View clinical trials related to Appointments and Schedules.
Filter by:NYU Langone Health outreaches to patients to remind them to schedule their appointments by phone or MyChart message.The proposed study will test different outreach methods using a predictive risk model. The goal is to increase gap closure rate by the end of the year.
This study is a prospective clinical trial designed to primarily test the impact of rideshare-based transportation services from a digital transportation network, Lyft, on reducing primary care clinic missed appointments--a composite outcome of no-shows and same day cancellations--for Medicaid patients. The study population consists of West Philadelphia residents who are established patients at two of the Penn Medicine Primary Care Practices within the University of Pennsylvania Health System. The study subjects are allocated into the intervention or control arm using a pseudorandomization approach - those receiving an appointment reminder on an even calendar day are in the intervention arm and odd calendar day calls are in the control arm. Secondary outcomes include the time of arrival to the clinics relative to actual appointment time (both arms), prospective utilization of acute care settings (both arms), prospective utilization of primary care (both arms), and description of programmatic metrics in the intervention arm (travel time, misuse, and costs). The investigators will assess the patient experience after each ride using a telephone-based survey and in-depth interviews. All adults with established primary care at the Penn Medicine Clinics, who have Medicaid, and do not require wheelchair accessible rides will be eligible for the rideshare service. The investigators hypothesize that individuals offered a rideshare-based transportation service will have a decreased proportion of missed appointments and same day cancellations as those not offered the service.
Our aim was to identify the causes non-attendance at scheduled appointments at the Clinical Medicine Outpatient care system at the Italian Hospital of Buenos Aires (HIBA).
Our objectives were to estimate the prevalence of nonattendance at outpatient offices, to identify the characteristics of appointments for which nonattendance was more likely to occur, and to generate a predictive model that could be applied to each appointment to estimate the probability of nonattendance.