scholarly journals Patient‐Reported Outcomes Predict Future Emergency Department Visits and Hospital Admissions in Patients With Stroke

2021 ◽  
Vol 10 (6) ◽  
Author(s):  
Irene L. Katzan ◽  
Nicolas Thompson ◽  
Andrew Schuster ◽  
Dolora Wisco ◽  
Brittany Lapin
Keyword(s):  
Emergency Department ◽  
Patient Reported Outcomes ◽  
Prediction Models ◽  
Hospital Admissions ◽  
Emergency Department Visits ◽  
Mitigation Strategies ◽  
Increased Risk ◽  
Patient Reported ◽  
Ed Visits ◽  
Unplanned Admissions

Background Identification of stroke patients at increased risk of emergency department (ED) visits or hospital admissions allows implementation of mitigation strategies. We evaluated the ability of the Patient‐Reported Outcomes Information Measurement System (PROMIS) patient‐reported outcomes (PROs) collected as part of routine care to predict 1‐year emergency department (ED) visits and admissions when added to other readily available clinical variables. Methods and Results This was a cohort study of 1696 patients with ischemic stroke, intracerebral hemorrhage, subarachnoid hemorrhage, or transient ischemic attack seen in a cerebrovascular clinic from February 17, 2015, to June 11, 2018, who completed the following PROs at the visit: Patient Health Questionnaire‐9, Quality of Life in Neurological Disorders cognitive function, PROMIS Global Health, sleep disturbance, fatigue, anxiety, social role satisfaction, physical function, and pain interference. A series of logistic regression models was constructed to determine the ability of models that include PRO scores to predict 1‐year ED visits and all‐cause and unplanned admissions. In the 1 year following the PRO encounter date, 1046 ED visits occurred in 548 patients; 751 admissions occurred in 453 patients. All PROs were significantly associated with future ED visits and admissions except PROMIS sleep. Models predicting unplanned admissions had highest optimism‐corrected area under the curve (range, 0.684–0.724), followed by ED visits (range, 0.674–0.691) and then all‐cause admissions (range, 0.628–0.671). PROs measuring domains of mental health had stronger associations with ED visits; PROs measuring domains of physical health had stronger associations with admissions. Conclusions PROMIS scales improve the ability to predict ED visits and admissions in patients with stroke. The differences in model performance and the most influential PROs in the prediction models suggest differences in factors influencing future hospital admissions and ED visits.

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 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 Frequent Emergency Department Visits After Spontaneous Intracerebral Hemorrhage: Who Is at Risk?

2018 ◽  
Vol 8 (4) ◽  
pp. 166-170
Author(s):  
Jerina Nogueira ◽  
Pedro Abreu ◽  
Patrícia Guilherme ◽  
Ana Catarina Félix ◽  
Fátima Ferreira ◽  
...  
Keyword(s):  
At Risk ◽  
Emergency Department ◽  
Intracerebral Hemorrhage ◽  
Hospital Discharge ◽  
Transitional Care ◽  
Neurological Impairment ◽  
Emergency Department Visits ◽  
Spontaneous Intracerebral Hemorrhage ◽  
Increased Risk ◽  
Ed Visits

Background: The long-term prognosis of spontaneous intracerebral hemorrhage (SICH) is poor. Frequent emergency department (ED) visits can signal increased risk of hospitalization and death. There are no studies describing the risk of frequent ED visits after SICH. Methods: Retrospective cohort study of a community representative consecutive SICH survivors (2009-2015) from southern Portugal. Logistic regression analysis was performed to identify sociodemographic and clinical factors associated with frequent ED visits (≥4 visits) within the first year after hospital discharge. Results: A total of 360 SICH survivors were identified, 358 (98.6%) of whom were followed. The median age was 72; 64% were males. The majority of survivors (n = 194, 54.2%) had at least 1 ED visit. Reasons for ED visits included infections, falls with trauma, and isolated neurological symptoms. Forty-four (12.3%) SICH survivors became frequent ED visitors. Frequent ED visitors were older and had more hospitalizations ( P < .001) and ED visits ( P < .001) prior to the SICH, unhealthy alcohol use ( P = .049), longer period of index SICH hospitalization ( P = .032), pneumonia during hospitalization ( P = .001), and severe neurological impairment at discharge ( P = .001). Pneumonia during index hospitalization (odds ratio [OR]: 3.08; confidence interval [CI]: 1.39-6.76; P = .005) and history of ED visits prior to SICH (OR: 1.64; CI: 1.19-2.26, P = .003) increased the likelihood of becoming a frequent ED visitor. Conclusions: Predictors of frequent ED visits are identifiable at hospital discharge and during any ED visit. Improvement of transitional care and identification of at-risk patients may help reduce multiple ED visits.

scholarly journals The impact of COVID-19 on hospital admissions and emergency department visits: A population-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0252441
Author(s):  
Elissa Rennert-May ◽  
Jenine Leal ◽  
Nguyen Xuan Thanh ◽  
Eddy Lang ◽  
Shawn Dowling ◽  
...  
Keyword(s):  
Public Health ◽  
Emergency Department ◽  
Hospital Admissions ◽  
Population Based ◽  
Emergency Department Visits ◽  
System Capacity ◽  
Population Based Study ◽  
Health Measures ◽  
Ed Visits ◽  
The Impact

Background As a result of the novel coronavirus disease 2019 (COVID-19), there have been widespread changes in healthcare access. We conducted a retrospective population-based study in Alberta, Canada (population 4.4 million), where there have been approximately 1550 hospital admissions for COVID-19, to determine the impact of COVID-19 on hospital admissions and emergency department (ED visits), following initiation of a public health emergency act on March 15, 2020. Methods We used multivariable negative binomial regression models to compare daily numbers of medical/surgical hospital admissions via the ED between March 16-September 23, 2019 (pre COVID-19) and March 16-September 23, 2020 (post COVID-19 public health measures). We compared the most frequent diagnoses for hospital admissions pre/post COVID-19 public health measures. A similar analysis was completed for numbers of daily ED visits for any reason with a particular focus on ambulatory care sensitive conditions (ACSC). Findings There was a significant reduction in both daily medical (incident rate ratio (IRR) 0.86, p<0.001) and surgical (IRR 0.82, p<0.001) admissions through the ED in Alberta post COVID-19 public health measures. There was a significant decline in daily ED visits (IRR 0.65, p<0.001) including ACSC (IRR 0.75, p<0.001). The most common medical/surgical diagnoses for hospital admissions did not vary substantially pre and post COVID-19 public health measures, though there was a significant reduction in admissions for chronic obstructive pulmonary disease and a significant increase in admissions for mental and behavioral disorders due to use of alcohol. Conclusions Despite a relatively low volume of COVID-19 hospital admissions in Alberta, there was an extensive impact on our healthcare system with fewer admissions to hospital and ED visits. This work generates hypotheses around causes for reduced hospital admissions and ED visits which warrant further investigation. As most publicly funded health systems struggle with health-system capacity routinely, understanding how these reductions can be safely sustained will be critical.

Using a chatbot to reduce emergency department visits and unscheduled hospitalizations among patients with gynecologic malignancies during chemotherapy: a pilot study (Preprint)

2021 ◽  
Author(s):  
Ming-Yuan Huang ◽  
Chia-Sui Weng ◽  
Hsiao-Li Kuo ◽  
Yung-Cheng Su
Keyword(s):  
Emergency Department ◽  
Pilot Study ◽  
Usual Care ◽  
Cancer Patients ◽  
Text Messaging ◽  
Emergency Department Visits ◽  
Control Group ◽  
Gynecologic Malignancies ◽  
Patient Reported ◽  
Ed Visits

BACKGROUND A chatbot is an automatic text-messaging tool that creates a dynamic interaction and simulates a human conversation through text or voice via smartphones or computers. A chatbot could be an effective solution for cancer patients’ follow-up during treatment, and could save time for healthcare providers. OBJECTIVE We conducted a retrospective cohort pilot study to evaluate whether a chatbot-based collection of patient-reported symptoms during chemotherapy, with automated alerts to clinicians, could decrease emergency department (ED) visits and hospitalizations. A control group received usual care. METHODS Self-reporting symptoms were communicated via the chatbot, a Facebook Messenger-based interface for patients with gynecologic malignancies. The chatbot included questions about common symptoms experienced during chemotherapy. Patients could also use the text-messaging feature to speak directly to the chatbot, and all reported outcomes were monitored by a cancer manager. The primary and secondary outcomes of the study were emergency department visits and unscheduled hospitalizations after initiation of chemotherapy after diagnosis of gynecologic malignancies. Multivariate Poisson regression models were applied to assess the adjusted incidence rate ratios (aIRRs) for chatbot use for ED visits and unscheduled hospitalizations after controlling for age, cancer stage, type of malignancy, diabetes, hypertension, chronic renal insufficiency, and coronary heart disease. RESULTS Twenty patients were included in the chatbot group, and 43 in the usual-care group. Significantly lower aIRRs for chatbot use for ED visits (0.27; 95% CI 0.11–0.65; p=0.003) and unscheduled hospitalizations (0.31; 95% CI 0.11–0.88; p=0.028) were noted. Patients using the chatbot approach had lower aIRRs of ED visits and unscheduled hospitalizations compared to usual-care patients. CONCLUSIONS The chatbot was helpful for reducing ED visits and unscheduled hospitalizations in patients with gynecologic malignancies who were receiving chemotherapy. These findings are valuable for inspiring the future design of digital health interventions for cancer patients.

Value and use of patient reported outcomes in sarcoma clinic through GetWell Loop app.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 179-179
Author(s):  
Ensi Voshtina ◽  
John A. Charlson
Keyword(s):  
Patient Satisfaction ◽  
High Risk ◽  
Healthcare Provider ◽  
Cancer Care ◽  
Patient Reported Outcomes ◽  
Hospital Admissions ◽  
Phone Call ◽  
Patient Reported ◽  
Ed Visits ◽  
Reported Data

179 Background: Symptom monitoring in cancer care through patient reported outcomes has been used as an approach to improve symptom detection and communication. Through monitoring of patient symptoms via a systematic way, previous studies have shown a reduction in ED visits and hospital admissions, an enhancement in patient-clinician communication and overall patient satisfaction and wellbeing. In this study, we evaluated outcomes of patient reported data by using the GetWell Loop app to help determine if it facilitates cancer care and improves clinical outcomes. Methods: We performed a retrospective, single-center analysis of sarcoma patients age > 18 who between December 2019 to January 2021 received systemic treatment and were enrolled to use GetWell Loop app to report their treatment related outcomes. We asked patients how they are feeling on a systematic basis post treatment by using a series of questions related to their therapy and potential symptoms. Through the use of GetWell Loop, patients are able to record symptoms information and prompt evaluation by a healthcare provider if they report severe or rapidly changing symptoms. Descriptive statistics were used to summarize use of the GetWell Loop through patient surveys, app generated data, and data available in EPIC electronic medical record. We noted the number of yellow (moderate) and red (severe) alerts generated by patient responses and the corresponding alert trigger to health care provider response. Healthcare provider communication and interventions were recorded, as were hospitalizations and ED visits while using the app. Results: A total of 75 patients were invited to join enrollment. Of those, 54 activated the app, with an activation rate of 72%. Engagement rate was 61% and 74 total alerts were generated. Of the severity of symptoms leading to an alert, 28% were red alters and 72% were yellow alerts. Red alerts most commonly comprised of decreased fluid intake, constipation, and fevers. The majority of red alert symptoms lead to an intervention from nursing staff that started with a phone call, while a minority of interventions were in app messages with the patient. Five red alerts led to an ED visit. The majority of yellow alert symptoms were addressed through in app messages. Both clinical staff and patients felt it helped them stay connected. Patients were most adherent with the first treatment. Patient satisfaction was 87.5% with the app usage. Conclusions: Using patient reported outcomes by using the GetWell Loop app yielded an overall positive patient experience. It provides an opportunity to intervene early with high risk patients and prevent ED visits. Focusing on the first cycle of a regiment and subsequent cycles if high risk seemed to provide the most benefit. There is utility to expand to other disease teams and use the app for survivorship support as well.

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