Shari J. Welch

Forecasting daily patient volumes in the emergency department.

BACKGROUNDnShifts in the supply of and demand for emergency department (ED) resources make the efficient allocation of ED resources increasingly important. Forecasting is a vital activity that guides decision-making in many areas of economic, industrial, and scientific planning, but has gained little traction in the health care industry. There are few studies that explore the use of forecasting methods to predict patient volumes in the ED.nnnOBJECTIVESnThe goals of this study are to explore and evaluate the use of several statistical forecasting methods to predict daily ED patient volumes at three diverse hospital EDs and to compare the accuracy of these methods to the accuracy of a previously proposed forecasting method.nnnMETHODSnDaily patient arrivals at three hospital EDs were collected for the period January 1, 2005, through March 31, 2007. The authors evaluated the use of seasonal autoregressive integrated moving average, time series regression, exponential smoothing, and artificial neural network models to forecast daily patient volumes at each facility. Forecasts were made for horizons ranging from 1 to 30 days in advance. The forecast accuracy achieved by the various forecasting methods was compared to the forecast accuracy achieved when using a benchmark forecasting method already available in the emergency medicine literature.nnnRESULTSnAll time series methods considered in this analysis provided improved in-sample model goodness of fit. However, post-sample analysis revealed that time series regression models that augment linear regression models by accounting for serial autocorrelation offered only small improvements in terms of post-sample forecast accuracy, relative to multiple linear regression models, while seasonal autoregressive integrated moving average, exponential smoothing, and artificial neural network forecasting models did not provide consistently accurate forecasts of daily ED volumes.nnnCONCLUSIONSnThis study confirms the widely held belief that daily demand for ED services is characterized by seasonal and weekly patterns. The authors compared several time series forecasting methods to a benchmark multiple linear regression model. The results suggest that the existing methodology proposed in the literature, multiple linear regression based on calendar variables, is a reasonable approach to forecasting daily patient volumes in the ED. However, the authors conclude that regression-based models that incorporate calendar variables, account for site-specific special-day effects, and allow for residual autocorrelation provide a more appropriate, informative, and consistently accurate approach to forecasting daily ED patient volumes.

Twenty Years of Patient Satisfaction Research Applied to the Emergency Department: A Qualitative Review

This clinical review article examines the patient satisfaction literature for the past 20 years. This literature is summarized for qualitative themes and general trends. Intended for the practicing clinician, these themes are then applied to the emergency department (ED) milieu. According to the Agency for Healthcare Research and Quality, the ED is the point of entry for more than half of all patients admitted to the hospital in the United States. Indeed, the ED is the “front door” to the hospital. According to Press Ganey, satisfaction with ED care is at an all-time low. A review of the literature revealed 5 major elements of the ED experience that correlate with patient satisfaction: timeliness of care, empathy, technical competence, information dispensation, and pain management. The literature supporting these 5 elements is summarized and applications to the ED setting are suggested. Other minor correlates with patient satisfaction are also presented.

A multivariate time series approach to modeling and forecasting demand in the emergency department

STUDY OBJECTIVEnThe goals of this investigation were to study the temporal relationships between the demands for key resources in the emergency department (ED) and the inpatient hospital, and to develop multivariate forecasting models.nnnMETHODSnHourly data were collected from three diverse hospitals for the year 2006. Descriptive analysis and model fitting were carried out using graphical and multivariate time series methods. Multivariate models were compared to a univariate benchmark model in terms of their ability to provide out-of-sample forecasts of ED census and the demands for diagnostic resources.nnnRESULTSnDescriptive analyses revealed little temporal interaction between the demand for inpatient resources and the demand for ED resources at the facilities considered. Multivariate models provided more accurate forecasts of ED census and of the demands for diagnostic resources.nnnCONCLUSIONnOur results suggest that multivariate time series models can be used to reliably forecast ED patient census; however, forecasts of the demands for diagnostic resources were not sufficiently reliable to be useful in the clinical setting.

Mapping the 24-Hour Emergency Department Cycle to Improve Patient Flow

BACKGROUNDnIntermountain Healthcare (Salt Lake City), in conjunction with emergency department (ED) staff at LDS Hospital, designed an integrated patient tracking system (PTS) and a specialized data repository (ED Data Mart) that was part of an overall enterprisewide data warehouse. After two years of internal beta testing the PTS and its associated data captures, an analysis of various ED operations by time of day was undertaken.nnnMETHODSnReal-time data, concurrent with individual ED patient encounters from July 1, 2004 through June 30, 2005 were included in a retrospective analysis.nnnRESULTSnA number of patterns were revealed that provide a starting point for understanding ED processes and flow. In particular, ED census, acuity, operations, and throughput vary with the time of day. For example, patients seen during low-census times, in the middle of the night, appear to have a higher acuity. Radiology and laboratory utilization were highly correlated with ED arrivals, and the higher the acuity, the greater the utilization.nnnDISCUSSIONnAlthough it is unclear whether or not these patterns will be applicable to other hospitals in and out of the cohort of tertiary care hospitals, ED cycle data can help all facilities anticipate the resources needed and the services required for efficient patient flow. For example, the fact that scheduling of most service departments falls off after 5:00 P.M., just when the ED is most in need of those services, illustrates a fundamental mismatch between service capacity and demand.

Exploring Strategies to Improve Emergency Department Intake

BACKGROUNDnThe emergency department (ED) is the point of entry for nearly two-thirds of patients admitted to the average United States (US) hospital. Due to unacceptable waits, 3% of patients will leave the ED without being seen by a physician.nnnOBJECTIVESnTo study intake processes and identify new strategies for improving patient intake.nnnMETHODSnA year-long learning collaborative was created to study innovations involving the intake of ED patients. The collaborative focused on the collection of successful innovations for ED intake for an improvement competition. Using a qualitative scoring system, finalists were selected and their innovations were presented to the members of the collaborative at an Association for Health Research Quality-funded conference.nnnRESULTSnThirty-five departments/organizations submitted abstracts for consideration involving intake innovations, and 15 were selected for presentation at the conference. The innovations were presented to ED leaders, researchers, and policymakers. Innovations were organized into three groups: physical plant changes, technological innovations, and process/flow changes.nnnCONCLUSIONnThe results of the work of a learning collaborative focused on ED intake are summarized here as a qualitative review of new intake strategies. Early iterations of these new and unpublished innovations, occurring mostly in non-academic settings, are presented.

The Concept of Reliability in Emergency Medicine

Despite the fact that the United States boasts one of the most advanced health care systems in the world, this system is highly “unreliable” and fraught with error. This article is an introduction to the concept of “reliability” in emergency medicine. It suggests ways in which the health care system could promote increased reliability of operations and processes in the emergency department by using reliability principles and tools that have proven successful in other high-risk settings. Through comparisons to aviation and nuclear power, this article illustrates the differences in culture between emergency medicine and other high-risk organizations and points to the qualities that promote reliability. Finally, a specific model for reliability in the emergency department, operations, and clinical processes is proposed.