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.

scholarly journals PERFORMANCE ANALYSIS OF A REAL-TIME ADAPTIVE PREDICTION ALGORITHM FOR TRAFFIC CONGESTION

2018 ◽  
Author(s):  
Khodabacchus Muhamad Nadeem ◽  
Tulsi Pawan Fowdur
Keyword(s):  
Real Time ◽  
Mean Square Error ◽  
Traffic Congestion ◽  
Selection Criterion ◽  
Prediction Algorithm ◽  
Mean Square ◽  
General Packet Radio Service ◽  
Prediction Algorithms ◽  
Cloud Server ◽  
Congestion Prediction

Traffic congestion is a major factor to consider in the development of a sustainable urban road network. In the past, several mechanisms have been developed to predict congestion, but few have considered an adaptive real-time congestion prediction. This paper proposes two congestion prediction approaches are created. The approaches choose between five different prediction algorithms using the Root Mean Square Error model selection criterion. The implementation consisted of a Global Positioning System based transmitter connected to an Arduino board with a Global System for Mobile/General Packet Radio Service shield that relays the vehicle’s position to a cloud server. A control station then accesses the vehicle’s position in real-time, computes its speed. Based on the calculated speed, it estimates the congestion level and it applies the prediction algorithms to the congestion level to predict the congestion for future time intervals. The performance of the prediction algorithms was analysed, and it was observed that the proposed schemes provide the best prediction results with a lower Mean Square Error than all other prediction algorithms when compared with the actual traffic congestion states.  

scholarly journals Optimizing Outpatient Phlebotomy Staffing: Tools to Assess Staffing Needs and Monitor Effectiveness

2014 ◽  
Vol 138 (7) ◽  
pp. 929-935 ◽  
Author(s):  
Aleksandar S. Mijailovic ◽  
Milenko J. Tanasijevic ◽  
Ellen M. Goonan ◽  
Rachel D. Le ◽  
Jonathan M. Baum ◽  
...  
Keyword(s):  
Health Care ◽  
Wait Time ◽  
Data Driven ◽  
Wait Times ◽  
Service Capacity ◽  
Staffing Levels ◽  
Number Of Patients ◽  
Patient Wait Time ◽  
Staffing Needs ◽  
Improve Patient

Context.—Short patient wait times are critical for patient satisfaction with outpatient phlebotomy services. Although increasing phlebotomy staffing is a direct way to improve wait times, it may not be feasible or appropriate in many settings, particularly in the context of current economic pressures in health care. Objective.—To effect sustainable reductions in patient wait times, we created a simple, data-driven tool to systematically optimize staffing across our 14 phlebotomy sites with varying patient populations, scope of service, capacity, and process workflows. Design.—We used staffing levels and patient venipuncture volumes to derive the estimated capacity, a parameter that helps predict the number of patients a location can accommodate per unit of time. We then used this parameter to determine whether a particular phlebotomy site was overstaffed, adequately staffed, or understaffed. Patient wait-time and satisfaction data were collected to assess the efficacy and accuracy of the staffing tool after implementing the staffing changes. Results.—In this article, we present the applications of our approach in 1 overstaffed and 2 understaffed phlebotomy sites. After staffing changes at previously understaffed sites, the percentage of patients waiting less than 10 minutes ranged from 88% to 100%. At our previously overstaffed site, we maintained our goal of 90% of patients waiting less than 10 minutes despite staffing reductions. All staffing changes were made using existing resources. Conclusions.—Used in conjunction with patient wait-time and satisfaction data, our outpatient phlebotomy staffing tool is an accurate and flexible way to assess capacity and to improve patient wait times.

scholarly journals Predicting Patient Wait Times in the Phlebotomy Unit

2020 ◽  
pp. 1-5
Author(s):  
Dilek Orbatu ◽  
Oktay Yıldırım ◽  
Eminullah Yaşar ◽  
Ali Rıza Şişman ◽  
Süleyman Sevinç
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
High Accuracy ◽  
Wait Times ◽  
Blood Collection ◽  
Time Prediction ◽  
Number Of Patients ◽  
Patient Will ◽  
Patient Waiting Time

Patients frequently complain of long waiting times in phlebotomy units. Patients try to predict how long they will stay in the phlebotomy unit according to the number of patients in front of them. If it is not known how fast the queue is progressing, it is not possible to predict how long a patient will wait. The number of prior patients who will come to the phlebotomy unit is another important factor that changes the waiting time prediction. We developed an artificial intelligence (AI)-based system that predicts patient waiting time in the phlebotomy unit. The system can predict the waiting time with high accuracy by considering all the variables that may affect the waiting time. In this study, the blood collection performance of phlebotomists, the duration of the phlebotomy in front of the patient, and the number of prior patients who could come to the phlebotomy unit was determined as the main parameters affecting the waiting time. For two months, actual wait times and predicted wait times were compared. The wait time for 95 percent of the patients was predicted with a variance of ± 2 minutes. An AI-based system helps patients make predictions with high accuracy, and patient satisfaction can be increased.

scholarly journals A novel prediction algorithm for multivariate data sets

2021 ◽  
Vol 4 (2) ◽  
pp. 225-240
Author(s):  
Pinki Sagar ◽  
◽  
Prinima Gupta ◽  
Rohit Tanwar ◽  
◽  
...  
Keyword(s):  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Multivariate Data ◽  
Prediction Algorithm ◽  
Series Data ◽  
Data Sets ◽  
Mean Square ◽  
Data Set ◽  
Independent Variables

Regression analysis is a statistical technique that is most commonly used for forecasting. Data sets are becoming very large due to continuous transactions in today's high-paced world. The data is difficult to manage and interpret. All the independent variables can’t be considered for the prediction because it costs high for maintenance of the data set. A novel algorithm for prediction has been implemented in this paper. Its emphasis is on extraction of efficient independent variables from various variables of the data set. The selection of variables is based on Mean Square Errors (MSE) as well as on the coefficient of determination r2p, after that the final prediction equation for the algorithm is framed on the basis of deviation of actual mean. This is a statistical based prediction algorithm which is used to evaluate the prediction based on four parameters: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and residuals. This algorithm has been implemented for a multivariate data set with low maintenance costs, preprocessing costs, lower root mean square error and residuals. For one dimensional, two-dimensional, frequent stream data, time series data and continuous data, the proposed prediction algorithm can also be used. The impact of this algorithm is to enhance the accuracy rate of forecasting and minimized average error rate.

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.

Delay to colectomy and survival for patients diagnosed with colon cancer.

2015 ◽  
Vol 33 (3_suppl) ◽  
pp. 541-541
Author(s):  
Sanjay Prakash Bagaria ◽  
Michael Heckman ◽  
Nancy N. Diehl ◽  
Alexander S. Parker ◽  
Nabil Wasif
Keyword(s):  
Colon Cancer ◽  
Cox Regression ◽  
Early Stage ◽  
Wait Time ◽  
Wait Times ◽  
Risk Of Death ◽  
Risk Of Mortality ◽  
Number Of Patients ◽  
Increased Risk ◽  
Early Stage Colon Cancer

541 Background: A long wait-time for colectomy for colon cancer should theoretically affect survival but, to date, the association between delay to colectomy and survival remains unresolved. Methods: We studied 4,692 patients who underwent colectomy for colon cancer between 1990 and 2012. Wait-time was defined as the number of days between diagnosis and colectomy. Cox regression models were used to estimate all-cause mortality across wait-time categories. Multivariable analyses were controlled for clinicopathologic variables and Charlson comorbidity index. Results: The number of patients in the wait-time group of 1-28 days was 3,950 (84.2%), 29-84 days was 681 (22.7%), and >84 days was 61 (1.3%). A wait-time of 29-84 days was not associated with an increased risk of death (HR=1.13; p=0.056) when compared to a wait-time of 1-28 days. Though a wait-time >84 days represented a small group, it was associated with an increased risk of death (HR=1.60; p=0.013). For patients with stage I and II disease, wait-times of 29-84 days (HR =1.44; p=0.0001) and >84 days (adjusted HR=2.24; p=0.0007) were associated with an increased risk of mortality. Conclusions: A wait-time for colon cancer surgery of up to 12 weeks is likely safe for most patients. However, those suspected to have early-stage colon cancer may benefit from less of delay. These data provide a framework to address concerns over prolonged wait-times and can direct efforts for timely surgical intervention in patients with colon cancer. [Table: see text]

The Bordeaux Automatic Meridian Circle

1978 ◽  
Vol 48 ◽  
pp. 227-228
Author(s):  
Y. Requième
Keyword(s):  
Mean Square Error ◽  
Mean Square ◽  
Meridian Circle ◽  
The Mean ◽  
Full Automation

In spite of important delays in the initial planning, the full automation of the Bordeaux meridian circle is progressing well and will be ready for regular observations by the middle of the next year. It is expected that the mean square error for one observation will be about ±0.”10 in the two coordinates for declinations up to 87°.

Investigation of Accuracy of a Calibrated Estimator of a Ratio by Modelling

2005 ◽  
Vol 10 (4) ◽  
pp. 333-342
Author(s):  
V. Chadyšas ◽  
D. Krapavickaitė
Keyword(s):  
Mean Square Error ◽  
Taylor Series ◽  
Random Sampling ◽  
Finite Population ◽  
Taylor Series Expansion ◽  
Simple Random Sampling ◽  
Population Parameter ◽  
Mean Square ◽  
Parameter Ratio ◽  
Using Data

Estimator of finite population parameter – ratio of totals of two variables – is investigated by modelling in the case of simple random sampling. Traditional estimator of the ratio is compared with the calibrated estimator of the ratio introduced by Plikusas [1]. The Taylor series expansion of the estimators are used for the expressions of approximate biases and approximate variances [2]. Some estimator of bias is introduced in this paper. Using data of artificial population the accuracy of two estimators of the ratio is compared by modelling. Dependence of the estimates of mean square error of the estimators of the ratio on the correlation coefficient of variables which are used in the numerator and denominator, is also shown in the modelling.

Performance analysis of global local mean square error criterion of stochastic linearization for nonlinear oscillator

2019 ◽  
Author(s):  
Nguyen Cao Thang ◽  
Luu Xuan Hung
Keyword(s):  
Performance Analysis ◽  
Mean Square Error ◽  
Nonlinear Oscillators ◽  
Degree Of Freedom ◽  
Equivalent Linearization ◽  
Mean Square ◽  
Error Criterion ◽  
Stochastic Linearization ◽  
Local Mean ◽  
Mean Square Error Criterion

The paper presents a performance analysis of global-local mean square error criterion of stochastic linearization for some nonlinear oscillators. This criterion of stochastic linearization for nonlinear oscillators bases on dual conception to the local mean square error criterion (LOMSEC). The algorithm is generally built to multi degree of freedom (MDOF) nonlinear oscillators. Then, the performance analysis is carried out for two applications which comprise a rolling ship oscillation and two degree of freedom one. The improvement on accuracy of the proposed criterion has been shown in comparison with the conventional Gaussian equivalent linearization (GEL).

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