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Automated ICD Coding of Primary Diagnosis Based on Machine Learning

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ClinicalTrials.gov Identifier: NCT04817423
Recruitment Status : Active, not recruiting
First Posted : March 26, 2021
Last Update Posted : March 26, 2021
Sponsor:
Information provided by (Responsible Party):
China National Center for Cardiovascular Diseases

Study Description
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Brief Summary:
This study aims to develop and validate machine learning model in ICD-10 coding of primary diagnosis related to cardiovascular diseases in Chinese corpus.

Condition or disease Intervention/treatment
Cardiovascular Diseases Other: No intervention

Detailed Description:

The accuracy and productivity of ICD coding has always been a concern of clinical practice. Errors of ICD codes may result in claim denials and missed revenue. However, ICD coding process is complex, time-consuming and error-prone. More experienced coders are in need, but there is an increasing lack of supply. Automated ICD coding has potential to facilitate clinical coders for improved efficiency and quality. Model performance of related studies is still far below coders and both the accuracy and interpretability need to be improved in great demand. Besides, studies in Chinese corpus are not sufficient.

In this study, the investigators will implement automated ICD coding study based on inpatient' data collected from electronic medical records from Fuwai Hospital, the world's largest medical center for cardiovascular disease. Feature engineering and machine learning methods will be used to develop classification models with good performance, interpretability and practicability for ICD codes of primary diagnosis.

Study Design
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Study Type : Observational
Estimated Enrollment : 74880 participants
Observational Model: Other
Time Perspective: Retrospective
Official Title: Automated ICD Coding of Primary Diagnosis Based on Machine Learning
Actual Study Start Date : March 1, 2021
Estimated Primary Completion Date : April 2021
Estimated Study Completion Date : April 2021
Groups and Cohorts
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Group/Cohort Intervention/treatment
Model training and test group
Data set will be split into training group and test group, where training group will be used for model building, and test group for subsequent evaluation and verification.
 Other: No intervention
No intervention


Outcome Measures
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Primary Outcome Measures :
  1. ICD code of primary diagnosis [ Time Frame: At the end of enrollment ]
    Each admission will be a sample in this study. The ICD code of primary diagnosis assigned by medical coders for each admission will be collected as the primary outcome.


Eligibility Criteria
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Information from the National Library of Medicine

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Ages Eligible for Study:   Child, Adult, Older Adult
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Admissions in Fuwai Hospital
Criteria

Inclusion Criteria:

  • Admissions in Fuwai Hospital, from January 1, 2019, to December 31, 2020

Exclusion Criteria:

  • Admissions stayed in nephrology department, Fuwai Hospital
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04817423


Locations
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China
Fuwai Hospital
Beijing, China
Sponsors and Collaborators
China National Center for Cardiovascular Diseases
Investigators
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Principal Investigator: Wei Zhao, PhD China National Center for Cardiovascular Diseases
More Information
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Responsible Party: China National Center for Cardiovascular Diseases
ClinicalTrials.gov Identifier: NCT04817423    
Other Study ID Numbers: 2021-1425
First Posted: March 26, 2021    Key Record Dates
Last Update Posted: March 26, 2021
Last Verified: March 2021
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Cardiovascular Diseases