This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2561) Clinical Informatics (1410) Clinical Information and Decision Making (2037) Personal Health Records, Patient-Accessible Electronic Health Records, Patient Portals (713) Machine Learning (2199) Artificial Intelligence (2297) Nursing Records (20) Infectious Diseases (non-STD/STI) (1744) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1544)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/53343, first published .
Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Identification of Predictors for Clinical Deterioration in Patients With COVID-19 via Electronic Nursing Records: Retrospective Observational Study

Authors of this article:

Sumi Sung1 Author Orcid Image ;   Youlim Kim2 Author Orcid Image ;   Su Hwan Kim3 Author Orcid Image ;   Hyesil Jung4 Author Orcid Image
Article Authors Cited by (3) Tweetations (2) Metrics

Journals

  1. Park S, Sung S, Min Y. A Comparative Study of End-of-Life Care Before and During the COVID-19 Pandemic Using Electronic Nursing Records. CIN: Computers, Informatics, Nursing 2025;43(6) View
  2. Seeiso T, Mhlongo E. Factors promoting eRegister and data use for evidence-based midwifery practice in Lesotho: a qualitative study. BMJ Open 2025;15(11):e093878 View
  3. Jung H, Yoo S, Kim S, Chung J, Lee H. Transforming nursing documentation data into the Observational Medical Outcomes Partners common data model. International Journal of Medical Informatics 2026;213:106358 View