Application of time series analysis in modelling and forecasting emergency department visits in a medical centre in Southern Taiwan

ObjectiveEmergency department (ED) overcrowding is acknowledged as an increasingly important issue worldwide. Hospital managers are increasingly paying attention to ED crowding in order to provide higher quality medical services to patients. One of the crucial elements for a good management strategy is demand forecasting. Our study sought to construct an adequate model and to forecast monthly ED visits.MethodsWe retrospectively gathered monthly ED visits from January 2009 to December 2016 to carry out a time series autoregressive integrated moving average (ARIMA) analysis. Initial development of the model was based on past ED visits from 2009 to 2016. A best-fit model was further employed to forecast the monthly data of ED visits for the next year (2016). Finally, we evaluated the predicted accuracy of the identified model with the mean absolute percentage error (MAPE). The software packages SAS/ETS V.9.4 and Office Excel 2016 were used for all statistical analyses.ResultsA series of statistical tests showed that six models, including ARIMA (0, 0, 1), ARIMA (1, 0, 0), ARIMA (1, 0, 1), ARIMA (2, 0, 1), ARIMA (3, 0, 1) and ARIMA (5, 0, 1), were candidate models. The model that gave the minimum Akaike information criterion and Schwartz Bayesian criterion and followed the assumptions of residual independence was selected as the adequate model. Finally, a suitable ARIMA (0, 0, 1) structure, yielding a MAPE of 8.91%, was identified and obtained as Visitt=7111.161+(at+0.37462 at−1).ConclusionThe ARIMA (0, 0, 1) model can be considered adequate for predicting future ED visits, and its forecast results can be used to aid decision-making processes.

Download Full-text

Emergency Department Visits for Opioid Overdoses Among Patients With Cancer

JNCI Journal of the National Cancer Institute ◽

10.1093/jnci/djz233 ◽

2019 ◽

Vol 112 (9) ◽

pp. 938-943 ◽

Cited By ~ 5

Author(s):

Vikram Jairam ◽

Daniel X Yang ◽

James B Yu ◽

Henry S Park

Keyword(s):

At Risk ◽

Emergency Department ◽

Statistical Tests ◽

Temporal Trends ◽

The United States ◽

Emergency Department Visits ◽

Multivariable Logistic Regression Analysis ◽

Opioid Overdose ◽

Patients With Cancer ◽

Ed Visits

Abstract Background Patients with cancer may be at risk of high opioid use due to physical and psychosocial factors, although little data exist to inform providers and policymakers. Our aim is to examine overdoses from opioids leading to emergency department (ED) visits among patients with cancer in the United States. Methods The Healthcare Cost and Utilization Project Nationwide Emergency Department Sample was queried for all adult cancer-related patient visits with a primary diagnosis of opioid overdose between 2006 and 2015. Temporal trends and baseline differences between patients with and without opioid-related ED visits were evaluated. Multivariable logistic regression analysis was used to identify risk factors associated with opioid overdose. All statistical tests were two-sided. Results Between 2006 and 2015, there were a weighted total of 35 339 opioid-related ED visits among patients with cancer. During this time frame, the incidence of opioid-related ED visits for overdose increased twofold (P < .001). On multivariable regression (P < .001), comorbid diagnoses of chronic pain (odds ratio [OR] 4.51, 95% confidence interval [CI] = 4.13 to 4.93), substance use disorder (OR = 3.54, 95% CI = 3.28 to 3.82), and mood disorder (OR = 3.40, 95% CI = 3.16 to 3.65) were strongly associated with an opioid-related visit. Patients with head and neck cancer (OR = 2.04, 95% CI = 1.82 to 2.28) and multiple myeloma (OR = 1.73, 95% CI = 1.32 to 2.26) were also at risk for overdose. Conclusions Over the study period, the incidence of opioid-related ED visits in patients with cancer increased approximately twofold. Comorbid diagnoses and primary disease site may predict risk for opioid overdose.

Download Full-text

Emergency Department Visits During Outpatient Parenteral Antimicrobial Therapy: A Cohort Study

Open Forum Infectious Diseases ◽

10.1093/ofid/ofx163.791 ◽

2017 ◽

Vol 4 (suppl_1) ◽

pp. S333-S334

Author(s):

So Lim Kim ◽

Angela Everett ◽

Susan J Rehm ◽

Steven Gordon ◽

Nabin Shrestha

Keyword(s):

Risk Factors ◽

Emergency Department ◽

Hospital Admission ◽

Vascular Access ◽

Antimicrobial Therapy ◽

Cleveland Clinic ◽

Emergency Department Visits ◽

Outpatient Parenteral Antimicrobial Therapy ◽

Ed Visits ◽

Access Complications

Abstract Background Outpatient parenteral antimicrobial therapy (OPAT) carries risk of vascular access complications, antimicrobial adverse effects, and worsening of infection. Both OPAT-related and unrelated events may lead to emergency department (ED) visits. The purpose of this study was to describe adverse events that result in ED visits and risk factors associated with ED visits during OPAT. Methods OPAT courses between January 1, 2013 and December 31, 2016 at Cleveland Clinic were identified from the institution’s OPAT registry. ED visits within 30 days of OPAT initiation were reviewed. Reasons and potential risk factors for ED visits were sought in the medical record. Results Among 11,440 OPAT courses during the study period, 603 (5%) were associated with 1 or more ED visits within 30 days of OPAT initiation. Mean patient age was 58 years and 57% were males. 379 ED visits (49%) were OPAT-related; the most common visit reason was vascular access complication, which occurred in 211 (56%) of OPAT-related ED visits. The most common vascular access complications were occlusion and dislodgement, which occurred in 99 and 34 patients (47% and 16% of vascular access complications, respectively). In a multivariable logistic regression model, at least one prior ED visit in the preceding year (prior ED visit) was most strongly associated with one or more ED visits during an OPAT course (OR 2.96, 95% CI 2.38 – 3.71, p-value < 0.001). Other significant factors were younger age (p 0.01), female sex (p 0.01), home county residence (P < 0.001), and having a PICC (p 0.05). 549 ED visits (71%) resulted in discharge from the ED within 24 hours, 18 (2%) left against medical advice, 46 (6%) were observed up to 24 hours, and 150 ED visits (20%) led to hospital admission. Prior ED visit was not associated with hospital admission among patients who visited the ED during OPAT. Conclusion OPAT-related ED visits are most often due to vascular access complications, especially line occlusions. Patients with a prior ED visit in the preceding year have a 3-fold higher odds of at least one ED visit during OPAT compared with patients without a prior ED visit. A strategy of managing occlusions at home and a focus on patients with prior ED visits could potentially prevent a substantial proportion of OPAT-related ED visits. Disclosures All authors: No reported disclosures.

Download Full-text

Predictors of Urgent Cancer Care Clinic and Emergency Department Visits for Individuals Diagnosed with Cancer

Current Oncology ◽

10.3390/curroncol28030165 ◽

2021 ◽

Vol 28 (3) ◽

pp. 1773-1789

Author(s):

Kathleen Decker ◽

Pascal Lambert ◽

Katie Galloway ◽

Oliver Bucher ◽

Marshall Pitz ◽

...

Keyword(s):

Emergency Department ◽

Mixed Models ◽

Cancer Care ◽

Median Number ◽

Emergency Department Visits ◽

Planning Service ◽

Care Clinic ◽

Future Studies ◽

Acute Complications ◽

Ed Visits

In 2013, CancerCare Manitoba (CCMB) launched an urgent cancer care clinic (UCC) to meet the needs of individuals diagnosed with cancer experiencing acute complications of cancer or its treatment. This retrospective cohort study compared the characteristics of individuals diagnosed with cancer that visited the UCC to those who visited an emergency department (ED) and determined predictors of use. Multivariable logistic mixed models were run to predict an individual’s likelihood of visiting the UCC or an ED. Scaled Brier scores were calculated to determine how greatly each predictor impacted UCC or ED use. We found that UCC visits increased up to 4 months after eligibility to visit and then decreased. ED visits were highest immediately after eligibility and then decreased. The median number of hours between triage and discharge was 2 h for UCC visits and 9 h for ED visits. Chemotherapy had the strongest association with UCC visits, whereas ED visits prior to diagnosis had the strongest association with ED visits. Variables related to socioeconomic status were less strongly associated with UCC or ED visits. Future studies would be beneficial to planning service delivery and improving clinical outcomes and patient satisfaction.

Download Full-text

Internet search and medicaid prescription drug data as predictors of opioid emergency department visits

npj Digital Medicine ◽

10.1038/s41746-021-00392-w ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Sean D. Young ◽

Qingpeng Zhang ◽

Jiandong Zhou ◽

Rosalie Liccardo Pacula

Keyword(s):

Emergency Department ◽

Prescription Drug ◽

Drug Utilization ◽

Prediction Models ◽

Emergency Department Visits ◽

Treatment Preferences ◽

Internet Search ◽

Utilization Data ◽

Ed Visits ◽

Medicaid Prescription

AbstractThe primary contributors to the opioid crisis continue to rapidly evolve both geographically and temporally, hampering the ability to halt the growing epidemic. To address this issue, we evaluated whether integration of near real-time social/behavioral (i.e., Google Trends) and traditional health care (i.e., Medicaid prescription drug utilization) data might predict geographic and longitudinal trends in opioid-related Emergency Department (ED) visits. From January 2005 through December 2015, we collected quarterly State Drug Utilization Data; opioid-related internet search terms/phrases; and opioid-related ED visit data. Modeling was conducted using least absolute shrinkage and selection operator (LASSO) regression prediction. Models combining Google and Medicaid variables were a better fit and more accurate (R2 values from 0.913 to 0.960, across states) than models using either data source alone. The combined model predicted sharp and state-specific changes in ED visits during the post 2013 transition from heroin to fentanyl. Models integrating internet search and drug utilization data might inform policy efforts about regional medical treatment preferences and needs.

Download Full-text

Interrupted time series design to evaluate ICD-9-CM to ICD-10-CM coding changes on trends in Colorado emergency department visits related to traumatic brain injury

Injury Epidemiology ◽

10.1186/s40621-021-00308-y ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Lauren Alexis De Crescenzo ◽

Barbara Alison Gabella ◽

Jewell Johnson

Keyword(s):

Public Health ◽

Traumatic Brain Injury ◽

Emergency Department ◽

Brain Injury ◽

Interrupted Time Series ◽

Emergency Department Visits ◽

Control And Prevention ◽

Icd 10 ◽

Ed Visits ◽

The Impact

Abstract Background The transition in 2015 to the Tenth Revision of the International Classification of Disease, Clinical Modification (ICD-10-CM) in the US led the Centers for Disease Control and Prevention (CDC) to propose a surveillance definition of traumatic brain injury (TBI) utilizing ICD-10-CM codes. The CDC’s proposed surveillance definition excludes “unspecified injury of the head,” previously included in the ICD-9-CM TBI surveillance definition. The study purpose was to evaluate the impact of the TBI surveillance definition change on monthly rates of TBI-related emergency department (ED) visits in Colorado from 2012 to 2017. Results The monthly rate of TBI-related ED visits was 55.6 visits per 100,000 persons in January 2012. This rate in the transition month to ICD-10-CM (October 2015) decreased by 41 visits per 100,000 persons (p-value < 0.0001), compared to September 2015, and remained low through December 2017, due to the exclusion of “unspecified injury of head” (ICD-10-CM code S09.90) in the proposed TBI definition. The average increase in the rate was 0.33 visits per month (p < 0.01) prior to October 2015, and 0.04 visits after. When S09.90 was included in the model, the monthly TBI rate in Colorado remained smooth from ICD-9-CM to ICD-10-CM and the transition was no longer significant (p = 0.97). Conclusion The reduction in the monthly TBI-related ED visit rate resulted from the CDC TBI surveillance definition excluding unspecified head injury, not necessarily the coding transition itself. Public health practitioners should be aware that the definition change could lead to a drastic reduction in the magnitude and trend of TBI-related ED visits, which could affect decisions regarding the allocation of TBI resources. This study highlights a challenge in creating a standardized set of TBI ICD-10-CM codes for public health surveillance that provides comparable yet clinically relevant estimates that span the ICD transition.

Download Full-text

Predicting emergency department visits in a large teaching hospital

International Journal of Emergency Medicine ◽

10.1186/s12245-021-00357-6 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Nathan Singh Erkamp ◽

Dirk Hendrikus van Dalen ◽

Esther de Vries

Keyword(s):

Emergency Department ◽

The Netherlands ◽

Teaching Hospital ◽

Time Trend ◽

Prediction Models ◽

Emergency Department Visits ◽

Weather Data ◽

Negative Consequences ◽

Weather Variables ◽

Ed Visits

Abstract Background Emergency department (ED) visits show a high volatility over time. Therefore, EDs are likely to be crowded at peak-volume moments. ED crowding is a widely reported problem with negative consequences for patients as well as staff. Previous studies on the predictive value of weather variables on ED visits show conflicting results. Also, no such studies were performed in the Netherlands. Therefore, we evaluated prediction models for the number of ED visits in our large the Netherlands teaching hospital based on calendar and weather variables as potential predictors. Methods Data on all ED visits from June 2016 until December 31, 2019, were extracted. The 2016–2018 data were used as training set, the 2019 data as test set. Weather data were extracted from three publicly available datasets from the Royal Netherlands Meteorological Institute. Weather observations in proximity of the hospital were used to predict the weather in the hospital’s catchment area by applying the inverse distance weighting interpolation method. The predictability of daily ED visits was examined by creating linear prediction models using stepwise selection; the mean absolute percentage error (MAPE) was used as measurement of fit. Results The number of daily ED visits shows a positive time trend and a large impact of calendar events (higher on Mondays and Fridays, lower on Saturdays and Sundays, higher at special times such as carnival, lower in holidays falling on Monday through Saturday, and summer vacation). The weather itself was a better predictor than weather volatility, but only showed a small effect; the calendar-only prediction model had very similar coefficients to the calendar+weather model for the days of the week, time trend, and special time periods (both MAPE’s were 8.7%). Conclusions Because of this similar performance, and the inaccuracy caused by weather forecasts, we decided the calendar-only model would be most useful in our hospital; it can probably be transferred for use in EDs of the same size and in a similar region. However, the variability in ED visits is considerable. Therefore, one should always anticipate potential unforeseen spikes and dips in ED visits that are not shown by the model.

Download Full-text

Prevalence and direct costs of emergency department visits and hospitalizations for selected diseases that can be transmitted by water, United States

Journal of Water and Health ◽

10.2166/wh.2017.083 ◽

2017 ◽

Vol 15 (5) ◽

pp. 673-683 ◽

Cited By ~ 2

Author(s):

E. A. Adam ◽

S. A. Collier ◽

K. E. Fullerton ◽

J. W. Gargano ◽

M. J. Beach

Keyword(s):

Emergency Department ◽

Otitis Externa ◽

Hepatitis A Virus ◽

Direct Costs ◽

Emergency Department Visits ◽

Escherichia Coli Infection ◽

Waterborne Transmission ◽

Ed Visits ◽

National Emergency ◽

Attribution Process

National emergency department (ED) visit prevalence and costs for selected diseases that can be transmitted by water were estimated using large healthcare databases (acute otitis externa, campylobacteriosis, cryptosporidiosis, Escherichia coli infection, free-living ameba infection, giardiasis, hepatitis A virus (HAV) infection, Legionnaires’ disease, nontuberculous mycobacterial (NTM) infection, Pseudomonas-related pneumonia or septicemia, salmonellosis, shigellosis, and vibriosis or cholera). An estimated 477,000 annual ED visits (95% CI: 459,000–494,000) were documented, with 21% (n = 101,000, 95% CI: 97,000–105,000) resulting in immediate hospital admission. The remaining 376,000 annual treat-and-release ED visits (95% CI: 361,000–390,000) resulted in $194 million in annual direct costs. Most treat-and-release ED visits (97%) and costs ($178 million/year) were associated with acute otitis externa. HAV ($5.5 million), NTM ($2.3 million), and salmonellosis ($2.2 million) were associated with next highest total costs. Cryptosporidiosis ($2,035), campylobacteriosis ($1,783), and NTM ($1,709) had the highest mean costs per treat-and-release ED visit. Overall, the annual hospitalization and treat-and-release ED visit costs associated with the selected diseases totaled $3.8 billion. As most of these diseases are not solely transmitted by water, an attribution process is needed as a next step to determine the proportion of these visits and costs attributable to waterborne transmission.

Download Full-text

US Emergency Department Visits for Adverse Drug Events From Antibiotics in Children, 2011–2015

Journal of the Pediatric Infectious Diseases Society ◽

10.1093/jpids/piy066 ◽

2018 ◽

Vol 8 (5) ◽

pp. 384-391 ◽

Cited By ~ 8

Author(s):

Maribeth C Lovegrove ◽

Andrew I Geller ◽

Katherine E Fleming-Dutra ◽

Nadine Shehab ◽

Mathew R P Sapiano ◽

...

Keyword(s):

Emergency Department ◽

Adverse Drug Events ◽

Emergency Department Visits ◽

Drug Event ◽

Injury Surveillance System ◽

Adverse Event Data ◽

Systemic Medication ◽

Ed Visits ◽

Pharmacy Dispensing Data

Abstract Background Antibiotics are among the most commonly prescribed medications for children; however, at least one-third of pediatric antibiotic prescriptions are unnecessary. National data on short-term antibiotic-related harms could inform efforts to reduce overprescribing and to supplement interventions that focus on the long-term benefits of reducing antibiotic resistance. Methods Frequencies and rates of emergency department (ED) visits for antibiotic adverse drug events (ADEs) in children were estimated using adverse event data from the National Electronic Injury Surveillance System–Cooperative Adverse Drug Event Surveillance project and retail pharmacy dispensing data from QuintilesIMS (2011–2015). Results On the basis of 6542 surveillance cases, an estimated 69464 ED visits (95% confidence interval, 53488–85441) were made annually for antibiotic ADEs among children aged ≤19 years from 2011 to 2015, which accounts for 46.2% of ED visits for ADEs that results from systemic medication. Two-fifths (40.7%) of ED visits for antibiotic ADEs involved a child aged ≤2 years, and 86.1% involved an allergic reaction. Amoxicillin was the most commonly implicated antibiotic among children aged ≤9 years. When we accounted for dispensed prescriptions, the rates of ED visits for antibiotic ADEs declined with increasing age for all antibiotics except sulfamethoxazole-trimethoprim. Amoxicillin had the highest rate of ED visits for antibiotic ADEs among children aged ≤2 years, whereas sulfamethoxazole-trimethoprim resulted in the highest rate among children aged 10 to 19 years (29.9 and 24.2 ED visits per 10000 dispensed prescriptions, respectively). Conclusions Antibiotic ADEs lead to many ED visits, particularly among young children. Communicating the risks of antibiotic ADEs could help reduce unnecessary prescribing. Prevention efforts could target pediatric patients who are at the greatest risk of harm.

Download Full-text

Using logic regression to characterize extreme heat exposures and their health associations: a time-series study of emergency department visits in Atlanta

BMC Medical Research Methodology ◽

10.1186/s12874-021-01278-x ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shan Jiang ◽

Joshua L. Warren ◽

Noah Scovronick ◽

Shannon E. Moss ◽

Lyndsey A. Darrow ◽

...

Keyword(s):

Emergency Department ◽

Time Series ◽

Health Outcomes ◽

Simulation Study ◽

Heat Exposure ◽

Emergency Department Visits ◽

Extreme Heat ◽

Logic Regression ◽

Adverse Health ◽

Adverse Health Outcomes

Abstract Background Short-term associations between extreme heat events and adverse health outcomes are well-established in epidemiologic studies. However, the use of different exposure definitions across studies has limited our understanding of extreme heat characteristics that are most important for specific health outcomes or subpopulations. Methods Logic regression is a statistical learning method for constructing decision trees based on Boolean combinations of binary predictors. We describe how logic regression can be utilized as a data-driven approach to identify extreme heat exposure definitions using health outcome data. We evaluated the performance of the proposed algorithm in a simulation study, as well as in a 20-year time-series analysis of extreme heat and emergency department visits for 12 outcomes in the Atlanta metropolitan area. Results For the Atlanta case study, our novel application of logic regression identified extreme heat exposure definitions that were associated with several heat-sensitive disease outcomes (e.g., fluid and electrolyte imbalance, renal diseases, ischemic stroke, and hypertension). Exposures were often characterized by extreme apparent minimum temperature or maximum temperature over multiple days. The simulation study also demonstrated that logic regression can successfully identify exposures of different lags and duration structures when statistical power is sufficient. Conclusion Logic regression is a useful tool for identifying important characteristics of extreme heat exposures for adverse health outcomes, which may help improve future heat warning systems and response plans.

Download Full-text