US 11,955,213 B2
Systems and methods for detecting documentation drop-offs in clinical documentation
Jonathan Matthews, Dripping Springs, TX (US); W. Lance Eason, Austin, TX (US); William Chan, Austin, TX (US); Michael Kadyan, Austin, TX (US); Frances Elizabeth Jurcak, Plymouth, MI (US); and Timothy Paul Harper, Austin, TX (US)
Assigned to IODINE SOFTWARE, LLC, Austin, TX (US)
Filed by Iodine Software, LLC, Austin, TX (US)
Filed on Feb. 13, 2023, as Appl. No. 18/168,435.
Application 18/168,435 is a continuation of application No. 17/129,598, filed on Dec. 21, 2020, granted, now 11,581,075.
Application 17/129,598 is a continuation of application No. 16/185,784, filed on Nov. 9, 2018, granted, now 10,886,013, issued on Jan. 5, 2021.
Claims priority of provisional application 62/586,629, filed on Nov. 15, 2017.
Prior Publication US 2023/0197221 A1, Jun. 22, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G16H 10/60 (2018.01); G06F 40/205 (2020.01); G06F 40/242 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06F 3/0482 (2013.01); G06F 3/0483 (2013.01)
CPC G16H 10/60 (2018.01) [G06F 40/205 (2020.01); G06F 40/242 (2020.01); G06F 40/30 (2020.01); G06N 20/00 (2019.01); G06F 3/0482 (2013.01); G06F 3/0483 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method, comprising:
responsive to new data arriving at a data store, processing, by a computer, entities of interest extracted from the new data into concepts specific to a patient;
automatically summarizing, by the computer, the concepts specific to the patient into concept groups, the concept groups indicative of medical conditions of the patient documented at time points during the patient's stay at a facility;
providing, by the computer, the concept groups to non-time-sensitive models and time-sensitive models;
wherein, on a per medical condition basis, a non-time-sensitive machine learning engine applies the non-time-sensitive models to the concept groups across all the medical conditions of the patient and generates an output containing a list of documented medical conditions that the patient likely has during the patient's stay; and
wherein, on a per medical condition basis, a time-sensitive machine learning engine applies the time-sensitive models to the concept groups across the medical conditions of the patient documented within a time period and generates an output containing a running total of documented medical conditions that the patient likely has sufficiently documented during the time period;
comparing, by the computer, the output from the non-time-sensitive machine learning engine and the output from the time-sensitive machine learning engine to identify any medical condition of the patient that is identified by the non-time-sensitive machine learning engine as having been documented during the patient's stay, but that is not identified by the time-sensitive machine learning engine as having been sufficiently documented during the time period, indicating that a documentation drop-off (DDO) has occurred; and
generating, by the computer, a notification that the DDO has occurred.