The Validity of Chief Complaint and Discharge Diagnosis in Emergency Department–based Syndromic Surveillance

Fleischauer, Aaron; Silk, Benjamin J.; Schumacher, Mare; Komatsu, Ken; Santana, Sarah; Vaz, Victorio; Wolfe, Mary E.; Hutwagner, Lori; Cono, Joanne; Berkelman, Ruth L.; Treadwell, Tracee A.

doi:10.1197/j.aem.2004.07.013

Cited by 50 publications

(37 citation statements)

References 15 publications

(19 reference statements)

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“…Our Kappa statistic findings comparing the agreement within categories for Discharge Diagnosis and Operational Code are consistent with the findings of Fleischauer13 and Begier14 despite their focus on syndromes while our work was focused on diagnostic categories. When looking at domestic emergency departments, Fleischauer13 and Begier14 found that there is high variability in agreement values across various syndrome classifications.…”

Section: Discussionsupporting

confidence: 89%

“…However, our high level of agreement for both respiratory and digestive system diseases aligned with the findings of Begier14 who identified chief complaint and discharge data aligned for that system. The low level of agreement between the Other category from the Discharge Diagnosis category and Other from the Operational Code category aligned with our hypothesis that additional effort on coding encounters provided more granularity, as compared to the use of primary discharge diagnosis alone 13. The perfect agreement existing in the diseases of the digestive system may have been due to low number of encounters in this category before recoding (n=152).…”

Section: Discussionsupporting

confidence: 63%

“…When looking at domestic emergency departments, Fleischauer13 and Begier14 found that there is high variability in agreement values across various syndrome classifications. However, our high level of agreement for both respiratory and digestive system diseases aligned with the findings of Begier14 who identified chief complaint and discharge data aligned for that system.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing the Use of Chief Complaint & Diagnosis for Operational Decision Making: An EMR Case Study of the 2010 Haiti Earthquake

et al. 2014

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Introduction: Data from an electronic medical record (EMR) system can provide valuable insight regarding health consequences in the aftermath of a disaster. In January of 2010, the U.S. Department of Health and Human Services (HHS) deployed medical personnel to Haiti in response to a crippling earthquake. An EMR system was used to record patient encounters in real-time and to provide data for decision support during response activities. Problem: During the Haiti response, HHS monitored the EMR system by recoding diagnoses into seven broad categories. At the conclusion of the response, it was evident that a new diagnosis categorization process was needed to provide a better description of the patient encounters that were seen in the field. After examining the EMRs, researchers determined nearly half of the medical records were missing diagnosis data. The objective of this study was to develop and test a new method of categorization for patient encounters to provide more detailed data for decision making. Methods: A single researcher verified or assigned a new diagnosis for 8,787 EMRs created during the Haiti response. This created a new variable, the Operational Code, which was based on available diagnosis data and chief complaint. Retrospectively, diagnoses recorded in the field and Operational Codes were categorized into eighteen categories based on the ICD-9-CM diagnostic system. Results: Creating an Operational Code variable led to a more robust data set and a clearer depiction emerged of the clinical presentations seen at six HHS clinics set up in the aftermath of Haiti’s earthquake. The number of records with an associated ICD-9 code increased 106% from 4,261 to 8,787. The most frequent Operational Code categories during the response were: General Symptoms, Signs, and Ill-Defined Conditions (34.2%), Injury and Poisoning (18.9%), Other (14.7%), Respiratory (4.8%), and Musculoskeletal and Connective Tissue (4.8%). Conclusion: The Operational Code methodology provided more detailed data about patient encounters. This methodology could be used in future deployments to improve situational awareness and decision-making capabilities during emergency response operations.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionsupporting

confidence: 63%

Section: Discussionmentioning

confidence: 99%

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Optimizing the Use of Chief Complaint & Diagnosis for Operational Decision Making: An EMR Case Study of the 2010 Haiti Earthquake

et al. 2014

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“…This level of agreement is slightly higher than seen in other studies [2-4]. However, over 25% of discharge diagnosis data were missing from the EHR, limiting our comparative analysis.…”

Section: Discussionmentioning

confidence: 58%

Validation of Los Angeles County Department of Public Health Respiratory Syndrome using Electronic Health Records

OYong

Kajita

Araki

et al. 2014

OJPHI

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In Los Angeles County, emergency department data is collected from hospitals and classified into syndromes based on chief complaints. To validate this respiratory syndromic surveillance categorization, chief complaint data were compared to discharge diagnoses extracted from electronic health records from one hospital emergency department in Los Angeles County during one week in January 2013. The agreement between syndrome classification and discharge diagnosis for respiratory reports is high (k=0.75), though over 25% of diagnosis data were missing. Further validation of additional syndromes is needed. Electronic health records are valuable sources of data and can enhance the validity of syndromic surveillance systems.

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“…Fleischauer and others [21] used Kappa statistics to assess agreement between syndromic grouping of the chief complaint on presentation in EDs with discharge diagnoses in patient records. They found moderate agreement overall and recommended that the validity of automated surveillance coding could be improved by including discharge diagnoses.…”

Section: Discussionmentioning

confidence: 99%

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance

Liljeqvist

Muscatello

Sara

et al. 2014

BMC Med Inform Decis Mak

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BackgroundSyndromic surveillance in emergency departments (EDs) may be used to deliver early warnings of increases in disease activity, to provide situational awareness during events of public health significance, to supplement other information on trends in acute disease and injury, and to support the development and monitoring of prevention or response strategies. Changes in mental health related ED presentations may be relevant to these goals, provided they can be identified accurately and efficiently. This study aimed to measure the accuracy of using diagnostic codes in electronic ED presentation records to identify mental health-related visits.MethodsWe selected a random sample of 500 records from a total of 1,815,588 ED electronic presentation records from 59 NSW public hospitals during 2010. ED diagnoses were recorded using any of ICD-9, ICD-10 or SNOMED CT classifications. Three clinicians, blinded to the automatically generated syndromic grouping and each other’s classification, reviewed the triage notes and classified each of the 500 visits as mental health-related or not. A “mental health problem presentation” for the purposes of this study was defined as any ED presentation where either a mental disorder or a mental health problem was the reason for the ED visit. The combined clinicians’ assessment of the records was used as reference standard to measure the sensitivity, specificity, and positive and negative predictive values of the automatic classification of coded emergency department diagnoses. Agreement between the reference standard and the automated coded classification was estimated using the Kappa statistic.ResultsAgreement between clinician’s classification and automated coded classification was substantial (Kappa = 0.73. 95% CI: 0.58 - 0.87). The automatic syndromic grouping of coded ED diagnoses for mental health-related visits was found to be moderately sensitive (68% 95% CI: 46%-84%) and highly specific at 99% (95% CI: 98%-99.7%) when compared with the reference standard in identifying mental health related ED visits. Positive predictive value was 81% (95% CI: 0.57 – 0.94) and negative predictive value was 98% (95% CI: 0.97-0.99).ConclusionsMental health presentations identified using diagnoses coded with various classifications in electronic ED presentation records offers sufficient accuracy for application in near real-time syndromic surveillance.

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The Validity of Chief Complaint and Discharge Diagnosis in Emergency Department–based Syndromic Surveillance

Cited by 50 publications

References 15 publications

Optimizing the Use of Chief Complaint & Diagnosis for Operational Decision Making: An EMR Case Study of the 2010 Haiti Earthquake

Optimizing the Use of Chief Complaint & Diagnosis for Operational Decision Making: An EMR Case Study of the 2010 Haiti Earthquake

Validation of Los Angeles County Department of Public Health Respiratory Syndrome using Electronic Health Records

Accuracy of automatic syndromic classification of coded emergency department diagnoses in identifying mental health-related presentations for public health surveillance

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