IP Library Granted Patent US 12,646,034
Granted Patent B2
US 12,646,034 · App. 18/825,425 · Granted Jun 2, 2026

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

Inventors: William Chan (Austin, TX); W. Lance Eason (Austin, TX); Timothy Harper (Austin, TX); Bryan Horne (Austin, TX); Michael Kadyan (Austin, TX); Jonathan Matthews (Dripping Springs, TX); Joshua Toub (Menlo Park, CA)
Assignee: IODINE SOFTWARE, LLC
G06Q10/10G06N7/01G06N20/00G06Q50/22G16H50/20G16H50/30
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,646,034
App. No.
18/825,425
Granted
Jun 2, 2026
Kind
B2
Abstract

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.

Claims (61)

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 electronic clinical documentation for a visit of the patient;

applying, by the CDI system, a machine learning diagnosis model to the real-time medical data to generate an under-documentation score, the under-documentation score representing a probability that a medical condition of the patient is not sufficiently documented;

applying, by the CDI system, a machine learning documentation model to the electronic clinical documentation 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.

2 . The method of claim 1 , further comprising:

determining special indicators applicable to the visit of the patient, wherein the special indicators represent significant events during the visit of the patient;

aggregating values of the special indicators; and

generating a special indicators score based on the values of the special indicators thus aggregated, wherein generating the CDI score for the visit of the patient further includes combining the under-documentation score for the medical condition, the over-documentation score for the medical condition, and the special indicators score.

3 . The method of claim 2 , wherein the special indicators score is independent of the under-documentation score for the medical condition, the over-documentation score for the medical condition, or a combination thereof.

4 . The method of claim 1 , further comprising:

evaluating the under-documentation score for the medical condition in conjunction with a queryability factor that represents a consideration applicable to the visit of the patient; and

determining an overall under-documentation score based at least in part on the evaluating.

5 . The method of claim 1 , further comprising:

aggregating over-documentation scores across medical conditions in the real-time medical data for the visit of the patient; and

generating an overall over-documentation score based on the over-documentation scores thus aggregated.

6 . The method of claim 1 , further comprising:

determining a cumulative probability that the medical condition determined from the real-time medical data is an undocumented complication or comorbidity, the determining the cumulative probability based at least in part on information about the patient, a number of progress notes by an attending physician for the patient, a number of queries made to the stored data items the CDI system has for the visit of the patient, or a count of undocumented medical conditions found in the stored data items the CDI system has for the visit of the patient.

7 . The method of claim 1 , further comprising:

adjusting the CDI score for the visit of the patient based at least in part on a location of the patient.

8 . A clinical documentation improvement (CDI) system, comprising:

a processor;

a non-transitory computer readable medium; and

stored instructions translatable by the processor to perform:

receiving real-time medical data from a data source, the real-time medical data comprising clinical data for a patient and electronic clinical documentation for a visit of the patient;

applying a machine learning diagnosis model to the real-time medical data to generate an under-documentation score, the under-documentation score representing a probability that a medical condition of the patient is not sufficiently documented;

applying a machine learning documentation model to the electronic clinical documentation 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, based at least in part on the under-documentation score and the over-documentation score, a CDI score for the visit of the patient.

9 . The CDI system of claim 8 , wherein the stored instructions are further translatable by the processor to perform:

determining special indicators applicable to the visit of the patient, wherein the special indicators represent significant events during the visit of the patient;

aggregating values of the special indicators; and

generating a special indicators score based on the values of the special indicators thus aggregated, wherein generating the CDI score for the visit of the patient further includes combining the under-documentation score for the medical condition, the over-documentation score for the medical condition, and the special indicators score.

10 . The CDI system of claim 9 , wherein the special indicators score is independent of the under-documentation score for the medical condition, the over-documentation score for the medical condition, or a combination thereof.

11 . The CDI system of claim 8 , wherein the stored instructions are further translatable by the processor to perform:

evaluating the under-documentation score for the medical condition in conjunction with a queryability factor that represents a consideration applicable to the visit of the patient; and

determining an overall under-documentation score based at least in part on the evaluating.

12 . The CDI system of claim 8 , wherein the stored instructions are further translatable by the processor to perform:

aggregating over-documentation scores across medical conditions in the real-time medical data for the visit of the patient; and

generating an overall over-documentation score based on the over-documentation scores thus aggregated.

13 . The CDI system of claim 8 , wherein the stored instructions are further translatable by the processor to perform:

determining a cumulative probability that the medical condition determined from the real-time medical data is an undocumented complication or comorbidity, the determining the cumulative probability based at least in part on information about the patient, a number of progress notes by an attending physician for the patient, a number of queries made to the stored data items the CDI system has for the visit of the patient, or a count of undocumented medical conditions found in the stored data items the CDI system has for the visit of the patient.

14 . The CDI system of claim 8 , wherein the stored instructions are further translatable by the processor to perform:

adjusting the CDI score for the visit of the patient based at least in part on a location of the patient.

15 . A computer program product comprising a non-transitory computer-readable medium storing instructions translatable by a processor of a clinical documentation improvement (CDI) system to perform:

receiving real-time medical data from a data source, the real-time medical data comprising clinical data for a patient and electronic clinical documentation for a visit of the patient;

applying a machine learning diagnosis model to the real-time medical data to generate an under-documentation score, the under-documentation score representing a probability that a medical condition of the patient is not sufficiently documented;

applying a machine learning documentation model to the electronic clinical documentation 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, based at least in part on the under-documentation score and the over-documentation score, a CDI score for the visit of the patient.

16 . The computer program product of claim 15 , wherein the instructions are further translatable by the processor to perform:

determining special indicators applicable to the visit of the patient, wherein the special indicators represent significant events during the visit of the patient;

aggregating values of the special indicators; and

generating a special indicators score based on the values of the special indicators thus aggregated, wherein generating the CDI score for the visit of the patient further includes combining the under-documentation score for the medical condition, the over-documentation score for the medical condition, and the special indicators score.

17 . The computer program product of claim 15 , wherein the special indicators score is independent of the under-documentation score for the medical condition, the over-documentation score for the medical condition, or a combination thereof.

18 . The computer program product of claim 15 , wherein the instructions are further translatable by the processor to perform:

evaluating the under-documentation score for the medical condition in conjunction with a queryability factor that represents a consideration applicable to the visit of the patient; and

determining an overall under-documentation score based at least in part on the evaluating.

19 . The computer program product of claim 15 , wherein the instructions are further translatable by the processor to perform:

aggregating over-documentation scores across medical conditions in the real-time medical data for the visit of the patient; and

generating an overall over-documentation score based on the over-documentation scores thus aggregated.

20 . The computer program product of claim 15 , wherein the instructions are further translatable by the processor to perform:

determining a cumulative probability that the medical condition determined from the real-time medical data is an undocumented complication or comorbidity, the determining the cumulative probability based at least in part on information about the patient, a number of progress notes by an attending physician for the patient, a number of queries made to the stored data items the CDI system has for the visit of the patient, or a count of undocumented medical conditions found in the stored data items the CDI system has for the visit of the patient.

Assignments (2)
SECURITY INTEREST Recorded Feb 17, 2026
From: IODINE SOFTWARE, LLC
To: JPMORGAN CHASE BANK, N.A., AS ADMINISTRATIVE AGENT AND COLLATERAL AGENT
Reel/Frame 073808/0661 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 12, 2024
From: CHAN, WILLIAM; EASON, W. LANCE; HARPER, TIMOTHY; HORNE, BRYAN; KADYAN, MICHAEL; MATTHEWS, JONATHAN; TOUB, JOSHUA
To: IODINE SOFTWARE, LLC
Reel/Frame 068572/0432 →
Continuity (5)
Continuation 18450946 · Aug 16, 2023
Continuation 17861801 · Jul 11, 2022
Continuation 16939790 · Jul 27, 2020
Continuation 15349679 · Nov 11, 2016
Related Publication 20240428194A1 · Dec 26, 2024
References Cited (9)
US 10394871B2 · Galia · 2019 [cited by examiner]
US 20110082712A1 · Eberhardt, III · 2011 [cited by examiner]
US 20120215551A1 · Flanagan · 2012 [cited by examiner]
US 20120304054A1 · Orf · 2012 [cited by examiner]
US 20130080187A1 · Bacon · 2013 [cited by examiner]
US 20130297348A1 · Cardoza · 2013 [cited by examiner]
US 20160012187A1 · Zasowski · 2016 [cited by examiner]
Kuhn et al., Clinical Documentation In The 21st Century: Executive Summary Of A Policy Position Paper From The American College Of Physicians, Annals Of Internal Medicine, vol. 162, No. 4, Feb. 17, 2015, 14 pages (Year:… [cited by examiner]
Optum, Insights And Best Practices For Clinical Documentation Improvement Programs, Jan. 15, 2015, https://www.optum.com/content/dam/optum3/optum/en/resources/white-papers/EHR.BestPracticesforClinicalDocumentationImprov… [cited by examiner]