Self-reporting and screening: Data with right-censored, left-censored, and complete observations.

We consider survival data that combine three types of observations: uncensored, right-censored, and left-censored. Such data arises from screening a medical condition, in situations where self-detection arises naturally. Our goal is to estimate the failure-time distribution, based on these three observation types. We propose a novel methodology for distribution estimation using both semiparametric and nonparametric techniques. We then evaluate the performance of these estimators via simulated data. Finally, as a case study, we estimate the patience of patients who arrive at an emergency department and wait for treatment. Three categories of patients are observed: those who leave the system and announce it, and thus their patience time is observed; those who get service and thus their patience time is right-censored by the waiting time; and those who leave the system without announcing it. For this third category, the patients' absence is revealed only when they are called to service, which is after they have already left; formally, their patience time is left-censored. Other applications of our proposed methodology are discussed.

A novel methodology to measure waiting times for community-based specialist care in a public healthcare system.

BACKGROUND: Monitoring waiting time (WT) in healthcare systems is essential, since long WT are associated with adverse health outcomes, reduced patient satisfaction and increased private financing. OBJECTIVE: To describe a methodology developed for routine national monitoring of WT for community-based non-urgent specialist appointments, in a public healthcare system. METHODS: The methodology is based on data from computerized appointment scheduling systems of all Health Maintenance Organizations (HMOs) in Israel. Data included first 50 available appointments for community-based specialists and actual number of visits. Five most frequent specialties: orthopedics, ophthalmology, gynecology, dermatology and otolaryngology, were included. WT offered to HMO members for non-urgent care was calculated for two scenarios: "specific" physician and "any" physician in the region. Distribution of offered WT was calculated separately for each specialty and geographical region, combined to create the nationwide distribution. RESULTS: The methodology was tested on data extracted between December 2018-June 2019. Estimated national median WT for "specific" physician, ranged from 9 days (ophthalmology/gynecology) to 20 days (dermatology), with large variation between geographic regions. WT were 26-56 % shorter for "any" than for "specific" physician. CONCLUSIONS: This novel method offers a solution for ongoing national WT measurement, using computerized scheduling systems. It integrates two scenarios for appointment scheduling and allows identification of differences between specialties and regions, setting the ground for interventions to strengthen public healthcare systems.