In April 2022, ExamOne and Sikka.ai introduced a collaboration to supply oral healthcare data by way of HealthPiQture. Sikka not too long ago printed a whitepaper on using Sikka Well being Indicators™ to determine well being dangers amongst insurance coverage candidates.
Sikka Well being Indicators whitepaper
Establish pre-existing well being situations from dental medical notes
Sikka.ai is the main API Platform within the retail healthcare business that features opt-in dentistry, veterinary, audiology, optometry, chiropractic, orthodontics, oral surgical procedure, and different medical practices. Sikka.ai API Platform seamlessly connects to 96% of the retail healthcare market all through the US and is processing billions of transactions a day. Sikka.ai API Platform gives the platform to assist practices optimize their enterprise, profitability, affected person communication, income cycle administration, affected person satisfaction, and affected person medical historical past evaluation, by enabling over 50 market purposes on its platform.
Sikka has over 41,000 opt-in dental practices put in within the US and Canada by way of its market-leading API integration platform. Sikka leverages this wealthy dataset to find out if life insurance coverage candidates have 1or extra of 10 pre-existing situations or habits with outsized affect on underwriting. These indicators are primarily based on precise medical notes from the licensed suppliers within the practices or affected person reported situations on well being historical past kinds. Sikka’s oral healthcare indicators will help decide the suitable threat class or adjustment primarily based on the applicant’s well being threat indicators. This will help make sure that “much less dangerous” life insurance coverage policyholders don’t find yourself subsidizing “dangerous” ones, no matter whether or not or not situations are mischaracterized by chance or deliberately.
10 Sikka Well being Indicators
Textual content Classification Mannequin
From Sikka’s huge database, sufferers with medical notes that comprise particular key phrases are recognized for every well being indicator. These medical notes have been preprocessed utilizing numerous NLP preprocessing methods. The Word2vec algorithm was used to generate a distributed illustration of phrases from medical notes as numerical vectors, capturing the semantics and relationships between phrases.
The embedded phrase was fed into the Lengthy Quick Time period Reminiscence (LSTM) mannequin Determine 2, which is a kind of recurrent neural community able to studying order dependence in sequence prediction issues. The LSTM mannequin is efficient in memorizing necessary data and, not like conventional classification algorithms, LSTM can use a a number of phrase string to search out out the category to which it belongs. The LSTM mannequin was skilled on a 400,000 balanced dataset with an accuracy of 99%.
As a part of enhancing the textual content categorization, a guidelines engine was developed to include any incorrect classifications discovered within the retrospective research.
Retrospective Research and Hit charge
Sikka’s knowledge has been validated in research performed by 3 main reinsurance corporations, 3 main knowledge suppliers, a number of carriers and MGAs in each the US and Canada, and a number one life settlement firm. These research vary from a choose 2,500 to an expansive 8,000,000 information and have match charges of as much as 54%. The tobacco indicator has recognized vital numbers of “smoking non-disclosers” that value carriers as a lot as $23,0001 per conventional time period policyholder in misplaced premiums on account of misclassification, primarily based on the evaluation of 1 of the information suppliers3. Individually Sikka Indicators are actually in manufacturing with a number of carriers and have been useful at figuring out lacking underlying situations that affect underwriting. Research of Sikka’s Tobacco indicators have been accomplished to determine gross protecting worth of just about 10x the associated fee.2 In 2019, Munich Re carried out a validation of Sikka’s Tobacco Rating utilizing insured information. Analysis confirms that details about dental well being might be informative about general well being. Munich Re recommends every service carry out a retrospective research to greatest assess the worth and utility of the Sikka Tobacco Rating on its company-specific insured inhabitants.3
Be part of Sikka and ExamOne for an interesting and well timed webinar dialogue with prime business panelists on find out how to leverage oral healthcare knowledge successfully for all times underwriting.
Register for our upcoming webinar on March 23, 2023.
1 https://www.verisk.com/siteassets/toprisks/how-audio-analytics-can-detect-undisclosed-tobacco-use-verisk-whitepaper.pdf 2 ExamOne Price Profit Evaluation, June, 2021, Brian Lanzrath 3 https://www.munichre.com/us-life/en/views/alternatives-for-stratifying-mortality-risk/oral-health-mortality-and-smoker-detection.html