جستجوی مقالات مرتبط با کلیدواژه « waiting time » در نشریات گروه « پزشکی »

  Many countries with universal healthcare have a parallel private healthcare sector due to the waiting time in the public sector. People purchase individual health insurance to pay for private services. Past studies on the relationship between the public sector’s waiting time and the demand for health insurance have two limitations: not considering the capacity of the private sector, and subsequently, the omission of a feedback loop. These limitations are also present in the health insurance policy discussion in Hong Kong, where the public sector is overstretched. A lack of understanding of market dynamics might lead to unrealistic expectations of public policy. This study highlights these limitations, and tries to answer the research question: whether the historical dynamics between the intersectoral imbalance of burden and the demand for health insurance in Hong Kong could be quantitatively explained.

  A system dynamics model was created based on a negative feedback loop. The model’s initial input was the percentage of population with health insurance in 2009, and to simulate the percentage continuously until 2019. Results from 2015 to 2019 were compared with actual figures to examine the model’s explanatory power. Multivariable sensitivity analysis was performed.

  With initial fluctuation, the simulated result stabilized and was within the acceptable error range from 2015 to 2019. The mean absolute percentage error (MAPE) was 0.94%. At the end of 2019, the simulated percentage of population with health insurance is 36.6% versus the “real value” of 36.7%. Simulated patient admissions and occupancy rates also approximate the reality. Sensitivity analysis demonstrates the robustness of the model.

  We can quantitatively explain the feedback loop between health system burden and demand for health insurance. With local parameterization, this model should be transferable to other universal health systems for a better understanding of the system dynamics and more informed policy-making.

The visit length is considered one of the indicators for assessing patients’ satisfaction. Factors such as waiting time for getting a visit affects the desirability of the visit.

This study aimed to investigate the visit length and waiting time of patients in public and private clinics in Tabriz.

This is a descriptive-analytic study conducted in five clinics in 2018. A questionnaire-based survey was used to collect data from 386 participants recruited through simple random sampling. Mann-Whitney U and Kruskal-Wallis tests were applied to analyze the data using SPSS version 22.0.

Overall, the mean visit length was 25.5 and 25.4 min in public and private centers, respectively, while the mean waiting time was 141.2 and 156.4 min in public and private centers, respectively. There was no significant difference between public and private centers regarding the visit length (P > 0.05); however, there was a significant difference between public and private centers in terms of waiting time (P < 0.05).

The waiting time was too much, especially in private clinics, which can negatively affect patient satisfaction. Therefore, suggested interventions may consist of using internet and telephone admission, scheduling a waiting list, and requiring physicians to be present on time.

Patients’ waiting time for healthcare services is identified as one of the key measurements of an effective health system. This factor has an important role in patients’ satisfaction as well. One factor that is related to the waiting time is patients’ punctuality. In this study the effect of patients’ and physicians’ punctuality on waiting time was assessed.

This was a cross- sectional study. The study population was outpatients that were admitted in clinics of a general non-educational hospital. 3500 samples were selected from all clinics. The sampling method was stratified randomized method. Samples were included after taking the informed consent. Data gathered by check lists that recorded the patients work flow at the clinic and the time of arrival to and departure of each station.

about 34% of patients had appointment that 98.5 % of them were unpunctual. The correlation of patient unpunctuality (positive and negative) and their waiting time indicated that these variables had positive correlation (p<0.001). Assessing the correlation of physicians’ punctuality and patients’ waiting time indicated that these variables also had positive correlation (p<0.001).

Appointment systems are very useful in controlling patients waiting time. This study identified that patient’s unpunctuality will increase patients waiting time. By the way negative punctuality will affect waiting time more that positive punctuality. Other important findings of this study was revealing the relation of physicians’ unpunctuality and patients’ waiting time. Punctuality of patients and providers are very important in the performance of appointment system in outpatient settings.

Waiting time is an important factor affecting the satisfaction of outpatients and also one of the indicators for assessing the level of access to health care services as well as service quality.

The aim of this study was to evaluate the waiting time and satisfaction in outpatients referring to Imam Hossein polyclinic, Zanjan in 2017 using patient flow analysis (PFA).

The present research is a descriptive-analytical study. A total of 392 outpatients were selected using a convenience sampling method. Data were collected using a timing checklist and Patient Satisfaction Questionnaire (PSQ) and analyzed using structural equation analysis and analysis of variance through LISREL 8.5, MINITAB 17.2, SPSS 24, and STATISTICA 12 software.

According to the obtained data, the longest average waiting time was in the radiology clinic (27±11 minutes), and the shortest was in the pharmacy (5±3 minutes). Also, the average duration of the examination was 9±9 minutes. The mean patient satisfaction was 3.38 out of 5 (average degree of satisfaction). The results showed that waiting time was inversely related to patient satisfaction and examination time was directly related to satisfaction, and waiting time in the laboratory and admission had the greatest effect on reducing patient satisfaction. Also, the patientchr('39')s waiting time to receive services and the duration of the doctorchr('39')s visit were at an acceptable level and patientschr('39') satisfaction with the waiting time was at a moderate level.

Since the radiology clinic was one of the bottlenecks of outpatient facilities in this polyclinic, measures to reduce waiting time and increase patient satisfaction are recommended.

 The velocity of providing services in health centers is crucial to reduce mortality and adverse outcomes.

 The present study aimed at determining the waiting time from entering the emergency department (ED) up to physician visiting based on congestion in the triple shift at Shahid Mohammadi Hospital in Bandar Abbas, Iran.

 The current cross-sectional, analytical study was conducted in 2019 on 1285 subjects selected from three shifts. The data collection tools included demographic data and standard triage forms, as well as a timetable with a stopwatch. The time between patient arrival and physician visit was recorded. SPSS software version 21 was employed to analyze the data.

 The highest percentage of patients, 65.1% (n = 837), was non-traumatic, 38.98% (n = 501) referred during the evening shift, and 47.54% (n = 611) were related to the triage level 3. The maximum average waiting time from the beginning to the end of the triage was 4.46, and up to the physician, the visit was 12.8 minutes. Waiting time from entering ED up to physician first visit in terms of gender, refer to ED, and cause of referral statistically divulged a significant difference (P < 0.05). Estimation of the maximum congestion in the department was from 16:00 to 20:00, which showed a significant difference with other day times (P < 0.05).

 The average waiting time for patients was higher than the global standard. The interventions based on the maximum congestion in ED can be effective in reducing patient waiting time.

Policies assigning low-priority patients treatment delays for care, in order to make room for patients of higher priority arriving later, are common in secondary healthcare services today. Alternatively, each new patient could be granted the first available appointment. We aimed to investigate whether prioritisation can be part of the reason why waiting times for care are often long, and to describe how departments can improve their waiting situation by changing away from prioritisation.  

We used patient flow data from 2015 at the Department of Otorhinolaryngology, Haukeland University Hospital, Norway. In Dynaplan Smia, Dynaplan AS, dynamic simulations were used to compare how waiting time, size and shape of the waiting list, and capacity utilisation developed with and without prioritisation. Simulations were started from the actual waiting list at the beginning of 2015, and from an empty waiting list (simulating a new department with no initial patient backlog).  

From an empty waiting list and with capacity equal to demand, waiting times were built 7 times longer when prioritising than when not. Prioritisation also led to poor resource utilisation and short-lived effects of extra capacity. Departments where prioritisation is causing long waits can improve their situation by temporarily bringing capacity above demand and introducing “first come, first served” instead of prioritisation. 

A poor appointment allocation policy can build long waiting times, even when capacity is sufficient to meet demand. By bringing waiting times down and going away from prioritisation, the waiting list size and average waiting times at the studied department could be maintained almost 90% below the current level – without requiring permanent change in the capacity/demand ratio.

Patient preparation process for surgery is one of the strategic control points in hospitals, which can improve the patient satisfaction and increase the quality of provided services. This study aimed to determine the impact of pre-surgery consultation unit startup on waiting time before surgery and cancellation of surgery.

This cross-sectional and analytical study was conducted in an Eye Hospital in Tehran. The study population included patients hospitalized for cataract surgery at Farabi Hospital in Tehran City, Iran. Data were collected using a researcher-made questionnaire containing information about the participants' gender, age, waiting time, cancellation of surgery, and length of stay. Data were analyzed by SPSS18 using Mann-Whitney and Chi-Square tests.

Pre-surgery consultation unit startup was able to significantly reduce the patient waiting time before surgery (P-value < 0.001). No significant change was observed in cancellation rate and length of stay after the pre-surgery consultation unit startup (P-value > 0.05).

Considering the reduction of patients' waiting time as one of the key indicators in eye surgery, preoperative consultation process and other effective processes should be improved to maximize benefits of establishing a preoperative consultation unit at Farabi Hospital.

Wait time at the emergency department is a global issue. However, most studies have relied on measured times to reduce and manage wait times at the emergency department. A quality improvement project using a mixed-method was conducted to reduce and manage the time patients spent at the emergency department. In-depth interviews with the business team staff showed that pharmacy was perceived by them as taking 30 minutes to complete the medication orders. Pharmacy was therefore seen as the bottleneck within the patient flow at the emergency department by these staff. However, when the times pharmacy completed the orders were measured using time stamps, the measured times were found to be 10 minutes. The business team staff found the measured time data as failing to capture their interactions with pharmacy. Hence, observations were further conducted with the business team staff to understand their perceptions. These observations found that patients were constantly approaching the business team staff for updates on their pharmacy orders which added anxiety to the business team staff. The led them to continuously check the hospital information system for any updates on the completions of the pharmacy orders as this was needed to finalize the bills and discharge the patients from the emergency departments. These anxiety and workload led to the perceived time to be 30 minutes among the staff. The combinations of perceived time and measured time data were used in this quality improvement project to advocate to the nurses and emergency department physicians to inform the patients that pharmacy would take time to thoroughly check and complete the orders and that they would be called to the counter by the business team staff when the orders were completed. This helped to manage the expectations of the patients and reduce the anxiety and workload of the staff.

Proper management of health systems requires the use of a suitable decision making logic. Simulation is a good tool for accurate and evidence-based decision making. The main objective of this study was to developing a simulation model to minimize the waiting time of patients in the cardiac subspecality clinic of Kowsar Hospital. This is a cross-sectional study. The statistical population consisted of 576 patients, referring to Kowsar cardiac clinic in the morning and afternoon shifts. Data collection was conducted according to a designed timetable form. Scenarios were defined to receive the best answer. These scenarios were as follows: scenario A: decrease of the average of the service time; scenario B: increase of the mean time between the two entries; and scenario C: decrease of the service time and increase of the time between two entries. ARENA software was used to simulate and review the scenarios and General Algebraic Modeling System (GAMS) softwarewas used to obtain a definite answer. Simulation results showed that in scenario A the mean time spent in the system was 85.55 minutes in the morning and 77.05 minutes in the evening shifts. In scenario B, the average time spent in the system was 65.95 minutes in the morning and 79.63 in the evening shifts. In scenario C, the mean time spent in the system was 73.90 minutes in the morning and 61.17 minutes in the evening shifts. The result of the final model showed that the average time spent in the system was 97.33 min in the morning and 86.85 min in the evening. According to the results obtained from the use of the definitive and simulated models, it was found that the simulation model, due to its probability, faces a percentage of error. Comparison of the definitive and simulated models revealed that the best scenario to the definitive answer was scenario B (increasing the mean time between the two logs).

Appointment scheduling system is a critical component in controlling patients’ waiting time, so can increase the efficiency and timely access to health services. It is also an important determinant of patient satisfaction. The aim of this study was to assess the relationship of using a scheduling system and outpatients’ waiting time in a general teaching hospital in Tehran, Iran.
This was a descriptive-analytical study performed cross-sectionally among outpatients referred to clinics of a non-teaching general hospital in Tehran in 2013. 3836 samples were selected from different stations. The sampling method was stratified randomized. Data were gathered by check lists. SPSS statistical software v.24 was used for data analysis. In addition to descriptive measures, Mann-Whitney non-parametric test and Pearson correlation test were used for data analysis.
The mean waiting time at admission was estimated 6.6 minutes. Analysing the waiting time of patients in admission indicated that walk-in patients wait more than scheduled patients for admission (P<0.001).
Mean waiting time for physician visit was 75.7 minutes. Analysing the differences of waiting time of scheduled and walk- in patients for visit show their waiting time were not different (P= 0.206).
The studied appointment system was successfull in controlling patient arrival but failed to control waiting time for physicians visit. It seems that considering physician arrival schedules will be helpful for designing appointment system and reducing outpatients’ waiting time.