On privacy and the prevention of unsolicited sessions in the IP multimedia subsystem

Includes abstract. Includes bibliographical references (leaves 168-175)

Waiting lists, waiting times and admissions: an empirical analysis at hospital and general practice level

We report an empirical analysis of the responses of the supply and demand for secondary care to waiting list size and waiting times. Whereas previous empirical analyses have used data aggregated to area level, our analysis is novel in that it focuses on the supply responses of a single hospital and the demand responses of the GP practices it serves, and distinguishes between outpatient visits, inpatient admissions, daycase treatment and emergency admissions. The results are plausible and in line with the theoretical model. For example: the demand from practices for outpatient visits is negatively affected by waiting times and distance to the hospital. Increases in waiting times and waiting lists lead to increases in supply; the supply of elective inpatient admissions is affected negatively by current emergency admissions and positively by lagged waiting list and waiting time. We use the empirical results to investigate the dynamic responses to one off policy measures to reduce waiting times and lists by increasing supply

The demand for private health insurance: do waiting lists or waiting times matter? CHERE Working Paper 2010/8

Besley, Hall, and Preston (1999) estimated a model of the demand for private health insurance in Britain as a function of regional waiting lists and found that increases in the number of people waiting for more than 12 months (the long-term waiting list) increased the probability of insurance purchase. In the absence of waiting time data, the length of regional long-term waiting lists was used to capture the price-quality trade-off of public treatment. We revisit Besley et al.?s analysis using Australian data and test the use of waiting lists as a proxy for waiting time in models of insurance demand. Unlike Besley et al., we find that the long-term waiting list is not a significant determinant of the demand for insurance. However we find that long waiting times do significantly increase insurance. This suggests that the relationship between waiting times and waiting lists is not as straightforward as is commonly assumed.waiting time, waiting lists, health insurance, regional aggregation

Measuring access: how accurate are patient-reported waiting times?

Introduction: A national audit of waiting times in England’s genitourinary medicine clinics measures patient access. Data are collected by patient questionnaires, which rely upon patients’ recollection of first contact with health services, often several days previously. The aim of this study was to assess the accuracy of patient-reported waiting times. Methods: Data on true waiting times were collected at the time of patient booking over a three-week period and compared with patient-reported data collected upon clinic attendance. Factors contributing to patient inaccuracy were explored. Results: Of 341 patients providing initial data, 255 attended; 207 as appointments and 48 ‘walk-in’. The accuracy of patient-reported waiting times overall was 52% (133/255). 85% of patients (216/255) correctly identified themselves as seen within or outside of 48 hours. 17% of patients (17/103) seen within 48 hours reported a longer waiting period, whereas 20% of patients (22/108) reporting waits under 48 hours were seen outside that period. Men were more likely to overestimate their waiting time (10.4% versus 3.1% p<0.02). The sensitivity of patient-completed questionnaires as a tool for assessing waiting times of less than 48 hours was 83.5%. The specificity and positive predictive value were 85.5% and 79.6%, respectively. Conclusion: The overall accuracy of patient reported waiting times was poor. Although nearly one in six patients misclassified themselves as being seen within or outside of 48 hours, given the under and overreporting rates observed, the overall impact on Health Protection Agency waiting time data is likely to be limited

A Lindley-type equation arising from a carousel problem

In this paper we consider a system with two carousels operated by one picker. The items to be picked are randomly located on the carousels and the pick times follow a phase-type distribution. The picker alternates between the two carousels, picking one item at a time. Important performance characteristics are the waiting time of the picker and the throughput of the two carousels. The waiting time of the picker satisfies an equation very similar to Lindley's equation for the waiting time in the PH/U/1 queue. Although the latter equation has no simple solution, we show that the one for the waiting time of the picker can be solved explicitly. Furthermore, it is well known that the mean waiting time in the PH/U/1 queue depends on to the complete interarrival time distribution, but numerical results show that, for the carousel system, the mean waiting time and throughput are rather insensitive to the pick-time distribution.Comment: 10 pages, 1 figure, 19 reference

Consumer Perception and Evaluation of Waiting Time

Telephone waiting times for a commercial service were varied in two different experiments. In the first experiment, the telephone rate was either zero or fixed at Dfl.1.- (approx. $0.40) per minute. Consumer perceptions of waiting times could be described best by a psychophysical power function. Furthermore, wait evaluations were mainly influenced by the difference between the consumers' acceptable and perceived waiting times. The negative effect of perceived waiting time on wait evaluations was increased by the monetary costs of waiting.In the second experiment, the waiting times were filled in different ways: music, queue information, and information about expected waiting time. Information about the expected waiting time significantly reduced the consumer's overestimation of waiting time, whereas information about wait duration and queue increased the negative effect of perceived waiting time on wait evaluations.customer satisfaction;experiment;psychophysics;telephone waiting times

Reconciliation of Waiting Time Statistics of Solar Flares Observed in Hard X-Rays

We study the waiting time distributions of solar flares observed in hard X-rays with ISEE-3/ICE, HXRBS/SMM, WATCH/GRANAT, BATSE/CGRO, and RHESSI. Although discordant results and interpretations have been published earlier, based on relatively small ranges ($< 2$ decades) of waiting times, we find that all observed distributions, spanning over 6 decades of waiting times ($\Delta t \approx 10^{-3}- 10^3$ hrs), can be reconciled with a single distribution function, $N(\Delta t) \propto \lambda_0 (1 + \lambda_0 \Delta t)^{-2}$, which has a powerlaw slope of $p \approx 2.0$ at large waiting times ($\Delta t \approx 1-1000$ hrs) and flattens out at short waiting times \Delta t \lapprox \Delta t_0 = 1/\lambda_0. We find a consistent breakpoint at $\Delta t_0 = 1/\lambda_0 = 0.80\pm0.14$ hours from the WATCH, HXRBS, BATSE, and RHESSI data. The distribution of waiting times is invariant for sampling with different flux thresholds, while the mean waiting time scales reciprocically with the number of detected events, $\Delta t_0 \propto 1/n_{det}$. This waiting time distribution can be modeled with a nonstationary Poisson process with a flare rate $\lambda=1/\Delta t$ that varies as $f(\lambda) \propto \lambda^{-1} \exp{-(\lambda/\lambda_0)}$. This flare rate distribution represents a highly intermittent flaring productivity in short clusters with high flare rates, separated by quiescent intervals with very low flare rates.Comment: Preprint also available at http://www.lmsal.com/~aschwand/eprints/2010_wait.pd