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.
Appendix:
https://www.techemergence.com/artificial-intelligence-medical-billing-coding/
http://www.a2ialab.com/doku.php?id=rimes_database:start
https://automatedinsights.com/blog/natural-language-generation-101
https://www.aha.org/system/files/research/reports/tw/chartbook/2016/2016chartbook.pdf
