Iodine Software, LLC

Timothy Harper

William Chan

Michael Kadyan

W Lance Eason

Jonathan Matthews

Joshua Toub

Bryan Horne

Frances Elizabeth Jurcak

Bryan Au-Young

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

In clinical documentation, mere documentation of a condition in a patient's records may not be enough. To be considered sufficiently documented, the patient's record needs to show that no documentation drop-offs (DDOs) have occurred over the course of the patient's stay. However, DDOs can be extremely difficult to detect. To solve this problem, the invention trains time-sensitive deep learning (DL) models on a per condition basis using actual and/or synthetic patient data. Utilizing an ontology, grouped concepts can be generated on the fly from real-time hospital data and used to generate time-series data that can then be analyzed by trained time-sensitive DL models to determine whether a DDO for a condition has occurred during the stay. Non-time-sensitive models can be used to detect all the conditions documented during the stay. Outcomes from the models can be compared to determine whether to notify a user that a DDO has occurred.

Systems and methods for detecting documentation drop-offs in clinical documentation

A patient case may be evaluated whenever new information is received or as scheduled. Evaluation may include resolving a Diagnosis-Related Group (DRG) code and determining a CDI scoring approach based at least in part on a result from the resolving. Resolving a DRG code may include determining whether a DRG code is associated with the patient case. If no DRG code is found, the system may search for an International Classification of Diseases code or ask a user to select or assign a DRG code. Using the determined CDI scoring approach, a first score may be generated and adjusted by at least one of length of stay, documentation accuracy, payer, patient location, documentation novelty, review timing, case size, or documentation sufficiency. The adjusted score may be normalized and presented to a CDI specialist, perhaps with multiple CDI scores in a sorted order.

Clinical documentation improvement (CDI) smart scoring systems and methods

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Austin, TX, United States of America

Timothy Harper