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Posts Tagged Big Data

HIMSS17 Sessions of Interest

We are thrilled to attend a number of sessions at HIMSS17 with topics pertaining to Wellpepper’s Vision and Goals!

Patient Engagement

Sessions that impact our ability to deliver an engaging patient experience that helps people manage their care to improve outcomes and lower cost:

Insight from Data

Sessions that impact our ability to derive insight from data to improve outcomes and lower cost:

Clinical Experience

Sessions that impact our ability to deliver more efficient experience for existing workflows and are non-disruptive for new workflows:

 

Posted in: big data, Healthcare Technology, Interoperability, M-health, patient engagement

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Population Health and Patient Engagement: A Reckoning Is Coming

Population health and patient engagement should be best friends. To draw conclusions for population health, you need a lot of data, and patient engagement that is, patients interacting digitally with treatment plans and healthcare providers, generates a ton of data. Population health tries to analyze the general to get to the specific and identify patients at risk. Patient engagement starts with the specific patient, and with enough data recorded by those patients, can find general trends.

With patient engagement, the information is real-time. With population health it is backwards-looking. Population health has the richness of the medical teams notes and diagnosis but it is missing the patient perspective. Patient-generated data will have diagnosis if it’s part of a treatment plan prescribed by a physician, but it won’t have the full notes. A blurring of the boundaries between population health and patient engagement presents a way forward to greater insights about both individuals and groups, and can make population health actionable at the individual patient level by providing personalized instructions (with or without care managers).

However, to get to this desired end-state, we need to clear some obstacles, first of which is the idea that patient engagement generates too much data for physicians.

Yes, an individual physician does not want to see or review each data point that a true patient engagement solution generates. However, this information can be extremely interesting to the patient, especially when looking for trends to help self-manage a chronic condition so it is worth enabling patients to collect it. For example, looking at whether certain foods trigger arthritis, or whether certain activities trigger headaches. However, to draw conclusions like this, you must record a lot of data points and in real-time, and this makes physicians nervous. They have enough to do, and not enough time to do it in, so this data cannot add to that workload.

As well, patient-generated data is messy, which can be intimidating, especially in an industry that is looking for deviations from norms. The challenge with patient-generated data is that it can uncover that the long-tail is actually longer than previously thought, that there are sub-groups within previously thought to be homogeneous groups of patients with a similar condition. In the long run, this will result in medical breakthroughs and personalized medicine. In the short run this can be difficult to deal with in the current systems.

the long-tail is actually longer than previously thought

Does that mean that we shouldn’t collect patient-generated data? Not at all. Helping patients track their experiences is a great first step to self-management. Knowing whether they are following a treatment plan, and what their experiences are with that treatment plan can help healthcare systems determine the impact of their instructions outside the clinic.

Although physicians don’t want all this data, healthcare organizations both providers and payers, should want it. Other industries would kill for this type of data. Data scientists and population health managers at health systems should be clamoring for this valuable patient-generated data.

Patient-generated data is usually collected in real-time so it may be more representative of the actual current population. The benefit of real-time collection is that further exploration of the actual patient experience is possible and can be used to prevent issues from escalating. With backwards looking data whatever was going to happen has happened, so you can only use it to impact new groups of patients not current groups.Patient-Generated Data

Finally, patient-generated data is less likely to be siloed, like clinical data often is, because the patient experience is broad and often messy and crosses clinical department thresholds (or more simply, patients are usually treated for more than one issue at a time.) Being relatively new to market, patient-engagement systems are built on modern and interoperable technology which also makes accessing data for analysis easier.

So where will we end up? To our team at Wellpepper, it seems inevitable that influencing and understanding patient experience outside the clinic. If you are making decisions for an individual patient with only a few clinical touch points, this is a very thin slice, often with a specific clinician’s specialty lenses on the actual situation. While healthcare systems are currently dipping their toes in the water on collecting and analyzing this data, if they don’t embrace the whole patient, patients will vote with their feet and pocket books towards organizations that are data and technology driven.

Posted in: Adherence, big data, Healthcare Technology, Healthcare transformation, Interoperability, M-health, patient engagement, population health

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Wellpepper Top Healthcare Blog Posts of 2016

We had a terrific year at Wellpepper and are anticipating great things in 2017. We’re looking forward to further improvement in the efficacy and effectiveness of mobile health and telehealth as well as advancement of new business models, value-based care, and interoperability between EMRs.

As we move forward, we’d like to take a moment to reflect and recap some of our most popular blog posts of 2016. In order of popularity they are:

Wellpepper Healthcare Christmas Wish List

Given the rush of the holiday season, it was a pleasant surprise to have gotten so many viewers (other than Santa) looking over our healthcare wish list, making it our most popular post of the year.

Not Patient Engagement with Jan Oldenburg

Unsurprisingly, our second most popular blog post happens to discuss a variety of topics ranging from shifting the healthcare mindset to utilizing digital tools to assist physicians, with nationally recognized consumer health information strategy leader Jan Oldenburg in this lively podcast that has listeners eagerly tuning in.

What’s True Now

With the uneasy condition of health systems and polices following the recent changes in leadership after the election, we are glad to see many turning to our blog post for some clarity. Will these factors remain true for the following years to come? We certainly hope so.

Better Living Through Big Data

We love sharing with our readers what we’ve gathered from panels and talks. This summary of our CEO discussing the benefits of collecting big data with the Seattle Health Innovator’s panel made this blog post our fourth most popular.

What Keeps Healthcare CEOs Up at Night

Last but not least, this recap of MATTER’s study about Accenture made our Top 5 by addressing the important values and actions that need to be implemented by healthcare CEOs in order to take a more patient-centered approach.

This next year, we are looking forward to sharing our new discoveries as we continue to tackle the challenges in healthcare and find more ways to improve mobile health and patient-centered technology.

Posted in: Healthcare motivation, Healthcare transformation

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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

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