Welcome to SMART Health
for Better Living

Our SMART Technology

By utilizing Deep Learning, Marpai achieves the dual goals of lowering the cost of claims for self-insured companies and maintaining excellent healthcare outcomes for members. We generate and collect troves of data from all aspects of our business, including healthcare claims, member engagement, internal operations, and customer experience. We systematically apply intelligent automation and AI to delight our end users, improve healthcare outcomes, and lower costs.

We use artificial intelligence in virtually every part of our business, including within our core systems as a third party administrator. These components include:

  • Claims management that enables accurate auto-adjudication of most claims, which reduces our operating costs and increases our operating efficiency. This also includes detecting and flagging suspicious claims before final adjudication.
  • Member engagement and self-service options via an intuitive and easy-to-use application, which also lowers the cost to serve employers and allows members to interact with us as often as they wish. We present the best providers in terms of quality and costs in a manner that is centered around the member’s needs and geography.
  • Care management with high-impact prediction via new, innovative AI modules, which helps us manage the overall cost of claims for our clients.
  • Continuous provider quality tracking enables us to identify the best providers as well as their usual and customary practices and related costs.
  • Smart plan design including cost optimization, performance monitoring, and risk prediction.

Our AI Capabilities: Deep Learning

In the last few years, we have witnessed the greatest leap in performance in the history of AI, largely due to a subfield of artificial intelligence known as “deep learning” (or “deep neural networks”). While traditional machine learning methods require extensive manual processing (e.g., for feature extraction), Deep Learning methods allow for “end-to-end” learning, as they directly operate on raw data and predict the final outcome.

The accuracy of Deep Learning-based models is significantly higher than traditional machine learning models, especially when more data and data types are available. This makes Deep Learning very relevant for big data platforms that involve large quantities of data as well as various types of data. Despite the huge success of Deep Learning in improving the fields of computer vision, speech recognition, and text understanding, few companies have the expertise to develop and deploy Deep Learning-based solutions due to the numerous technical challenges involved. We believe that is why the application of Deep Learning remains limited in the healthcare space, as it is mostly confined to use in medical image processing thus far.

We are creating a robust Deep Learning infrastructure that will allow us to analyze nearly any type of data. More importantly, this infrastructure is expected to have the capability to automatically train on multiple data types at once and obtain unified predictions from that data. Specifically, we expect our Deep Learning capabilities to be used for the following data types:

  • Structured data: This includes any form of data that results in a tabular database. Examples of such data include laboratory tests, claims data, and payment details to healthcare providers. Our Deep Learning models for structured data, which rely on fully connected neural networks, allow us to automatically train on any such structured databases and incorporate those insights into predictive models.
  • Images: While traditional image processing requires cumbersome development for each specific use case, Deep Learning-based models can directly operate on any image data without having to go through a preprocessing feature extraction phase. We are developing a series of Deep Learning models based on convolutional neural networks that could be extended to process medical images, including radiology imaging such as MRIs, CT scans, x-rays, ultrasounds, and PET scans, as well as other types of medical images, such as those used in dermatology, pathology, and ophthalmology.
  • Text: Traditionally, analyzing text data requires lengthy natural language processing, or “NLP.” Recent breakthroughs in Deep Learning, especially the advent of transformer networks (e.g., BERT), allow for the end-to-end training of language models, which results in significantly higher accuracy than previous NLP methods. Our infrastructure includes support for these Deep Learning-based language models, allowing us to automatically process textual data as well. A prime example of textual medical data is the plain text description of doctors’ notes, which include information about symptoms, diagnosis, and treatment. Even though some of this data, such as disease codes, appears in structured databases as well, the textual data contains a large amount of information that does not appear elsewhere.
  • Multi-type data: Deep Learning can also be applied to multiple data sources and data types. During the first stage, the relevant Deep Learning models are applied to each data type (e.g., CNN for images, Transformers for text, and so on), and then, during the second stage, the processed information from those various data sources is fed into a secondary Deep Learning model that provides unified predictions. This is one of the major advantages of Deep Learning over traditional AI, as it allows the incorporation of multiple big data sources into a single unified prediction model. This approach far exceeds the accuracy rate achieved by traditional methods.
  • Data fusion: With Deep Learning, we have the ability to study different data types together, such as data in the form of images (e.g., CT scans) with data in tabular form (e.g., figures from healthcare claims). Putting these different data types together is referred to as data fusion.

The SMART Health Plan Services System

Our SMART system is fully integrated with our user platform and can automatically deliver alerts to internal staff, members, and other stakeholders. It includes:

  • Mass Scale Data Fusion: Our system fuses massive amounts of data from various sources, and in many formats, into a single environment for analysis.
  • Data: Our system includes a unified healthcare schema that can ingest and make use of any healthcare data type including claims, social determinants of health, psychographic data, blood test results, pathology images, and radiology images.
  • A data lake: Our system includes a massive data lake with our unified schema that stores and structures healthcare data in order to enable analysis.
  • AI Models: Our system automatically applies our AI models to healthcare data to enable pattern recognition, advanced querying and anomaly detection.

The Future Sight Advantage and Proactive Health

Our AI capabilities in the area of Deep Learning enable us to make more accurate predictions about costly healthcare events using data types that are hard or impossible to understand with traditional artificial intelligence. This allows us to help our members prevent costly healthcare events while bending the cost curve for employers.

Our Deep Learning technology is able to process, analyze, and store myriad types of data on a large scale. Our AI models recognize patterns and detect anomalies, giving us greater insights into a patient’s medical trajectory. Simulating that trajectory allows us to intervene early with the best healthcare recommendations. The current data types our AI  technology uses to predict healthcare needs include, but are not limited to, the following:

  • Claims data
  • Social determinants of health and psychographic data
  • Blood tests and other laboratory results
  • Pathology images
  • Advanced images from X-rays, magnetic resonance imaging (MRI), and computed tomography (CT)
  • Electronic health records
  • Health risk assessments
  • Information gathered from applications used by members
  • Information gathered from wearables like Fitbit devices and Apple watches

Our innovative technology will help self-insured employers manage their employees’ healthcare needs proactively. Our AI-enabled predictions are presented as real-time alerts that our team then uses to guide the employee towards the best preventive care management providers at the earliest possible time. It also directs members towards  lower-cost but high-quality healthcare providers. The key components of this technology include:

  • Deep Member Profiles: We map a member’s health based on their medical history, demographic information, historical claims, and in some cases, what they shared with us in their own health risk assessments. All this data is fed through various Deep Learning modules and automatically processed.
  • Claims Trajectories: Our AI models have studied millions of claims and mapped out expected trajectories for members. The predictions are based on all the available data on each member. Currently, the data includes structured data, such as demographic information, historical claims, and laboratory test data when available. In the future, the data will include unstructured data, such as medical images, text-based assessments, and other types of health records. All this information will be fed first into Deep Learning models for each data type, and then into a unified Deep Learning model that finds patterns and insights across all data and data types available for the member.
  • Alerts: Our AI models come to life via alerts, which are triggered by events such as claims. Each alert represents a new data point for each member, and invokes the entire model to update its predictions. Deep Learning models are especially superior in finding non-linear patterns and correlations. For example, a new data point X — that seems to be completely unrelated to prediction Y — may actually affect it through complex non-linear patterns that are very difficult for humans and traditional artificial intelligence to find. However, Deep Learning models are very good at finding those patterns, and they in turn trigger new actionable alerts. These alerts allow the Care Guide team to start reaching out to an at-risk member.

For example, an alert may say that a member is likely to develop Type 2 diabetes in the next twelve months, even though that member is not currently diagnosed with the illness. Our Care Guide team would reach out to the member and recommend a visit to a primary care physician to try to find a a root cause or to drill down analysis. The doctor can then develop an actionable healthcare plan with the member.

Our AI-enabled predictions helps both members and their providers be proactive and get in front of costly events, so they can plan around them or even prevent them entirely. Our services deliver not just lower costs, but also better value for the money spent on healthcare. For this reason, they also greatly reduce waste related to care coordination and care delivery.

TopCare™: Matching members with high quality providers

Matching members with high-quality providers is a key component of our services. Provider quality is continuously tracked and measured by feeding various provider quality databases to our AI models. Monitoring provider outcomes, costs, and proper conduct allows our models to rate providers as well as measure cost efficiency. This information will allow us to assist members in making more informed decisions regarding their healthcare provider options.

Using in-depth insights from our AI models with data from laboratory results, imaging, and electronic health records helps us detect suspicious claims and potential over-treatment. These cases can be re-examined by clinicians and may be used to educate both members and providers, or otherwise guide members to better healthcare solutions. High-performing providers will be used by our AI models to develop the “gold standard” when it comes to pricing models and understanding what is usual and customary in terms of costs.

Given the high price variability for expensive medical procedures, we want to guide members to cost-effective but also high-quality providers. For instance, we may generate an alert for a member who is on a trajectory to have a knee replacement surgery in the next twelve months. In that case, we will educate the member early on regarding their options in terms of highly-rated orthopedic surgeons for that exact procedure within their geographic area. This may happen months before the actual surgery, and it involves the member’s choice of care. This helps reduce waste related to not obtaining the best pricing for healthcare options. Company health plans may also allow for incentives whereby the member’s co-insurance is reduced or eliminated if they select a high-quality and cost-effective surgeon.

For our self-insured companies, we use AI to design and optimize plans and costs in light of the historical data of its employee population and its predicted costly healthcare events. This information forms the building blocks of plan design, risk management, and plan optimization. Continuous monitoring of any at-risk members as well as measuring how mitigation strategies are working are also key components to lowering the overall cost of claims.

Part of the technology also tracks member outreach to see how and whether a member has responded, and what communication vehicles are the most successful for a particular member (e.g. text, email, or phone call). This data helps us tailor the outreach that best facilitates member responses and thus reduces waste related to care coordination and care delivery. The response is also fed through a feedback loop to Deep Learning models to not only improve predictions, but more importantly, the actionable alerts as well.

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