Coded Chief Complaints—Automated Analysis of Free-text Complaints

Thompson, David A.; Eitel, David; Fernandes, Christopher M.B.; Pines, Jesse M.; Amsterdam, James T.; Davidson, Steven J.

doi:10.1111/j.1553-2712.2006.tb01718.x

Cited by 7 publications

(17 citation statements)

References 20 publications

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“…Chief complaint was the patient’s reason for the ED visit captured as free text. To group similar chief complaints to facilitate analysis, all chief complaints from all ED visits were classified by regular expression matching into 154 categories by two emergency physicians based on previously developed methodologies 19–21 . For example, we classified both “chest pain” and “chest discomfort” into the chief complaint group “chest pain.” We tabulated these chief complaint groups by frequency for the study population and explored the 20 highest‐frequency groups.…”

Section: Methodsmentioning

confidence: 99%

Association Between Emergency Physician Length of Stay Rankings and Patient Characteristics

Chang

Baugh

Brown

et al. 2020

Academic Emergency Medicine

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Objective: Emergency physicians are commonly compared by their patients' length of stay (LOS). We test the hypothesis that LOS is associated with patient characteristics and that accounting for these features impacts physician LOS rankings. Methods: This was a retrospective observational study of all encounters at an emergency department in 2010 to 2015. We compared the characteristics of patients seen by physicians in different quartiles of LOS. Primary outcome was variation in patient characteristics at time of physician assignment (age, sex, comorbidities, Emergency Severity Index [ESI], and chief complaint) across LOS quartiles. We also quantified the change in LOS rankings after accounting for difference in characteristics of patients seen by different physicians. Results: A total of 264,776 encounters seen by 62 attending physicians met inclusion criteria. Physicians in the longest LOS quartile saw patients who were older (age = 49.1 vs 48.6 years, difference = +0.5 years, 95% confidence interval [CI] = 0.3 to 0.7) with more comorbidities (Gagne score = 1.3 vs. 0.9, difference = +0.4, 95% CI = 0.4 to 0.4) and higher acuity (ESI = 2.8 vs. 2.9, difference = À0.1, 95% CI = 0.1 to 0.1) than physicians in the shortest LOS quartile. The odds ratio (OR) of physicians in the longest LOS quartile seeing patients over age 50 compared to the shortest LOS quartile was 1.1 (95% CI = 1.0 to 1.1); the OR of physicians in the longest LOS quartile seeing patients with ESI of 1 or 2 was also 1.1 (95% CI = 1.0 to 1.1). Accounting for variation in patient characteristics seen by different physicians resulted in substantial reordering of physician LOS rankings: 62.9% (39/62) of physicians reclassified into a different quartile with mean absolute percentile change of 25.8 (95% CI = 20.3 to 31.3). A total of 62.5% (10/16) of physicians in the shortest LOS quartile and 56.3% (9/16) in the longest LOS quartile moved into a different quartile after accounting for variation in patient characteristics. Conclusions: Length of stay was significantly associated with patient characteristics, and accounting for variation in patient characteristics resulted in substantial reordering of relative physician rankings by LOS. Comparisons of emergency physicians by LOS that do not account for patient characteristics should be reconsidered.

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Section: Methodsmentioning

confidence: 99%

Association Between Emergency Physician Length of Stay Rankings and Patient Characteristics

Chang

Baugh

Brown

et al. 2020

Academic Emergency Medicine

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“…We realized that non-standard naming conventions for clinical conditions could make merging multiple definitions difficult, so we provided each participant with a standard terminology of clinical conditions as a starting point. We developed a modified version of the coded chief complaints for emergency department systems (CCCeEDS) terminology for chief complaints 16 as our initial terminology by removing terms that were not relevant to biosurveillance, resulting in a subset of 91 of the original set of 228 terms. Participants compiled their syndrome definitions by including relevant CCCeEDS terms and by adding terms that were not present in the CCCeEDS terminology but were mapped by the surveillance system to a relevant syndrome.…”

Section: Consensus Processmentioning

confidence: 99%

“…Participants included in their clinical definitions 59 of the 91 CCCeEDS conditions 16 used as our initial terminology, and they added 145 unique new conditions. Of the 204 unique clinical conditions submitted by the 10 participants, 127 were included by only a single surveillance system, indicating the diversity of syndrome definitions monitored by the participants' surveillance systems.…”

Section: Generating Baseline Syndrome Definitionsmentioning

confidence: 99%

Developing syndrome definitions based on consensus and current use

Chapman

Dowling

Baer

et al. 2010

Journal of the American Medical Informatics Association

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Objective: Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems. Design: Clinical condition–syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions. Results: Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created. Limitations: The consensus definitions have not yet been validated through implementation. Conclusion: The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.

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“…In the July article, ''Coded Chief Complaints-Automated Analysis of Free-text Complaints,'' Thompson and colleagues describe a computer algorithm to classify patients' free-text complaints into 228 categories. 11 What types of questions could be addressed with an optimal automated algorithm? ''Are African American patients presenting to U.S. EDs with abdominal pain less likely to receive an opiate than are white patients?''…”

mentioning

confidence: 99%

“…Thompson et al conclude that their categorization system is ''comprehensive, granular, and useful.'' 11 How accurate is their algorithm? Thompson et al 11 note that the study does not evaluate the algorithm's accuracy, and further studies will need to determine this.…”

mentioning

confidence: 99%