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

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.

Identification of patients at high risk for preventable emergency department visits and inpatient admissions after starting chemotherapy: Machine learning applied to comprehensive electronic health record data.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. 1511-1511
Author(s):  
Dylan J. Peterson ◽  
Nicolai P. Ostberg ◽  
Douglas W. Blayney ◽  
James D. Brooks ◽  
Tina Hernandez-Boussard
Keyword(s):  
Machine Learning ◽  
Emergency Department ◽  
Electronic Health Record ◽  
Acute Care ◽  
Cancer Care ◽  
Model Performance ◽  
Emergency Department Visits ◽  
Health Record ◽  
Patients At Risk ◽  
Ed Visits

1511 Background: Acute care use is one of the largest drivers of cancer care costs. OP-35: Admissions and Emergency Department Visits for Patients Receiving Outpatient Chemotherapy is a CMS quality measure that will affect reimbursement based on unplanned inpatient admissions (IP) and emergency department (ED) visits. Targeted measures can reduce preventable acute care use but identifying which patients might benefit remains challenging. Prior predictive models have made use of a limited subset of the data available in the Electronic Health Record (EHR). We hypothesized dense, structured EHR data could be used to train machine learning algorithms to predict risk of preventable ED and IP visits. Methods: Patients treated at Stanford Health Care and affiliated community care sites between 2013 and 2015 who met inclusion criteria for OP-35 were selected from our EHR. Preventable ED or IP visits were identified using OP-35 criteria. Demographic, diagnosis, procedure, medication, laboratory, vital sign, and healthcare utilization data generated prior to chemotherapy treatment were obtained. A random split of 80% of the cohort was used to train a logistic regression with least absolute shrinkage and selection operator regularization (LASSO) model to predict risk for acute care events within the first 180 days of chemotherapy. The remaining 20% were used to measure model performance by the Area Under the Receiver Operator Curve (AUROC). Results: 8,439 patients were included, of whom 35% had one or more preventable event within 180 days of starting chemotherapy. Our LASSO model classified patients at risk for preventable ED or IP visits with an AUROC of 0.783 (95% CI: 0.761-0.806). Model performance was better for identifying risk for IP visits than ED visits. LASSO selected 125 of 760 possible features to use when classifying patients. These included prior acute care visits, cancer stage, race, laboratory values, and a diagnosis of depression. Key features for the model are shown in the table. Conclusions: Machine learning models trained on a large number of routinely collected clinical variables can identify patients at risk for acute care events with promising accuracy. These models have the potential to improve cancer care outcomes, patient experience, and costs by allowing for targeted preventative interventions. Future work will include prospective and external validation in other healthcare systems.[Table: see text]

scholarly journals Characteristics of Emergency Department Visits and Select Predictors of Hospitalization for Adults With Newly Diagnosed Cancer in a Safety-Net Health System

2019 ◽  
Vol 15 (6) ◽  
pp. e490-e500 ◽  
Author(s):  
Arthur S. Hong ◽  
Navid Sadeghi ◽  
Valorie Harvey ◽  
Simon Craddock Lee ◽  
Ethan A. Halm
Keyword(s):  
Emergency Department ◽  
Health System ◽  
Safety Net ◽  
Emergency Department Visits ◽  
Urgent Care ◽  
Newly Diagnosed ◽  
Care Clinic ◽  
Medication Refill ◽  
Urban Safety ◽  
Ed Visits

PURPOSE: There is little description of emergency department (ED) visits and subsequent hospitalizations among a safety-net cancer population. We characterized patterns of ED visits and explored nonclinical predictors of subsequent hospitalization, including time of ED arrival. PATIENTS AND METHODS: This was a retrospective cohort study of patients with cancer (excluding leukemia and nonmelanoma skin cancer) between 2012 and 2016 at a large county urban safety-net health system. We identified ED visits occurring within 180 days after a cancer diagnosis, along with subsequent hospitalizations (observation stay or inpatient admission). We used mixed-effects multivariable logistic regression to model hospitalization at ED disposition, accounting for variability across patients and emergency physicians. RESULTS: The 9,050 adults with cancer were 77.2% nonwhite and 55.0% female. Nearly one-quarter (24.7%) of patients had advanced-stage cancer at diagnosis, and 9.7% died within 180 days of diagnosis. These patients accrued 11,282 ED visits within 180 days of diagnosis. Most patients had at least one ED visit (57.7%); half (49.9%) occurred during business hours (Monday through Friday, 8:00 am to 4:59 pm), and half (50.4%) resulted in hospitalization. More than half (57.5%) of ED visits were for complaints that included: pain/headache, nausea/vomiting/dehydration, fever, swelling, shortness of breath/cough, and medication refill. Patients were most often discharged home when they arrived between 8:00 am and 11:59 am (adjusted odds ratio for hospitalization, 0.69; 95% CI, 0.56 to 0.84). CONCLUSION: ED visits are common among safety-net patients with newly diagnosed cancer, and hospitalizations may be influenced by nonclinical factors. The majority of ED visits made by adults with newly diagnosed cancer in a safety-net health system could potentially be routed to an alternate site of care, such as a cancer urgent care clinic.

A solution to reducing avoidable emergency department visits: Implementation of an outpatient oncology acute care clinic.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e13517-e13517
Author(s):  
Sadaf Charania ◽  
Judy Devlin ◽  
Edie Brucker ◽  
Shayna Simon ◽  
Christine Hong ◽  
...  
Keyword(s):  
Emergency Department ◽  
Cancer Treatment ◽  
Acute Care ◽  
The United States ◽  
Emergency Department Visits ◽  
Urgent Care ◽  
Outpatient Basis ◽  
Oncology Patients ◽  
Care Clinic ◽  
Ed Visits

e13517 Background: Emergency Department (ED) utilization by oncology patients accounts for more than 4.5 million visits in the United States annually, leading to hospitalization four times the rate of the general population.1,2 Many ED visits are the result of symptoms related to cancer or cancer treatment that can be managed on an outpatient basis. Unnecessary admissions lead to possible delays in cancer treatment and increased burden on healthcare resources.3 Simmons Acute Care (SAC), an advanced practice provider (APP)-led clinic, was established in August 2020 to provide an alternative model of oncology care to address these issues. Methods: A multidisciplinary team of key stakeholders was formed to develop an action plan. Institutional data was reviewed to identify the timing and volume of ED visits by oncology patients. Clinic hours were set Monday through Friday, 7:00am – 7:00pm, and referrals were made from primary oncology providers. Evidence-based clinical pathways were developed to standardize patient management, and a data collection plan was implemented to measure outcomes. Internal communications to patients and presentations at staff and faculty meetings occurred to inform patients and clinical staff/providers. Results: From August to December 2020, 165 patient visits were completed in SAC, 141 patients discharged home, 14 patients directly admitted to the hospital, and 10 patients transferred to the ED for a higher level of care. Based on data from 2020, the average cost of an ED visit for an oncology patient was $5,500 and increased to $28,500 if the patient is admitted. Patients with hematologic and gastrointestinal malignancies represented approximately 30% of all visits. Gastrointestinal symptoms were the most frequent presenting chief complaint. Conclusions: Supporting oncology patients in the ambulatory setting provided a reduction in admissions and unnecessary ED visits, leading to cost savings/avoidance to the patient and health system. Based on internal cost analyses, there are potential savings of over $2 million to the organization during this 5-month period. Additional studies are underway to assess patient satisfaction, as well as the economic impact for patients. 1. Rui PKK. National Hospital Ambulatory Medical Care Survey: 2015 emergency department summary tables. https://www.cdc.gov/nchs/data/nhamcs/web_tables/2015_ed_web_tables.pdf 2. Hong AS, Froehlich T, Clayton Hobbs S, Lee SJC, Halm EA. Impact of a Cancer Urgent Care Clinic on Regional Emergency Department Visits. J Oncol Pract. 2019;15(6):e501-e509. doi:10.1200/JOP.18.00743 3. Roy M, Halbert B, Devlin S, Chiu D, Graue R, Zerillo JA. From metrics to practice: identifying preventable emergency department visits for patients with cancer. Support Care Cancer Off J Multinatl Assoc Support Care Cancer. Published online November 7, 2020. doi:10.1007/s00520-020-05874-3

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

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.

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

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.

scholarly journals 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

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.

scholarly journals Predicting emergency department visits in a large teaching hospital

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.

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

2017 ◽  
Vol 15 (5) ◽  
pp. 673-683 ◽  
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.

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

2018 ◽  
Vol 8 (5) ◽  
pp. 384-391 ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document