Clinical Research

Archive for Clinical Research

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

Leave a Comment (0) →

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

Leave a Comment (0) →

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

Leave a Comment (0) →

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

Leave a Comment (1) →

Digital Transformation in Pharma: Digital Pharma West

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

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

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

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

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

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

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

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

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

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

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

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

Leave a Comment (0) →

Sidelined by mindlines?

Evidence-based medicine (EBM), a movement that emerged roughly 30 years ago, advocates for the use of current best evidence from high quality research studies in healthcare decision making. This logical and straightforward way of delivering healthcare often fails in modern day practice. One simple reason that clinicians cannot execute point of care decision making with EBM is due to the overwhelming volume of scientific evidence that is ever changing and available within severe time constraints. A more pervasive reason is found in the way clinicians practice and incorporate knowledge into their daily work – they tend to follow what ethnographers Gabbay and Le May have coined as mindlines: collectively reinforced, iterative, internalized, and tacit guidelines. Clinicians’ practice is primarily influenced by trusted colleagues, mediated by cultural and organizational features of their practices, and is constantly refined as knowledge-in-practice-in-context.

Through my own wandering through various clinical settings, I have often heard phrases from respected clinicians including “there is evidence…and then there is actual practice.” The five part concept of EBM appears intuitively important in a science-based profession – define the problem, search for sources of information, critically evaluate that information, apply the information to the patient encounter, and evaluate the efficacy of the application of that information for that specific patient. It seems that an exciting opportunity would be data analytics enhanced by artificial intelligence that could search high volume clinical research and identify patient-matching criteria in order to assist clinician judgment on relevant treatment protocols.

How much of this is naïve rationalism? Upon evaluating a typical clinical scenario, what I used to think was a clear set of facts in a one-dimensional reality is now more like an interaction of temporary realities of patients, clinicians, researchers, and guideline/policy makers. Mindlines are therefore:

  • More than intuition.
    Mindlines that clinicians abide by undergo a validation process despite being mainly tacit. They are built off of shared sense-making in the local settings of patient care, which leads to coherence and negotiation with real-time environmental influences. They provide for more accuracy than the reductionist tools and beliefs of EBM.
  • More patient centered.
    Mindlines allow for incorporation of valid knowledge to occur from the patient’s perspective, as opposed to the paternalistic model of clinician knowing all and only being able to derive more information from EBM.
  • Meaningful and effective.
    Mindlines are not very far off from the way typical high performers solve problems – they consciously and unconsciously adjust their frameworks through contextual experience, colleagues, and the physical world. EBM can negotiate with these frameworks, but likely can never replace them.

The paradigm of mindlines offers insight into the way clinicians practice and how western medicine operationally works in an environment with varying expectations from the patient and the overall industry where innovative work is being attempted. The secular trend for the future hopefully will be the risk-adjusted incorporation of EBM with assistance from artificial intelligence into the tacit world of clinical medicine.

Posted in: big data, Clinical Research, Research

Leave a Comment (0) →

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

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

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

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

Interoperability: Universal doesn’t mean one

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

Privacy: The government is okay, the US is not

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

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

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

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

Blockchain: It’s not about currency

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

“E” HR

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

Other Voices: Patients!

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

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

Manels

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

Best Quote

 

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

Leave a Comment (0) →

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

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

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

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

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

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

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

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

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

Leave a Comment (0) →

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

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

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

Patient Experience

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

User Journey from Wellpepper on Vimeo.

Outcomes

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

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

Technology

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

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

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

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

Leave a Comment (0) →

In Defense of Patient-Generated Data

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

There are a few flaws in this plan:

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

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

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

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

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

Leave a Comment (0) →

Falls Challenge

How might we enable older adults to live their best possible life by preventing falls? We have entered a challenge with AARP and IDEO to bring our proven falls solutions to the masses. Along side our partners at Harvard and Boston University, we believe that using mobile technology to enhance and scale a proven falls prevention program will lead to better life by increasing access to care and decreasing costs.

The challenge started with over 220 submissions and recently weeded down to the top 40. We’re thrilled to have made the first cut. Our method is proven and we invite you to participate in the next round to refine our idea and help achieve greater impact.

Click here to check out our entry!

 

 

Posted in: Aging, Clinical Research, Healthcare Technology, Outcomes, Physical Therapy, Research, Uncategorized

Leave a Comment (0) →

HIMSS 2017 Recap: What’s Hot and What’s Hype

Wellpepper had a great HIMSS 2017 Conference with a very busy booth in the Innovation Zone, a panel on the current state of innovation, and a talk on Delivering Empathy Through Telehealth. Here are a few of our thoughts on the conference compiled from our team.Empathetic Care Through Telehealth

Cognitive and AI: Hype

Starting with Ginni Romety’s keynote, Cognitive and AI were definitely the buzzwords of the conference. Everyone is excited about the promise but it seems like the current status is not ready for takeoff. First, there’s a lot of work to get data out of the EMR, and second, no one seems quite sure what the killer use case is going to be. Immediately before HIMSS, MD Anderson announced that after a $62M investment they weren’t seeing value in IBM Watson and were pulling out of the program. That did not stop them from co-presenting with Mayo Clinic and Watson at the conference. The main use case seemed to be shortening the time to identify cancer patients for clinical trials from 30 minutes to 8 minutes. Another example, which just highlights the sorry state of clincial technology, was to use Watson on top of Epic to help staff figure out how to use features. During the session, Mayo CIO Christopher Ross referred to Watson as a toddler. While all of this was disappointing, it’s heartening that for once healthcare is on trend with the rest of the tech world, and possibly pointing to an accelerated evolution of health IT.

IMG_0611Patient Engagement: Hot

In 2016, patient engagement was also hot, but this year, we’d also say it was real. Buyers visited our booth with checklists of capabilities they wanted to see. Pilots were completed last year, and now they are making platform decisions for patient engagement. We’ve noticed this ourselves in the past 6 months, we’ve seen the patient engagement purchase decision elevated to the C-suite, and the decision being made based on capabilities that will address the needs of all patients and all service lines.

Interoperability: Hot

Compared to the previous year, we saw a lot more talk about interoperability, whether that was EMRs building out APIs and developer programs, the CommonWell Alliance, or talk about how block-chain could be used to both secure and transfer healthcare data. Understanding that data needs to flow with the patient, and also that a heck of a lot of data is being created outside the EMR (in patient engagement solutions for example), is driving a greater commitment to interoperability in the industry.

Healthcare Investment: Hot

The Sharks said so, so it must be hot. The HIMSS Venture+ Investment forum this year had a much more diverse set of pitches than previously, including a social venture. and was won by DiaCardio, a woman-led company from Israel automating evaluation of heart ultrasound.

The Affordable Care Act: Prognosis Unclear

Make no mistake, the potential repeal of the ACA is looming heavy even in health IT. Health systems Boehner, HIMSSare concerned about impact on Medicare and Medicaid revenue. While bundles and value-based care have been quite positively received, the current uncertainty is putting a hold on capital expenditures. (Did we mention that Saas can be accounted for as operating expense?) Possibly the most entertaining speculation on the ACA came from former house speaker John Boehner and former governor Ed Rendell. Rendell suggested that we repeal Obamacare and replace it with the Affordable Care Act. Boehner mused that repealing without a plan would place all the blame and problems with the current system firmly on the sitting government, and recommended that it not be repealed.

The Takeway?

We’re still optimistic. IT is increasingly having a seat at the table within healthcare. Although not all EMR implementations have been seen as a success for clinicians, we are seeing a shift to an expectation of better software for both patients and providers, for data to move smoothly, and the promise of insights and better care when that data can be analyzed and acted on. We’re already looking forward to HIMSS 2018 Las Vegas.

Posted in: big data, Clinical Research, Interoperability, patient engagement

Leave a Comment (0) →

Better Living Through Big Data

This week I had the opportunity to participate on a lively panel at General Assembly Seattle organized by Seattle Health Innovators, and moderated by Corinne Stroum of Caradigm. Fellow panelists included Randy Wise formerly of Group Health and now at EveryMove, Ang Sun of Regence/Cambia, Lifesprite founder Swatee Surve, and Daniel Newton of Accolade.

Corrine sent us a series of great questions in advance, and we had a rich discussion and so many questions from the audience that we didn’t even get to half of them. It’s a big topic, and with payers, providers, and technologists on the panel there was a lot of opportunity for broad perspectives. There’s a discussion of having a follow-up to this panel to continue the conversation—stay tuned for more on that. The general themes of the discussion included the value of big data to influence individual health with examples like the quantified-self movement, but more generally how our ability to collect and analyze can lead to more personalized and better healthcare. img_3265

At Wellpepper, we have a lot of data to analyze. As Wellpepper CTO Mike Van Snellenberg pointed out in his Stanford MedX talk and I’ve also talked about in this paper in The Journal of MHealth, having data provides an opportunity to get answers faster than using the traditional scientific method. Rather than formulating a hypothesis, setting up an experiment, collecting data, analyzing the data, and then going back to the drawing board if your hypothesis is not born out, data enables you to ask a series of questions and get immediate and sometimes surprising answers.

The panel kicked off with the sharing of some surprising things that we’ve found from the data,  ranging from which mental health tools were favored by different populations to the ability to predict hospital readmissions. In addition to finding trends from explicit patient input, we also discussed the ability to draw insight from activities including social media and mobile usage patterns. Swatee mentioned the Instagram analysis that showed color scheme on photos was a predictor of depression.

The ability to combine both passive and active patient-generated data, and draw conclusions from broad date sets these data sources can help to deliver better care – resulting in what Daniel Newton referred to as “small data.” That is, I’m going to learn as much as I can about you, and then tailor care to you, which is the approach Accolade takes.

As with any talk on tracking and data, questions of privacy came up. While all the panelists thought that there have become standard terms for people to opt-in to sharing health data, describing the use of that data was deemed important. At this point, Ang Sun from Cambia (who admitted that, as a healthcare plan, they had a heck of a lot of data on people), mused that he wished his physician knew as much about him as Google did. Generally, there was consensus that, if the purpose of the data sharing was for connecting people with the appropriate healthcare services, people would opt in.

Our panel was pretty aligned on the idea that there is big value in big data for healthcare, but that the general applications and usage are still in early days. First, there are the privacy concerns and even laws. Second, current healthcare organizations using this first generation of EMRs have limited ability to look at aggregate data for trends. However, with new technology and personalized approaches to care, we see great promise in big data and predictive analytics for healthcare.

Posted in: Clinical Research, Healthcare Research, Research, Seattle

Leave a Comment (0) →

MHealth and Big Data Are Catalysts for Personalized Patient Care

Although there are many complexities wrapped around our healthcare system, Stanford University’s 2016 Medicine X Conference starts finding solutions to improving patient care by focusing on increasing patient engagement and transforming how patients are treated in the system.

Wellpepper CTO Mike Van Snellenberg, who spoke at MedX in September with digital health entrepreneur and physician Dr. Ravi Komatireddy, addressed several important aspects of big data collection.

“Collecting big data is like planting trees. You need to plant the seed of the process or tooling,” says Van Snelleberg. “Over time, this matures and produces data.”

Mr. Van Snellenberg, who has collected and analyzed patient data at Wellpepper, discovered several key aspects of data collection that could improve care continuity for both patient and providers. He shared this to his MedX audience.

img_5792

“Wellpepper has already uncovered new understandings about which patients are most adherent as well as indicators of readmissions,” says Van Snellenberg. “That’s very valuable information.”

“We’ve discovered that, as you collect patient-generated data, these types of insights as well indications about the effectiveness of certain clinical protocols will be available to you. This will help allow for providers to encourage positive patient behavior,” he stated.

Mr. Van Snellenberg spoke further at an interview in October about collecting and using patient-generated data.

 

Question: What groups can benefit off the collecting of big data?

Snellenberg: Collecting patient-generated data can ultimately produce better outcomes and patient care for hospital and clinics as well as the patients themselves. The more in quantity and detail, the better it is to help produce good results. Data collection has tremendous value that can allow hospitals and clinics to learn more about their patients in between hospital visits, thereby filling in missing gaps in patient information. We also realized that collecting big data can potentially prevent complications or readmissions by identifying warning flags before the patient needs to return to the clinic.

And as mentioned, analyzing big data has provided us insights about which patients are most adherent. For example, we have found that patients with 5-7 tasks are adherent while patients with 8-10 tasks are not.

 

Q: What are some things you have discovered using patient-generated data?

MS: We were able to make observations on the patterns. We also discovered a strong linear correlation between the level of pain and difficulty of patients.

Traditionally, patient data remained in the hospital. This often left big gaps in knowledge about the patient in between hospital visits. By collecting and data in between visits to the hospital, you can discover important correlations that would not have been discoverable without data.

 

Q: What are some possible methods to collect patient data?

MS: Dr. Ravi Komatireddy, who worked in digital health, suggested several programs such as Storyvine and AugMedix.

Usually, data is collected by patients recording symptoms and experiences on a daily basis in a consistent manner and then managed afterwards. For example, patients themselves tend to keep track of their progress in diaries or using the FitBit to record the number of steps and heart rate.

 

Q: What are some of the most unique aspects about this year’s MedX?

MS: One unique aspect about the MedX Conference is that it provided more opportunities for diverse voices to be heard in addition to health professionals – including a mix of health patients, providers, and educators.

The mindset was also encouraged to change. Some of the convention’s most progressive talks on stage happened when phrases such as “How might we…” and “Everybody included” are brought up in the discussion.

The term “Everyone included” came up most often, pushing for more perspectives outside of JUST the physicians. MedX’s solution-oriented focus proves to be heading down a successful route to improving patient care in the healthcare system as well as acting as the initiative to open doors for new voices to be heard.

Posted in: Clinical Research, Healthcare motivation, Healthcare Research, Healthcare Technology, Outcomes, patient engagement, Research, Seattle

Leave a Comment (0) →

A CJR Primer

Recently, I had the opportunity to attend a CJR Bootcamp put on by the Healthcare Education Associates in Miami, Florida. The boot camp setting was intimate, collegial, and well targeted. With the exception of a trio of cardio folks who wanted to get ahead of their bundles, all attendees were directly responsible for implementing bundles at their health systems . The two days were jam-packed with information ranging from understanding the legislation to influencing surgeon behavior to assembling a great team to implement CJR. I recommend that if you’re on the hook for bundles in your organization that you check out this or a similar training yourself.

There is too much to recap in a single blog post, so I’ll share some high-level takeaways:

Bundles Are Complex

Even advanced organizations had gaps in their knowledge and understanding when it comes to the complexity associated with bundles. CMS continues to evolve the requirements and guidelines, causing some implementation approaches to have to rely on predicting what’s going to stick.

For example, the original PRO guidelines were for HOOS and KOOS, which have now been changed to HOOSJR and KOOSJR. If you’re concerned about requirements changing, consider adopting requirements that will benefit you even if they change. Organizations that started tracking HOOS and KOOS have a leg (or knee or hip) up because they have historical outcome data and have hopefully streamlined their processes.

Bundles Require Multi-Disciplinary and Multi-Organizational Teams

Within an organization, you’ll need a multi-disciplinary team that includes clinical, administrative, operational and finance, technology, procurement and so on. You’ll also require an executive sponsor who will make sure senior leadership is aware of and supporting your initiative.

A recommended working group looks like this:

  1. Executive Sponsor(s)
  2. Physician Lead
  3. Project Manager(s)
  4. Care Navigator/Care Coordination Lead
  5. HER/IT Lead
  6. Data Analytics & Quality Leads
  7. Compliance Lead
  8. Legal Lead
  9. Communications Lead
  10. Gainsharing Program Support

You’ll need to be skilled in both project management as well as the ability to influence change. Consider all the stakeholders that need to be influenced – who are the best people to influence them and how?

Think about the rhythm of communication to different stakeholders. Too much and you overwhelm. Too little and people aren’t part of the process.

 Influencing Surgeons

One of the sessions focused on how to change behavior of surgeons. It was presented by Claudette Lajam, M.D. Assistant Professor of Orthopedic Surgery Chief Safety Officer at NYU Langone Orthopedics, who had the task of decreasing costs for implants and improving quality by getting Langone’s to use the right selection criteria. Dr. Lajam studied behavior change theory to implement the change, but it came down to understanding surgeon behavior. She presented them with data, and encouraged competition: each surgeon was able to see in a weekly report where they stood with respect to costs and quality against everyone else in the department.

img_0095

In the new model, hospitals are responsible for gain sharing with both upstream and downstream partners where they have less influence and insight. Understanding your top performing orthopedic and skilled nursing partners is key to a successful bundle. In some areas, this risk-and-gain sharing is causing consolidation where orthopedic groups are joining hospitals.

Note that with CJR, different from BPCI, conveners are not allowed. That is, hospitals can only share risk with orthopedic groups and skilled nursing facilities. Organizations that offer to manage your program and share the risk are not allowed to participate in any gain sharing.

Bundles Need Data: But People Don’t Have It

If you need to improve outcomes and lower costs, you need to know where you’re starting from.  To know where you’re starting from, you will need lots of data so that the impact of outliers is harmonized. Not many organizations have this level of detail across their entire pathway, either from organizational challenges or challenges of the system.

Sometimes, this is from a variation of care. For example, one surgeon has most of the complex cases, or another surgeon uses a different combination of implants and auxiliary materials.

Sometimes this is from the challenges of inter-organizational communication. For example, the handoffs between hospital and skilled nursing are notoriously bad – usually with hospitals not knowing where their patients ended up and skilled nursing not knowing why they are there.

Add to this that you can’tthis on top of not being able to find out if a patient is even in the CJR bundle for a period until the CMS data comes back.

So, you’ve got a complex challenge, with large and heterogeneous teams and organizations, and a lack of data. What do you do? Give up? Of course not.

First, attend a boot camp like this one.

Then, treat every patient like they are in a bundle and work on improving outcomes.

Finally, take a look at your position, risk, and low hanging fruit. Even if you only have a few patients in the bundle today, the private payers and self-insured employers are monitoring this closely.

There is Low Hanging Fruit

There are a few areas that have been identified as opportunities to lower costs without impacting quality:

  • Inpatient rehab has been targeted, and often cut. Patients need to get moving soon after surgery, but they may not need as many sessions with a PT directly. We have patients who are following their PT care plan through Wellpepper even in an inpatient setting.
  • Standardization and optimization of implants. Often the implant companies charge separately for each component for the implant and try to upsell on items like screws. Negotiating a standardized bundle can decrease costs here, as can evaluating patients for the best joint for their situation rather than using the surgeon’s favorite. (This was the project undertaken at NYU Langone.)
  • Decreasing the length of inpatient and skilled nursing stay. Equipping patients to be more self-sufficient with joint camps, educational materials, and mobile care plans can enable them to go home faster.

You are Here

Possibly because it’s early days and people are still figuring this out, there isn’t a consistent, phased approach to rolling out the CJR bundle. In fact, you can start anywhere. Or maybe you don’t have to.

First off, make sure you’re in one of the X areas where the bundle is being rolled out. If you are, find out who else is in your region. Your cost accountability is for the average for your region. If there are big spenders in your region, you may already be delivering total joints more effectively than others and may not need to change much besides starting to collect PROs.

Also, take a look at your Medicare population for joint replacement. If it’s low, you may only have a few patients that qualify for the bundle each year – which doesn’t mean that you shouldn’t strive to improve, but it may impact the amount of effort you put in initially.

Figure out where you are today and plan your efforts accordingly. Don’t try to do everything at once and understand that both your process and the information available will continue to improve.

Good luck!

Posted in: Behavior Change, Clinical Research, Healthcare Legislation, Healthcare motivation, Healthcare Research

Leave a Comment (0) →

Finding Change and Honesty at Mayo Transform Conference 2016

mayo-clinic-logoAlthough the theme of this year’s Mayo Transform conference was “Change,” it might as well have been dubbed “Honesty.”

From keynotes to breakout sessions, there was a raw sense of honesty and acceptance of the fact that change is hard, and we’ve reached a point where the evolution in healthcare doesn’t seem to be happening fast enough.

When you’re as successful as Mayo, it might be easy to brush failure under the rug – which made this session, “We Made This Thing, But It Didn’t Go as Planned. Now What?” unique. Now that some of the initial hype for digital health has died down, we are in a phase of realistic optimism where sharing both wins and misses represents a realistic way forward.

This interactive session in three parts by Steve Ommen, MD, Kelli Walvatne, and Amy Wicks unfolded a bit like a mystery. Questions were posed to the audience at each phase for our input on what might have gone right and wrong. Not surprisingly, the attentive audience proved as capable as the presenters, and some of the most valuable insights came from the audience questions.

The case study in this session was a three-year process to develop a new interface and workflow for the cardiology clinic. Dr. Ommen and the other presenters did not tip their hands to whether the project was successful or not, and we had to tease out the wins and losses that occurred during each phase.

The presenters shared stories, but did not show any artifacts of the process such as flow diagrams, screenshots, or personas. This methodology was effective because, instead of getting bogged down in critique of particular elements, we were able to see the bigger picture of challenges that could apply to any innovation or clinical change.

At the end of the session, the presenters summarized their top takeaways as:

  • Not having enough credibility and evidence

Much of the Transformation team were experts in design, but not necessarily the clinical experience for this service line. There were some misunderstandings between what could work in theory and in practice, although the team did identify areas of workflow improvement that saved time regardless of whether the technology was implemented.

  • Change fatigue (or “Agile shouldn’t be rigid”)

The team tried to use a lean or agile methodology with two-week product sprints: iterating on the design and introducing new features as well as interface changes biweekly. This pace was more than what the clinical users – especially the physicians – could handle, but the design aimed to stay true to the agile process. In this situation, the process was not flexible to the needs of the end users and possibly exacerbated the first point of lack of credibility.

  • Cultural resistance

The team lost champions because of the process. It also seemed like they may have spent too much effort convincing skeptics rather than listening to their champions. One physician in the audience wondered aloud whether the way physicians were included in the process had an outsized impact on the feedback the team received about what was working and wasn’t working. From his own experience, he noticed that a physician’s authority is often a barrier to collaboration and brainstorming.

From audience observations, it seemed like there may have been some other challenges such as:

  • Scope/Success Definition

There wasn’t a clear definition of success for the project. While the problem was identified that the current process was clunky and the technology was not adaptive and usable, not all parties had a clear understanding of what constituted success for the project.

Looking back, Dr. Ommen suggested that rather than trying to build a solution that addressed all co-morbidities, they should have chosen one that worked for the most common or “happy path” scenario. The too-broad scope and lack of alignment on goals made it challenging to conclude success.

  • Getting EPIC’ed

When the project started, the team was largely solving for usability problems created by having two instances of Cerner and one of GE used in the clinical workflow. During the course of this three-year project, Mayo made the decision to ink a deal with Epic, rendering the current problem they were solving for obsolete.

Going for a smaller win early on might have delivered value to end users before this massive shift in the underlying medical records software.

So what happened?

You can probably tell from the recap that the project was shelved. However, the team did have some wins, certainly in their understanding of how to better run a project like this in the future as well as in helping the clinical team optimize their workflow.

What should you take away?

Know your users, iterate, and move quickly to deploy quick wins – but not so quickly as to alienate your stakeholders.

Finally, ask your peers: we’re facing similar problems and can learn together.

Posted in: Clinical Research, Healthcare motivation, Healthcare Research, Healthcare transformation, Outcomes, Research, Uncategorized

Leave a Comment (0) →

Let’s Talk About Poop

The ups and downs of the first two keynotes at the 2016 Mayo Transform Conference were mirrored in the session The Challenges of Change which highlighted the story of Cologuard. Cologuard is a joint venture between Mayo Clinic and Exact Sciences whose sole goal for the venture was to create a less invasive way for early detection of colon cancer. They succeeded in this goal and were also the first product to receive FDA clearance and CMS reimbursement on the first day. Cologuard launched to much fanfare on national news.

Did they knock it out of the park? Yes. Are they wildly successful today? No. Why? Keep reading and I’ll tell you.

First let’s start with the problem. Colonoscopies, while effective, are not favored by most people. The preparation is extremely uncomfortable, they require general or partial anesthesia, and people need to take time off work. In addition, in some remote communities, it is difficult to get access to care from specialists. As a result, people put off or skip getting colonoscopies and by the time cancer is detected it is often too late. A clinical challenge with colonoscopies is that they are good at detecting left-side tumors but not right side tumors, the incidence of which has been increasing since the 1980s.

CologuardCologuard solves all of these problems. The test is designed to be used at home and is basically a nicely-packaged stool collection kit combined with specialized testing at Cologuard’s lab. No time, and no procedure required for an individual. As well, Cologuard is more effective than colonoscopy at detecting right side tumors, and comparably effective at left-side tumors. Since it’s a home collection, and all tests are processed at Cologuard, access to care is not an issue either and it’s widely used in the Alaska Native Tribal Health Consortium, which was presented as a success story.

Sounds great, yes? Everyone (aka people who at some point will need a colonoscopy or have already had one) I talked to about it thought so. So what’s the problem? As usual, what’s preventing this innovation is an issue of reimbursement. Colonoscopies are a profit center for healthcare organizations, and they are effective, so this isn’t necessarily a case of a better technology losing. It’s the case of a more patient-friendly technology losing, except in Alaska where there really isn’t a viable option for delivering colonoscopies. As well in violation of CMS, some payers are refusing to cover Cologuard.

Cologuard CEO Kevin Conroy was evasive when asked about pricing, which is more expensive than other screenings but pales in comparison to the coimg_0060sts of a procedure that requires booking an operating room and an anesthesiologist.

Let’s hope that a shift to value-based care changes this. From a patient’s perspective it can’t come soon enough.

PS Apparently a lot of single Cologuard kits are being ordered by cardiologists and other specialists. Conroy thinks they’ve recognized the value and are using the kits on themselves. Harrumph.

Posted in: Clinical Research, Health Regulations, Healthcare Disruption, Healthcare Legislation, Outcomes, Patient Advocacy, patient engagement, Patient Satisfaction

Leave a Comment (0) →
Page 1 of 2 12
Google+