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At least 19 recordsLinked to original sources
PubMed2004

Trends in waiting time to date and total time waited: are the sources compatible?

Department of Health Waiting List returns routinely measure the number of patients waiting for elective NHS admitted patient care on a given day, grouped by long they have been waiting to date. The trend in the average waiting time estimated from this istribution is different from the trend in the average total time waited by patients admitted over a period of time, calculated from Hospital Episodes Statistics. This article investigates whether the results from the two sources are consistent.

Data Interpretation, Statistical↗
PubMed1996

Effects of actual waiting time, perceived waiting time, information delivery, and expressive quality on patient satisfaction in the emergency department.

STUDY OBJECTIVE: To determine the effects of actual waiting time, perception of waiting time, information delivery, and expressive quality on patient satisfaction. METHODS: During a 12-month study period, a questionnaire was administered by telephone to a random sample of patients who had presented to a suburban community hospital emergency department during the preceding 2 to 4 weeks. Respondents were asked several questions concerning waiting times (ie, time from triage until examination by the emergency physician and time from triage until discharge from the ED), information delivery (eg, explanations of procedures and delays), expressive quality (eg, courteousness, friendliness), and overall patient satisfaction. RESULTS: There were 1,631 respondents. The perception that waiting times were less than expected was associated with a positive overall satisfaction rating for the ED encounter (P < .001). Satisfaction with information delivery and with ED staff expressive quality were also positively associated with overall satisfaction during the ED encounter (P < .001). Actual waiting times were not predictive of overall patient satisfaction (P = NS). CONCLUSION: Perceptions regarding waiting time, information delivery, and expressive quality predict overall patient satisfaction, but actual waiting times do not. Providing information, projecting expressive quality, and managing waiting time perceptions and expectations may be a more effective strategy to achieve improved patient satisfaction in the ED than decreasing actual waiting time.

Adolescent↗
PubMedDate not supplied

Improving patient satisfaction with waiting time.

Waiting times are a significant component of patient satisfaction. A patient satisfaction survey performed in the author's health center showed that students rated waiting time lowest of the listed categories--A ratings of 58% overall, 63% for scheduled appointments, and 41% for the walk-in clinic. The center used a quality improvement process and instituted schedule and reception area changes. A follow-up survey 3 months later showed A ratings of 73% overall, 81% for scheduled appointments, and 56% for the walk-in clinic. A repeat survey 4 months later showed A satisfaction ratings of 79% overall, 85% for scheduled appointments, and 68% for the walk-in clinic. The author discusses strategies the center used to improve patient satisfaction with waiting time.

Adolescent↗
PubMed1996

A study of waiting times and waiting lists for radiation therapy patients.

A simple mathematical model based on queueing theory is introduced, and used to obtain a sensitive measure of the capacity of a radiation therapy centre to provide service. The model illustrates the relationships among the relevant variables: the patient waiting time, the number on the waiting list, the rate at which requests for radiation therapy are received and the rate at which courses of treatment are commenced. In particular, the rate at which the waiting time increases is equal to the difference between the request rate and the start rate, expressed as a fraction of the start rate, and is therefore a measure of the deficiency in service capacity of a facility. A study of the records of patients treated on the linear accelerators of the Tom Baker Cancer Centre from January 1991 to June 1994 shows that this relationship holds to a high degree of accuracy for average values of the various parameters in spite of the considerable variation in these values, and particularly variation in the individual waiting times. This finding suggests that such an approach would be useful in assessing and comparing the performance of radiation therapy facilities.

Alberta↗
PubMed2006

A dynamic model of supply of elective surgery in the presence of waiting times and waiting lists.

This paper analyses hospitals' decisions regarding the supply of elective surgery in a dynamic model in the presence of waiting times and waiting lists. We show that periods of increasing (decreasing) supply and increasing (decreasing) waiting list may be optimal paths towards the steady state. We also analyse the hospitals' response to exogenous shocks on the demand and the supply. For example, an increase in the marginal tariff for the provider generates overshooting in the supply: supply jumps upwards when the shock is introduced and then slowly reduces towards the steady-state value.

Decision Making, Organizational↗
PubMed2002

Marginal estimation for multi-stage models: waiting time distributions and competing risks analyses.

We provide non-parametric estimates of the marginal cumulative distribution of stage occupation times (waiting times) and non-parametric estimates of marginal cumulative incidence function (proportion of persons who leave stage j for stage j' within time t of entering stage j) using right-censored data from a multi-stage model. We allow for stage and path dependent censoring where the censoring hazard for an individual may depend on his or her natural covariate history such as the collection of stages visited before the current stage and their occupation times. Additional external time dependent covariates that may induce dependent censoring can also be incorporated into our estimates, if available. Our approach requires modelling the censoring hazard so that an estimate of the integrated censoring hazard can be used in constructing the estimates of the waiting times distributions. For this purpose, we propose the use of an additive hazard model which results in very flexible (robust) estimates. Examples based on data from burn patients and simulated data with tracking are also provided to demonstrate the performance of our estimators.

Burns↗
PubMed2002

[Gender and age differences in waiting time on hospital waiting list.].

OBJECTIVE: The size of waiting lists has traditionally been viewed as a fairly good measure of the quality of health care services. No statistical analysis exists in Iceland of the length of waiting times and the potential variation between groups of patients. This study was conducted within the office of the Directorate of Health in Iceland. This location was convenient since standardized information on waiting lists is collected by the office three times a year. Variations in waiting times were studied based on gender on the one hand and on age on the other. MATERIAL AND METHOD: Data from the largest waiting lists, those amounting to 400 or more patients, were included in the study. The most frequently awaited operations were identified and the groups of people waiting for them analyzed. The departments and prospective operations included in the study were: Dept. of General Surgery at the University Hospital (UH) (laparoscopic gastro-oesophageal antireflux operation), Opthalmology at UH (phakoemulsification with implantation of artificial lens in posterior chamber), Orthopedic Surgery at UH (primary total prosthetic replacement of hip joint using sement), The Rehabilitation Center at Reykjalundur (rehabilitation, not specified), Ear, Nose and Throat (ENT) at UH (tonsillectomy), and Reconstructive Surgery at UH (reduction mammoplasty with transposition of areola). The lists were sorted by gender and age, with the latter consisting of two categories, older and younger patients. Every attempt was made as to ensure similar sample sizes for both age groups within each department. Finally, the median waiting time was determined and a Mann-Whitney test conducted in order to test for significance. RESULTS: The median waiting time for males at the General Surgery Dept. was 73 weeks as compared to 60 weeks for females. This was the only department where the median waiting time was significantly longer for males than for females (p<0.05). At three of the departments the older group had a longer median waiting time than the younger group, 18 weeks compared to 14 at Opthalmology (p<0.001), 26 versus 17 weeks at Reykjalundur (p<0.025) and 33 versus 21 weeks at ENT (p<0.01). Waiting times for females was significantly longer than for males at two departments, Reykjalundur (21 vs. 17 weeks, p<0.05) and ENT (33 vs. 29 weeks, p<0.05). CONCLUSION: This study revealed age and gender differences in median waiting times at Icelandic hospitals. These differences were in many cases marked and statistically significant. Various explanations have been put forward, however, further research is needed in order to determine if it these differences are due to actual clinical needs assessments or to age or gender discrimination.

English Abstract↗
PubMedDate not supplied

Are your patients patiently waiting? What to do about patient wait times.

Patient wait times are inevitable, but their reduction or elimination is an important marketing tool. Patients feel increased time demands, and if the wait is too long, they may not come back. However, if wait times are reasonable and handed well, patients are more likely to have a positive perception of services and return to the practice. In this article, the authors review the reality of wait times in medical practices today, sources of patient dissatisfaction with wait times, the treatment patients want while waiting, and a checklist of steps to reduce wait times.

Appointments and Schedules↗
PubMed2005

Why do not all hip- and knee patients facing long waiting times accept re-referral to hospitals with short waiting time? Questionnaire study.

Patients' preferences are often assumed to be homogeneous and to favour hospitals with a short waiting time and high quality. Due to long waiting times (6 months) for artificial hip or knee implantation a Danish county in 1999-2000 offered patients on a waiting list a choice between remaining on the local hospital's waiting list with the long waiting time, or re-referral to a hospital outside the county with a shorter waiting time. Fewer patients than expected took advantage of the offer of re-referral ("accepters"): 89 of 149 patients (60%). In 2003, we asked patients about the reasons for their choice: 87% of patients responded. Respondents and non-respondents were similar by decision, choice of hospital, diagnosis and age; men were significantly more likely to respond than women. Accepters and decliners were similar by age, sex, diagnosis and the presence of a car in the household. Short distance, short transport time and previous experience with the nearby hospital were the most important reasons for choosing that hospital. Some patients appeared to be willing to accept a long waiting time, if they were told exactly when they would undergo surgery. The results of this study question the validity of the conventional wisdom, that patients are willing to travel long distances in order to receive treatment with short waiting time.

Arthroplasty, Replacement, Hip↗
PubMed1991

[Waiting time for treatment in hospital. How can we measure and regulate the waiting time?].

We discuss different measures of waiting time, as well as the factors that influence the waiting time, the number of patients accumulated on the waiting list, the inflow of patients to the list, the number of patients removed from the list without treatment, and the number of patients removed from the list as treated. We describe a method for continuous registration and surveillance of these factors, and of the waiting time for treated and waiting patients. We also present a mathematical model to describe the relation between these factors. This model has proved to be a useful tool in predicting waiting time and in estimating the treatment capacity required to reach defined goals for waiting time.

Hospital Administration↗
PubMed2003

Parental perception of waiting time and its influence on parental satisfaction in an urban pediatric emergency department: are parents accurate in determining waiting time?

OBJECTIVE: The objective of this study was to determine whether parental perception of waiting time in an urban pediatric emergency department (ED) is accurate and whether the actual waiting times or their perception of waiting times impact on parental satisfaction. METHODS: A prospective convenience sample study in which the on-duty emergency physicians randomly administered a questionnaire at the time of the ED visit was used. During a 3-week period from December 15, 1999, through January 7, 2000, 500 parents or legal guardians of children who visited our ED were questioned about their perceived waiting time, and the responses were compared with the actual waiting time. The parents or guardians were also asked if they were satisfied with the waiting time. RESULTS: The majority (84%) of parents overestimated waiting time in the ED (median difference, 26 min; interquartile range, 9-50 min). Parents with perceived or actual waiting times that exceeded 2 hours were significantly more likely to be dissatisfied than parents with actual or perceived waiting times that were 1 hour or less (P < 0.001). Satisfaction was not related to the age (P = 0.35), sex (P = 0.30), race/ethnicity (P = 0.90), or mode of arrival (P = 0.28). CONCLUSION: Parents tend to overestimate waiting time. Both perceived and actual waiting times that exceed 2 hours were associated with parental dissatisfaction. ED administrators may need to keep this in mind when arranging ED staffing patterns to match peak patient hours to achieve optimal parental satisfaction.

Adolescent↗
PubMed2000

Maximum waiting time - a threat to clinical freedom? Implementation of a policy to reduce waiting times.

This article focuses on physicians as implementers of health policy reforms. In 1992, a maximum waiting-time guarantee was introduced in Sweden. Initially the policy was a successful way to come to terms with long waiting times. However, after 2 years the waiting lists started to increase. To understand this development it is important to look at the reactions to the policy among the implementers, i.e. the physicians. Three questions are addressed: Did the implementers understand the intentions and the goals of the reform? Were they able to fulfil the guarantee? And, did they approve of the initiative? The study subjects were chief physicians at the hospital departments involved with the guarantee. Their attitudes towards the policy were ascertained by two surveys. Other material, such as statistics on waiting times, was also used. The study shows that the physicians approved of the guarantee initially. The measures taken in the first years were effective and did not conflict with earlier practice. However, increased demand in combination with economic restraints necessitated new priorities among patient groups. These changes of clinical practice did not coincide with the physicians' professional values and hence they became more critical to the initiative and finally chose to abandon the intentions in the guarantee.

Attitude of Health Personnel↗
PubMed2005

Time trends in waiting time to pregnancy among Danish twins.

BACKGROUND: Little is known about time trends in fecundity because few population-based data are available. In a survey among female twins born from 1953 to 1976, their time to pregnancy did not differ from singletons and can be considered to represent the fecundity of the general population. METHODS: Information was collected by interview about waiting time to first pregnancy (TTP) and any periods of subfecundity among both male and female twins born between 1931 and 1952. Trends were analysed by considering the year of birth of the index person (birth cohort effect) or year at which the first attempt started (period effect). RESULTS: Eighty-five percent of male and 81.3% of female eligible twins participated. A total of 1598 male twins and 1653 female twins reported a TTP value and 1671 men and 1715 women had a value for their first attempt to conceive including unsuccessful attempts. No overall trends in either male or female TTP were observed with increasing year of birth or of starting time, after adjustment for confounders, but for attempts fecundity increased among female twins by year of birth or of starting the attempt. A decreasing risk of severe infertility with increasing year of birth or year for start of the attempt was observed among male twins, but this observation was based on only 81 men and was not seen among female twins. CONCLUSION: No decreasing trend in fecundity was observed among Danish twins born between 1931 and 1952 who had completed their reproduction. Female twins had a slight increase in fecundity, and men a decrease in severe infertility. In addition, TTP was apparently well reported and recalled for up to 50 years among both male and female twins.

Adult↗
PubMed2000

Liver transplant waiting time does not correlate with waiting list mortality: implications for liver allocation policy.

Factors associated with the risk for mortality once placed on the liver transplant waiting list and how this risk relates to center-specific waiting time and transplant activity have not been adequately evaluated. We performed this study to determine the association between center-specific waiting time and waiting list mortality among liver transplant candidates stratified by medical urgency at the time of registration. A Cox proportional hazards model was used to calculate 2-year mortality risk for a cohort of 16, 414 registrants added to the United Network for Organ Sharing liver transplant waiting list between January 1, 1997, and December 31, 1997. After controlling for confounding variables, we calculated the mortality risk for centers, organ procurement organizations (OPOs), and states. The relation between center-specific waiting list mortality risk and median waiting time or transplant activity was determined by linear regression. In multivariate analyses, higher initial medical urgency status (relative risk [RR] = 12.8; P <.001), increasing age (P <.001), black ethnicity (RR = 1.29; P <.001), history of previous transplant (RR = 1.2; P =.009), certain liver diagnoses, and smaller center size (RR = 1.39; P =.008) were associated with significantly increased waiting list mortality. Candidates with blood type A (RR = 0.87; P <.001) and those with cholestatic cirrhosis as the primary diagnosis (RR = 0.73; P < 0. 001) had a reduced risk for dying. There were significant variations in 2-year waiting list mortality risk among centers, OPOs, and states. However, when stratified by medical urgency status at waiting list entry, center-specific waiting time and transplantation rates accounted for almost none of the center-specific waiting list mortality. Although there are variations in waiting list mortality risk among centers, OPOs, and states, there is very little relation between center-specific waiting list mortality and center-specific median waiting time or center-specific transplantation rates when stratified by medical urgency. Waiting time and center transplant rates should not influence liver allocation policy.

Cohort Studies↗
PubMedDate not supplied

Coronary bypass grafting in South Thames: the correlation between clinical scores and waiting times.

To determine waiting times before surgery and their correlation with clinical need, we examined the files of 1049 patients on the waiting list for coronary bypass grafting in 1996. The total waiting time to bypass grafting was 279 (SD 209) days (range 1-1579 days). Waiting time to specialist consultation was 36 (SD 43) days, and time on the waiting list for coronary angiography was 85 (SD 89) days. The mean time on the surgical waiting list was 133 (SD 134) days. Patients with a Birmingham clinical score below 10 waited between 27 and 879 days, and patients with scores above 35 waited between 3 and 282 days. Total waiting time was weakly associated with the priority score (Pearson correlation = -0.51). We conclude that waiting times were long with wide variation at every stage between referral and coronary bypass grafting. There was little correlation between clinical scores and waiting times.

Adult↗
PubMed1990

[Effects of waiting times within simple problems: an analogy to waiting times in human-computer interaction].

The impact of forced intra-task waiting periods was evaluated in a laboratory study designed in analogy to forced waiting periods in human-computer interaction. Each task consisted of two lines of grouped capital letters (so-called Sterzinger lines) that had to be processed individually, separated by the experimental waiting period. The final response to both lines was taken after removal of the second line. The experiment was conducted according to a within-subjects design with the order of the two factors "duration" (2 s vs. 8 s) and "variability of the waiting periods" (constant vs. variable) controlled. The dependent variables were performance indices, heart rate, electrodermal activity, and subjective measures of mood and physical complaints. The results showed effects of the duration of waiting periods for the performance and the physiological variables, and effects of the variability factor for the subjective variables. Shorter waiting periods were associated with higher scores of heart rate and skin resistance responses, variable waiting periods seemed to confuse the subjects more than constant ones. The results are discussed with respect to the effects of the two experimental factors. With respect to dialog structures in human-computer interaction, the results lead to the conclusion that response interference could be avoided by giving the user the possibility to immediately input problem solutions. Partial and preliminary inputs should be permanently kept visible during the whole task cycle by the software.

Adult↗