Chief Complaints, Emergency Department Clinical Documentation Systems, and the Challenge of Dealing with the Patient's Own Words

Husk, Gregg; Akhtar, Saadia

doi:10.1197/j.aem.2006.07.012

Cited by 2 publications

(1 citation statement)

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“…Unstructured and semi-structured data have remained largely untapped in biomedical research as these datasets present unique challenges in analyses [37]. Howinformation that exists within the free-text narratives of health records, such as a patient's own reason for visit, provide potentially important data to better predict health outcomes to improve overall care [38,39,40]. Using natural language processing and principal component analysis to extract information contained in freetext fields, we found that the text contains data that improves the accuracy of predictive models for hospital admission: the AUC increased from 0.824 to 0.846 for LR and 0.823 to 0.844 for MLNN with the inclusion of free-text field variables.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

Zhang¹,

Kim²,

Patzer³

et al. 2017

Methods Inf Med

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Section: Discussionmentioning

confidence: 99%

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

Zhang¹,

Kim²,

Patzer³

et al. 2017

Methods Inf Med

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Chief Complaint–Based Performance Measures: A New Focus for Acute Care Quality Measurement

Griffey

Pines

Farley

et al. 2015

Annals of Emergency Medicine

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Performance measures are increasingly important to guide meaningful quality improvement efforts and value-based reimbursement. Populations included in most current hospital performance measures are defined by recorded diagnoses using International Disease Classification (ICD)-9 codes in administrative claims data. While the diagnosis-centric approach allows the assessment of disease-specific quality, it fails to measure one of the primary functions of emergency department (ED) care which involves diagnosing, risk-stratifying, and treating patients’ potentially life-threatening conditions based on symptoms (i.e. chief complaints). In this paper we propose chief complaint-based quality measures as a means to enhance the evaluation of quality and value in emergency care. We discuss the potential benefits of chief-complaint based measures, describe opportunities to mitigate challenges, propose an example measure set, and present several recommendations to advance this paradigm in ED-based performance measurement.

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Chief Complaints, Emergency Department Clinical Documentation Systems, and the Challenge of Dealing with the Patient's Own Words

Cited by 2 publications

References 20 publications

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

Prediction of Emergency Department Hospital Admission Based on Natural Language Processing and Neural Networks

Chief Complaint–Based Performance Measures: A New Focus for Acute Care Quality Measurement

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