scholarly journals Managing waiting times to predict no-shows and cancelations at a children’s hospital

2016 ◽  
Vol 9 (5) ◽  
pp. 1107 ◽  
Author(s):  
Miguel Rodríguez-García ◽  
Aldo A McLean-Carranza ◽  
J. Carlos Prado-Prado ◽  
Pablo Domínguez-Caamaño
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Hospital Performance ◽  
Strong Impact ◽  
Distribution Analysis ◽  
Missed Appointments ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
Appointment Keeping

Purpose: Since long waits in hospitals have been found to be related to high rates of no-shows and cancelations, managing waiting times should be considered as an important tool that hospitals can use to reduce missed appointments. The aim of this study is to analyze patients’ behavior in order to predict no-show and cancelation rates correlated to waiting times.Design/methodology/approach: This study is based on the data from a US children’s hospital, which includes all the appointments registered during one year of observation. We used the call-appointment interval to establish the wait time to get an appointment. Four different types of appointment-keeping behavior and two types of patients were distinguished: arrival, no-show, cancelation with no reschedule, and cancelation with reschedule; and new and established patients.Findings: Results confirmed a strong impact of long waiting times on patients’ appointment-keeping behavior, and the logarithmic regression was found as the best-fit function for the correlation between variables in all cases. The correlation analysis showed that new patients tend to miss appointments more often than established patients when the waiting time increases. It was also found that, depending on the patients’ appointment distribution, it might get more complicated for hospitals to reduce missed appointments as the waiting time is reduced.Originality/value: The methodology applied in our study, which combines the use of regression analysis and patients’ appointment distribution analysis, would help health care managers to understand the initial implications of long waiting times and to address improvement related to patient satisfaction and hospital performance.

scholarly journals Improvement in outpatient services using the WeChat calling system in the Shanghai Children’s Hospital

2021 ◽  
Vol 37 (4) ◽  
Author(s):  
Dan Wu ◽  
Wenbin Cui ◽  
Xiulian Wang ◽  
Yanyan Huo ◽  
Guangjun Yu ◽  
...  
Keyword(s):  
Waiting Time ◽  
Wait Time ◽  
Idle Time ◽  
Outpatient Services ◽  
Traditional System ◽  
Children's Hospital ◽  
Creative Commons ◽  
Children’S Hospital ◽  
Patient Waiting Time ◽  
The Mean

Objectives: We explored the utility of WeChat applet as part of the Outpatient Department (OPD) to provide patients with timely queuing information and compared it with the traditional calling system. Methods: Data for the WeChat calling system was extracted for the period of May 2018 to September 2018. Data for the traditional system was extracted for the same period from the year 2017. We compared the effective patient waiting time and nurse idle time i.e. nonproductive time spent on factors outside of employees’ control with the two systems. We also analyzed the relationship between the length of waiting time and conflicts between doctors and patients. Results: The mean wait time for the traditional calling system was 126 minutes, while the average idle time for nurses was 96 minutes/day. On the other hand, the mean wait time for the WeChat calling system was 33 minutes, and the average idle time for nurses was 72 minutes/day. The incremental profit (cost of traditional calling system – cost of WeChat calling system) achieved from switching systems was 13,879 yuan/month. Behavioral observations showed that wait time (OR=2.745, 95%CI 1.936~3.892 P<0.0001) was a risk factor for staff-patient conflict. Conclusion: The cost of the WeChat calling system was significantly lower than the traditional system. Also, the traditional calling system was time-consuming. Longer waiting time was the main factor affecting OPD quality and caused conflicts between doctors and patients. doi: https://doi.org/10.12669/pjms.37.4.4301 How to cite this:Wu D, Cui W, Wang X, Huo Y, Yu G, Chen J. Improvement in outpatient services using the WeChat calling system in the Shanghai Children’s Hospital. Pak J Med Sci. 2021;37(4):---------. doi: https://doi.org/10.12669/pjms.37.4.4301 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Reducing overcrowding in an emergency department: a pilot study

2019 ◽  
Vol 65 (12) ◽  
pp. 1476-1481
Author(s):  
Fábio Ferreira Amorim ◽  
Karlo Jozefo Quadros de Almeida ◽  
Sanderson Cesar Macedo Barbalho ◽  
Vanessa de Amorim Teixeira Balieiro ◽  
Arnaldo Machado Neto ◽  
...  
Keyword(s):  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Federal District ◽  
Arrival Times ◽  
Process Maps ◽  
Manchester Triage System ◽  
Simulation Scenario ◽  
Reduce Wait Time ◽  
Capacity Data

SUMMARY OBJECTIVE Exploring the use of forecasting models and simulation tools to estimate demand and reduce the waiting time of patients in Emergency Departments (EDs). METHODS The analysis was based on data collected in May 2013 in the ED of Recanto das Emas, Federal District, Brasil, which uses a Manchester Triage System. A total of 100 consecutive patients were included: 70 yellow (70%) and 30 green (30%). Flow patterns, observed waiting time, and inter-arrival times of patients were collected. Process maps, demand, and capacity data were used to build a simulation, which was calibrated against the observed flow times. What-if analysis was conducted to reduce waiting times. RESULTS Green and yellow patient arrival-time patterns were similar, but inter-arrival times were 5 and 38 minutes, respectively. Wait-time was 14 minutes for yellow patients, and 4 hours for green patients. The physician staff comprised four doctors per shift. A simulation predicted that allocating one more doctor per shift would reduce wait-time to 2.5 hours for green patients, with a small impact in yellow patients’ wait-time. Maintaining four doctors and allocating one doctor exclusively for green patients would reduce the waiting time to 1.5 hours for green patients and increase it in 15 minutes for yellow patients. The best simulation scenario employed five doctors per shift, with two doctors exclusively for green patients. CONCLUSION Waiting times can be reduced by balancing the allocation of doctors to green and yellow patients and matching the availability of doctors to forecasted demand patterns. Simulations of EDs’ can be used to generate and test solutions to decrease overcrowding.

scholarly journals A Cross-Sectional Analysis of the BC Children’s Hospital Cleft Palate Program Waitlist

2019 ◽  
Vol 27 (4) ◽  
pp. 311-318 ◽  
Author(s):  
Leslie Tze Fung Leung ◽  
Christine A. Loock ◽  
Rebecca Courtemanche ◽  
Douglas J. Courtemanche
Keyword(s):  
Cleft Palate ◽  
Plastic Surgery ◽  
Wait Time ◽  
Wait Times ◽  
Service Needs ◽  
Cross Sectional ◽  
Clinical Assessments ◽  
Children's Hospital ◽  
Children’S Hospital ◽  
Sectional Analysis

Objective: A 2016 review of the BC Children’s Hospital Cleft Palate - Craniofacial Program (CPP) revealed that one-third of patients met the program’s care recommendations and half met the American Cleft Palate-Craniofacial Association guidelines. This study reviews patients on the CPP waitlist and determines median wait times and missed clinical assessments as well as identifies how wait times are influenced by medical complexity, specialized speech service needs, vulnerability, and distance from clinic. Design: Cross-sectional. Setting: BC Children’s Hospital Cleft Palate—Craniofacial Program. Patients: Five hundred seventy-six waitlisted patients. Main Outcome Measures: Additional wait time after recommended appointment date. Correlation of additional wait time with diagnosis, number of specialists required, speech services needed, vulnerability, and distance from the clinic. Missed plastic surgery, speech, and orthodontic assessments according to CPP team recommendations and ACPA guidelines. Results: Patients had a median additional wait time of 11 months (interquartile range: 5-27). Longer additional wait times were associated with a craniofacial diagnosis ( P = .019), a need for formal speech assessments or evaluations ( P < .001), or a requirement to see multiple specialists ( P < .001). Vulnerability and distance from clinic did not affect wait times. Plastic surgery assessments were not available at the preschool and preteen time points for 45 (8%) patients, 355 (62%) patients were unable to access speech assessments, and 120 (21%) were unable to complete an orthodontic assessment. Conclusion: Patients wait up to an additional year to be seen by the CPP and miss speech, orthodontic, and surgical assessments at key developmental milestones. Additional resources are required to address these concerns.

scholarly journals The Obstetrics Gynecology and Children’s Hospital Emergency Room waiting time before hospitalization

2014 ◽  
Vol 13 (4) ◽  
pp. 1162
Author(s):  
T Ocak ◽  
M Bekdas ◽  
A Duran ◽  
SB Göksügür ◽  
B Küçükbayrak
Keyword(s):  
Emergency Room ◽  
Waiting Time ◽  
Hospital Emergency ◽  
Children's Hospital ◽  
Hospital Emergency Room ◽  
Children’S Hospital

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 Prevalence and factors associated with missed hospital appointments: a retrospective review of multiple clinics at Royal Hospital, Sultanate of Oman

BMJ Open ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. e046596
Author(s):  
Ahmed Alawadhi ◽  
Victoria Palin ◽  
Tjeerd van Staa
Keyword(s):  
Waiting Time ◽  
Conditional Logistic Regression ◽  
Waiting Times ◽  
Outpatient Clinics ◽  
Secondary Outcome ◽  
Missed Appointments ◽  
Tertiary Referral Hospital ◽  
Cross Sectional ◽  
Royal Hospital ◽  
Lack Of Information

ObjectivesMissed hospital appointments pose a major challenge for healthcare systems. There is a lack of information about drivers of missed hospital appointments in non-Western countries and extent of variability between different types of clinics. The aim was to evaluate the rate and predictors of missed hospital appointments and variability in drivers between multiple outpatient clinics.SettingOutpatient clinics in the Royal hospital (tertiary referral hospital in Oman) between 2014 and 2018.ParticipantsAll patients with a scheduled outpatient clinic appointment (N=7 69 118).Study designRetrospective cross-sectional analysis.Primary and secondary outcome measuresA missed appointment was defined as a patient who did not show up for the scheduled hospital appointment without notifying or asking for the appointment to be cancelled or rescheduled. The outcomes were the rate and predictors of missed hospital appointments overall and variations by clinic. Conditional logistic regression compared patients who attended and those who missed their appointment.ResultsThe overall rate of missed hospital appointments was 22.3%, which varied between clinics (14.0% for Oncology and 30.3% for Urology). Important predictors were age, sex, service costs, patient’s residence distance from hospital, waiting time and appointment day and season. Substantive variability between clinics in ORs for a missed appointment was present for predictors such as service costs and waiting time. Patients aged 81–90 in the Diabetes and Endocrine clinic had an adjusted OR of 0.53 for missed appointments (95% CI 0.37 to 0.74) while those in Obstetrics and Gynaecology had OR of 1.70 (95% CI 1.11 to 2.59). Adjusted ORs for longer waiting times (>120 days) were 2.22 (95% CI 2.10 to 2.34) in Urology but 1.26 (95% CI 1.18 to 1.36) in Oncology.ConclusionPredictors of a missed appointment varied between clinics in their effects. Interventions to reduce the rate of missed appointments should consider these factors and be tailored to clinic.

Emergency Department Logistic Optimization Using Design of Experiments

2017 ◽  
pp. 386-400
Author(s):  
Jose Antonio Vazquez-Ibarra ◽  
Rodolfo Rafael Medina-Ramirez ◽  
Irma Jimenez-Saucedo
Keyword(s):  
Emergency Department ◽  
Design Of Experiments ◽  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Time Factors ◽  
Healthcare Services ◽  
Public Healthcare ◽  
Response Variable ◽  
Main Entrance

Public healthcare services face a growing demand and Emergency department is the main entrance to these services. Waiting times at Emergency departments are increasing at risky levels, causing that people die in wait rooms due to a lack of staff to serve timely every patient. Present chapter describes one research project conducted in a mexican public hospital which was in the process of adopting a triage systems in order to reach the goal of a maximum wait time in department. Design of experiments is the tool proposed to analyze waiting time factors and define the best levels to reduce the response variable value.

Emergency Department Logistic Optimization Using Design of Experiments

2016 ◽  
pp. 293-308
Author(s):  
Jose Antonio Vazquez-Ibarra ◽  
Rodolfo Rafael Medina-Ramirez ◽  
Irma Jimenez-Saucedo
Keyword(s):  
Emergency Department ◽  
Design Of Experiments ◽  
Waiting Time ◽  
Waiting Times ◽  
Wait Time ◽  
Time Factors ◽  
Healthcare Services ◽  
Public Healthcare ◽  
Response Variable ◽  
Main Entrance

Public healthcare services face a growing demand and Emergency department is the main entrance to these services. Waiting times at Emergency departments are increasing at risky levels, causing that people die in wait rooms due to a lack of staff to serve timely every patient. Present chapter describes one research project conducted in a mexican public hospital which was in the process of adopting a triage systems in order to reach the goal of a maximum wait time in department. Design of experiments is the tool proposed to analyze waiting time factors and define the best levels to reduce the response variable value.

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