NLG for Healthcare Billing

Pitch: NLG for Healthcare Billing


Opportunity: healthcare bills are positively inscrutable.


Medical billing in the healthcare space is widely known to be overly complicated. Both the end consumer and the service provider have to deal with a painful process in order to properly consummate the purchase (and delivery) of healthcare services. For the consumer, the billing and coding process of the medical services they received is completely jargon based and not understandable. On the service provider side, code accuracy (entered by the practitioner’s administrator) is more often than not a key driver of improper payments.


An inscrutable medical bill… $165 for what?! (Table 1)


The problem of healthcare bill non-payment is massive. According to Reuters, “U.S. hospitals had nearly $36 billion in uncompensated care costs in 2015, according to the industry’s largest trade group, a figure that is largely made up of unpaid patient bills.” And, “the largest publicly-traded hospital chain, HCA Holdings Inc, reported in the fourth quarter of 2016 that its ratio of bad debt to gross revenues of more than $11 billion was 7.5 percent.”


The broader medical billing outsourcing market is projected to reach $16.9 billion by 2021. According to the Centers for Medicare & Medicaid Services (CMS), errors resulted in $36.21 billion in improper payments in FY2017. The cost to both parties is not only frustration, but also a negative patient experience which strains the long term care relationship between the patient and healthcare services provider.


Solution: NLG to produce patient-friendly bills


We can improve the accuracy and efficiency of the healthcare billing and coding process, for both patients and service providers, by leveraging both natural language processing (NLP) and natural language generation (NLG) technology.

For service providers, we will leverage NLP to deliver automated medical coding. We can train our algorithm on large datasets of medical terminology and automate the coding process by analyzing physician documentation from the text of clinical records and using this information to automatically identify the correct billing codes.

For consumers, we will leverage NLG to clarify billing for patients. In practice, the NLG technology would turn the same billing codes explained in the NLP strategy above into natural language, with clear and concise explanations for patients about charges and their diagnosis. By making the billing more transparent we will not only make the billing process better for the patient. This more comprehensible but we will also to introduce a more impactful sense of trust in the healthcare billing process. This trust will

Patients feel better about what they are paying for and service providers gain clarity and efficiency in the billing process.

MVP Development: develop and iterate in the field


To develop and test an initial minimum viable product, we must first partner with a healthcare services company. We have spoken to a family friend that operates three different outpatient centers in the Southern California area, and he is excited about the opportunity to test this concept at his locations.


Our partner has been operating the outpatient centers as a family-owned business for thirty years. From our initial diligence, it is clear that the company’s data trove is both large and nearly all of it is on physical paper. Prior to building our own technology, we will use off-the-shelf versions that can be deployed after purchasing a software license to prove whether or not, we can successfully generate the value that we believe our concept is capable of producing. There are a number of companies that offer both NLP text extraction and understanding applications and NLG text generation applications. These include: 3M, A2iA, EMscribe, and Popul8.


We think that deploying these technologies will help us better understand where they are most effective and where they break down. This knowledge will drive the development of our technology and its application to our partner’s outpatient locations.


Commercial Viability


The recent shift in the way that healthcare services companies are measured has placed a spotlight on the quality of service and has driven services companies to focus on measured impact like throughput. As a result, there has been a steady decline in inpatient admissions (and inpatient days) in community hospitals and a simultaneous increase in outpatient visits (over 600+ million) and visits per thousand persons (over 2,000). The percent share of market between inpatient and outpatient care is currently at ~35% and ~65%, respectively.


These market trends signal an important opportunity for our concept. Higher turnover within healthcare services centers further strains the billing system, increasing negative patient experiences and putting downward pressure on billing efficiency for services companies. The need for our product could not be greater.


In terms of competition, as previously stated, there are a number of companies building similar products for the largest healthcare services institutions in the U.S. These large healthcare institutions not only have a larger budget, but they attract an extremely large patient base and want the security of a larger technology provider. Our opportunity is in the long tail, where we will target the small healthcare services companies with a software solution that is well within their scope of service. By building volume, regionally, we will both amass scale, and become an attractive M&A target to larger scale technology solution providers.



Augmented Judgment – Autonomous Vehicle


Autonomous vehicle industry and the problem it solves

The global autonomous vehicle market is estimated to be $42B by 2025. This is segmented by Partially Autonomous Vehicles and Fully Autonomous Vehicles, representing $36B and $6B, respectively. The main benefit from autonomous vehicles is the expected increase in safety. Thousands of people die in car accidents and autonomous vehicles are expected to be less error-prone than humans. Autonomous vehicles also have additional benefits such as helping those with physical limitations to mobilize easier. Other benefits include: reducing the number of vehicles on the road, lowering the amount of traffic violations, and providing a more comfortable and efficient way of transportation. These solutions can then be expanded to optimize ride-sharing services and reduce traffic congestion and lower fuel consumption.

Optimus Ride is at the forefront of creating an entirely autonomous vehicle. It leverages a system of hardware products (and machine learning algorithms) in concert with human drivers to develop semi-autonomous vehicles. Initial use cases of the vehicles include shuttle services in communities, commercial developments, airports, college campuses, amusement parks, and other relatively low traffic areas.

The solution is two-fold – hardware augmenting people becomes software augmenting people.

Vehicles come with two lightweight Velodyne lidar sensors, eight cameras, GPUs and motion sensors, and a proprietary switchboard that translates the sensor data into mechanical responses from the vehicle. The system uses cameras and lidar sensors, but dependence on lidar will decrease as Optimus’ accumulates data, which will train computer vision algorithms. The system will eventually shift reliance to computer vision, which will use less expensive hardware and is more scalable.

Path to autonomous vehicle

Time to prepare, calibrate, test, and deploy a vehicle currently takes several weeks. That timeframe is expected to decrease as the company solidifies formal production partnerships with OEMs. Discussions are ongoing for several pilots, including some in Massachusetts and Florida. The product is currently comprised of a full-stack autonomous solution encompassing lidar-based perception (front and rear), vision-based perception (via front and rear cameras), motion planning (via wheel encoders), computer integration (via the NvidiaDrive PX platform), and drive-by-wire control. Lidar is the industry standard, but Optimus is working towards an advanced computer vision-based autonomous solution through a multi-layered rendering from three distinct visual input techniques: visual slam, deep learning, and stereo vision. The resultant camera-focused autonomous system, complete with sensor fusion, will represent the core IP and proprietary software. The unique solution will have the capability to detect objects and obstacles in the vehicle’s path and determine the location of the vehicle in proximity to its surroundings with centimeter-level accuracy. With this scalable turnkey solution available, Optimus could deploy full fleets of autonomous vehicles controlled by client platforms.

Roadmap to vision-based autonomy                Vehicle use cases


The fully autonomous vehicle landscape is highly competitive:

Robust proprietary data is a key competitive differentiator in the autonomous vehicle space. Driving data will train machine learning algorithms, underpinning self-driving technology. To scale, video or image-based data will be the most valuable because it can lessen dependence on expensive lidar technology by shifting reliance to computer vision and software. Incumbent players have accumulated road-mileage but capturing vision-based data remains to be an arms race.

Competitive Landscape

  • Automakers (Ford, GM, Tesla) have been actively establishing partnerships with technology startups and making strategic acquisitions and investments in the autonomous vehicle space.
  • Ride Hailing Companies (Uber, Lyft) are well-aware of the transition to self-driving cars and are developing in-house, through partnerships or via acquisitions.
  • Technology Companies (Google, Apple)–Alphabet leverages GPS, Waze, and Google Maps to generate routes for autonomous vehicles and is developing autonomous offerings via Waymo.
  • Autonomous Software Startups (Nexar, Nauto,, nuTonomy, Varden Labs, Aurora, NextEV)–have received significant funding to power the autonomous shift

But Optimus Ride benefits from a proprietary dataset and pilot partnerships:

Over time, Optimus Ride can leverage its multifunctional ride complex and license to operate in Boston’s self-driving vehicle zone, along with partnerships it has signed with private developers and community and transit authorities to use controlled city zones to accumulate driving data. One of the most obvious constraining market factors is the limited pool of talent, which is a crucial factor driving the numerous strategic acquisitions by incumbents that have defined the autonomous vehicle market in the last year. The technical expertise of the founding team provides a strong competitive advantage.

Team Members: Brentt Baltimore, Moises Numa, Corey Ritter, and Mitchell Stubbs