Partnering with 23andMe for a Population Health Initiative

Renown Health’s recent partnership with 23andMe and the Desert Research Institute has been an astounding success. Just little over a month ago we announced the program which offered free personalized health and ancestry information to over 5,000 Northern Nevadans. We expected it would take two days to get that many people to sign up. You can imagine our surprise when the day after the launch of the event, all 5,000 slots has been filled up! So we decided to open up another 5,000 slots, which filled almost equally as fast.

Some reasons to which the success of this event could be owed would be the cost (it’s one hundred percent free for participants), the perks of genetic analysis (it’s something that many people are curious about, but would not think of paying for), and the simplicity of our system (all that is required is that participants show up, watch an informational video about the project, sign some forms, and spit into a tube). There’s also the additional incentive of knowing that those who participate are contributing to the overall benefit of the Nevada community.

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via Renown Health

Through our work with 23andMe, Renown Health now has access to over 10,000 samples of genetic analysis. By combining this data with our data on the medical histories of over 300,000 people, we will have a better overall picture of the health of those in Northern Nevada. The Desert Research Institute will draw even more insights out of this data by looking at it in conjunction with a number of social and environmental figures. The overall goal of all of this data-crunching is to figure out how different environmental, demographic, and environment factors interact. Once we have a better idea of this, we will be able to better allocate resources to those who need them most and may even be able to stop illness before it happens.

This data-gathering, data analysis, data sharing, and all around cross-organization collaboration truly epitomizes what population health can become. I’m very proud of everyone who has been involved in this initiative–from the participants, to the researchers, to the countless administrative workers who have made this possible. Additionally, this project would not be possible without the generous support of Governor Brian Sandoval and the rest of the government of Nevada.

I’m also taken aback at how warmly the community has received this project. For instance, KUNR gave Joe Grzymski of 23andMe a great interview and the Reno Gazette-Journal has provided us with some glowing press. We’re taking steps toward the future of medicine.

Mark Behl

Mentrics: Redefining the Possibilities of Population Health

IBM Watson is one of the most exciting developments in population heath. It has an unprecedented capability for processing high volumes of data and turning that data into natural language answers. IBM Watson is like Siri on steroids. Through partnering with a number of organizations, IBM has been able to increase the range and power of the Watson. One of the most recent partnerships in this direction has been its partnership with ODH Inc. The progeny of this collaboration is called Mentrics.

Mentrics gives Managed Care Organizations (MCOs) and Behavioral Health Organizations (BHOs) like Renown Health the ability to “understand the whole population, segment the population needing intervention AND manage and monitor the key determinants of health for each segment.” Not only will Mentrics give population health organizations more data to work with, it will make providing healthcare cheaper and more streamlined–specifically when targeting those segments that suffer from a chronic comorbidity.

The Cost of Comorbidity

When patients have a physical and a comorbid (coinciding) mental health disorder, treatment becomes more costly and often less effective. For instance, a patient with diabetes and depression has a 200% higher mortality patient compared to a patient who only has diabetes. Furthermore, individuals with coronary artery disease and depression are 2 to 3 times more likely to incur a future cardiac event. Those who fall into this segment of comorbidity of chronic physical and mental health cost 300% more than those with only only chronic health condition.

Bottom line: the greatest healthcare expenditures are going to a small group. We need a way of addressing this specific group. Big data, population health, and specifically Mentrics is the answer.

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Mentrics in Action

HITConsultant.com has identified three specific areas in behavioral health that Mentrics will benefit

1) Behavioral Health Population Management

Using 22 dimensions including a model for behavioral health specific risk stratification (the only commercially-available one on the market thanks to IBM Watson), Mentrics will be able to create clinically meaninful poplulation segments.

Bottom line:  This will help medical professionals to identify which segments will most likely benefit from interaction.

2) Provider Network Performance

With Mentrics, providers will be able to assess their own effectiveness and efficiency, allowing for a comprehensive comparing of providers and an analysis of the entire provider network. This will in turn work towards value-based payment arrangements.

Bottom line: Money will be allocated more accurately, efficiently, and economically.

3) Patient Care Coordination

Using a user-friendly, “Longitudinal Data Visualization tool”, cross-network providers will be able to notice detailed patterns in medication use and clinical history. Furthermore, providers will be able to receive notifications based on evidence-based care gaps like patients receiving duplicate prescriptions and providers will be able to customize cross-network care via these notifications.

Bottom line: Providers will have a more holistic view of patients and will have an interoperable system in place for communicating with other providers.

Realizing the Vision of Population Health

Years ago, population health was a vague though ambitious field. It promised an integrated cross-network system that optimized patient outcome and reduced patient cost. Yet, it seemed that there was no shortage of hurdles blocking population health from ever realizing its vision.

But recent developments in digital health, such as Mentrics prove that there is indeed hope for an ubiquitous population health model. Mentrics provides providers with unprecedented awareness of the millions of variables that go into a healthcare system. Now it’s just a matter of this brilliant software finding its way into practice.

How Apps and Wearables are Changing the Population Health Landscape

At the center of modern population health practices is data. Traditional forms of gathering data include culling medical records, analyzing ever scrap of demographic information available, looking at medical studies conducted, and gleaning information from other people/systems/organizations that are compiling their own health databases. But the advent of smartphones and wearable apps allows a whole new sort of data collection. It allows access to real-time, uniquely tailored data for an individual, and at the same time (when enough individuals are using the app, mobile apps and wearables) allow access to lots and lots of numbers. Numbers that can change the landscape of population health for a community and for the world.

Health Apps

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Northwestern’s Intellicare app suite opens a new door into quickly and easily collecting data on mental health.

From Apple’s Healthkit to Samsung’s SHealth, apps that help people measure their fitness activity and diet are becoming more and more popular. But we’re also seeing the emergence of a level of unprecedented apps, such as Northwestern University’s Intellicare  app suite that focuses on mental health. Using a variety of approaches to treating mental health based on Cognitive Behavioral Therapy (CBT), the Intellicare suite holds the potential to provide data on mental health from a variety of patients. Although not a complete substitute for face-to-face interaction, the app suite grants patients an accessible and cheap first line of defense resource. Yet another a tool in the value-based healthcare model.

Wearables

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Wearables adoption is set to double from from 2014 to 2019.

A recent study in 2015 indicates that patients with multiple sclerosis were much more likely to engage in an activity tracking physical fitness with wearables. Over 24o patients enrolled in the study to track their activity with Fitbits and 77% made it all the way through to the post-study follow-up survey. For starters, wearables are cool. Just like it’s fashionable to have a smart phone, it’s fashionable to have a Fitbit. According to a survey conducted by RBC in 2015, Fitbits are one of the most in-demand wearables out there.

And the wearable market is only going to grow. The investment firm Piper Jaffray expects that from 2014 to 2019, wearable technologies are expected to nearly double in adoption across the world. They point out three trends behind the widespread growth of wearables (which could be attributed to health apps as well):

  1. Health and fitness becoming a growing concern among individuals in more and more demographics
  2. People wanting to be able to quantify and analyze more and more of their activity
  3. Convergence between brands and tech e.g. UnderArmor acquring MyFitnessPal, etc.

Privacy Concerns

As more and more consumers use apps and wearables, more data becomes available. But as the availability of data grows, health organizations and other businesses need to make sure that they are harvesting this data in ethical fashion. There have already been several law suits in regards to companies not being upfront about their privacy policy. Moving forward, companies need to work with consumers’ full awareness and trust if they are to make the most out of the data available.

What are your thoughts on apps and wearables? Is there a particular company or product that you think is making strides? Let me know @MarkBehl.

How IBM Watson is Revolutionizing Healthcare

If you’ve been following big data the past several years, chances are you’ve heard of IBM’s Watson. To recap, Watson is IBM’s revolutionary commercial computing capability that utilizes the cloud in order to process high volumes of data and then turns this data into evidence-based answers when presented with questions in natural language. Put simply, Watson is a cloud-based supercomputer that companies can make use of. Watson’s ability to comb the cloud for data makes it a catalyst for great change in the healthcare industry. In fact, in 2015 IBM launched IBM Watson Health and the Watson Health Cloud platform specifically designed to assist physicians, researchers, and insurers in harnessing the great amount of personal health data being circulated around the cloud.

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IBM Watson is revolutionizing how the world analyzes health data.

IBM’s recent collaboration with Welltok and and Pathway Genomics illustrate two ways that Watson is revolutionizing population health management.

Welltok and Improving Heart Health

In line with a number of other initiatives this National Heart Month, IBM has revealed plans to team up with the social health management company Welltok and the American Heart Association (AHA) in order to develop workplace technology that improves heart health. The unnamed app will make use of AHA’s Workplace Health Achievement Index, which uses best practices “to measure and rank corporate health initiatives” and give an overall assessment on workplace health culture.

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Welltok’s most recent project comes on the heels of a similar workplace health platform developed specifically for IBM offices.

Employees could take advantage of Welltok’s platform by filling out the AHA’s My Life Questionnaire, at the core of which is the AHA’s Simple 7 key cardiovascular health indicators: not smoking, eating healthy, being physically active, achieving and maintaing a healthy weight, managing blood pressure, controlling cholesterol, and reducing blood sugar. Using the data from this questionnaire, as well as that from other of IoT devices–from wearable fitness trackers to wifi-connected scales–Welltok would be able to tailor health recommendations to the individual needs of each employee.

Optimal population health management is all about pooling resources. It’s about pooling data.  In this case, we see how IBM’s computing capability, Welltok’s health platform, and AHA’s metrics would all work together for better healthcare outcomes.

Pathway Genomics

In January, IBM teamed up with Pathway Genomics teamed up to create another personalized healthcare app. Instead of targeting workplace heart health, Pathway’s OME makes use of information from Pathway’s “FIT” Test, which takes a look at a number of metrics including exercise and genetic predisposition, and pulls metrics from a number of IoT sources, such as wearable health monitors and Apple HealthKit.

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Pathway Genomics’ OME delivers health recommendations based on your genetic traits.

Critical to this platform is the analysis of genetic traits and their effects on health. After receiving approval from a licensed physician, participants receive a saliva DNA collection kit. Pathway uses this to conduct their FIT test, which looks at 75 genes that deal with a number of health factors. The insights gained from the FIT test are then incorporated into personal wellness plans.

More to Come

Both the Welltock and Pathway Genomics collaborations with IBM Watson illustrate the boon of integrated systems in population health. But we’re just scratching the surface. Welltok’s platform focuses on workplace health and Pathway Genomics’ OME focuses on genetic predisposition, but what if the two technologies could work together instead of separately? What if both genetic predisposition and genetics were compared in one’s personalized healthcare recommendations? We’re well on our way to finding out.

Population Health Platforms

The purpose of population health management is manifold: it helps improve the the health outcomes of people by improving the quality of care, increases preventive care, and provides access to better care. The golden promise is that healthcare providers will be able to deliver better care at a lower price. In order for this to happen, caregivers at all sides of a patient need to be interconnected with a digital framework.

As mentioned in another blog post, this past year has been a booming time for digital health companies that harness population health. There are a number of ways that digital health companies can approach population health. Below is a breakdown of the platforms that make up population health management as detailed by Miguel McInnis, the founder of McInnis & Associates, a healthcare management and consulting firm. As with any attempt at categorization, there is some overlap.

NueMd mark behl

Population health intelligence platforms can provide plan administrators and care teams with cloud-stored access to extensive financial and clinical information, and access clinical data from a number of sources. They may also link to other population health platforms like risk stratification, hospital admission data, and referral data. Example: Conifer Health Intelligence

Medical Management systems combine people and information to effective and personalized services for acute care management, chronic care management, etc. These systems use accurately integrated data to identify at-risk patients, track results, analyze care and support wellness management. Example: NueMD

Risk Stratification tools identify different population needs across all levels of risk. WIth this info, providers can determine appropriate courses of action with which to approach the needs of a population. Of prime importance here are demographics, medical conditions, cre patterns and resource utilization. From here, patients can be stratified into five main categories: episode of care patients; high risk patients; chronically ill patients; healthy patients but with conditions; and healthy patients. Example: HExL

Patient Engagement services AthenaHealth mark behlhelp help patients take part in their own healthcare. The goal here is to help create a supportive, long-term relationship with a patient using third-party data to figure out the needs of patients and facilitate more effective relationships with providers. Example: AthenaHealth

Predictive Analytics tools model medical conditions within a population to identify high people who may be risk. By identifying these patients ahead of the curve, predictive analytics can prevent these people from needing to shell out for expensive healthcare. Example: Evolent Health

Better Patient Access platforms help patients interact with providers. This is especially useful when patients have poor access to healthcare. One way of doing this is with telehealth which ties together some of the above platforms in order to provide not only better care to patients, but also better training to providers. Example: Doctor on Demand

 

For the full article, check out Forbes.com.

 

Getting the Bigger Picture in Population Health

When it comes to ensuring the healthcare of an individual, leveraging data is helpful. But when we it comes to addressing the healthcare of whole communities that can range from a hundred to several billion people, harnessing every ounce of data is down-right essential. Still, the integration of data in population health still faces a number of hurdles. A recent article from Health Data Management touches upon some of the challenges. In this post, I extract and further explain this challenges and illustrate how they might be overcome.

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Explorys.

Not all of the data needed for clients can be gathered from a quick office visit or simply by perusing a database. There are a lot of gaps in current databases and so the challenge there is in 1) determining what those gaps are and 2) addressing them.

For instance, determinants like access to clean water, access to good food, access to a gym, reliable climate control, and even whether or not someone has the right clothing to keep them warm enough when walking about town during winter are all factors that need to be considered. If a client doesn’t have access to any of the above, that will have an effect on the likelihood of them being admitted and/or readmitted to a hospital. Getting data in these fields will help providers increase the overall well-being of their patients by looking at care as something that transcends the hospital/clinic/office.

One example of a company that is looking to overcome this gap is Exlplorys. Working with 23 health systems that represent about 360 hospitals, Explorys’ database contains claims and clinical data on 55 million patients, which can in turn be utilized by clients to organize patient populations by risk.

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Evolvent Health

Another company that is doing great work is Evolvent. Starting with work at the University of Pittsburgh Medical Center, Evolvent now helps organize analytics for almost a million patients in 25 markets.

Companies like Explorys and Evolvent incorporate data from a variety of sources, including air quality data, census data, and real estate information, drivers’ license data, and credit scores. Indeed, the sky is (almost) the limit when it comes to where data is pulled from.

Population healthcare is about leveraging data to provide the best healthcare possible. To ensure that providers are providing the best healthcare possible, it would seem that no rock of data should be left unturned. Yet, as mentioned in a previous post, it’s not just a matter of gathering data–it’s about gathering relevant data.

I’ve already managed the Health Management News article above. But for more great information on this topic, check out Forbes.com.