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Debbie A Travers

Publications and source records attributed to Debbie A Travers.

10 recordsLinked to original sources
PubMed2006

Unified medical language system coverage of emergency-medicine chief complaints.

BACKGROUND: Emergency department (ED) chief-complaint (CC) data increasingly are important for clinical-care and secondary uses such as syndromic surveillance. There is no widely used ED CC vocabulary, but experts have suggested evaluation of existing health-care vocabularies for ED CC. OBJECTIVES: To evaluate the ED CC coverage in existing biomedical vocabularies from the Unified Medical Language System (UMLS). METHODS: The study sample included all CC entries for all visits to three EDs over one year. The authors used a special-purpose text processor to clean CC entries, which then were mapped to UMLS concepts. The UMLS match rates then were calculated and analyzed for matching concepts and nonmatching entries. RESULTS: A total of 203,509 ED visits was included. After cleaning with the text processor, 82% of the CCs matched a UMLS concept. The authors identified 5,617 unique UMLS concepts in the ED CC data, but many were used for only one or two visits. One thousand one hundred thirty-six CC concepts were used more than ten times and covered 99% of all the ED visits. The largest biomedical vocabulary in the UMLS is the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT), which included concepts for 79% of all ED CC entries. However, some common CCs were not found in SNOMED CT. CONCLUSIONS: The authors found that ED CC concepts are well covered by the UMLS and that the best source of vocabulary coverage is from SNOMED CT. There are some gaps in UMLS and SNOMED CT coverage of ED CCs. Future work on vocabulary control for ED CCs should build upon existing vocabularies.

Data Collection↗
PubMed2004

Evaluation of emergency medical text processor, a system for cleaning chief complaint text data.

OBJECTIVES: Emergency Medical Text Processor (EMT-P) version 1, a natural language processing system that cleans emergency department text (e.g., chst pn, chest pai), was developed to maximize extraction of standard terms (e.g., chest pain). The authors compared the number of standard terms extracted from raw chief complaint (CC) data with that for CC data cleaned with EMT-P and evaluated the accuracy of EMT-P. METHODS: This cross-sectional observation study included CC text entries for all emergency department visits to three tertiary care centers in 2001. Terms were extracted from CC entries before and after cleaning with EMT-P. Descriptive statistics included number and percentage of all entries (tokens) and all unique entries (types) that matched a standard term from the Unified Medical Language System (UMLS). An expert panel rated the accuracy of the CC-UMLS term matches; inter-rater reliability was measured with kappa. RESULTS: The authors collected 203,509 CC entry tokens, of which 63,946 were unique entry types. For the raw data, 89,337 tokens (44%) and 5,081 types (8%) matched a standard term. After EMT-P cleaning, 168,050 tokens (83%) and 44,430 types (69%) matched a standard term. The expert panel reached consensus on 201 of the 222 CC-UMLS term matches reviewed (kappa=0.69-0.72). Ninety-six percent of the 201 matches were rated equivalent or related. Thirty-eight percent of the nonmatches were found to match UMLS concepts. CONCLUSIONS: EMT-P version 1 is relatively accurate, and cleaning with EMT-P improved the CC-UMLS term match rate over raw data. The authors identified areas for improvement in future EMT-P versions and issues to be resolved in developing a standard CC terminology.

Cohort Studies↗
PubMed2003

Diagnosis clusters for emergency medicine.

OBJECTIVES: Aggregated emergency department (ED) data are useful for research, ED operations, and public health surveillance. Diagnosis data are widely available as The International Classification of Diseases, version, 9, Clinical Modification (ICD-9-CM) codes; however, there are over 24,000 ICD-9-CM code-descriptor pairs. Standardized groupings (clusters) of ICD-9-CM codes have been developed by other disciplines, including family medicine (FM), internal medicine (IM), inpatient care (Agency for Healthcare Research and Quality [AHRQ]), and vital statistics (NCHS). The purpose of this study was to evaluate the coverage of four existing ICD-9-CM cluster systems for emergency medicine. METHODS: In this descriptive study, four cluster systems were used to group ICD-9-CM final diagnosis data from a southeastern university tertiary referral center. Included were diagnoses for all ED visits in July 2000 and January 2001. In the comparative analysis, the authors determined the coverage in the four cluster systems, defined as the proportion of final diagnosis codes that were placed into clusters and the frequencies of diagnosis codes in each cluster. RESULTS: The final sample included 7,543 visits with 19,530 diagnoses. Coverage of the ICD-9-CM codes in the ED sample was: AHRQ, 99%; NCHS, 88%; FM, 71%; IM, 68%. Seventy-six percent of the AHRQ clusters were small, defined as grouping <1% of the diagnosis codes in the sample. CONCLUSIONS: The AHRQ system provided the best coverage of ED ICD-9-CM codes. However, most of the clusters were small and not significantly different from the raw data.

Cluster Analysis↗
PubMed2003

The emergency severity index triage algorithm version 2 is reliable and valid.

OBJECTIVES: Initial studies have shown improved reliability and validity of a new triage tool, the Emergency Severity Index (ESI), over conventional three-level scales at two university medical centers. After pilot implementation and validation, the ESI was revised to include pediatric and updated vital signs criteria. The goal of this study was to assess ESI version (v.) 2 reliability and validity at seven emergency departments (EDs) in three states. METHODS: In part 1, interrater reliability was assessed using weighted kappa analysis of written training cases and postimplementation by a random sampling of actual patient triages. In part 2, validity was analyzed using a prospective cohort with stratified random sampling at each site. The ESI was compared with outcomes including resource consumption, inpatient admission, ED length of stay, and 60-day all-cause mortality. RESULTS: Weighted kappa analysis of interrater reliability ranged from 0.70 to 0.80 for the written scenarios (n = 3289) and 0.69 to 0.87 for patient triages (n = 386). Outcomes for the validity cohort (n = 1042) included hospitalization rates by ESI triage level: level 1, 83%; 2, 67%; 3, 42%; 4, 8%; level 5, 4%. Sixty-day all-cause mortality by triage level was as follows: level 1, 25%; 2, 4%; 3, 2%; 4, 1%; and 5, 0%. CONCLUSIONS: ESI v. 2 triage produced reliable, valid stratification of patients across seven sites. ESI triage should be evaluated as an ED casemix identification system for uniform data collection in the United States and compared with other major ED triage methods.

Algorithms↗
PubMed2003

Emergency Department data for bioterrorism surveillance: electronic data availability, timeliness, sources and standards.

Emergency Department (ED) data are a key component of bioterrorism surveillance systems. Little research has been done to examine differences in ED data capture and entry across hospitals, regions and states. The purpose of this study was to describe the current state of ED data for use in bioterrorism surveillance in 2 regions of the country. We found that chief complaint (CC) data are available electronically in 54% of the North Carolina EDs surveyed, and in 100% of the Seattle area EDs. Over half of all EDs reported that CCs are recorded in free text form. Though all EDs have electronic diagnosis data, less than half report that diagnoses are coded within 24 hours of the ED visit.

Bioterrorism↗
PubMed2002

Five-level triage system more effective than three-level in tertiary emergency department.

INTRODUCTION: The study objectives were to compare reliability and validity of a 3-level (3L) triage system with a new 5-level (5L) triage system and determine the effect of nursing experience on triage reliability. METHODS: The study was conducted in a southeastern tertiary emergency department. With a stratified random sample, reliability of 3L triage ratings was measured with weighted kappa (time 1). The 5L system was then implemented, and weighted kappa was remeasured (time 2). Validity was assessed by comparing case mix, sensitivity, and specificity at times 1 and 2, and comparing 5L ratings with physician billing (Evaluation and Management) codes and nursing resource intensity at time 2. RESULTS: Time 1 case mix (15,324 patients) was: level 1, 6%; level 2, 36%; level 3, 59%, and time 2 (16,024 patients) was: level 1, 1%; level 2, 8%; level 3, 38%; level 4, 41%; level 5, 13%. Three hundred-five triage ratings were evaluated from time 1, and 303 were evaluated from time 2. Weighted kappa was 0.53 for time 1 and 0.68 for time 2. Spearman correlations were: 5L and nursing resource intensity, 0.55 (P <.0001); and 5L and Em, 0.57 (P <.0001). Sensitivity was 58% for the 3L and 68% for the 5L. Specificity was 83% for the 3L and 91% for the 5L. Under-triage rates were 28% for the 3L and 12% for the 5L, and less-experienced nurses were more likely to under-triage using the 3L system. DISCUSSION: The 5L triage system is safer and provides greater discrimination, better reliability, and improved sensitivity and specificity than the 3L triage system.

Diagnosis-Related Groups↗
PubMedDate not supplied

Using nurses' natural language entries to build a concept-oriented terminology for patients' chief complaints in the emergency department.

Information about the chief complaint (CC), also known as the patient's reason for seeking emergency care, is critical for patient prioritization for treatment and determination of patient flow through the emergency department (ED). Triage nurses document the CC at the start of the ED visit, and the data are increasingly available in electronic form. Despite the clinical and operational significance of the CC to the ED, there is no standard CC terminology. We propose the construction of concept-oriented nursing terminologies from the actual language used by experts. We use text analysis to extract CC concepts from triage nurses' natural language entries. Our methodology for building the nursing terminology utilizes natural language processing techniques and the Unified Medical Language System.

Computational Biology↗