Healthcare transformation

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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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Healthcare Summer Reading: Invisible Women

As artificial intelligence and machine-learning are increasingly touted as the solution for everything from shopping to healthcare, the need to better understand the data that goes into these solutions increases. We’ve written before about how trying to solve patient problems using only the EMR data delivers only half the equation because it leaves out everything that happened to the patient outside the clinic, and the patient’s own experience.

Machine Learning in Healthcare: How To Avoid GIGO

Self-Driving Healthcare

However, it turns out that this data may only deliver ¼ or less of the solution when it comes to women’s health, as the fact-packed “Invisible Women: Data Bias In a World Designed For Men” points out.

While Invisible Women tackles politics, healthcare, manufacturing, career, and finances, so many of the problem areas where key decisions are made without enough data result in population health and healthcare problems, even decisions about automobile manufacturing, snow shoveling, and portable cookstoves.

In example after example from every industry, the “normal” male is used as the standard, and women are seen as aberrations, resulting in health, safety, and finance inequality. One size fits all is actually one size fits average male.

If you need to make a case, either business or healthcare related, for the need for diversity of thinking, and for having the data for evidence-based decision making, this book will help you. You’ll also realize that in evidence-based decision making in healthcare, the data is missing for 51% of the population.

Examples include:

  • Crash test dummies that don’t approximate women’s bodies so that women are 17% more likely to die in a car crash.
  • Drug testing that does not require evaluation of outcomes by gender. (The UK does not require any gender evaluation for randomized control trials so researchers are advised to look at studies from other countries to ensure gender inclusion.)
  • Drug dosages that are not adjusted for size or hormones
  • Health trackers that underestimate women’s activities and don’t include menstruation tracking
  • Increased risk of hip fracture by making female solders match an arbitrary male gate length
  • Public transportation safety issues
  • Greater risk of women being misdiagnosed for heart failure because symptoms present differently
  • Portable cookstoves intended to decrease indoor pollution but aren’t used because they need constant tending, and mean that women can’t get other chores done.

The list goes on. Practically every paragraph in this book has a practical example where getting the right data, either qualitative or quantitative would have resulted in better quality of life for women (and everyone really.)

The good news is that this implicit bias that normal can be overcome with a contentious approach to collecting data and feedback, and a rigor of examining the data and outcomes by gender. Interestingly, when reading this book, I realized that this should be another way that we evaluate our Wellpepper care plans. We currently mostly segment our data analysis by age because there has been previous skepticism about older patients ability to use technology. Now I’m thinking that gender differences in care plan outcomes might be a really interesting source of insight. What might we learn about recovery? We know women experience pain and medication differently. Are their gender based clinical insights in our outcome data as well?

While Invisible Women is probably not beach reading, it’s still highly recommended book to add to your healthcare and data reading list.

Posted in: big data, Clinical Research, Healthcare Research, Healthcare transformation, machine learning, patient-generated data

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Summer Reading: “Get Well Soon: History’s Worst Plagues and the Heroes Who Fought Them”

Summer Reading: Get Well Soon: History’s Worst Plagues and the Heroes Who Fought Them, by Jennifer Wright

At Wellpepper, we’re huge proponents of evidence, and have worked for years with researchers from Boston University and Harvard University to prove that the things that seem like common sense, like providing help outside the clinic in a digital format, will truly improve patient outcomes. Given today’s focus on evidence-based medicine, and even the sometimes dismissal of common sense if there’s no randomized control trial (even chicken soup is subject to peer review), it’s amazing to remember that we once knew so little about what makes us sick, or the difference between correlation and causation.

If you don’t think you’re interested in plagues, think again. This book is a rollicking journey through a history of plagues that is both funny, sarcastic, and tragic. It reminds us that things that seem obvious today might not have been in the past, and that we’re never that far from mass hysteria when we don’t understand the root cause of a new healthcare epidemic.

While there is a chapter dedicated to each historical epidemic, Wright does not talk about the AIDs epidemic of the 1980s. She believes that history needs to be shared by the ones who were there, while her job is to amplify the voices of history so that we stop making the same mistakes. By uncovering how society, medical professionals, or government either did or didn’t cope with a particular epidemic, Wright offers valuable lessons for today.

For example, when exploring leprosy (which by the way, was a required medical test to get a Russian visa when I moved there in 2008 with Microsoft: spoiler alert, I don’t have it), Wright says:

“Diseases don’t ruin lives just because they rot off noses. They destroy people if the rest of society isolates them and treats them as undeserving of help and respect.”

When people blame others for their diseases, or treat them differently, we are not acting better than our ancestors.

Wright also puts into perspective why all types of people fall for information that now may seem ridiculous, with this analogy:

“If you were a peasant and someone said, “If you live in a sewer, the bubonic plague won’t kill you,” your reaction likely wouldn’t be, “I am curious to hear the science behind that.” Your response would be, “Point me to the nearest sewer.”

It’s up to medical professionals to understand why someone believes what they believe, and then try to provide alternate evidence, rather than dismiss it out of hand. It doesn’t mean that you can’t debunk the value of living in a sewer, but do it by understanding where the information came from in the first place. (And also don’t forget that the fake healthcare information is much easier to access than medical journals locked behind firewalls.

Stories of the Spanish flu, and government-sanctioned and media campaign to downplay (aka ignore or bury) the seriousness of the illness so as to not divert energy and enthusiasm for the war effort, versus the example of Marcus Aurelius during the Antonine Plague taking care of business by offering government burials and time off to go to funerals, which both kept bodies from piling up and acknowledged there was a serious problem.

Wright admonishes us to choose leaders well.

“When we are electing government officials, it is not stupid to ask yourself, “If a plague broke out, do I think this person could navigate the country through those times, on a spiritual level, but also on a pragmatic one? Would they be able to calmly solve one problem, and then another one, and then the next one? Or would bodies pile up in the streets?”

As we start to repeat the mistakes of the past (measles anyone?)Wright makes sure to remind us that with our natural human instinct to lean away from bad news, we often forget how bad things were. Measles, anyone?

“Polio was effectively eliminated throughout the world. And then people just … kind of forgot all about polio. This seems to be the human response to any disease. People forget diseases ever existed the minute they are no longer being affected by them. Maybe that’s understandable. Maybe if we all thought about all the potential diseases the world is teeming with, and the extent to which we are, every day, dancing on the edge of a volcano, the world would seem too terrifying to walk around in at all. Or we’d just vaccinate our kids.”

If you’re interested in medical history, policy, or historical epidemiology this makes a light summer read. I’m not kidding. Also, the chapter on Spanish flu should be made into a dystopian/future past film. It’s got everything: media and government cover up, bodies in the street, a mystery, and a hero fighting against the status quo.

Posted in: Behavior Change, Clinical Research, Healthcare motivation, Healthcare transformation, population health, Rare disease

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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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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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Calculating Return On Investment For Interactive Care Plans

Patient engagement and education is perhaps one of the most under-utilized resources in the care continuum. Patients who are engaged in their care cost healthcare systems [cost] 8% less than non-engaged patients in their base year, and 21% less in future years.[1]

Interactive treatment plans deliver return on investment in three key ways:

Improving patient satisfaction and outcomes

  • Patients look for quality and convenience when they choose a provider organization. They want to be able to interact with their healthcare organization on the same terms as everything else in their lives. Improving outcomes also increases patient satisfaction.

Increasing access to care

  • By monitoring what patients are doing outside the clinic and enabling patients to self-manage, you can increase access to care by seeing the right patients, seeing more patients, and improving recall. Making sure patients are prepared for surgery decreases no-shows and increases utilization and access to care.

Reducing costs

  • Cost reductions are the most dependent on model of care since a readmission or ED visit could be a source of revenue. However, you can look for reducing hard costs of seminars and handouts as well as costs of readmissions, extra visits in capitated models, and of complications. For patients poor outcomes increase costs both out of pocket and in quality of life. Manual labor costs of administering surveys and follow up questionaries can also be avoided with automated systems.

Long-term impact

Patient engagement can have a much stronger long-term impact, including reducing:

  • Hidden costs of variability in care delivery
  • Hidden costs of lack of standardization and manual processes
  • Costs of poor patient outcomes that result in worsening patient problems

As well, as an industry have only begun to scratch the surface of the types of clinical and behavioral insights that will be derived from patient-reported data, that will enable more efficient and effective treatment based on predictive models, and stronger patient participation in their own care.

For our full whitepaper, and ROI economic models contact sales@wellpepper.com or call (844) 899-7377 and press 1 for Sales.

[1] Health Affairs, 32, no.2 (2013):207-214 What The Evidence Shows About Patient Activation: Better Health Outcomes And Care Experiences; Fewer Data On Costs

Posted in: Adherence, Healthcare Legislation, Healthcare transformation, patient-generated data, Return on Investment

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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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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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