Iodine Software, LLC

Sprinklr, Inc.

Bryan Horne

Joshua Toub

William Chan

W Lance Eason

Michael Kadyan

Timothy Harper

Jonathan Matthews

Erik Lee Huddleston

Brandon Kearby

Timothy Joseph Potter

David Chi-Fine Yu

Clinton Frederick Miller

John Joseph De Oliveira

Jacob Andrew Perkins

Kevin Troy Safford

Ramon Renteria

William Lance Eason

Stephen Michael Vaughan, Ii

Jeffrey Dachis

Steven Michael Vaughan

Roger Louis Cauvin

Jeremy Hanna

Shannon Paul Hardt

Brian Justin Huddleston, Ii

Jason Westigard, Ii

Jason Samuel Westigard

John Joseph De Olivera

A clinical documentation improvement (CDI) smart scoring method may include predicting, via per-condition diagnosis machine learning (ML) models and based on clinical evidence received by a system, a probability that a medical condition is under-documented and, via per-condition documentation ML models and based on documentation received by the system, a probability that a medical condition is over-documented. The under- and over-documentation scores are combined in view of special indicators and queryability factors, which can also be evaluated using ML query prediction models, to generate an initial CDI score. This CDI score can be further adjusted, if necessary or desired, to account for factors such as length of stay, payer, patient location, CDI review timing, etc. The final CDI score can be used to prioritize patient cases for review by CDI specialists to quickly and efficiently identify meaningful CDI opportunities.

High fidelity clinical documentation improvement (CDI) smart scoring systems and methods

A social analytic system collects signals from different social network accounts. Social metrics are derived for the accounts and the accounts classified as different types of constituents for a company or primary account based on the social metrics. The constituents may include any combination of advocates, detractors, influencers, spammers, employees, partners, and/or market. Some of the social metrics used for classifying the different types of constituents may include a volume of the signals, types of message interactions, number of unique messages, sentiment, number of subscribers, alignment of constituent and company messages, and/or average signal length.

Apparatus and method for identifying constituents in a social network

A model based social analytic system collects social signals from social network accounts for different companies and constituents. The social signals can be used to benchmark social network performance for different contextual dimensions, such as industries, companies, brands, etc. Conversations may be identified in the signals and the types of constituents participating in the conversations may be identified. Analytics can then be determined for the contextual dimensions based on the related conversations and the types of constituents participating in the conversations.

Apparatus and method for model-based social analytics

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Austin, TX, United States of America

Bryan Horne