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 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.

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.

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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


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