Determinants of hospital waiting time for outpatient care in India: how demographic characteristics, hospital ownership, and ambulance arrival affect waiting tim

Background: Waiting time in hospital outpatient clinics affects patient satisfaction, access to care, health outcomes, trust, willingness to return and hospital revenue. Only a few studies have explored length and variability of waiting times among patients. This study is an attempt to understand factors affecting waiting time experienced by patients in outpatient clinics.Methods: For this study, data were collected in 2012 from 830 patients seeking care from outpatient clinics located in 30 randomly selected hospitals in the district of Nellore, India. Linear regression and logistic regression models have been used to identify the effect of various determinants on hospital waiting times.Results: The average waiting time in government hospitals was 20.3 minutes compared to 15.5 minutes in private hospitals and 39.71 minutes in voluntary hospitals. Waiting time of men was about six minutes lower than women. After controlling for other patient related and hospital related factors, median wait time was 19% lower for male patients compared to females. Length of waiting declines with patient's age. Patients arriving by ambulance waited 64% less that patients not arriving by ambulance but this pattern was not valid for public hospitals.Conclusions: Significant gender bias was present in all facility-types implying that policy and legal interventions would be required. For-profit hospitals had lower waiting time of patients to ensure higher demand for their services by the economically better-off sections of the population. The results highlight the importance of lowering the waiting time in public sector hospitals, especially for patients arriving in ambulances.

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493 Evaluation of Patient Waiting Times for Plain Radiographs Whilst Attending Outpatient Clinics in A Tertiary Orthopaedic Centre

British Journal of Surgery ◽

10.1093/bjs/znab134.435 ◽

2021 ◽

Vol 108 (Supplement_2) ◽

Author(s):

Z Hayat ◽

E Kinene ◽

S Molloy

Keyword(s):

Waiting Time ◽

Waiting Times ◽

Time Frame ◽

Outpatient Clinics ◽

Contributing Factors ◽

Time Data ◽

Work Related ◽

Mean Waiting Time ◽

Appointment Time ◽

Related Stress

Abstract Introduction Reduction of waiting times is key to delivering high quality, efficient health care. Delays experienced by patients requiring radiographs in orthopaedic outpatient clinics are well recognised. Method To establish current patient and staff satisfaction, questionnaires were circulated over a two-week period. Waiting time data was retrospectively collected including appointment time, arrival time and the time at which radiographs were taken. Results 84% (n = 16) of radiographers believed patients would be dissatisfied. However, of the 296 patients questioned, 56% (n = 165) were satisfied. Most patients (89%) felt the waiting time should be under 30 minutes. Only 36% were seen in this time frame. There was moderate negative correlation (R=-0.5); higher waiting times led to increased dissatisfaction. Mean waiting time was 00:37 and the maximum 02:48. Key contributing factors included volume of patients, staff shortages (73.7%), equipment shortages (57.9%) and incorrectly filled request forms. Eight (42.1%) had felt unwell from work related stress. Conclusions A concerted effort is needed to improve staff and patient opinion. There is scope for change post COVID. Additional training and exploring ways to avoid overburdening the department would benefit. Numerous patients were open to different days or alternative sites. Funding requirements make updating equipment, expanding the department and recruiting more staff challenging.

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Understanding Gap Acceptance Behavior at Unsignalized Intersections using Naturalistic Driving Study Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211007140 ◽

2021 ◽

pp. 036119812110071

Author(s):

Yingfeng (Eric) Li ◽

Haiyan Hao ◽

Ronald B. Gibbons ◽

Alejandra Medina

Keyword(s):

Road Traffic ◽

Wait Time ◽

Study Data ◽

Gap Acceptance ◽

Factors Affecting ◽

Unsignalized Intersections ◽

Research Activities ◽

Stop Sign ◽

Naturalistic Driving Study ◽

Naturalistic Driving

Even though drivers disregarding a stop sign is widely considered a major contributing factor for crashes at unsignalized intersections, an equally important problem that leads to severe crashes at such locations is misjudgment of gaps. This paper presents the results of an effort to fully understand gap acceptance behavior at unsignalized intersections using SHPR2 Naturalistic Driving Study data. The paper focuses on the findings of two research activities: the identification of critical gaps for common traffic/roadway scenarios at unsignalized intersections, and the investigation of significant factors affecting driver gap acceptance behaviors at such intersections. The study used multiple statistical and machine learning methods, allowing a comprehensive understanding of gap acceptance behavior while demonstrating the advantages of each method. Overall, the study showed an average critical gap of 5.25 s for right-turn and 6.19 s for left-turn movements. Although a variety of factors affected gap acceptance behaviors, gap size, wait time, major-road traffic volume, and how frequently the driver drives annually were examples of the most significant.

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Reducing overcrowding in an emergency department: a pilot study

Revista da Associação Médica Brasileira ◽

10.1590/1806-9282.65.12.1476 ◽

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.

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Predicting Patient Wait Times in the Phlebotomy Unit

Global Journal of Medical Research ◽

10.34257/gjmrkvol20is1pg1 ◽

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.

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

BMJ Open ◽

10.1136/bmjopen-2020-046596 ◽

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.

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Emergency Department Logistic Optimization Using Design of Experiments

Health Care Delivery and Clinical Science ◽

10.4018/978-1-5225-3926-1.ch019 ◽

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.

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Emergency Department Logistic Optimization Using Design of Experiments

Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations - Advances in Logistics, Operations, and Management Science ◽

10.4018/978-1-4666-9779-9.ch015 ◽

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

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How does clinical space utilisation impact patient flow?

BMJ Open Quality ◽

10.1136/bmjoq-2018-000542 ◽

2019 ◽

Vol 8 (3) ◽

pp. e000542 ◽

Cited By ~ 1

Author(s):

Alexandra von Guionneau ◽

Charlotte M Burford

Keyword(s):

Quality Improvement ◽

Waiting Time ◽

Primary Outcome ◽

Patient Flow ◽

Waiting Times ◽

Wait Time ◽

Limiting Factor ◽

Electronic Systems ◽

Improvement Project ◽

Medical Wards

BackgroundLong waiting times in accident and emergency (A&E) departments remain one of the largest barriers to the timely assessment of critically unwell patients. In order to reduce the burden on A&Es, some trusts have introduced ambulatory care areas (ACAs) which provide acute assessment for general practitioner referrals. However, ACAs are often based on already busy acute medical wards and the availability of clinical space for clerking patients means that these patients often face long waiting times too. A cheap and sustainable method to reducing waiting times is to evaluate current space utilisation with the view to making use of underutilised workspace. The aim of this quality improvement project was to improve accessibility to pre-existing clinical spaces, and in doing so, reduce waiting times in acute admissions.MethodsData were collected retrospectively from electronic systems and used to establish a baseline wait time from arrival to having blood taken (primary outcome). Quality improvement methods were used to identify potential implementations to reduce waiting time, by increasing access to clinical space, with serial measurements of the primary outcome being used to monitor change.ResultsData were collected over 54 consecutive days. The median wait time increased by 55 min during the project period. However, this difference in waiting time was not deemed significant between the three PDSA cycles (p=0.419, p=0.270 and p=0.350, Mann-Whitney U). Run chart analysis confirmed no significant changes occurred.ConclusionIn acute services, one limiting factor to seeing patients quickly is room availability. Quality improvement projects, such as this, should consider facilitating better use of available space and creating new clinical workspaces. This offers the possibility of reducing waiting times for both staff and patients alike. We recommend future projects focus efforts on integration of their interventions to generate significant improvements.

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The Predictive Effectiveness of Blood Biochemical Indexes for the Severity of COVID-19

Canadian Journal of Infectious Diseases and Medical Microbiology ◽

10.1155/2020/7320813 ◽

2020 ◽

Vol 2020 ◽

pp. 1-6 ◽

Cited By ~ 1

Author(s):

Yingchu Zhou ◽

Bo Li ◽

Jiyang Liu ◽

Dong Chen

Keyword(s):

Clinical Data ◽

Peripheral Blood ◽

Severe Disease ◽

Treatment Center ◽

Intergroup Difference ◽

Related Factors ◽

Factors Affecting ◽

Logistic Regression Models ◽

Blood Biochemical ◽

Wbc Count

Objective. We aimed to explore the predictive effectiveness of blood biochemical indexes for COVID-19 severity. Method. We retrospectively analyzed the clinical data of COVID-19 patients who were cured and discharged from the Public Health Treatment Center of Changsha from January 30, 2020, to February 19, 2020. According to the clinical classification of the disease, the patients were divided into severe and nonsevere groups. General clinical data and underlying medical conditions were recorded through the electronic medical record (EMR) system. Laboratory examination results of the patients during their hospitalization were collected, including the first results for erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), peripheral blood lymphocyte ratio and count, and peripheral blood white blood cell (WBC) count. Univariate and multivariate logistic regression models were used to analyze the predictive effectiveness of blood biochemical indexes and other related factors for COVID-19 severity. Result. In all, 108 COVID-19 patients (median age: 43.9 years (range: 1–75); male patients: 56 (51.85%)) were enrolled, of whom 24 (22.22%) showed severe disease and 84 (77.78%) showed nonsevere disease, and two in 24 patients with severe disease developed into a critically severe type and died. Fever was the most common onset symptom (67.59%), followed by cough (48.15%) and fatigue (37.04%). Comorbidities were important factors affecting the severity of COVID-19, and among the patients with severe disease, the proportion with comorbidities was 70.83%, and the proportion without comorbidities was 29.17%. The intergroup difference was significant P<0.05. In patients with CRP levels (mg/L) of ≤8, >8–≤20, >20–≤40, and >40, the proportions of those with severe and nonsevere disease were 0 to 32, 7 to 19, 6 to 23, and 11 to 10, respectively; the intergroup difference was significant P<0.05. Conclusion. The presence or absence of comorbidities and CRP elevation were independent significant predictors of COVID-19 severity, and hypertension was found as the most common comorbidity in patients with severe disease.

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Integral patient scheduling in outpatient clinics under demand uncertainty to minimize patient waiting times

Health Informatics Journal ◽

10.1177/1460458219832044 ◽

2019 ◽

Vol 26 (1) ◽

pp. 435-448 ◽

Cited By ~ 1

Author(s):

Jyoti R Munavalli ◽

Shyam Vasudeva Rao ◽

Aravind Srinivasan ◽

GG van Merode

Keyword(s):

Real Time ◽

Waiting Time ◽

Outpatient Clinic ◽

Demand Uncertainty ◽

Waiting Times ◽

Optimal Path ◽

Outpatient Clinics ◽

Patient Scheduling ◽

Patient Waiting Time ◽

Patient Waiting

This study addressed the problem of scheduling walk-in patients in real time. Outpatient clinics encounter uncertainty in patient demand. In addition, the disparate departments are locally (department-centric) organized, leading to prolonged waiting times for patients. The proposed integral patient scheduling model incorporates the status and information of all departments in the outpatient clinic along with all possible pathways to direct patients, on their arrival, to the optimal path. The developed hybrid ant agent algorithm identifies the optimal path to reduce the patient waiting time and cycle time (time from registration to exit). An outpatient clinic in Aravind Eye Hospital, Madurai, has a huge volume of walk-in patients and was selected for this study. The simulation study was performed for diverse scenarios followed by implementation study. The results indicate that integral patient scheduling reduced waiting time significantly. The path optimization in real time makes scheduling effective and efficient as it captures the changes in the outpatient clinic instantly.

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