scholarly journals P098: Staff and patient attitudes towards influenza vaccination availability during wait times at the Queen Elizabeth II Emergency Department, Halifax, Nova Scotia (in progress)

CJEM ◽  
2019 ◽  
Vol 21 (S1) ◽  
pp. S99
Author(s):  
N. Ozog ◽  
A. Steenbeek ◽  
J. Curran ◽  
N. Kelly
Keyword(s):  
Emergency Department ◽  
Data Collection ◽  
Influenza Vaccine ◽  
Wait Time ◽  
Wait Times ◽  
Research Assistant ◽  
Data Collection Tool ◽  
Online Data ◽  
Convenience Sample ◽  
Online Data Collection

Introduction: Influenza is a preventable infectious disease that causes a yearly burden to Canada. While an influenza vaccine is available free of charge in most provinces, uptake is below target rates. 15% of Canadians who did not get the influenza vaccine reported that they “didn't get around to it”; this presents an opportunity to combine the task of influenza prevention with the logistical issue of another health system challenge: escalating emergency department (ED) wait times. At the Queen Elizabeth II Health Sciences Centre (QEII) in Halifax, NS, average wait time is 4.6 hours. Offering the influenza vaccine during this time could increase convenient access to health services, and ultimately, improve vaccination rates. Methods: This observational, cross-sectional design study is currently in progress. It aims to gauge public interest, health care provider (HCP) support, perceived barriers and perceived facilitators to influenza vaccine availability at the QEII ED. Data is being collected via short, anonymous, close-ended questionnaires over a 7-week period, set to end Dec 14, 2018. Client participants are a convenience sample of low-acuity (Canadian Triage and Acuity Scale score 4/5), adult clients who use the QEII ED during the study period, anticipated n = 150. Client questionnaires are completed, with the help of a research assistant, on an iPad that inputs data directly into a secure online data collection tool. The HCP group is a convenience sample of nurses, physicians and paramedics currently working in the QEII ED, anticipated n = 80. Questionnaires are available to HCPs either on paper outside the staff lounge, or online. Data is being collected via short, anonymous, close-ended questionnaires over a 7-week period, set to end Dec 14, 2018. Client participants are a convenience sample of low-acuity (Canadian Triage and Acuity Scale score 4/5), adult clients who use the QEII ED during the study period, anticipated n = 150. Client questionnaires are completed, with the help of a research assistant, on an iPad that inputs data directly into a secure online data collection tool. The HCP group is a convenience sample of nurses, physicians and paramedics currently working in the QEII ED, anticipated n = 80. Questionnaires are available to HCPs either on paper outside the staff lounge, or online. Results: Following completion of data collection, descriptive statistics, such as the frequency of support for ED influenza vaccination and the proportion of unvaccinated clients willing to receive the vaccine if available in the ED, will be calculated using IBM SPSS Statistics 25. This will provide meaningful data that can be used by the QEII to inform future program planning (i.e. should the influenza vaccine be made available in the ED). Conclusion: An ED vaccination program could add value to the hours clients spend waiting to be seen, and make ED care more cohesive. It is essential that clients and ED staff are approached prior to any new initiative; this study is one way we can lay the necessary groundwork for a public health program that would utilize patient “wait time” more effectively.

Time for an open access secure online data collection tool

BMJ ◽  
2012 ◽  
Vol 345 (jul16 2) ◽  
pp. e4805-e4805
Author(s):  
X. H. Chan ◽  
W. Wynn-Jones ◽  
C. Lobban
Keyword(s):  
Open Access ◽  
Data Collection ◽  
Data Collection Tool ◽  
Online Data ◽  
Online Data Collection

National Health Interview Survey, COVID-19, and Online Data Collection Platforms: Adaptations, Tradeoffs, and New Directions

2021 ◽  
Vol 111 (12) ◽  
pp. 2167-2175
Author(s):  
Stephen J. Blumberg ◽  
Jennifer D. Parker ◽  
Brian C. Moyer
Keyword(s):  
Data Collection ◽  
National Health ◽  
National Health Interview Survey ◽  
Quality Data ◽  
Online Data ◽  
Health Interview Survey ◽  
Related Data ◽  
Health Interview ◽  
Interview Survey ◽  
Online Data Collection

High-quality data are accurate, relevant, and timely. Large national health surveys have always balanced the implementation of these quality dimensions to meet the needs of diverse users. The COVID-19 pandemic shifted these balances, with both disrupted survey operations and a critical need for relevant and timely health data for decision-making. The National Health Interview Survey (NHIS) responded to these challenges with several operational changes to continue production in 2020. However, data files from the 2020 NHIS were not expected to be publicly available until fall 2021. To fill the gap, the National Center for Health Statistics (NCHS) turned to 2 online data collection platforms—the Census Bureau’s Household Pulse Survey (HPS) and the NCHS Research and Development Survey (RANDS)—to collect COVID-19‒related data more quickly. This article describes the adaptations of NHIS and the use of HPS and RANDS during the pandemic in the context of the recently released Framework for Data Quality from the Federal Committee on Statistical Methodology. (Am J Public Health. 2021;111(12):2167–2175. https://doi.org/10.2105/AJPH.2021.306516 )

scholarly journals P021: A novel way of hiding beds: manipulating wait time predictions to alter future patient flows into the ED

CJEM ◽  
2020 ◽  
Vol 22 (S1) ◽  
pp. S72-S72
Author(s):  
S. Strobel
Keyword(s):  
Mean Square Error ◽  
Administrative Data ◽  
Wait Time ◽  
Care Physician ◽  
Unintended Consequences ◽  
Prediction Algorithm ◽  
Wait Times ◽  
Mean Square ◽  
Online Data ◽  
Number Of Patients

Introduction: Wait time predictions have become more common in emergency departments in Canada. These estimate the wait times a patient faces to see providers and they are usually provided in an accessible way such as through an online interface. One purpose of these trackers is to improve ED system efficiency. Patients can self-triage to alternative care such as their primary care physician, defer care until a later time or could move from oversubscribed to undersubscribed EDs. However, these mechanisms could also be abused. If providers can artificially influence the wait time this may provide a possible lever to change patients flows to an ED. I investigate whether there is evidence suggestive of manipulation of online wait time trackers at an ED system in Ontario. Methods: Inputs into the wait time prediction algorithm, like patient volumes are taken from the ED EMR. This is the most likely place where staff can manipulate the wait time tracker by retaining patients in the EMR system even after they are discharged. I examine two sets of data to assess whether the online tracker displays differences in patient volumes from “true” data. The first is scraped data of patient volumes from the wait times website. The second are the accurate patient volumes from administrative data which includes when a physician discharged patients from the ED. I compare values of the true patient volumes to the online values and plot distributions of these differences. I also employ measures of accuracy such as mean square error and root mean square error to provide a value of how accurate the online data is compared to the true data. I examine these by ED and over time. Results: There are differences between the number of patients that are posted online and those in the administrative data. The distributions of these differences are skewed towards positive values suggesting that the online data more often overcounts rather than undercounts patients. Measures of accuracy increase during times when EDs are congested but do not decrease when EDs become less congested. This inaccuracy persists for a period after EDs cease to be busy. Conclusion: ED wait time trackers have the potential to be manipulated. When staff have incentive to reduce patient volumes, online data becomes more inaccurate relative to true data. This suggests that wait time trackers may have unintended consequences and that the information that they provide may not be entirely accurate.

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