About our project

The problem we solve: Physicians do not have the time to write or read all the unstructured information about each patient. Physicians are not able to check/peer review their work. They spend extensive time documenting each case and they tend to burn out with the extensive amount of information they are required to absorb and read.

About our solution: Neural Network-based SAS platform improves physician productivity and quality. The platform is able to interpret information similar to what an experienced physician would be able to, generate and write impressions and conclusions similar to what an experienced physician would be able to and is able to assist and reduce the burden of reading and writing notes. Radiology reports, Pathologist reports, Discharge notes as well as general SOAP notes wil be able to get processed through the IANN platform.

The IANN infrastructure has already been developed. We have 3 pilots already, University of Chicago Medicine, Tahoe Radiology, and Chicago Teleradiology. We are also in discussions and have generated interest from Mayo Clinic, Johns Hopkins, Emery University as well as University of Wisconsin.

We are co-presenting our results with the University of Chicago at the upcoming RSNA. In addition, Amazon Web Services (AWS) will be hosting us as part of their marketplace algorithms   

About Our Company

How We Help Patients

In radiology we expect that IANN will be able to reduce errors in radiology, hence patients will have a lower risk associated with physician errors as a result of using our platform. However, our platform is not geared for direct use by patients. 

How We Help Physicians

Physician productivity should increase by as much as 20% by using our platform, this has been attested through private practice use. Their error rate should also decrease by at least 50%. We have been able to see 20% physician productivity improvement and are willing to work with additional providers willing to use the system for further validation. 

Physician productivity should increase by as much as 20% by using our platform, this has been attested through private practice use. Their error rate should also decrease by at least 50%. We have been able to see 20% physician productivity improvement and are willing to work with additional providers willing to use the system for further validation. 

How We Help Hospitals

Hospitals have the potential to increase throughput from their radiologists and to reduce errors and risk by using our AI platform IANN.  By using our AI platform IANN, we predict that radiologists will be able to see more patients since the impression suggestion will shorten the time spent in writing each report. In turn, hospitals should see higher overall throughput. Additionally, the quality of reports should increase, resulting in a reduction on the number of errors in reports, and risks associated with those errors. 

How We Help Partners

Partners with established relationships with large hospital systems will have the opportunity to bring us in and share the revenue generated. We are developing these partnerships with software companies such as Nuance and hence would welcome the chance to work with additional partners who possess established relationships with large hospital systems.

Partners with established relationships with large hospital systems will have the opportunity to bring us in and share the revenue generated. We are developing these partnerships with software companies such as Nuance and hence would welcome the chance to work with additional partners who possess established relationships with large hospital systems.

Challenge Mission

Innovation Details

Intellectual Property Summary

The architecture and technique used, as well the infrastructure used for IANN is patent pending. Our patent filings are in:

• Multidimensional corpus where every healthcare term is mapped into a 300 dimensional space called a "Narrative Cloud".
• Individual algorithms for specific improvements in radiology workflows. 

Clinical Information

We are co-presenting our work with the University of Chicago Medicine at the upcoming RSNA. We have been able to get validations from the University of Chicago on the accuracy of our algorithms, our accuracy has been higher than 95% in comparison to human-generated impressions in certain specific modalities. We expect to continue to progress in improving the algorithms while we are advancing new use cases and algorithms related to those use cases. 

Regulatory Status

FDA clearance is not required as we are working with human-generated data, i.e. not using AI to interpret images directly but looking at the text that is generated by physicians and providing the results as a reference back to the physicians to correct or improve their conclusions.

How we will use the funds raised

We have raised $580K, any additional capital raised would go into patent applications and GPU cluster costs 

Thank You

Healthcare has been inundated with the maintenance demands of EHR's to the extent that physicians are spending an extensive amount of time managing and entering needed information into these systems. This focus on data entry as part of the care delivery process has negated the experience for both patients as well as their physicians and provider teams. The impacts from this are far-reaching ranging from productivity and quality improvements all the way to physician burn out. Already there are numerous cases that I have personally observed of physicians who have moved away from practicing because of the way that care is being provided. IANN is meant to be a Physician’s assistant/helper to make sure Physicians get back to providing care and not become stuck in entering or deciphering data. We believe that care will be significantly improved for both the physician as well as the patient as the focus moves away from data entry and towards care delivery.

Investor Info

Market Size

We estimate our Total Market Opportunity to be $1.7B built from four distinct areas where extensive documentation or narratives/notes are being generated:

• Radiology 34,000 physicians generate 28,000 reports/year on average = approx. 1 Billion radiology reports/year.  We would charge $.50 cents per report = $476M  
• Pathology 18,000 physicians generate 12,000 report/year on average= approx. 216 Million pathology reports/year. We would charge $2 per report = $432M
• Discharge Notes 35 million hospitalizations/year on average generating a discharge note.   We would charge $12 per note = $420M
• SOAP Notes for applicable specialties.  Approximately 4 billion notes across specialties (excluding procedural/surgical specialties)/ year on average.   We would charge $.09 per report = $360M

Projected 3 Year Growth

We expect to be servicing approximately 6,600 physicians, which accounts for approximately 19.4% of the total radiology market within 3 years. Generating revenues of about $22.5M, with gross margins of about 83%. 

Revenue Model

Radiology is our first focal market. Per our opportunity analysis, it is obvious that this is the largest segment that we are pursuing.

In radiology we are targeting the following customer segments:

• Academic Hospital Systems - given our access to key leading advisors in Academic environments, we have received strong validation and acceptance for out work - Events such as the SIIM (Society of Information Informatics in Medicine) and RSNA participation as a presenter will allow us to get access to leading academic institutions. We expect to charge these institutions our standard fee of 50 cents/report. 
• Strategically Targeted Small, Medium and Large Sized private practices to start off with. We have targeted key influencers in private practices for our pilot programs.  These key influencers have strong credibility and acceptance in the market.  By turning our existing private pilots into customers, we expect to be able to have strong references that will facilitate our acceptance by other prospects. We expect to maintain the .50 cents/report price point
• Large hospital systems-- our goal is to use partners to penetrate the large hospital system world. Through reseller models that we are currently exploring, we will leverage incumbent vendors to penetrate these systems, leveraging their relationships in the process. The price point would remain at 50 cents / report. 

Competitors

• Radi AI - this is the closest radiology competitor. Their platform is not able to produce the level of speed with which our impression calculations occur. Our advantage over them is our platform and overall technology architecture. 
• Agamon health  - this is a general text-oriented deep learning platform. They are not radiology focused, hence not a strong direct competitor
• Amazon Comprehend - We are partnering with AWS, and leverage their GPU's, additionally we expect to launch our algorithms in their market place. Their inability to get access to training data sets as well as a more general-purpose capability makes them more of a partner than a competitor. 
• Optum - They are based on rules-based NLP as opposed to automated, deep learning-based models. There is a limitation on what a rules-based system can achieve.

We offer advantages in prediction, speed and quality versus our competition due to our comprehensive architecture which combines a unique healthcare corpus that we have trained, in addition to our individual unique algorithms that work for each specific use case.

Traction

Today we are being piloted at one of the leading academic hospital systems with a focus on radiology. We are presenting our work in impression prediction jointly with the University of Chicago at the upcoming RSNA. We have 2 private practices who have been using IANN, they have seen 20% productivity improvement by using the platform. We are expecting to convert these to revenue-generating customers. We have partnered with Nuance, the leading software platform for radiologists in report writing. We have been able to integrate with their API, which they do not expose to many partners. We expect to be able to leverage this partnership for further traction.

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Supporters

• 
  Alan Sullivan
  11/12/2019 - Liked the project.
• 
  Erik Thomas
  11/08/2019 - Followed the project.
• 
  Robert DeLeonardis
  11/07/2019 - Liked the project.
• 
  Eric Puster, DO
  11/05/2019 - Liked the project.
• 
  Jonathan Fair
  11/03/2019 - Liked the project.
• 
  Sunnie Southern, MS, RDN, LDN
  11/02/2019 - Liked the project.
• 
  Sunnie Southern, MS, RDN, LDN
  11/02/2019 - Followed the project.
• 
  Faisal Sami,M.D.
  11/01/2019 - Followed the project.
• 
  Faisal Sami,M.D.
  11/01/2019 - Liked the project.
• 
  Ms. Barbara Rapchak
  10/29/2019 - Liked the project.
• 
  Frank Naeymi-Rad
  10/27/2019 - Backed the project for $1000
• 
  Frank Naeymi-Rad
  10/27/2019 - Liked the project.
• 
  Mary Beth Arzac, MBA
  10/27/2019 - Followed the project.
• 
  Imran Aftab
  10/26/2019 - Followed the project.
• 
  Imran Aftab
  10/26/2019 - Liked the project.
• 
  Adrian Lawrence, Masters
  10/25/2019 - Backed the project for $1
• 
  Robin Prasad
  10/25/2019 - Liked the project.
• 
  Adrian Lawrence, Masters
  10/25/2019 - Followed the project.
• 
  Vincent Lepreux
  10/23/2019 - Followed the project.
• 
  Vincent Lepreux
  10/23/2019 - Liked the project.
• 
  Tom Gibbs
  10/23/2019 - Liked the project.
• 
  Michael Williams
  10/22/2019 - Liked the project.
• 
  Gizem Aydin
  10/16/2019 - Followed the project.
• 
  Gizem Aydin
  10/16/2019 - Liked the project.
• 
  Baris U. Agar, Ph.D.
  10/16/2019 - Liked the project.
• 
  Irem Pamuksuz
  10/16/2019 - Liked the project.
• 
  Tomislav Deur
  10/16/2019 - Liked the project.
• 
  Tomislav Deur
  10/16/2019 - Followed the project.
• 
  Steven Collens
  10/15/2019 - Liked the project.
• 
  Adesh Ghadge
  10/15/2019 - Liked the project.
• 
  Adesh Ghadge
  10/15/2019 - Followed the project.
• 
  Ashish Mahadik
  10/15/2019 - Liked the project.
• 
  Ashish Mahadik
  10/15/2019 - Followed the project.
• 
  Khan Siddiqui
  10/15/2019 - Liked the project.
• 
  Mary Beth Arzac, MBA
  10/08/2019 - Liked the project.
• 
  Mary Beth Arzac, MBA
  10/08/2019 - Interested in helping your project as a mentor or team member.
• 
  Utku Pamuksuz, Ph.D.
  10/08/2019 - Liked the project.
• 
  Farrukh Khan
  10/08/2019 - Liked the project.

Comments

• 
  Adrian Lawrence posted on 25th October, 2019

  I like the concept - best of luck with it.

  Report as Spam

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