Computer-facilitated review of electronic medical records reliably identifies emergency department interventions in older adults.

OBJECTIVES: An estimated 14% to 25% of all scientific studies in peer-reviewed emergency medicine (EM) journals are medical records reviews. The majority of the chart reviews in these studies are performed manually, a process that is both time-consuming and error-prone. Computer-based text search engines have the potential to enhance chart reviews of electronic emergency department (ED) medical records. The authors compared the efficiency and accuracy of a computer-facilitated medical record review of ED clinical records of geriatric patients with a traditional manual review of the same data and describe the process by which this computer-facilitated review was completed. METHODS: Clinical data from consecutive ED patients age 65 years or older were collected retrospectively by manual and computer-facilitated medical record review. The frequency of three significant ED interventions in older adults was determined using each method. Performance characteristics of each search method, including sensitivity and positive predictive value, were determined, and the overall sensitivities of the two search methods were compared using McNemar's test. RESULTS: For 665 patient visits, there were 49 (7.4%) Foley catheters placed, 36 (5.4%) sedative medications administered, and 15 (2.3%) patients who received positive pressure ventilation. The computer-facilitated review identified more of the targeted procedures (99 of 100, 99%), compared to manual review (74 of 100 procedures, 74%; p < 0.0001). CONCLUSIONS: A practical, non-resource-intensive, computer-facilitated free-text medical record review was completed and was more efficient and accurate than manually reviewing ED records.

Development and validation of a prioritization rule for obtaining an immediate 12-lead electrocardiogram in the emergency department to identify ST-elevation myocardial infarction.

BACKGROUND: Current guidelines recommend an immediate (eg, <10 minutes) 12-lead electrocardiogram (ECG) to identify ST-elevation myocardial infarction (STEMI) among patients presenting to the emergency department (ED) with chest pain. Yet, one third of all patients with myocardial infarction do not have chest pain. Our objective was to develop a practical approach to identify patients, especially those without chest pain, who require an immediate ECG in the ED to identify STEMI. METHODS: An ECG prioritization rule was derived and validated using classification and regression tree analysis among >3 million ED visits to 107 EDs from 2007 to 2008. RESULTS: The final study population included 3,575,178 ED patient visits; of these, 6,464 (0.18%) were diagnosed with STEMI. Overall, 1,413 (21.9%) of patients with STEMI did not present to the ED with chest pain. Major predictors of those requiring an immediate ECG in the ED included age ≥30 years with chest pain; age ≥50 years with shortness of breath, altered mental status, upper extremity pain, syncope, or generalized weakness; and those with age ≥80 years with abdominal pain or nausea/vomiting. When the ECG prioritization rule was applied to a validation sample, it had a sensitivity of 91.9% (95% CI 90.9%-92.8%) for STEMI and a negative predictive value 99.98% (95% CI 99.98%-99.98%). CONCLUSION: A simple ECG prioritization rule based on age and presenting symptoms in the ED can identify patients during triage who are at high risk for STEMI and therefore should receive an immediate 12-lead ECG, often before they are seen by a physician.

Developing syndrome definitions based on consensus and current use.

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.

Defining and applying a method for improving the sensitivity and specificity of an emergency department early event detection system.

The sensitivity and specificity of syndrome definitions used in early event detection (EED) systems affect the usefulness of the system for end-users. The ability to calculate these values aids system designers in the refinement of syndrome definitions to better meet public health needs. Utilizing a stratified sampling method and expert review to create a gold standard dataset for the calculation of sensitivity and specificity, we describe how varying syndrome structure impacts these statistical parameters and discuss the relevance of this to outbreak detection and investigation.

Evaluation of a chief complaint pre-processor for biosurveillance.

Emergency Department (ED) chief complaint (CC) data are key components of syndromic surveillance systems. However, it is difficult to use CC data because they are not standardized and contain varying semantic and lexical forms for the same concept. The purpose of this project was to revise a previously-developed text processor for pre-processing CC data specifically for syndromic surveillance and then evaluate it for acute respiratory illness surveillance to support decisions by public health epidemiologists. We evaluated the text processor accuracy and used the results to customize it for respiratory surveillance. We sampled 3,699 ED records from a population-based public health surveillance system. We found equal sensitivity, specificity, and positive and negative predictive value of syndrome queries of data processed through the text processor compared to a standard keyword method on raw, unprocessed data.