Factors Influencing Time-Dependent Quality Indicators for Patients With Suspected Acute Coronary Syndrome.

OBJECTIVES: Rapid risk stratification and timely treatment are critical to favorable outcomes for patients with acute coronary syndrome (ACS). Our objective was to identify patient and system factors that influence time-dependent quality indicators (QIs) for patients with unstable angina/non-ST elevation myocardial infarction (NSTEMI) in the emergency department (ED). METHODS: A retrospective, cohort study was conducted during a 42-month period of all patients 24 years or older suspected of having ACS as defined by receiving an electrocardiogram and at least 1 cardiac biomarker test. Cox regression was used to model the effects of patient characteristics, ancillary service use, staffing provisions, equipment availability, and ED and hospital crowding on ACS QIs. RESULTS: Emergency department adherence rates to national standards for electrocardiogram readout time and biomarker turnaround time were 42% and 37%, respectively. Cox regression models revealed that chief complaints without chest pain and the timing of stress testing and medication administration were associated with the most significant delays. CONCLUSIONS: Patient and system factors both significantly influenced QI times in this cohort with unstable angina/NSTEMI. These results illustrate both the complexity of diagnosing patients with NSTEMI and the competing effects of clinical and system factors on patient flow through the ED.

The International Classification of Functioning, Disability and Health (ICF) in Electronic Health Records. A Systematic Literature Review.

BACKGROUND: The International Classification of Functioning, Disability and Health (ICF) is the World Health Organization's standard for describing health and health-related states. Examples of how the ICF has been used in Electronic Health Records (EHRs) have not been systematically summarized and described yet. OBJECTIVES: To provide a systematic review of peer-reviewed literature about the ICF's use in EHRs, including related challenges and benefits. METHODS: Peer-reviewed literature, published between January 2001 and July 2015 was retrieved from Medline®, CINAHL®, Scopus®, and ProQuest® Social Sciences using search terms related to ICF and EHR concepts. Publications were categorized according to three groups: Requirement specification, development and implementation. Information extraction was conducted according to a qualitative content analysis method, deductively informed by the evaluation framework for Health Information Systems: Human, Organization and Technology-fit (HOT-fit). RESULTS: Of 325 retrieved articles, 17 publications were included; 4 were categorized as requirement specification, 7 as development, and 6 as implementation publications. Information regarding the HOT-fit evaluation framework was summarized. Main benefits of using the ICF in EHRs were its unique comprehensive perspective on health and its interdisciplinary focus. Main challenges included the fact that the ICF is not structured as a formal terminology as well as the need for a reduced number of ICF codes for more feasible and practical use. CONCLUSION: Different approaches and technical solutions exist for integrating the ICF in EHRs, such as combining the ICF with other existing standards for EHR or selecting ICF codes with natural language processing. Though the use of the ICF in EHRs is beneficial as this review revealed, the ICF could profit from further improvements such as formalizing the knowledge representation in the ICF to support and enhance interoperability.

Impact of an Electronic Clinical Decision Support Tool for Emergency Department Patients With Pneumonia.

STUDY OBJECTIVE: Despite evidence that guideline adherence improves clinical outcomes, management of pneumonia patients varies in emergency departments (EDs). We study the effect of a real-time, ED, electronic clinical decision support tool that provides clinicians with guideline-recommended decision support for diagnosis, severity assessment, disposition, and antibiotic selection. METHODS: This was a prospective, controlled, quasi-experimental trial in 7 Intermountain Healthcare hospital EDs in Utah's urban corridor. We studied adults with International Classification of Diseases, Ninth Revision codes and radiographic evidence for pneumonia during 2 periods: baseline (December 2009 through November 2010) and post-tool deployment (December 2011 through November 2012). The tool was deployed at 4 intervention EDs in May 2011, leaving 3 as usual care controls. We compared 30-day, all-cause mortality adjusted for illness severity, using a mixed-effect, logistic regression model. RESULTS: The study population comprised 4,758 ED pneumonia patients; 14% had health care-associated pneumonia. Median age was 58 years, 53% were female patients, and 59% were admitted to the hospital. Physicians applied the tool for 62.6% of intervention ED study patients. There was no difference overall in severity-adjusted mortality between intervention and usual care EDs post-tool deployment (odds ratio [OR]=0.69; 95% confidence interval [CI] 0.41 to 1.16). Post hoc analysis showed that patients with community-acquired pneumonia experienced significantly lower mortality (OR=0.53; 95% CI 0.28 to 0.99), whereas mortality was unchanged among patients with health care-associated pneumonia (OR=1.12; 95% CI 0.45 to 2.8). Patient disposition from the ED postdeployment adhered more to tool recommendations. CONCLUSION: This study demonstrates the feasibility and potential benefit of real-time electronic clinical decision support for ED pneumonia patients.

A systematic review of the implementation and impact of asthma protocols.

BACKGROUND: Asthma is one of the most common childhood illnesses. Guideline-driven clinical care positively affects patient outcomes for care. There are several asthma guidelines and reminder methods for implementation to help integrate them into clinical workflow. Our goal is to determine the most prevalent method of guideline implementation; establish which methods significantly improved clinical care; and identify the factors most commonly associated with a successful and sustainable implementation. METHODS: PUBMED (MEDLINE), OVID CINAHL, ISI Web of Science, and EMBASE. STUDY SELECTION: Studies were included if they evaluated an asthma protocol or prompt, evaluated an intervention, a clinical trial of a protocol implementation, and qualitative studies as part of a protocol intervention. Studies were excluded if they had non-human subjects, were studies on efficacy and effectiveness of drugs, did not include an evaluation component, studied an educational intervention only, or were a case report, survey, editorial, letter to the editor. RESULTS: From 14,478 abstracts, we included 101 full-text articles in the analysis. The most frequent study design was pre-post, followed by prospective, population based case series or consecutive case series, and randomized trials. Paper-based reminders were the most frequent with fully computerized, then computer generated, and other modalities. No study reported a decrease in health care practitioner performance or declining patient outcomes. The most common primary outcome measure was compliance with provided or prescribing guidelines, key clinical indicators such as patient outcomes or quality of life, and length of stay. CONCLUSIONS: Paper-based implementations are by far the most popular approach to implement a guideline or protocol. The number of publications on asthma protocol reminder systems is increasing. The number of computerized and computer-generated studies is also increasing. Asthma guidelines generally improved patient care and practitioner performance regardless of the implementation method.

Implementation and evaluation of an integrated computerized asthma management system in a pediatric emergency department: a randomized clinical trial.

OBJECTIVE: The use of evidence-based guidelines can improve the care for asthma patients. We implemented a computerized asthma management system in a pediatric emergency department (ED) to integrate national guidelines. Our objective was to determine whether patient eligibility identification by a probabilistic disease detection system (Bayesian network) combined with an asthma management system embedded in the workflow decreases time to disposition decision. METHODS: We performed a prospective, randomized controlled trial in an urban, tertiary care pediatric ED. All patients 2-18 years of age presenting to the ED between October 2010 and February 2011 were screened for inclusion by the disease detection system. Patients identified to have an asthma exacerbation were randomized to intervention or control. For intervention patients, asthma management was computer-driven and workflow-integrated including computer-based asthma scoring in triage, and time-driven display of asthma-related reminders for re-scoring on the electronic patient status board combined with guideline-compliant order sets. Control patients received standard asthma management. The primary outcome measure was the time from triage to disposition decision. RESULTS: The Bayesian network identified 1339 patients with asthma exacerbations, of which 788 had an asthma diagnosis determined by an ED physician-established reference standard (positive predictive value 69.9%). The median time to disposition decision did not differ among the intervention (228 min; IQR=(141, 326)) and control group (223 min; IQR=(129, 316)); (p=0.362). The hospital admission rate was unchanged between intervention (25%) and control groups (26%); (p=0.867). ED length of stay did not differ among intervention (262 min; IQR=(165, 410)) and control group (247 min; IQR=(163, 379)); (p=0.818). CONCLUSIONS: The control and intervention groups were similar in regards to time to disposition; the computerized management system did not add additional wait time. The time to disposition decision did not change; however the management system integrated several different information systems to support clinicians' communication.

Exploring the frontier of electronic health record surveillance: the case of postoperative complications.

BACKGROUND: The aim of this study was to build electronic algorithms using a combination of structured data and natural language processing (NLP) of text notes for potential safety surveillance of 9 postoperative complications. METHODS: Postoperative complications from 6 medical centers in the Southeastern United States were obtained from the Veterans Affairs Surgical Quality Improvement Program (VASQIP) registry. Development and test datasets were constructed using stratification by facility and date of procedure for patients with and without complications. Algorithms were developed from VASQIP outcome definitions using NLP-coded concepts, regular expressions, and structured data. The VASQIP nurse reviewer served as the reference standard for evaluating sensitivity and specificity. The algorithms were designed in the development and evaluated in the test dataset. RESULTS: Sensitivity and specificity in the test set were 85% and 92% for acute renal failure, 80% and 93% for sepsis, 56% and 94% for deep vein thrombosis, 80% and 97% for pulmonary embolism, 88% and 89% for acute myocardial infarction, 88% and 92% for cardiac arrest, 80% and 90% for pneumonia, 95% and 80% for urinary tract infection, and 77% and 63% for wound infection, respectively. A third of the complications occurred outside of the hospital setting. CONCLUSIONS: Computer algorithms on data extracted from the electronic health record produced respectable sensitivity and specificity across a large sample of patients seen in 6 different medical centers. This study demonstrates the utility of combining NLP with structured data for mining the information contained within the electronic health record.

Detecting temporal expressions in medical narratives.

BACKGROUND: Clinical practice and epidemiological information aggregation require knowing when, how long, and in what sequence medically relevant events occur. The Temporal Awareness and Reasoning Systems for Question Interpretation (TARSQI) Toolkit (TTK) is a complete, open source software package for the temporal ordering of events within narrative text documents. TTK was developed on newspaper articles. We extended TTK to support medical notes using veterans' affairs (VA) clinical notes and compared it to TTK. METHODS: We used a development set consisting of 200 VA clinical notes to modify and append rules to TTK's time tagger, creating Med-TTK. We then evaluated the performances of TTK and Med-TTK on an independent random selection of 100 clinical notes. Evaluation tasks were to identify and classify time-referring expressions as one of four temporal classes (DATE, TIME, DURATION, and SET). The reference standard for this test set was generated by dual human manual review with disagreements resolved by a third reviewer. Outcome measures included recall and precision for each class, and inter-rater agreement scores. RESULTS: There were 3146 temporal expressions in the reference standard. TTK identified 1595 temporal expressions. Recall was 0.15 (95% confidence interval [CI] 0.12-0.15) and precision was 0.27 (95% CI 0.25-0.29) for TTK. Med-TTK identified 3174 expressions. Recall was 0.86 (95% CI 0.84-0.87) and precision was 0.85 (95% CI 0.84-0.86) for Med-TTK. CONCLUSION: The algorithms for identifying and classifying temporal expressions in medical narratives developed within Med-TTK significantly improved performance compared to TTK. Natural language processing applications such as Med-TTK provide a foundation for meaningful longitudinal mapping of patient history events among electronic health records. The tool can be accessed at the following site: http://code.google.com/p/med-ttk/.

An asthma management system in a pediatric emergency department.

INTRODUCTION: Pediatric asthma exacerbations account for >1.8 million emergency department (ED) visits annually. Asthma guidelines are intended to guide time-dependent treatment decisions that improve clinical outcomes; however, guideline adherence is inadequate. We examined whether an automatic disease detection system increases clinicians' use of paper-based guidelines and decreases time to a disposition decision. METHODS: We evaluated a computerized asthma detection system that triggered NHLBI-adopted, evidence-based practice to improve care in an urban, tertiary care pediatric ED in a 3-month (7/09-9/09) prospective, randomized controlled trial. A probabilistic system screened all ED patients for acute asthma. For intervention patients, the system generated the asthma protocol at triage for intervention patients to guide early treatment initiation, while clinicians followed standard processes for control patients. The primary outcome measures included time to patient disposition. RESULTS: The system identified 1100 patients with asthma exacerbations, of which 704 had a final asthma diagnosis determined by a physician-established reference standard. The positive predictive value for the probabilistic system was 65%. The median time to disposition decision did not differ among the intervention (289 min; IQR = (184, 375)) and control group (288 min; IQR = (185, 375)) (p=0.21). The hospital admission rate was unchanged between intervention (37%) and control groups (35%) (p = 0.545). ED length of stay did not differ among the intervention (331 min; IQR = (226, 581)) and control group (331 min; IQR = (222, 516)) (p = 0.568). CONCLUSION: Despite a high level of support from the ED leadership and staff, a focused education effort, and implementation of an automated disease detection, the use of the paper-based asthma protocol remained low and time to patient disposition did not change.

Relationships among initial hospital triage, disease progression and mortality in community-acquired pneumonia.

BACKGROUND AND OBJECTIVE: Appropriate triage of patients with community-acquired pneumonia (CAP) may improve morbidity, mortality and use of hospital resources. Worse outcomes from delayed intensive care unit (ICU) admission have long been suspected but have not been verified. METHODS: In a retrospective study of consecutive patients with CAP admitted from 1996-2006 to the ICUs of a tertiary care hospital, we measured serial severity scores, intensive therapies received, ICU-free days, and 30-day mortality. Primary outcome was mortality. We developed a regression model of mortality with ward triage (and subsequent ICU transfer within 72 h) as the predictor, controlled by propensity for ward triage and radiographic progression. RESULTS: Of 1059 hospital-admitted patients, 269 (25%) were admitted to the ICU during hospitalization. Of those, 167 were directly admitted to the ICU without current requirement for life support, while 61 (23%) were initially admitted to the hospital ward, 50 of those undergoing ICU transfer within 72 h. Ward triage was associated with increased mortality (OR 2.6, P = 0.056) after propensity adjustment. The effect was less (OR 2.2, P = 0.12) after controlling for radiographic progression. The effect probably increased (OR 4.1, P = 0.07) among patients with ≥ 3 severity predictors at admission. CONCLUSIONS: Initial ward triage among patients transferred to the ICU is associated with twofold higher 30-day mortality. This effect is most apparent among patients with ≥ 3 severity predictors at admission and is attenuated by controlling for radiographic progression. Intensive monitoring of ward-admitted patients with CAP seems warranted. Further research is needed to optimize triage in CAP.

Predictors of patient length of stay in 9 emergency departments.

OBJECTIVES: Prolonged emergency department (ED) length of stay (LOS) is linked to adverse outcomes, decreased patient satisfaction, and ED crowding. This multicenter study identified factors associated with increased LOS. METHODS: This retrospective study included 9 EDs from across the United States. Emergency department daily operational metrics were collected from calendar year 2009. A multivariable linear population average model was used with log-transformed LOS as the dependent variable to identify which ED operational variables are predictors of LOS for ED discharged, admitted, and overall ED patient categories. RESULTS: Annual ED census ranged from 43,000 to 101,000 patients. The number of ED treatment beds ranged from 27 to 95. Median overall LOS for all sites was 5.4 hours. Daily percentage of admitted patients was found to be a significant predictor of discharged and admitted patient LOS. Higher daily percentage of discharged and eloped patients, more hours on ambulance diversion, and weekday (vs weekend) of patient presentation were significantly associated with prolonged LOS for discharged and admitted patients (P < .05). For each percentage of increase in discharged patients, there was a 1% associated decrease in overall LOS, whereas each percentage of increase in eloped patients was associated with a 1.2% increase in LOS. CONCLUSIONS: Length of stay was increased on days with higher percentage daily admissions, higher elopements, higher periods of ambulance diversion, and during weekdays, whereas LOS was decreased on days with higher numbers of discharges and weekends. This is the first study to demonstrate this association across a broad group of hospitals.

Hospital admission decision for patients with community-acquired pneumonia: variability among physicians in an emergency department.

STUDY OBJECTIVE: We examine variability among emergency physicians in rate of hospitalization for patients with pneumonia and the effect of variability on clinical outcomes. METHODS: We studied 2,069 LDS Hospital emergency department (ED) patients with community-acquired pneumonia who were aged 18 years or older during 1996 to 2006, identified by International Classification of Diseases, Ninth Revision coding and compatible chest radiographs. We extracted vital signs, laboratory and radiographic results, hospitalization, and outcomes from the electronic medical record. We defined "low severity" as PaO(2)/FiO(2) ratio greater than or equal to 280 mm Hg, predicted mortality less than 5% by an electronic version of CURB-65 that uses continuous and weighted elements (eCURB), and less than 3 Infectious Disease Society of America-American Thoracic Society 2007 severe pneumonia minor criteria. We adjusted hospitalization decisions and outcomes for illness severity and patient demographics. RESULTS: Initial hospitalization rate was 58%; 10.7% of patients initially treated as outpatients were secondarily hospitalized within 7 days. Median age of admitted patients was 63 years; median eCURB predicted mortality was 2.65% (mean 6.8%) versus 46 years and 0.93% for outpatients. The 18 emergency physicians (average age 44.9 [standard deviation 7.6] years; years in practice 8.4 [standard deviation 6.9]) objectively calculated and documented illness severity in 2.7% of patients. Observed 30-day mortality for inpatients was 6.8% (outpatient mortality 0.34%) and decreased over time. Individual physician admission rates ranged from 38% to 79%, with variability not explained by illness severity, time of day, day of week, resident care in conjunction with an attending physician, or patient or physician demographics. Higher hospitalization rates were not associated with reduced mortality or fewer secondary hospital admissions. CONCLUSION: We observed a 2-fold difference in pneumonia hospitalization rates among emergency physicians, unexplained by objective data.

Interventions to safeguard system effectiveness during periods of emergency department crowding.

This article summarizes the proceedings of a breakout session, "Interventions to Safeguard System Effectiveness," at the 2011 Academic Emergency Medicine consensus conference, "Interventions to Assure Quality in the Crowded Emergency Department." Key definitions fundamental to understanding the effectiveness of emergency care during periods of emergency department (ED) crowding are outlined. Next, a proposed research agenda to evaluate interventions directed at improving emergency care effectiveness is outlined, and the paper concludes with a prioritization of those interventions based on breakout session participant discussion and evaluation.

The effect of computerized provider order entry (CPOE) on ordering patterns for chest pain patients in the emergency department.

This study addressed the effect of CPOE implementation on chest pain ordering patterns for patients in the emergency department. Retrospective order data was collected to assess the implementation. 300 randomly selected, time matched patients with a chief complaint of chest pain were selected in a before/after study. Patient demographics, treatment and disposition data were collected on clinical orders. Order volume, completeness and completion times were assessed before and after implementation. Overall order volume increased significantly from 11.6 pre-CPOE to 19.9 post-implementation (p<.01). Order documentation deficiencies were noted pre-implementation with 35.6% containing all order elements. Order completion times were unchanged; however, laboratory completion times increased for admitted patients post-implementation. Order volume increased after CPOE implementation, likely due to improved ED-based admission order capture for admitted patients. Order completeness improved significantly including standing order documentation. Overall, CPOE implementation is associated with improved clinical documentation with limited effect on clinical testing turn-around times.

Variations in crowding and ambulance diversion in nine emergency departments.

OBJECTIVES: The primary study aim was to examine the variations in crowding when an emergency department (ED) initiates ambulance diversion. METHODS: This retrospective, multicenter study included nine geographically disparate EDs. Daily ED operational variables were collected during a 12-month period (January 2009 to December 2009), including total number of ED visits, mean overall length of stay (LOS), number of ED beds, and hours on ambulance diversion. The primary outcome variable was the "ED workload rate," a surrogate marker for daily ED crowding. It was calculated as the total number of daily ED visits multiplied by the overall mean LOS (in hours) and divided by the number of ED beds available for acute treatment in a given day. The primary predictor variables were ambulance diversion, as a dichotomous variable of whether or not an ED went on diversion at least once during a 24-hour period, diversion hour quintiles, and sites. RESULTS: The annual ED census ranged from 43,000 to 101,000 patients. The percentage of days that an ED went on diversion at least once varied from 4.9% to 86.6%. On days with ambulance diversion, the mean ED workload rate varied from 17.1 to 62.1 patient LOS hours per ED bed among sites. The magnitude of variation in ED workload rate was similar on days without ambulance diversion. Differences in ED workload rate varied among sites, ranging from 1.0 to 6.0 patient LOS hours per ED bed. ED workload rate was higher on average on diversion days compared to nondiversion days. The mean difference between diversion and nondiversion was statistically significant for the majority of sites. CONCLUSIONS: There was marked variation in ED workload rates and whether or not ambulance diversion occurred during a 24-hour period. This variability in initiating ambulance diversion suggests different or inconsistently applied decision-making criteria for initiating diversion.

A computerized pneumococcal vaccination reminder system in the adult emergency department.

BACKGROUND: Pneumococcal vaccination is an effective strategy to prevent invasive pneumococcal disease in the elderly. Emergency department (ED) visits present an underutilized opportunity to increase vaccination rates; however, designing a sustainable vaccination program in an ED is challenging. We examined whether an information technology supported approach would provide a feasible and sustainable method to increase vaccination rates in an adult ED. METHODS: During a 1-year period we prospectively evaluated a team-oriented, workflow-embedded reminder system that integrated four different information systems. The computerized triage application screened all patients 65 years and older for pneumococcal vaccine eligibility with information from the electronic patient record. For eligible patients the computerized provider order entry system reminded clinicians to place a vaccination order, which was passed to the order tracking application. Documentation of vaccine administration was then added to the longitudinal electronic patient record. The primary outcome was the vaccine administration rate in the ED. Multivariate logistic regression analysis was used to estimate the odds ratios and their 95% confidence intervals, representing the overall relative risks of ED workload related variables associated with vaccination rate. RESULTS: Among 3371 patients 65 years old and older screened at triage 1309 (38.8%) were up-to-date with pneumococcal vaccination and 2062 (61.2%) were eligible for vaccination. Of the eligible patients, 621 (30.1%) consented to receive the vaccination during their ED visit. Physicians received prompts for 428 (68.9%) patients. When prompted, physicians declined to order the vaccine in 192 (30.9%) patients, while 222 (10.8%) of eligible patients actually received the vaccine. The computerized reminder system increased vaccination rate from a baseline of 38.8% to 45.4%. Vaccination during the ED visit was associated younger age (OR: 0.972, CI: 0.953-0.991), Caucasian race (OR: 0.329, CI: 0.241-0.448), and longer ED boarding times (OR: 1.039, CI: 1.013-1.065). CONCLUSION: The integrated informatics solution seems to be a feasible and sustainable model to increase vaccination rates in a challenging ED environment.

Measures of crowding in the emergency department: a systematic review.

OBJECTIVES: Despite consensus regarding the conceptual foundation of crowding, and increasing research on factors and outcomes associated with crowding, there is no criterion standard measure of crowding. The objective was to conduct a systematic review of crowding measures and compare them in conceptual foundation and validity. METHODS: This was a systematic, comprehensive review of four medical and health care citation databases to identify studies related to crowding in the emergency department (ED). Publications that "describe the theory, development, implementation, evaluation, or any other aspect of a 'crowding measurement/definition' instrument (qualitative or quantitative)" were included. A "measurement/definition" instrument is anything that assigns a value to the phenomenon of crowding in the ED. Data collected from papers meeting inclusion criteria were: study design, objective, crowding measure, and evidence of validity. All measures were categorized into five measure types (clinician opinion, input factors, throughput factors, output factors, and multidimensional scales). All measures were then indexed to six validation criteria (clinician opinion, ambulance diversion, left without being seen (LWBS), times to care, forecasting or predictions of future crowding, and other). RESULTS: There were 2,660 papers identified by databases; 46 of these papers met inclusion criteria, were original research studies, and were abstracted by reviewers. A total of 71 unique crowding measures were identified. The least commonly used type of crowding measure was clinician opinion, and the most commonly used were numerical counts (number or percentage) of patients and process times associated with patient care. Many measures had moderate to good correlation with validation criteria. CONCLUSIONS: Time intervals and patient counts are emerging as the most promising tools for measuring flow and nonflow (i.e., crowding), respectively. Standardized definitions of time intervals (flow) and numerical counts (nonflow) will assist with validation of these metrics across multiple sites and clarify which options emerge as the metrics of choice in this "crowded" field of measures.

CURB-65 pneumonia severity assessment adapted for electronic decision support.

BACKGROUND: Accurate severity assessment is crucial to the initial management of community-acquired pneumonia (CAP). The CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) score contains data that are entered routinely in electronic medical records and are, thus, electronically calculable. The aim of this study was to determine whether an electronically generated severity estimate using CURB-65 elements as continuous and weighted variables better predicts 30-day mortality than the traditional CURB-65. METHODS: In a retrospective cohort study at a US university-affiliated community teaching hospital, we identified 2,069 patients aged 18 years or older with CAP confirmed by radiographic findings in the ED. CURB-65 elements were extracted from the electronic medical record, and 30-day mortality was identified with the Utah Population Database. Performance of a severity prediction model using continuous and weighted CURB-65 variables was compared with the traditional CURB-65 in the US derivation population and validated in the original 1,048 patients from the CURB-65 international derivation study. RESULTS: The traditional, binary CURB-65 score predicted mortality in the US cohort with an area under the curve (AUC) of 0.82. Our severity prediction model generated from continuous, weighted CURB-65 elements was superior to the traditional CURB-65, with an out-of-bag AUC of 0.86 (P < .001). This finding was validated in the international database, with an AUC of 0.85 for the electronic model compared with 0.80 for the traditional CURB-65 (P = .01). CONCLUSIONS: Using CURB-65 elements as continuous and weighted data improved prediction of 30-day mortality and could be used as a real-time, electronic decision support tool or to adjust outcomes by severity when comparing processes of care.

Factors affecting stress in emergency medicine residents while working in the ED.

OBJECTIVES: The aim of this study was to identify factors other than work hours in the emergency department (ED) work environment contributing to resident stress. METHODS: This study involved a prospective cohort evaluation of emergency medicine residents in the ED. Twelve surveys were collected from 18 subjects, 4 each from the day, evening, and night shifts. The Perceived Stress Questionnaire and a visual analog stress scale were administered. Data collected included the shift number of a given consecutive sequence of shifts, number of procedures performed, number of adverse events, average age of the patients seen by the resident, triage nurse-assigned acuities of the patients seen by the resident during the shift, the number of patients seen during a shift, the number of patients admitted by the resident during the shift, anticipated overtime after a shift, and shift-specific metrics related to overcrowding, including average waiting room time both for the individual residents and for all patients, average waiting room count for all patients, and average occupancy of the ED for all patients. RESULTS: Among the 216 studied shifts, there was considerable variability in stress both within and between residents. In the multivariate mixed-effect regression analysis, only anticipated overtime and process failures were correlated with stress. Factors related to ED overcrowding had no significant effect on resident stress. CONCLUSIONS: Resident stress was most impacted by anticipation of overtime and adverse events. Overcrowding in the ED and traditional measures of workload did not seem to affect stress as much.

Characterizing waiting room time, treatment time, and boarding time in the emergency department using quantile regression.

OBJECTIVES: The objective was to characterize service completion times by patient, clinical, temporal, and crowding factors for different phases of emergency care using quantile regression (QR). METHODS: A retrospective cohort study was conducted on 1-year visit data from four academic emergency departments (EDs; N = 48,896-58,316). From each ED's clinical information system, the authors extracted electronic service information (date and time of registration; bed placement, initial contact with physician, disposition decision, ED discharge, and disposition status; inpatient medicine bed occupancy rate); patient demographics (age, sex, insurance status, and mode of arrival); and clinical characteristics (acuity level and chief complaint) and then used the service information to calculate patients' waiting room time, treatment time, and boarding time, as well as the ED occupancy rate. The 10th, 50th, and 90th percentiles of each phase of care were estimated as a function of patient, clinical, temporal, and crowding factors using multivariate QR. Accuracy of models was assessed by comparing observed and predicted service completion times and the proportion of observations that fell below the predicted 10th, 50th, and 90th percentiles. RESULTS: At the 90th percentile, patients experienced long waiting room times (105-222 minutes), treatment times (393-616 minutes), and boarding times (381-1,228 minutes) across the EDs. We observed a strong interaction effect between acuity level and temporal factors (i.e., time of day and day of week) on waiting room time at all four sites. Acuity level 3 patients waited the longest across the four sites, and their waiting room times were most influenced by temporal factors compared to other acuity level patients. Acuity level and chief complaint were important predictors of all phases of care, and there was a significant interaction effect between acuity and chief complaint. Patients with a psychiatric problem experienced the longest treatment times, regardless of acuity level. Patients who presented with an injury did not wait as long for an ED or inpatient bed. Temporal factors were strong predictors of service completion time, particularly waiting room time. Mode of arrival was the only patient characteristic that substantially affected waiting room time and treatment time. Patients who arrived by ambulance had shorter wait times but longer treatment times compared to those who did not arrive by ambulance. There was close agreement between observed and predicted service completion times at the 10th, 50th, and 90th percentile distributions across the four EDs. CONCLUSIONS: Service completion times varied significantly across the four academic EDs. QR proved to be a useful method for estimating the service completion experience of not only typical ED patients, but also the experience of those who waited much shorter or longer. Building accurate models of ED service completion times is a critical first step needed to identify barriers to patient flow, begin the process of reengineering the system to reduce variability, and improve the timeliness of care provided.