Literature DB >> 34035789

Clinical Data Abstraction: A Research Study.

Valerie J M Watzlaf, Patty T Sheridan, Amal A Alzu'bi, Laura Chau.   

Abstract

This is the second part in a two-part research study on clinical data abstraction.1 Clinical data abstraction is the process of capturing key administrative and clinical data elements from a medical record. Very little is known about how the abstraction function is organized and managed today. A research study to gather data on how the clinical data abstraction function is managed in healthcare organizations across the country was performed. Results show that the majority of the healthcare organizations surveyed have a decentralized system, still perform the abstraction in-house as part of the coding workflow, and use manual abstraction followed by natural language processing (NLP) and simple query. The qualifications and training of abstractors varied across abstraction functions, however coders followed by nurses and health information management (HIM) professionals were the three top performers in abstraction. While, in general, abstraction is decentralized in most enterprises, two enterprise-wide abstraction models emerged from our study. In Model 1, the HIM department is responsible for coding, as well as all of the abstraction functions except the cancer registry and trauma registry abstraction. In Model 2, the quality department is responsible for all of the abstraction functions except the cancer registry, trauma registry, and coding function.
Copyright © 2021 by the American Health Information Management Association.

Entities:  

Keywords:  Abstraction; clinical; descriptive research study; electronic health record; models; natural language processing; query

Year:  2021        PMID: 34035789      PMCID: PMC8120675     

Source DB:  PubMed          Journal:  Perspect Health Inf Manag        ISSN: 1559-4122


  2 in total

1.  Artificial Intelligence Tool for Optimizing Eligibility Screening for Clinical Trials in a Large Community Cancer Center.

Authors:  J Thaddeus Beck; Melissa Rammage; Gretchen P Jackson; Anita M Preininger; Irene Dankwa-Mullan; M Christopher Roebuck; Adam Torres; Helen Holtzen; Sadie E Coverdill; M Paul Williamson; Quincy Chau; Kyu Rhee; Michael Vinegra
Journal:  JCO Clin Cancer Inform       Date:  2020-01

2.  Natural language processing of prehospital emergency medical services trauma records allows for automated characterization of treatment appropriateness.

Authors:  Christopher J Tignanelli; Greg M Silverman; Elizabeth A Lindemann; Alexander L Trembley; Jon C Gipson; Gregory Beilman; John W Lyng; Raymond Finzel; Reed McEwan; Benjamin C Knoll; Serguei Pakhomov; Genevieve B Melton
Journal:  J Trauma Acute Care Surg       Date:  2020-05       Impact factor: 3.313

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.