIMSS Innovation Summit, where leaders from Virginia Mason, Providence, Overlake, Seattle Children’s, UW Medicine, Vera Whole Health and Confluence spoke about innovation in their organizations. A lot of great themes and takeaways. These are the ones that stood out most to me.

Technology Adoption

Several panelists mentioned they have problems with their health systems adopting new technologies. Executives tend to bring in new technologies, get pilots kicked off, but struggle in the system-wide adoption. A lot of times new technologies are implemented and expected to work immediately. The reality is that no matter what Health Systems are implementing, they need to invest resources. Physicians and end users need to be engaged early on to really take ownership of the new technology. A well-defined change management process is also key to ensuring a successful adoption. Lastly, even though organizations are piloting the new technology, call it Phase 1 vs Pilot. Pilots imply a short-term project with and end date. Phase 1 makes the technology more real and gets people thinking about what Phase 2 and 3 look like.

Return on Investment

One of the panelists challenged any technology vendor to show him a technology that has ROI. He said his organization does over $200M in uncompensated care per year so he must evaluate new technologies against cost of patient care, which is a valid point. This brought up an interesting discussion about what health systems consider to be a ROI. Not all technologies will give Health Systems dollar-for-dollar return. Some technologies will. ROI can be a blend of hard and soft cost, so it’s important to spend time thoroughly defining a business case and make sure that success metrics align with the overall mission of the Health System.

Patients

I was surprised at how much of the discussion was focused around clinician-facing vs patient-facing technologies. I agree better tools and algorithms for clinicians will directly influence the quality of care that patients receive. Virginia Mason panelists did a great job bringing everything back to the patients. Patients should be the center and they should have access to all their data, regardless of where it comes from, in one place. They should have one seamless app and experience for all their healthcare needs. We at Wellpepper could not agree more!

Key Takeaways

When evaluating and implementing new technologies:

  • Define a realistic business case and what financial and non-financial ROI looks like
  • Ensure alignment to Health System’s mission and goals
  • Don’t assume that new technologies can just be plugged in and solve all problems
  • Allocate resources and engage providers and end users from the beginning
  • Treat it as a multi-year, phased journey; call it Phase 1 instead of a Pilot
  • Have a solid change management process
  • Keep patients’ experience and needs at the top of mind

Posted in: Adherence, Behavior Change, Healthcare costs, Healthcare Disruption, Healthcare Technology, Healthcare transformation, HIMSS, patient engagement, Patient Satisfaction, patient-generated data, Return on Investment, Uncategorized, Using Wellpepper

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Myth Busting and Data Gathering At Parks Connected Health

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

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

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

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

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

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

We couldn’t agree more.

Connected Health, Wellpepper results

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

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You Are Here: Voice Assistants In Healthcare

“We’re at the beginning of the end of the first phase of voice” said James Poulter, CEO of Vixen Labs.

The beginning of the end of the first phase might be optimistic for voice, but that did not dampen the enthusiasm at the second annual Voice.Summit in New Jersey this week. Another speaker likened voice to being at the same stage of technology as the release of the first iPhone. If you remember, at that time everyone else had feature phones, and Android didn’t exist. Voice Summit Keynote

Not surprisingly, this year’s summit reminded me a bit of the early days of mobile development: more talk about how than why, more developer content than business content, and just an inkling that we really don’t know what we don’t know about where voice will go. One of the great things about this conference is that it’s a cross-industry event, so was a specific healthcare track, with topics ranging from best-practices for designing voice care plans to ethical considerations of voice, AI, and bots in healthcare, there was also the opportunity to learn from other industries, and also directly from the key technology leaders in the space.

The following are some highlights, learnings, and implications for healthcare as well.

Amazon Alexa

Dave Isbitski, Alexa Developer Evangelist, kicked off the summit with a couple of announcements for Alexa, skill connections which enable one skill to invoke another skill, for example if you want to print content from a skill you can call another skill designed for printing, like the HP skill. While this is currently limited to named skills, it has huge value in healthcare where every skill shouldn’t have to recreate medication lists or a full lexicon of disease education, and it would be better to call on a proven authority like Mayo Clinic or WebMD to get more information.

Dave also announced Dialog Flow, which uses neural networks to develop skills with less coding, and less manual tagging, although admittedly we’re realistically still in the turn>multi-turn phase rather than full machine-learning and AI. This is probably okay for healthcare: let’s focus on getting patient feedback on structured conversations, like triage surveys, before trying to design a system that is completely responsive to any healthcare need.

Another announcement, the ability to have one skill that uses two languages, rather than installing a separate skill for each language would also be beneficial in healthcare to provide patient instructions, especially to family members who may have different “first” languages.

Samsung

Samsung has jumped into the voice fray with a voice assistant called Bixby, which is designed to be a developer platform for people to insert voice into any type of device. Microsoft has this strategy with Cortana, the difference is that Samsung themselves ship televisions and refrigerators that can be voice-enabled. Samsung is also in the “voice and” category with screens being key part of their delivery. This has some really interesting implications for healthcare if you think about the television as the focal point of the living room. Health reminders and actions delivered there could have great impact. We’re working on a mobile version of the MIND diet, which would have huge impact if delivered through voice and visual reminders on the refridgerator door. The challenges with these modalities though are that it may take generations for the technology to become ubiquitous, versus the $39 Echo Dot. Samsung sees a world where your voice assistant knows you across all your devices, which would definitely be helpful in maintaining health context.

Microsoft

Voice as an ingredient, and part of an iOT and AI strategy was echoed by Microsoft. No surprise since Cortana doesn’t have a body or even a hockey puck. This strategy could be very interesting in healthcare if you think about the talking EPI pen. Why wouldn’t all devices and complex equipment have voice prompts for both patients and providers? There was also a meetup group at the conference demonstrating voice running on an ARM chip, which could be very interesting for the cheaper medical devices.

Designers and Developers

The tradeshow floor was full of mostly developer tools for building, testing, and securing voice applications, and the rallying cry in sessions was for the platform providers (Amazon, Google, Apple, Samsung, Microsoft) to standardize on their approach to voice, if not for the developers, then for the end-users. One of the key areas for some standardization is in the lack of standard interface, just as the APIs from platform vendors use different terminology, there’s no standard interface or reusable components aside from the idea of a wake word, to help users navigate. Mobile had the same problem in the early days, and still does to some extent, something that was solved with on-boarding experiences and tours built into apps, something voice has yet to do, but if done consistently could really improve usability.

“Complexity and ecosystem lock-in are threats to ubiquity and frictionless experience. Let’s not build an ecosystem that locks that in.” James Poulter, CEO of Vixen Labs

There was also an admonition to not build apps for the sake of building apps, but to focus on user need, and understand that what users want most of all is convenience, and that they will use the most convenient interface for the task (web, mobile, TV, phone, voice).

What does this mean in healthcare? Context is very important. Make sure your users know exactly what your skill can and can’t do so they don’t expect the full canon of medical knowledge from your one skill. At Wellpepper, we are firmly in the “Voice and” category, (and yet I still get invited to speak at these events): our voice interactions are a subset of the patient’s care plan, and just as they don’t expect the mobile app to do more than deliver the care plan prescribed by their physicians, the same holds true for the voice experiences for Wellpepper interactive care plans.

Ethics

Ethics in designWhile I didn’t hear anyone talking specifically about the ethical issues of both the eavesdropping scandals, and the need for humans to manually tag voice snippets in order to improve machine learning, I did attend a great session by Brooke Hawkins on ethics and design implications in healthcare. Issues tackled included considerations for disclosure of the exact conditions for the efficacy of an app, whether A/B testing on patients can even be done, and understanding the implications of focusing on specific measures in a care plan. On this last one, she suggested that care plans that include weight, like our Sugarpod diabetes care plan

Brand

Along the same lines of not building voice apps for the sake of it, there was also a lot of talk of how your brand is reflected in your app. Not all healthcare systems think of their brand impact, although they should, and the voice skill is an extension of that. Interestingly David Ciccarelli from Voices.com which has voice talent, mentioned that most developers use the standard Alexa voice. It’s not surprising, as it’s expensive to have someone record every possible response for your application, although it’s interesting to think about a world where your healthcare app is speaking in the voice of your own doctor. Given what we’ve seen with the correlation between adherence and healthcare provider engagement, this could give a huge boost to patient outcomes. The technology is not that far off to synthesis voice from other recordings, so it wouldn’t require that your doctor record everything. Or perhaps it would make more sense to have a specific doctor be the voice of all of your apps, which might be more credible than Alexa dispensing healthcare information. Ciccarelli provided a nice matrix of when to use synthetic voice and when to use real humans that applies well in healthcare.

Users

There’s no question that voice spans all ages. Dave Isbitski opened the conference by saying that his kids and his parents were equally excited by voice applications. Speakers at on the Best Practices for Developing Voice Care Plans panel (myself included) were developing specialized care plans for children, seniors, and everyone else. While there are definite generational differences in usage patterns for voice assistant, and there is also a “voice-first” generation coming. One speaker mentioned how his child who had Alexa from birth knew the limitations of the device, and didn’t ask more than Alexa was capable to deliver, however a 6-year-old family friend who didn’t have that experience wanted to ask things like “do you know my teacher.”  In our testing we found similar differences between generations as well, with seniors more likely to try to have a conversation, and younger people sticking to the script of the care plan a bit more. I heard one developer say that they track slang used by end user to determine age and adjust the interactions accordingly.

Wrapping It Up For Healthcare

We’ve written before about the use cases for voice in healthcare, and there are many, from documenting clinic visits, transcribing physician notes, to medication adherence, education, and patient care plans, as well, voice biomarkers, which my fellow panelists called a huge pool of untapped diagnostic data. If we’re at the early days of voice apps, we’re also at the early days of voice data. There’s a ton to be discovered, and the research, especially in healthcare, is just starting.

Our expectations for voice are high. Let’s hope it delivers.

If you’re interested in learning more about voice in healthcare here are some great resources:

Posted in: Adherence, alexa, Behavior Change, big data, Healthcare Technology

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Partnership Announcement: eVideon and Wellpepper

eVideon and Wellpepper Announce Partnership to Deliver Comprehensive Patient Experience Across Care Settings

 

eVideon, a leader in hospital patient experience, and Wellpepper, a leader in interactive patient care plans, today announced a partnership to deliver comprehensive patient experiences across inpatient and outpatient settings. Both companies work with health systems to deliver a better patient experience. The partnership enables health systems to deploy integrated solutions from best-of-breed vendors. eVideon and Wellpepper are integrating their solutions so providers can deliver a consistent experience across care settings from hospital to home.

 

“Increasingly, health systems are looking for patient experiences that span inpatient and outpatient settings,” said Wellpepper CEO Anne Weiler. “The patient journey begins before a patient enters the hospital and continues long after discharge. This partnership enables an engaging and usable experience for patients across their entire journey of care.”

Wellpepper’s clinically validated digital treatment plans guide users through all the activities they need to do outside the clinic to manage an episode of care: from preparing for a clinic or hospital visit, to managing a chronic condition or recovering after hospital discharge, via patients’ preferred mobile, web, or voice devices. eVideon enhances the inpatient experience, providing personalized interactive bedside capabilities that empower patients to control their environment, learn about their conditions, access hospital services, order meals or watch movies on demand. Both companies are passionate about improving the patient experience while providing data and analytics to streamline clinician workflows and improve outcomes.

“eVideon and Wellpepper share a passion for bringing long overdue self-service to healthcare through personalized, engaging systems,” said Jeff Fallon, CEO of eVideon. “Interoperability is vital, and the value of better outcomes and streamlined workflows is undeniable – not just for patients, but for clinicians and staff too. We’re excited about the new possibilities this comprehensive solution will bring across care environments for our customers.”

The partnership enables health systems to own the end-to-end patient care pathway. Health systems can deploy eVideon’s and Wellpepper’s solutions simultaneously or sequentially. Solution architects from eVideon and Wellpepper are available to consult with healthcare technology and patient experience groups to determine the best approach for rolling out a comprehensive solution.

About eVideon

eVideon digitizes hospitals with a suite of bedside engagement systems, hospital TV, digital signage, and other interactive displays. Patients enjoy the convenience of self-service at the bedside with access to meal ordering, entertainment, education, hospital services and more, while staff enjoy streamlined, paperless workflows. Interactive surveys and patient feedback help patients communicate immediate needs, and help hospitals respond to those needs quickly. The platform leverages integrations with EHRs, building management systems, food service systems, real-time location systems and more. Data analytics provide valuable insights into patient behaviors and pain points, letting hospitals improve service and outcomes. Learn more at www.evideon.com.

About Wellpepper

Wellpepper is a healthcare technology company with an award-winning and clinically validated patient engagement platform used by major health systems to improve outcomes and lower costs of care. Wellpepper treatment plans can be customized for each health system’s own protocols and best practices and personalized for each patient. Wellpepper’s patented adaptive notification system helps drive over 70 percent patient engagement with treatment plans. Wellpepper was founded in 2012 to help healthcare organizations lower costs, improve outcomes and improve patient satisfaction. The company is headquartered in Seattle, Washington. Visit www.wellpepper.comfor more information.

Full press-release available at PRWeb.

Contact:

eVideon
info@evideon.com
616-588-8109

Wellpepper
Jennifer Allen Newton
Bluehouse Consulting Group, Inc.
jennifer@bluehousecg.com
503-805-7540

Posted in: Healthcare Technology, Patient Satisfaction, Press Release

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Transforming Health at Montana HIMSS Annual Spring Conference

Possibly the most interesting thing in healthcare technology is the breadth of scope that health tech needs to cover, and the talks at the HIMSS Montana annual spring conference represented that with talks about security, how to find money for projects, consumer engagement, and how to create a state-wide initiative for healthcare IT. Just like the state of Montana, talks covered a lot of territory.

 

 

 

Here’s a small review of what attendees experienced:

Voice Technology

I had the honor of kicking off the HIMSS Montana Chapter “Transforming Healthcare” conference with an introduction to how voice technologies show promise in patient care. There’s still a lot of concern in the industry about what these voice assistants are tracking, and the speaker immediately after me talked about a surgeon using Alexa to play music in the operating room (a non-compliant use as Alexa might be ‘listening’ to the conversation). However, today’s news that Comcast is also getting into the voice healthcare game shows that there is real promise and high stakes. If you’re interested in this topic you might want to check out our white paper on considerations for designing voice interfaces for patient care, or join me at Voice Summit in Newark this fall for a workshop.

Security

Not surprisingly security remains a hot topic in healthcare, probably because of the surface area of devices and IOT devices. While bad actors and hackers remain a constant threat, people and process are as important, and speakers stressed that often breaches are not malicious but when people don’t follow proper process like the backup company driver who left a van full of backup tapes in his driveway overnight where it was broken into.

Interestingly according to Fred Langston, CISSP, CCSK Executive VP of Professional Services CI Security, imaging systems account for almost 50% of security alerts, possibly because the systems involve both hardware and software, and have often been installed for years. EMRs are seen as relatively safe, and other risks come from devices, attached to the hospital network, where manufacturers have stopped upgrading or patching devices, or simply stopped support for them. The reason is that any sort of software or firmware upgrade requires new FDA certification, which may be cost prohibitive on a discontinued product. There are startups trying to solve this problem, however the FDA may also want to reconsider the unintended consequences of their certification program.

Generally, it takes 205 days within a hospital system until a compromised asset is detected. Decreasing this time and the time from the realization of the compromise and fix (known as dwell time), should be the goal of all IT departments. Hiring a security consultant organization may be the best bet for the broad scope of monitoring that needs to happen.

Finding Money for Innovation

Dianna Linder, MPA, FACHE Director of Grants and Program Development, Billings Clinic is a grant-writer who has been successful at finding funding sources for innovative projects. Much like targeting sales, donor targeting involves figuring out the value proposition you can offer to a particular donor. The Billings Clinic has a shark-tank day where everyone comes with their projects to request funding. Projects are stack-ranked and budget is applied. For those that don’t get budget, Linder looks for other sources like grants. She warns that grants are best used for projects that are new experiments and where the headcount is not part of the spend since they cannot ensure someone of a job when the grant money runs out. A great example of a use of grant money was for building an intake facility for mental health, so that people did not languish in the ED. This program used staff that were already at the system and proved successful enough that it became operationalized the following year.

At Wellpepper, we’ve seen a few projects start with grants, like the one that the Schultz Foundation provided to EvergreenHealth to kick off a patient engagement project that has since been operationalized. Grants for research projects like the one with Harvard are also interesting.

Consumer Experience

Ben WanamakerHead, Consumer Technology & Services from Aetna made us promise not to blog or tweet about his session where he shared some results from Aetna’s partnership with Apple’s smart watch. So, go see for yourself how the application uses behavioral economics and design principles to reward people for healthy behavior.

Building a State-Wide Healthcare IT Strategy

Did you know that 10 states have a state-wide healthcare IT strategy? No? Neither did I. These strategies, when aligned with Medicare and Medicaid initiatives can help drive adoption and support for healthcare technology, innovation, and modernization initiatives. The benefits of the roadmaps are to focus on healthier residents, and freeing information. Another important benefit is funding that is matched by the federal government. While this type of program may be out of reach for the average healthcare technology enthusiast, knowing that they exist can offer opportunities to align with larger initiatives.

Posted in: Adherence, Behavior Change, Health Regulations, Healthcare costs, Healthcare Disruption, Healthcare Policy, Healthcare Research, Healthcare Technology, Healthcare transformation, HIMSS, M-health, patient engagement

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Way Way Beyond HCAHPs: Cleveland Clinic’s Patient Experience Summit

It was the 10th year for the Cleveland Clinic’s innovation & empathy conference, a journey at Cleveland Clinic which started from a question from an MBA student to then CEO Toby Cosgrove asking if the Clinic’s physicians learned empathy.

You can hear him tell the story himself, and it’s personal for him.

And, if you haven’t seen Cleveland Clinic’s quintessential video on empathy, be sure to check it out.

Dr Toby Cosgrove at Cleveland Clinic

This year, Dr Cosgrove was back to talk about what Google could learn from healthcare and visa versa, as he is a newly appointed advisor to Google Health. As a surprise, he was interviewed by the same student who asked the question so many years ago. Not surprisingly, Dr. Cosgrove believes that healthcare needs to embrace big data, and care outside the clinic. He didn’t have much to offer about what Google could learn, but we’d say protecting personal data would be the biggest thing.

Possibly because he’s no longer running a physician organization and yet he is a physician himself, Dr Cosgrove was pretty blunt about the biggest barrier to transform an organization to deliver empathetic care: the doctors themselves.

Dr Victor Montori at Cleveland ClinicAnother notable keynote came from Mayo Clinic’s Dr. Victor Montori, an endocrinologist, and author of Why We Revolt: A Patient Revolution for Kind and Careful Care. Dr Montori asks us to question our biases, assumptions, and language. He decries “industrial healthcare” where we “provide care.” Care is already a verb. He advocates a person-centered approach where the goals and needs of the individual, not billing or the organization are center. Dr. Montori talked about the phenomenon of doctors doing volunteer travel vacations in other countries because it gets them back to why they became physicians: to care for people.

 

The example of a woman struggling to understand her medication, and make good food choices while being aware of her culture reminded me of visiting my mom when she was in a rehabilitation hospital. When we started bringing homecooked meals and even restaurant takeout, she ate. Physicians couldn’t understand her weight loss and hair loss, blaming it on medication. The problem was the terrible nutritional value and taste of the food.

Patient Stories

Patient stories were a key feature in the conference, while backing research up with data is important, it’s the stories that people remember.

How Walmart Started a Movement of Engagement

The power of human stories was prevalent in the presentation from Walmart’s David Hoke, who has created a movement of better health activities within the Walmart employee base, a challenging job when some stores have 100 per cent employee turnover. To create a movement that inserted a healthy virus into stores, David turned to military strategy:

  • Compelling reason to join
  • Place to join
  • Have to have something to do
  • Have something to share with people they love
  • People follow people

Instead of going directly to digital health, the program was designed to be analog to have the broadest reach, and to overcome people’s fears of being tracked. The program featured story booklets in breakrooms that highlighted other employees journey’s to health. Participants described thinking “well if that person can do it, so can I” after reading the stories, and seeing videos of successful program participants.  By the way, if you’re a Walmart customer, you can also join the program, which is now available digitally as well as analog.

Nebraska Medicine’s Situational Interviewing

Observational patient interviewingIn order to find the patient stories, you have to ask the right questions, and HCHAPs isn’t doing that. We see this all the time at Wellpepper: You need to talk to patients to get the story behind the data points. In this example, a patient had rated Nebraska Medicine highly for caring about her. Rather than just accepting this as praise, researchers dug deeper and asked how the patient perceived this, and the patient’s example was of a nurse who noticed she had dry skin and applied lotion. Another patient rated the facility high on cleanliness because he saw a physician pick up some garbage in the patient’s room. The key takeaway from this session was that patients infer intent.

Geisinger Longitudinal Patient JourneyGeisinger’s Longitudinal Patient Record

Chanin Wendling from Geisinger talked about their implementation of a CRM to be able to track a longitudinal patient experience. Knowing when and where patients are interacted with by the health system will go a long way towards understanding their overall experience.

Wellpepper Digital Intervention for Seniors

Dr. Jonathan Bean from Harvard, talked about why interventions for seniors are so important, and how design impacts whether someone is considered “able” by sharing an example of a cross walk timer being decreased so that slower people could no longer get across the street. Dr. Bean the Director of the New England GREC at the VA, professor at Harvard, and our research partner at Wellpepper, and we were extremely proud when he presented results of the REACH digital intervention using Wellpepper that reduced ED visits in seniors by 73%. We’ll share more when the study outcomes are published in the journal of PM&R.

Financial Impact of Care

Another theme that bubbled up in so many sessions at the conference is the financial impact of care, and the intertwined aspects of financial and physical health. A few key points:

  • Walmart has introduced a banking/payday loan application for employees so that they don’t have to pay the exorbitant rates of quick loan companies.
  • People cut back in other areas of their lives to pay for healthcare
  • 95% of patients want to talk to their provider about healthcare costs but providers aren’t equipped to do so. They don’t want to talk to the health plan or billing/collections department.

This was my first time at the summit, but it won’t be the last, especially as it evolves to encompass more aspects of patient experience outside the clinic, and through non-traditional methods like chatbots, virtual assistants, and virtual reality.

It’s hard to encapsulate all the learning at the conference, and no one person can attend all the sessions, but MobiHealth News has a great recap of the keynotes and individual sessions as well.

https://www.mobihealthnews.com/content/patients-more-vulnerable-other-consumers-technology-must-keep-human-empathy-center

https://www.mobihealthnews.com/content/north-america/without-co-design-technologys-healthcare-potential-wasted

https://www.mobihealthnews.com/content/north-america/providence-st-joseph-patient-engagement-begins-call-center

https://www.mobihealthnews.com/content/patient-stories-inspire-new-digital-tools-singapore-health-systems-sutter-health

Posted in: Adherence, Healthcare costs, Healthcare Disruption, Healthcare Research, Healthcare Technology, Healthcare transformation, HIMSS, M-health, Outcomes, patient engagement, Patient Satisfaction, patient-generated data, physician burnout

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Self-Driving Healthcare

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

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

The Promise of Self-Driving Cars

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

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

Machine Learning in Healthcare

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

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

All The Wrong Data

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

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

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

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

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

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

 

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

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The Healthcare of the Future: Equity and Access

If the sold-out “Healthcare of the Future” event presented by Puget Sound Business Journal, is any indication, Seattle is ready for healthcare transformation. Last week’s event at the Fairmont Olympic featured prominent local healthcare leaders discussing equity and access to care, and why Seattle is the right community to deliver.

Healthcare of the Future

Interesting, the panel discussion started with a definition of access, framed as not just being able to receive care, but also to navigate and understand care. Speakers mentioned that in the transition from uninsured to insured, people might now have access to care but not enough health literacy to receive care. This was exemplified when one of the panelists, a physician herself talked about a recent experience with getting care for her son where she navigated multiple providers and care settings, and all the while had a firm handle on who she was talking to, why her concerns were important, and the eventual diagnosis.

You can see examples of this everyday on #medtwitter when physicians or other healthcare professionals talk about how hard it was for them to navigate the system as caregivers for their families. Now, just imagine that you’ve never had healthcare coverage before. How do you know what your options are, how much you may have to pay, or when and where to see a doctor?

Panelists also felt that in addition to access, health equity needed to include helping patients make decisions based on their own values, not the values of the system. Again, a difficult situation to navigate for someone new to the system, or potentially being intimidated by the ‘white coat.’ Equity also looks at whether anyone is being left behind in the system, which could be from a myriad of reasons: language, cultural, or even geographical barriers. Given the problems of staffing rural clinics and hospitals, are people in remote areas able to receive the same level of care as those in the cities?

Unsurprisingly, panelists were bullish on Seattle’s ability to deliver, both from the collaborative nature of the healthcare organizations in the region, and from our stronghold of technology. Tech and healthcare partnerships were cited as the best opportunity to shorten the 17 year cycle from research to clinical practice, with technology disruption in the areas of big data, AI, and cloud infrastructure from local tech giants Microsoft and Amazon pushing the healthcare industry forward. (Let’s hope they can also solve the interoperability of data problems, as one speaker equated having the largest Epic installation at Providence as being a substitute for data portability and interoperability.)

All in all, this was a great event, and discussions at my table ranged from best practices for oncology care, especially with patient-facing tools outside the clinic, to how smart hospital room design includes sensors in the furniture to predict patient falls. That the room was buzzing before 7:30 on a Friday morning shows that we have a lot of great momentum for solving hard problems in healthcare in Seattle.

Posted in: Healthcare costs, Healthcare Disruption, Healthcare Technology, Healthcare transformation

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Wellpepper Deployment Options

Health Systems have 3 ways to deploy Interactive Treatment Plans at their organization:

  1. Using Wellpepper templates
  2. Leveraging the best practices from Mayo Clinic
  3. Custom Care Plan based on own best practices

Wellpepper Templates

We have a partnership with University of Georgia Medical School, that allows residents to join us on rotation for a month. Through this partnership, and with our excellent research partners, we’ve been able to build care plan templates based on best practice templates for over 30 chronic and acute conditions.

Health Systems may choose to implement these Wellpepper Templates, with minimal effort, which makes this deployment option the quickest.

Mayo Clinic Best Practices

At HIMSS 2018, we announced a partnership with Mayo Clinic to make their best practices available on the Wellpepper platform (here). This allows for health systems to leverage interactive care plans developed with Mayo Clinic content. This is also a very fast deployment and only requires a few configuration decisions from the health system.

Custom Care Plans

The third and most commonly selected option, especially for comprehensive care plans, is to develop an interactive treatment plan based on the Health System’s own best practices. These implementations typically take a bit more time to deploy. One of our tenants is if we can’t do better than paper, then we shouldn’t be doing it. Because of this, we’ll spend additional time going through the existing care plan documentation/discharge instructions and provide guidance and recommendations for how to deliver content digitally in context of where the patients are in their care.

EMR Integration

For initial deployments, we’ll typically see Health Systems choose to start without EMR integration. This is due to competing priorities with IT and allows the Health System to get up and running more quickly.

Shortly after that initial deployment, or in parallel with, we will start to map out what EMR integration looks like, with the goal of streamlining the clinical experience. The graphic below shows several ways that we integrate with EMRs, with the first step frequently being single sign on for patients and clinicians, followed by an ADT feed to onboard patients.

For more information on how to get the most of your deployment, please email me at luke@wellpepper.com.

Posted in: Healthcare Technology, Interoperability, patient engagement, Using Wellpepper

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Engaging Patients and Impact to Clinical Workflow

One of our goals at Wellpepper is to enable patients to self-manage and we know that if given the right tools, they will do so. While we strive for that exceptional patient experience, we also put a lot of effort into streamlining clinical workflow for onboarding and monitoring patients.

Patient Onboarding

The process for inviting a patient is meant to be simple with a minimal impact to clinical workflow. Capturing basic patient demographics and assigning the appropriate care plan(s); a process that takes 30 seconds to complete. Typically, a scheduler, navigator or health coach will invite patients when they have been identified for an interactive care plan. Ideally, this is done prior to the patient coming into the clinic, but depending on the care plan, this can place at the front desk or in the patient’s room. EMR integration is another way Wellpepper can help streamline this process. You can read a bit more about some of our EMR Integration options in my post about deployment options (here).

Patient Monitoring

Our professional services team will work with your clinical teams to understand all the scenarios where you want to know what your patients are doing when they’re not in the clinic. Using Alerts & Notifications and Machine Learning, we can help make sure that you’re focusing your time on the patients that need help.

Alerting & Notifications

Our sophisticated rules engine enables health systems to build out simple or complex alerting scenarios. These alerting scenarios will generate an alert and notify the care team.

Patients reporting a symptom or side effect is the most commonly used alerting scenario. Our analysis has found that in surgical scenarios, patients that report a symptom or side effect within 3 days after surgery are 3 times as likely to readmit within 30 days. By alerting the care team that a patient is experiencing a symptom or side effect, a care team member can take action and possibly prevent a readmission.

Other Alerting scenarios may include things like patients not doing their exercises, or reporting a blood sugar reading out of the target range.

Machine Learning

One of the areas that we apply machine learning to help streamline clinical workflow is in our HIPAA-compliant messaging system, which allows communication between patients and their care team. Our analysis has shown that 98% of the messages that patients send are not urgent, and 70% of them don’t need a response. Our message classifier looks for the 2% that are urgent and escalates those to the care team.

It’s important to understand all of the points where patients may reach out for help and optimize workflow accordingly. This is another area where integrating with the EMR can help.

For more information on how to streamline clinical workflow while still providing a great patient experience, please email me at luke@wellpepper.com.

Posted in: Healthcare Disruption, Healthcare motivation, Healthcare Technology, Healthcare transformation, Interoperability, Managing Chronic Disease, patient engagement

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The HIMSS Flu

As usual HIMSS was an overwhelming whirlwind of meetings, opportunities, and information. We had a great show at Wellpepper, and were impressed by a few things. First we heard a lot less about wanting the EMR to do everything. People have realized that especially for all of the patient-facing digital experience, that there need to be interoperable solutions, that are designed with the needs of the end-user in mind. Another thing we noticed was less hype that any one technology (AI, blockchain) was going to be the savior of healthcare. It seems like the market is maturing and there’s an understanding that technology is a key underpinning but only when it’s solving real problems for patients and clinicians. John Moore from Chilmark, who was attending his 11th HIMSS has a great take on this.

Each year, we come away from HIMSS with something we didn’t expect. While it’s usually new leads, partnerships, or competitive intelligence, this year for me, it was the HIMSS flu. Being in a conference center full of technology to diagnose, manage, connect with, and treat sick people, made it seem like a solution should be close by. Ironically, I had meetings with a number of physicians who said that it looked like I had the flu, but couldn’t treat me because they weren’t licensed in Florida. Also, my primary care physician couldn’t help me for this reason as well.

After seeing CirrusMD tweet at my friend and fellow patient-centered care advocate Jan Oldenburg with an offer of a consult, I thought that telemedicine might be the answer.

MDLive came through with a visit code, and I signed up. The sign-up process was pretty painless although an option to clarify where I was physically versus where I lived might have been helpful.

Once I signed up, the app told me it would notify me when it found a physician. This was the slightly confusing part, as when I exited the app and opened it again there was no record that I was in a queue for an appointment, so I started trying to sign up again. Eventually, a video visit came through while I was trying to re-register.

My doctor looked like she was taking calls from home, from the video. Unfortunately, video didn’t work very well from the HIMSS floor—not surprising given the status of the network, so we switched to phone. After a 10 minute conversation, she concluded I had the flu (she was right), and prescribed Tamiflu.

As Jan also found out when she had her asthma attack, the pharmacies near the convention center weren’t actually pharmacies, that is they didn’t offer prescription medication. For Jan it was an expensive Uber to pick up her prescription. For me it was finding a pharmacy that would be open between Orlando and Tampa where were were headed for customer meetings on Friday. By the time I got the prescription, it was 7 hours later, and with Tamiflu the timing matters.

While I was thankful to get care, here are a number of points of friction that made it more difficult than it needed to be, and also show how healthcare really hasn’t adapted to the needs of people:

  • State-based licensure makes telemedicine prohibitive. It also means that you can’t get care from your primary care or other specialists if you’re traveling. Kind of ridiculous that because the patient is physically in Florida suddenly the physician is not licensed to practice.
  • Pharmacies need more delivery options. Even locally, I’ve ended up at pharmacies that don’t take my insurance. Driving around when you’re sick is annoying, and showing up in person when you’ve got the flu is unhelpful for everyone else there.

On the licensure, it’s slow going, but states are starting to have agreements to solve this. On the delivery options, Amazon-drone delivery can’t come fast enough. Overall, the experience wasn’t terrible, and the technology worked but it certainly wasn’t seamless or convenient, and I probably infected a bunch of people while trying to get care. I’d like to apologize to anyone I may have passed the flu along to. I’m not the type to work when sick, but when you’re on the road it’s hard not to.

Also, we’d like HIMSS and all conferences to consider pop-up urgent care. The bandaids in the first-aid room weren’t enough.

Posted in: Healthcare costs, Healthcare Disruption, Healthcare Technology, HIMSS, M-health, patient engagement, Telemedicine

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See you at HIMSS19

HIMSS19 is a couple weeks away and we have a lot to be excited for!

Stop by and see us in the Personalized Health Experience, Booth 888-96. Alongside our great partners at Ensocare, we will be showcasing our latest product updates, discussing ROI for patient engagement platforms, promoting care plans based on Mayo Clinic best practices, and sharing our vision for the future of patient engagement.

We have a long list of booths to visit and sessions to attend. Below are some of the topics that we’re particularly interested in this year:

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

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Machine Learning In Healthcare: How To Avoid GIGO

There’s a commonly used phrase in technology called “garbage in, garbage out” which means that if you start with flawed data or faulty code, you’re going to get lousy output. It results in high-levels of rigor whether that’s in doing user or market research or designing algorithms. Garbage in/Garbage out (or GIGO) is why, although, we are using machine-learning to improve patient engagement and outcomes, at Wellpepper, we’re also slightly skeptical of efforts by big tech (Google, Microsoft, and Amazon) to partner with healthcare organizations to mine their EMR data using machine learning to drive medical breakthroughs. I’ve talked to a number of physicians who are equally skeptical. The reason is that we and especially many physicians are skeptical is that the data in the EMR is frequently poor quality, much of it highly unstructured, and that a major piece of data is missing: the actual patient outcomes. It has what the doctor prescribed but not what the patient did and often what the result was. As well, the data collected in the EMR is designed for billing not diagnosis so it’s more likely the insights will be about billing codes not diagnosis. Why is the data poor quality?

  • A JAMA study found that only 18 percent of EMR notes are original. 46 percent were imported (without attribution) and 36 percent were copied and pasted. Let’s assume that the 18 percent of original notes have no errors, you’re still dealing with 80% of the notes that have a questionable source. This copying and pasting has also contributed to “note bloat” if the data is bad, having more of it will actually hinder the process of finding insights, even for a machine.
  • The data is not standardized. Since so much of the data in the EMR is in these notes, physicians are using different words for the same issue.
  • The dataset from an EMR is biased in several important ways. First, it was entered by physicians and other practitioners, rather than by a broad set of users. The language in healthcare is very different than how patients talk about their health, so these algorithms are unlikely to generalize well outside of the setting where their training data was acquired. Second, data in the EMR has a built-in selection bias towards sick people. Healthy people are probably missing, or at least substantially underrepresented in the dataset. So don’t be surprised if a classifier trained in this setting decides that everyone is sick.
  • Even without copy and paste errors, the data is often just wrong. I once had an intern read back a note to me where she’d recorded my profession as “construction worker”. Yes, I make things, but it’s not nearly as physically taxing and if a physician treating me thought I regularly did heavy labor with my small frame, you can see where over-treatment might be the result.

CNBC’s Christina Farr wrote more about this data problem, the potential for medical errors, and a strange unwillingness to correct the data. A patient quoted in the story understands all too well the problem of GIGO:

“I hope that companies in tech don’t start looking at the text in physician notes and making determinations without a human or someone who knows my medical history very well,” she said. “I’m worried about more errors.”

  • In addition to incorrect data, there are incorrect semantics or examples of physicians using different words for the same issue. In addition to learning medical synonyms which is no small feat, these EMR ML algorithms are going to have to learn grammar too to be truly effective.

Of course, there are solutions to all of these problems and the data quality can be improved with approaches like more standardized input, proof-reading, and possibly using virtual scribes (ironically using machine-learning to speed up input and improve the quality of the data). However the current issues with it make me question whether this is a garbage in/garbage out effort where everyone would be better off starting from cleaner data. The challenge today is that the experts in ML (big tech), don’t have the data, and the experts in the data (healthcare) don’t have the experts in machine learning, so they are partnering and trying to gain some insights from what they have which is arguably very messy data. Another, and possibly more interesting approach is to get a new data set. In 2014, HealthMap showed that you could glean social media data like Twitter, Facebook, and Yelp for health data, and even predict food poisoning faster than the CDC, and now government health organizations have adopted the approach. This is a great example of finding a new data set and seeing what comes of it. At Wellpepper, our growing body of patient-generated data is starting to show insights. In particular we’ve been able to analyze data to find the following, and use this to automate and improve care:

  • Indicators of adverse events in patient-generated messages
  • Patients at 3-times greater risk of readmission from their own reported side-effects
  • The optimal number of care plan tasks for adherence
  • The most adherent cohort of patients
  • The correlation between provider messages and patient adherence to care plan

We also use machine-learning in our patented adaptive notification system that learns from patient behavior and changes notifications and messages based on their behavior. This is a key drive in our high-levels of patient engagement, and can be applied to other patient interactions. While it’s still hard work to find these insights, and then train algorithms on the data sets, we have an advantage because we are also responsible for creating the structure (the patient engagement platform) in which we collect this data :

  • We know exactly what the patient has been asked to do as part of the care plan
  • We have structured and unstructured data
  • Through EMR integration we also have the diagnosis code and other demographic insights on the patient

If you’re interested in gaining new insights about the effectiveness of your own patient-facing care plans delivered to patients outside the clinic, get in touch. You can create a new and clean data stream based on patient-generated data that can start delivering new insights immediately.

Posted in: Clinical Research, Healthcare Technology, patient engagement

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Voice, Podcasts, Partnerships, and Amazon: Wellpepper’s most popular blog posts of 2018

Looking back over the past year, at our most popular blog posts, we suspect that these topics will remain popular in 2019 as well. New technologies and approaches to consumer and digital health have still not reached their full potential, and there’s still lots to learn.

Not surprisingly, a few of our most popular blog posts related to Amazon. In talking with regular people, healthcare organizations, and digital health folks, we find that they are split on whether they want the retail and cloud services giant to get into healthcare, but whatever side of the fence you’re on, there’s no denying they could have a big impact. Right Alexa?

Wellpepper 2018 Blog, Most Viewed Posts

Voice.Health Shows The Promise of Conversational Interfaces

Voice First or Voice And? Dispatches from Voice Summit

This post was actually from 2017, but our CTO’s take on why AWS Lambda serverless architecture is going to be really important for healthcare IT remains a popular topic.

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

We’re also seeing more and more interest in a continuum of care approach: technologies and experiences that span the patient experience, and really help patients and care-givers self-manage, so our announcement of our partnership with Ensocare was also a very popular post.

Ensocare and Wellpepper Streamline Patient Discharge and Engagement

Continual learning is something both technology folks and healthcare folks share, so it comes as no surprise that our round-up of great podcasts for healthcare transformation enthusiasts, was also a popular post.

Podcasts for Healthcare Transformation Enthusiasts

 

 

Posted in: Healthcare Disruption, Healthcare Social Media, Healthcare Technology, Healthcare transformation, M-health, Voice

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Simple Patient-Centered Design

At Wellpepper, we work hard to make sure our software is intuitive, including working with external academic researchers on randomized control trials for people who may have cognitive or other disabilities. This is both to make sure our software is easy-to-use for all abilities, and to overcome a frequent bias we hear about older people not being able to use applications, and also to provide valuable feedback. We’ve found from these studies, the results of which will be published shortly in peer-reviewed journals, that software can be designed for long-term adherence, and this adherence to programs can lead to clinically-meaningful patient outcome improvements.

User-centered design relies on three principles, all of which can be practiced easily, but require continual discipline to practice. It’s easy to assume you know how your users or patients will react either based on your own experiences, or based on prior knowledge. There’s really no substitute for direct experience though. When we practice user-centered design, we think about things from three aspects:

Immersion

Place ourselves in the full experience through the eyes of the user. This is possibly the most powerful way to impact user-centered design, but sometimes the most difficult. Virtual reality is proving to be a great way to experience immersion. At the Kaiser Permanente Center For Total Health in Washington, DC, participants experience a virtual reality tour by a homeless man showing where he sleeps and spends his days. It’s very powerful to be right there with him. While this is definitely a deep-dive immersion experience, there are other ways like these physical therapy students who learned what it was like to age through simple simulations like braces, and crutches. Changing the font size on your screens can be a really easy way to see whether your solution is useable by those with less than 20/20 vision. With many technology solutions being built by young teams, immersion can be a very powerful tool for usable and accessible software.

Observation

Carefully watch and examine what people are actually doing. It can be really difficult to do this without jumping in and explaining how to use your solution. An interesting way to get started with observation is to start before you start building a solution: go and visit your end-user’s environment and take notes, video, and pictures.

Understanding what is around them when they are using your solution may give you much greater insight. When possible we try to visit the clinic before a deployment of Wellpepper. Simple things like whether wifi is available, how busy the waiting room is, and who is initiating conversations with patients can help us understand how to better build administrative tools that fit into the clinician’s workflow. Once you’ve started with observing your users where they will use your solution, the next step is to have them test what you’ve built. Again, it doesn’t have to be complicated. Starting with asking them how they think they would use paper wireframes or voice interface testing with Wizard of Oz scenarios can get you early feedback before you become too attached to your creations.

Conversation

Accurately capture conversations and personal stories. The personal stories will give you insight into what’s important to your users, and also uncover things that you can’t possibly know just by looking at usage data. Conversations can help you with this. The great thing about conversations is that they are an easy way to share feedback with team members who can’t be there, and personal stories help your team converge around personas. We’ve found personal stories to be really helpful in thinking about software design, in particular understanding how to capture those personal stories from patients right in the software by letting them set and track progress against their own personal goals.

Doctor’s often talk about how becoming a patient or becoming a care-giver for a loved one changes their experiences of healthcare and makes them better doctors. This is truly user-centered design, but deeply personal experience is not the only way to learn.

To learn more:

Check out the work Bon Ku, MD is doing at Jefferson University Hospital teaching design to physicians.

Visit the Kaiser Permanente Innovation Center.

Learn about our research with Boston University and Harvard to show patient adherence and outcome improvements.

Read these books from physicians who became patients.
In Shock: My Journey from Death to Recovery and the Redemptive Power of Hope, Rana Adwish, MD
When Breath Becomes Air Paul Kalanithi, MD

Posted in: Adherence, Aging, Behavior Change, Clinical Research, Healthcare Technology, Healthcare transformation, patient engagement, Patient Satisfaction, Research

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Promise, Adoption, and Reality: Dispatches from Connected Health 2018

It’s a rare feat to be engaged, educated, and entertained at the same time, but the Oxford-style debate at Connected Health on telehealth’s effectiveness did all three. Moderated by new Chief Digital Officer for Partners Healthcare, Alistair Erskine, MD, with Ateev Mehrotra, MD debating that telehealth is not effective, and head of the American Telemedicine Association, Andrew Watson, MD debating that it is, the format and discussion delivered a provocative closing session on Day 1 of Connected Health. As decided by the audience, the winner was Dr. Watson, citing effective programs like telestroke, consults and expert referrals, and rural medicine. However, applause for Dr. Mehrotra was also strong, and I suspect that his major points that telehealth has not reached broad adoption, and in fact there have been observations that telehealth is actually increasing utilization as people follow a telehealth visit with an in-person visit. The question is whether that visit wouldn’t have happened and we’d see worse health outcomes, or whether the person had a problem that couldn’t be helped with telehealth.

In another deep dive session on telehealth, “Making Connected Health Work for Physicians”,  Kevin Fickenscher, MD talked about a unique program to train clinicians on virtual visits. Given that the diagnostic capabilities are different, for example, you can’t touch the patient, this makes perfect sense. Questioning and listening skills are going to be more important than physical exam, and observation may be limited by (current) video technology. Also in this session, Ami Blatt MD from Partners, talked about how her young and mobile patients essentially lead her to telemedicine, by insisting that was how they wanted to communicate: the consumerization of healthcare in action. She also recommended to any physicians wanting to deploy a telemedicine solution to make sure that the goals of the program align with the financial incentives available for the hospital.

So, what do we take away from this? Twenty years later, telemedicine is still in the promise stage. Practice and reimbursement needs to change even more to find true breakthroughs, and perhaps we should look at pattern matching to find other successful workflows and outcomes that resemble the benefits for telestroke.

In no particular order, here are some other observations from the conference:

  • Patients are taking a bigger role, whether that was a patient co-presenting in a session on Patient Generated Health data, the Wego Health Awards honoring LupusLady as an activated and collaborative patient, or the society for Participatory Medicine pre-day with patients included, the voice of patients is increasingly being listened to with a real seat at the table.
  • Digital therapeutics and behavioral health are hot. There was a special pavilion on the tradeshow floor dedicated to digital therapeutics where our fellow Seattle health innovators, 2Morrow presented great results from their smoking cessation programs.
  • Patient-generated data is starting to show promise and much greater acceptance by clinicians, particularly in the ability for clinicians and patients to talk to each other. However, we’d still like to see a better connection of data and actionable care plans, and there was still some mention of the data being better because patients cheat when verbally relating data like blood sugar after the fact. Data alone isn’t enough to support patients or change behavior, and it shouldn’t be continued punitive.


From Session: PGHD End User Experience: Patients and Providers

  • There’s a continual blurring of the lines with engagement, particularly member and patient engagement, and there were a ton of new companies in this space (again), all offering to get members and patients engaged. From their overviews it was hard to tell how targeting providers and payers was even different aside from the terminology.
  • Although a full-day devoted to voice interfaces definitely showed it’s a hot topic, AI was definitely the buzzword of the show.

We’re already gearing up for HIMSS 2019 where we hope the buzzword of the show will be “outcomes”. We just heard that our talk on the (really positive) results of the REACH study has been accepted. See you there?

Posted in: Health Regulations, Healthcare motivation, Healthcare Technology, Healthcare transformation, HIMSS, M-health, patient engagement, Patient Satisfaction, patient-generated data, Telemedicine, Voice

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