David A. Thompson

Effects of Actual Waiting Time, Perceived Waiting Time, Information Delivery, and Expressive Quality on Patient Satisfaction in the Emergency Department

STUDY OBJECTIVEnTo determine the effects of actual waiting time, perception of waiting time, information delivery, and expressive quality on patient satisfaction.nnnMETHODSnDuring a 12-month study period, a questionnaire was administered by telephone to a random sample of patients who had presented to a suburban community hospital emergency department during the preceding 2 to 4 weeks. Respondents were asked several questions concerning waiting times (ie, time from triage until examination by the emergency physician and time from triage until discharge from the ED), information delivery (eg, explanations of procedures and delays), expressive quality (eg, courteousness, friendliness), and overall patient satisfaction.nnnRESULTSnThere were 1,631 respondents. The perception that waiting times were less than expected was associated with a positive overall satisfaction rating for the ED encounter (P < .001). Satisfaction with information delivery and with ED staff expressive quality were also positively associated with overall satisfaction during the ED encounter (P < .001). Actual waiting times were not predictive of overall patient satisfaction (P = NS).nnnCONCLUSIONnPerceptions regarding waiting time, information delivery, and expressive quality predict overall patient satisfaction, but actual waiting times do not. Providing information, projecting expressive quality, and managing waiting time perceptions and expectations may be a more effective strategy to achieve improved patient satisfaction in the ED than decreasing actual waiting time.

Predicting patient satisfaction : A study of two emergency departments

To identify perceptions that predict overall patient (dis)satisfaction with Emergency Department (ED) care, we studied responses to a survey mailed to all discharged patients over a 6-month period (Academic Hospital), and to a telephone interview of a random sample of discharged patients over a 1-year period (Community Hospital). The survey and interview both assessed overall satisfaction, as well as satisfaction with perceived waiting times, information delivery, and expressive quality of physicians, nurses, and staff. Data for 1176 patients (training sample) and 1101 patients (holdout sample) who rated overall satisfaction as either “very good” or “very poor” (Academic Hospital), and for 856 patients (training sample) and 431 patients (holdout sample) who rated overall satisfaction as either “excellent” or “poor” (Community Hospital), were retained for analysis. For both hospitals, nonlinear tree models efficiently achieved overall classification accuracy exceeding 98% in training analysis and 95% in holdout analysis (all p < .0001). The findings suggest that overall patient (dis)satisfaction with care received in the ED is nearly perfectly predictable on the basis of patient-rated expressive qualities of ED staff, particularly physicians and nurses. Interventions designed to reinforce positive (and extinguish negative) expressive health-care provider behaviors may cut the number of extremely dissatisfied patients in half.

How Accurate Are Waiting Time Perceptions of Patients in the Emergency Department

STUDY OBJECTIVEnTo assess the ability of patients to accurately estimate specific waiting times in the emergency department.nnnMETHODSnA questionnaire was administered by telephone to a random sample of 776 patients (or parents or responsible caretakers, if appropriate) who had been treated within the previous 2 to 4 weeks in the ED of a suburban hospital. Respondents were asked their perceptions of two particular time frames: (1) the time elapsed from triage until initial examination by the emergency physician (physician waiting time [PWT]), and (2) the time elapsed from triage until departure from the ED (total waiting time [TWT]). Corresponding actual times were extracted from a computerized database. Time frames were divided into discrete periods for comparison. The correspondence between actual and perceived times was assessed by optimal data analysis.nnnRESULTSnOnly 22.3% of the respondents accurately estimated PWT. Although this level of accuracy is statistically significant (P < .0001), it reflects only 11% of the theoretically possible improvement in accuracy beyond chance. More respondents overestimated than underestimated PWT (49.9% versus 27.8%, respectively). In contrast, TWT was accurately estimated by 36.6% of the respondents (P < .0001), reflecting 18% of the theoretically possible improvement in accuracy beyond chance. Fewer respondents overestimated than underestimated TWT (24.5% versus 38.9%, respectively).nnnCONCLUSIONnPatients are not very accurate in their estimation of actual waiting times. Although fewer than one fourth of the respondents overestimated the TWT spent in the ED, almost half the respondents overestimated the PWT.

The full moon and ED patient volumes: Unearthing a myth

To determine if there is any effect of the full moon on emergency department (ED) patient volume, ambulance runs, admissions, or admissions to a monitored unit, a retrospective analysis of the hospital electronic records of all patients seen in an ED during a 4-year period was conducted in an ED of a suburban community hospital. A full moon occurred 49 times during the study period. There were 150,999 patient visits to the ED during the study period, of which 34,649 patients arrived by ambulance. A total of 35,087 patients was admitted to the hospital and 11,278 patients were admitted to a monitored unit. No significant differences were found in total patient visits, ambulance runs, admissions to the hospital, or admissions to a monitored unit on days of the full moon. The occurrence of a full moon has no effect on ED patient volume, ambulance runs, admissions, or admissions to a monitored unit.

Relative bradycardia in patients with isolated penetrating abdominal trauma and isolated extremity trauma

A relative bradycardia is sometimes seen in patients with hemorrhagic shock. The phenomenon of relative bradycardia in civilian patients with isolated penetrating abdominal trauma and isolated severe extremity trauma who presented to an urban trauma center was studied retrospectively. Relative bradycardia was defined as a pulse rate of less than 100 with a concomitant systolic blood pressure of less than 100 mm Hg. There were 256 patients with isolated penetrating abdominal trauma and 938 patients with isolated severe extremity trauma. The incidence of relative bradycardia was 3.1% (eight of 256) in the group with abdominal trauma and 1.8% (17 of 938) in the group with extremity trauma. A pulse rate less than 100 was documented in 35.2% of all patients presenting with a systolic blood pressure less than 100 mm Hg (25 of 71). A pulse rate of less than 100 was documented in 45.8% of all patients presenting with a systolic blood pressure less than 90 mm Hg (11 of 24). No increased mortality was seen in the patients who evidenced relative bradycardia. The effect of intraperitoneal bleeding on the normal tachycardic response to hemorrhage also was studied. After controlling for volume status using various operational definitions of shock, no statistically significant (P less than .01) difference in pulse rates was noted between patients with isolated penetrating abdominal trauma and isolated extremity trauma. This result suggests that the previously theorized vagal-mediated bradycardia unique to intraperitoneal bleeding may not exist.

Successful Resuscitation of a Severely Hypothermic Neonate

A profoundly hypothermic 5-hour-old infant in cardiac arrest was brought to the emergency department by paramedics. The infant was found wrapped in a garbage bag inside a freezer. She had been in the freezer for approximately four hours. Her initial rectal temperature was 16.2 degrees C. Active external and core warming modalities, including warm blanket, radiant heat lamp, warm humidified air, heated gastric lavage, and heated bladder lavage, were used to rewarm the infant. Her temperature rose to 30.5 degrees C in three hours (4.8 degrees C/hr). The infant converted from a slow idioventricular rhythm to sinus bradycardia at 49 minutes (20.4 degrees C) into the resuscitation. At 53 minutes (21.5 degrees C), the infant moved both upper extremities. At the time of discharge from the hospital, she had no significant physical or neurologic problems. Neurologic examination at 4 months was normal. This report supports prior recommendations to aggressively rewarm severely hypothermic infants in cardiac arrest.

Developing syndrome definitions based on consensus and current use.

OBJECTIVEnStandardized 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.nnnDESIGNnClinical 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.nnnRESULTSnConsensus 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.nnnLIMITATIONSnThe consensus definitions have not yet been validated through implementation.nnnCONCLUSIONnThe 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.

Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology

Objective Our objective was to apply neural network methodology to determine whether adding coded chief complaint (CCC) data to triage information would result in an improved hospital admission prediction model than one without CCC data. Participants and methods We carried out a retrospective derivation and validation cohort study of all adult emergency department visits to a single center. We downloaded triage, chief complaint, and admission/discharge data on each included visit. Using a CCC algorithm and the Levenberg–Marquardt back-propagation learning method, we derived hospital admission prediction models without and with CCC data and applied these to the validation cohort, reporting the prediction models’ characteristics. Results A total of 74 056 emergency department visits were included in the derivation cohort, 85 144 in the validation cohort with 213 CCC categories. The sensitivity/specificity of the derivation cohort models without and with CCC data were 64.0% [95% confidence interval (CI): 63.7–64.3], 87.7% (95% CI: 87.4–88.0), 59.8% (95% CI: 59.5–60.3%), and 91.7% (95% CI: 91.4–92.0) respectively. The sensitivity/specificity of the derived models without and with CCC data applied to the validation cohort were 60.7% (95% CI: 60.4–61.0), 87.7% (95% CI: 87.4–88.0), 59.8% (95% CI: 59.5–60.3), and 90.6% (95% CI: 90.3–90.9) respectively. The area under the curve in the validation cohort for the derived models without and with CCC data were 0.840 (95% CI: 0.838–0.842) and 0.860 (95% CI: 0.858–0.862). Net reclassification index (0.156; 95% CI: 0.148–0.163) and integrated discrimination improvement (0.060; 95% CI: 0.058–0.061) in the CCC model were significant. Conclusion Neural net methodology application resulted in the derivation and validation of a modestly stronger hospital admission prediction model after the addition of CCC data.

Semantic-enhanced models to support timely admission prediction at emergency departments

With the rapid outstripping of limited health care resources by the demands on hospital care, it is of critical importance to find more effective and efficient methods of managing care. Our research addresses the problem of emergency department (ED) crowding by building classification models using various types of pre-admission information to help predict the hospital admission of individual patients. We have developed a framework of hospital admission prediction and proposed two novel approaches that capture semantic information in chief complaints to enhance prediction. Our experiments on an ED data set demonstrate that our proposed models outperformed several benchmark methods for admission prediction. These models can potentially be used as decision support tools at hospitals to improve ED throughput rate and enhance patient care.

Do chief complaints allow targeting of SBIRT in the Emergency Department

Background Emergency Department (ED)-based Screening, Brief Interventions and Referral for Treatment (SBIRT) for alcohol and drug use has the potential to impact public health greatly. Time and resource constraints limit implementation [1]. Targeted intervention may be more efficient and practical. We hypothesized that we could use chief complaints to identify patients at highest risk of positive drug or alcohol use assessments.