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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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Voice.Health Shows The Promise of Conversational Interfaces

“By embracing voice, healthcare has the opportunity to leapfrog technology from other industries” John Brownstein, PhD, Chief Innovation Officer, Boston Children’s Hospital

Dr. Brownstein was speaking in shared keynote at the Voice.Health summit about why he and other healthcare innovators are so pumped about the opportunity for voice in healthcare. On a later panel Shivdev Rao, MD from UPMC Enterprises described what makes voice a natural fit.

75-80 percent of the signal in a hospital is voice-driven
Shivdev Rao, MD, Vice President, UPMC Enterprises

The one-day concentrated pre-day at Connected Health focused on all things voice tech in healthcare and was kicked off by Klick Health founder and CEO Leerom Segal, who talked about the factors that made this time ripe for voice in the tech industry overall. Putting technology in context is exactly what’s needed at more healthcare events versus a sometimes myopic view of healthcare technology.

So why is voice having a moment?

  • Compute power necessary for processing the large amounts of data that voice creates and requires is now available and relatively inexpensive through cloud offerings from Amazon, Google, and Microsoft
  • Devices are cheap and ubiquitous
  • We’re already trained to expect instant answers but starting to be sensitive to the impact of screen time
  • Voice is seen as more accessible to broader groups
  • And of course, voice is being used as a Trojan horse for commerce (at least by Amazon), for Google it’s for more data

In addition to panels on clinical and consumer impact of voice in healthcare, there was an immersive experience with examples of voice technology in different healthcare settings including clinic, hospital room, operating room, senior home, and an actual home living room. We participated on the consumer panel, and showcased Sugarpod (in the living room since there wasn’t a bathroom.) During the course of the day, and in the keynote at least a hundred potential uses for voice in healthcare were explored. At the same time, participants didn’t shy away from challenges either, like using voice for the wrong purposes like converting pages and pages of web content, or the challenges for people with hearing, cognition, or speech problems to use the devices, all of which can be mitigated with thoughtful voice interaction design, accessibility design, and user testing.

Clinicians have particular concerns about voice. From UPMC, Dr. talked about the challenges of any new and shiny technology in healthcare

As well, similar to what we’ve seen with other technology starting with the real problem of EMR screen time but also including mobile outside the clinic to machine-learning and artificial intelligence, clinicians are concerned about any technology getting between them and their patients. From Robert Stevens, Executive Director and Head of Digital for Novartis summed up what he had heard from physicians “I don’t to be usurped by a smart hockey puck at patient point of care.”

We’re bullish on voice, and agree with Brownstein, that embracing this technology puts healthcare on the cutting-edge technology-wise. It’s also an opportunity for new players, as the incumbents have not proved themselves capable of embracing consumer or end-user centric design that voice requires. We’re also still firmly in the “voice and” camp, looking at voice user interface as one of a number of tools for engaging patients as part of a comprehensive overall digital strategy. Planning and delivering on  a context-aware omni-channel adoption strategy for digital health is another way healthcare has an opportunity to evolve with the overall technology and consumer markets who also haven’t solved this thorny problem.

If you’d like to talk about how to deliver a consistent and engaging omni-channel experience that improves patient outcomes, get in touch sales@wellpepper.com

If you’re interested in voice, check out our other blog posts on the topic:

Posted in: Adherence, Healthcare Social Media, Healthcare Technology, Healthcare transformation, M-health, patient engagement, Patient Satisfaction, Voice

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Podcasts for Healthcare Transformation Enthusiasts

If you like to nerd out about healthcare, we’ve got a cornucopia of great podcasts for you to choose from in no particular order. Happy listening!

White Coat Black Art

This long-running podcast from CBC radio in Canada is hosted by Brian Goldman, MD, and does not shy away from tough topics like assisted suicide, medical errors, or the health impacts of legalizing marijuana.

 

A Healthy Dose

Steve Kraus of Bessemer Partners and Trevor Price of Oxeon partners interview a who’s who of health tech pioneers and entrepreneurs. Their conversation with AthenaHealth founder Jonathan Bush on cold medicine is not to be missed.

 

 

Inside Health

This BBC podcast hosted by Mark Porter, MD, explores fact and fiction for common health issues, and the state of the National Health System in the UK. It’s worth listening both for the medical advice and for insight into a different system of care.

 

 

Tech Tonics

Tech Tonics David Shaywitz, MD, PhD, and Venture Valkyrie, Lisa Suennen weigh in on unicorns and reality, and interview physicians and founders in this health tech focused podcast.

 

 

 

This Week In Healthcare IT

This Week In Health ITFormer hospital CIO current expert in cloud computing for healthcare, Bill Russell interviews health IT experts, with a heavy emphasis on hospital healthcare IT experts on topics like security, interoperability, and the shift to the cloud.

 

 

Well Connected

Innovation veteran, Joe Kvedar, MD from Partners Health interviews peers and colleagues on both current and new technologies.

 

Outcomes Rocket


This podcast from Saul Marquez delivers with a focus on outcomes, value, and cost-savings in healthcare.

 

 

 

Voice First Health

Alexa enthusiast, and Canadian physician, Teri Fisher, MD is bullish on the potential for voice interactions in healthcare.

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

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Reflecting on Wellpepper

I am a third year internal medicine resident from the University of Georgia. For the last four weeks I had the good fortune to travel from Athens, Georgia to Seattle, Washington and work with Wellpepper as a resident physician consultant. As a young physician, I have a lot of hope for electronic health systems and their ability to decrease our workload, increase our efficiency, and improve patient care. In residency, we spend anywhere from 25% to 75% of our time looking at the electronic medical record, but we do not get the opportunity to see it from the other side. When I found out about the opportunity to work with a health tech company for an elective rotation, it seemed like a great way to see things from different perspective. There was the added benefit of escaping a humid Georgia summer month and instead spending it in the beautiful Pacific Northwest where I hope to work after residency.

While at Wellpepper I worked on a variety of projects in several different roles. My primary responsibility was to work on care plan development. A particular care plan they were interested in based on feedback from their customers was pain and opioid management. Considering the opioid epidemic we are currently facing in medicine, this seemed like a great idea. Many of the patients in our resident clinic are chronic pain patients or come to us already on opioids from other providers. Unfortunately, I have received very little training in opioid management (our residency clinic is not allowed to prescribe opioids or benzodiazepines) . While I understand the sentiment behind this, it is not helpful to residents who need to learn how to manage these types of medications for their future practices . Developing a care plan around opioid management presented a wonderful learning opportunity. I designed the opioid care plan and taper program with the opioid-naïve physician in mind, providing a platform to help guide patients and physicians through the intricacies of opioid management and withdrawal. Many of the other care plans I helped work on throughout the month were more on the surgical side of things, but closely related to internal medicine because of how often we work with pre and post-surgical management of patients and these also provided great learning opportunities.

The month culminated in a trip to meet with Mayo Clinic in Rochester, Minnesota. Wellpepper has a unique partnership with Mayo Clinic to build their care plan best practices into the Wellpepper platform to help improve patient care and outcomes. Participating in meetings with administrators, secretaries, clinical research nurses, and physicians at the forefront of their specialties was an extremely unique opportunity . I thought my medical school, the University of Kansas, was a big hospital. It paled in comparison to the small city of Mayo Clinic. It was quite the experience just to be there.

In short, my month with Wellpepper provided a glimpse into the medical tech industry and provided a unique opportunity to work as a consultant outside of patient care. In the electronic medical record world, the focus is on functionality for the healthcare providers. Apps for patient use present an interesting challenge in creating something that is clinically useful for providers but also user friendly and not bogged down in medical jargon for the patients to be able to navigate. It was nice to experience seeing what creating those types of tools for patients looked like from a perspective other than the provider. There were plenty of learning opportunities throughout the month (as well as plenty of extremely valuable study time with board exams on the horizon). While I do not see working exclusively as a physician consultant in my immediate future, I plan to continue to champion electronic health records and mobile services to pursue continued improvement in patient care and outcomes .

From left to right: Myself, Anne Weiler, and Luke Feaster visiting Mayo Clinic.

Posted in: Healthcare Technology, Healthcare transformation

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Machine Learning in Medicine

As a new intern, I remember frequently making my way to the Emergency Department for a new admission; “Chest pain,” the attending would tell me before sending me to my next patient. Like any good intern I would head directly to the paper chart where I knew the EKG was supposed to be waiting for me, already signed off on by the ER physician. Printed in standard block print, “Normal Sinus Rhythm, No significant ST segment changes” I would read and place the EKG back on the chart. It would be later in the year before I learned to ignore that pre-emptive diagnosis or even give a thought to about how it got there. This is one of many examples how machine learning has started to be integrated into our everyday life in medicine. It can be helpful as a diagnostic tool, or it can be a red herring.

Example of machine-learning EKG interpretation.

Machine learning is the scientific discipline that focuses on how computers learn from data and if there is one thing we have an abundance of in medicine, data fits the bill. Data has been used to teach computers how to play poker, learn laws of physics, become video game experts, and provide substantial data analysis in a variety of fields. Currently in medicine, the analytical power of machine learning has been applied to EKG interpretation, radiograph interpretation, and pathology specimen identification, just to name a few. But this scope seems limited. What other instances could we be using this technology in successfully? What are some of the barriers that could prevent its utilization?

Diagnostic tools are utilized in the inpatient and outpatient setting on a regular basis. We routinely pull out our phones or Google to risk stratify patients with ASCVD scoring, or maybe MELD scoring in the cirrhotic that just got admitted. Through machine learning, these scoring systems could be applied when the EMR identifies the correct patient to apply it to, make those calculations for the physician, and present it in our results before we even have to think about making the calculation ourselves. Imagine a patient with cirrhosis who is a frequent visitor to the hospital. As a patient known to the system, a physician has at some point keyed in the diagnosis of “cirrhosis.” Now, on their next admission, this prompts this EMR to automatically calculated and provide a MELD Score, a Maddrey Discriminant Function (if a diagnosis of “alcoholic hepatitis” is included in the medical history). The physician can clinically determine relevance of the provided scores; maybe they are helpful in management, or maybe they are of little consequence depending on the reason for admission. You can imagine similar settings for many of our other risk calculators that could be provided through the EMR. While machine learning has potential far beyond this, it is a practical example where it could easily be helpful in every day workflow. However, there are some drawbacks to machine learning.

Some consequences of machine learning in medicine include reducing the skills of physician, the lack of machine learning to take data within context, and intrinsic uncertainties in medicine. One study includes that when internal medicine residents were presented with EKGs that had computer-annotated diagnoses, similar to the scenario I mentioned at the beginning of this post, diagnostic accuracy was actually reduced from 57% to 48% went compared to a control group without that assistance (Cabitza, JAMA 2017). An example that Cabitza brings up regarding taking data in context is regarding pneumonia patients with and without asthma and in-hospital mortality. The machine-learning algorithms used in this scenario identified that patients with pneumonia and asthma had a lower mortality, and drew the conclusion that asthma was protective against pneumonia. The contextual data that was missing from the machine learning algorithm was that the patient with asthma who were admitted with pneumonia were more frequently admitted to intensive care units as a precaution. Intrinsic uncertainties in medicine are present in modern medicine as physician who have different opinions regarding diagnosis and management of the same patient based on their evaluation. In a way, this seems like machine-learning could be both an advantage and disadvantage. An advantage this offers is removing physician bias. On the same line of thought, it removes the physician’s intuition.

At Wellpepper, with the Amazon Alexa challenge, machine learning was used to train a scale and camera device (named “Sugarpod“) in recognizing early changes in skin breakdown to help detect diabetic foot ulcers. Given the complications that come with diabetic foot ulcers, including infections and amputations, tools like this can be utilized by the provider to catch foot wounds earlier and provide appropriate treatment, ideally leading to less severe infections, less hospitalizations, less amputations, and lower burden on healthcare system as a whole. I believe these goals can be projected across medicine and machine learning can help assist us with them. With healthcare cost rising (3.3 Trillion dollars in 2016), most people can agree that any tools which can be used to decrease that cost should be utilized to the best of its ability. Machine learning, even in some of its simplest forms, can certainly be made to do this.

Posted in: Healthcare costs, Healthcare Technology, Healthcare transformation

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Digital Transformation in Pharma: Digital Pharma West

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

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

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

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

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

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

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

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

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

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

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

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

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The Challenge of Challenges: Determining When To Participate

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

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

Costs

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

Benefits

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

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

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

Low cost/medium benefit

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

Medium cost/medium benefit

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

High cost/migh benefit

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

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

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

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

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

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

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

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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

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Healthcare Transformation: Emulating Disney Is Not A Bad Idea

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

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

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

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

Recently I received this in the mail from UnitedHealthcare.

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

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

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

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

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Wellpepper now an Amazon Partner Network Advanced Technology Partner

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

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

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

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

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

Posted in: Healthcare Technology

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

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

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

“You’re late to the party.”

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

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

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

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

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

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

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

Until next year’s party, cheers!

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

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

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HIMSS 2018…See you there!

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

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

Monday, March 5th

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

Tuesday, March 6th

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

Wednesday, March 7th

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

Thursday, March 8th

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

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

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

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