View clinical trials related to No-Show Patients.
Filter by:In this observational study, the investigators will analyze all patients who have scheduled appointments in the Urology Department from twelve months before the start date of the e-mail reminder dispatch (01/02/2023) to twelve months after (01/01/2022 to 31/12/2023). The investigators will divide them into two groups based on whether they have received the reminder or not. The investigators are going to compare the rate of no-show rates in both groups and then obtain the relative risk of the association between appointment reminders and no-show rates.
Non-attendance to pediatric outpatient appointments is a frequent and relevant public health problem. Using different approaches it is possible to build non-attendance predictive models and these models can be used to guide strategies aimed at reducing no-shows. However, predictive models have limitations and it is unclear which is the best method to generate them. Regardless of the strategy used to build the predictive model, discrimination, measured as area under the curve, has a ceiling around 0.80. This implies that the models do not have a 100% discrimination capacity for no-show and therefore, in a proportion of cases they will be wrong. This classification error limits all models diagnostic performance and therefore, their application in real life situations. Despite all this, the limitations of predictive models are little explored. Taking into account the negative effects of non-attendance, the possibility of generating predictive models and using them to guide strategies to reduce non-attendance, we propose to generate non-attendance predictive models for outpatient appointments using traditional logistic regression and machine learning techniques, evaluate their diagnostic performance and finally, identify and characterize the population misclassified by predictive models.
Unused clinic visits due to patient no-shows continue to plague American healthcare as a large source of waste and avoidable constraint on access. The average no-show rate across 105 studies was 23% though with wide variation (4% to 79%). No-show behavior has adverse effects on patients, providers, and healthcare organizations' operational and financial outcomes. Patients that miss clinic visits are more likely to need acute care and suffer poor health outcomes. There have been increasingly sophisticated efforts focused on predicting which patients are likely to no-show. This can allow for tactful over-booking and/or patient outreach. At Hopkins, investigators have implemented a novel machine learning based approach for identifying those patients at high-risk for no-show. Offering home visits for patients who are most likely to no-show is an appealing strategy to connect medical providers with patients who need care but are otherwise unlikely to receive it. Yet, it is unclear if this would be helpful to engage patients in their care, and encourage subsequent attendance, or if it would encourage future missed appointments, fostering a reliance on possible ongoing home visits. This study would link existing efforts with no-show prediction to home visits by internal medicine residents and evaluate its clinical impact. Patients at high-risk for no-show will be randomized into the control arm where patients will be called to remind patients of their visits. Those randomized into the intervention arm will be offered a one time home visit in lieu of their in-person visit to help understand barriers to in-person care and build rapport. Outcomes evaluated include future in-person show rates and healthcare cost/utilization
Importing quality improvement practices and enhances health education to improving quality of bowel preparation for health screening colonoscopy This is a retrospective data analysis study aimed at obtaining the types of purgative treatments used, the results of bowel preparation, and each subject's demographic characteristics (Age & Gender).