US 12,112,296 B2
High fidelity clinical documentation improvement (CDI) smart scoring systems and methods
William Chan, Austin, TX (US); W. Lance Eason, Austin, TX (US); Timothy Harper, Austin, TX (US); Bryan Horne, Austin, TX (US); Michael Kadyan, Austin, TX (US); Jonathan Matthews, Dripping Springs, TX (US); and Joshua Toub, Menlo Park, CA (US)
Assigned to IODINE SOFTWARE, LLC, Austin, TX (US)
Filed by IODINE SOFTWARE, LLC, Austin, TX (US)
Filed on Aug. 16, 2023, as Appl. No. 18/450,946.
Application 18/450,946 is a continuation of application No. 17/861,801, filed on Jul. 11, 2022, granted, now 11,775,932.
Application 17/861,801 is a continuation of application No. 16/939,790, filed on Jul. 27, 2020, granted, now 11,423,356, issued on Aug. 23, 2022.
Application 16/939,790 is a continuation of application No. 15/349,679, filed on Nov. 11, 2016, granted, now 10,733,566, issued on Aug. 4, 2020.
Prior Publication US 2023/0394437 A1, Dec. 7, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06Q 10/10 (2023.01); G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06Q 50/22 (2024.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01)
CPC G06Q 10/10 (2013.01) [G06N 7/01 (2023.01); G06N 20/00 (2019.01); G06Q 50/22 (2013.01); G16H 50/20 (2018.01); G16H 50/30 (2018.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
receiving, by a clinical documentation improvement (CDI) system, real-time medical data from a data source, the real-time medical data comprising clinical data for a patient and documentation for a visit of the patient;
determining, by the CDI system based on the real-time medical data, a medical condition for the patient;
determining, by the CDI system utilizing the real-time medical data, a difference between portions of the real-time medical data and stored data items the CDI system has for the visit of the patient;
applying, by the CDI system, a machine learning diagnosis model to the portions of the real-time medical data to generate an under-documentation score, the under-documentation score representing a probability that the medical condition is clinically valid but is not sufficiently documented;
applying, by the CDI system, a machine learning documentation model to the stored data items the CDI system has for the visit of the patient to generate an over-documentation score, the over-documentation score representing a probability that the medical condition is documented but is not supported by the clinical data for the patient; and
generating, by the CDI system based at least in part on the under-documentation score and the over-documentation score, a CDI score for the visit of the patient.