© 2018 IEEE. The rate at which people miss hospital appointments has decreased but remains a big concern for health care professionals as well as funding agencies. This research paper used an open data obtained from the NHS database to determine the factors that may lead to missed appointments and create a model that can be used to predict the likelihood of a patient missing an appointment. Logistic regression models and bivariate analysis were used to determine whether there was a meaningful relationship/association between 'did not attend' and forgetfulness, gender, apathy, and transportation. An extensive literature review was conducted to narrow down the reasons that might lead to missed appointments. In conclusion, the research showed ...
From PubMed via Jisc Publications RouterPublication status: epublishMissed hospital appointments pos...
This study sought to explore the root causes of missed appointments in the health care safety net by...
Healthcare systems across the world generate large volumes of data about patients including informat...
© 2018 IEEE. The rate at which people miss hospital appointments has decreased but remains a big con...
Abstract Background and aim: No-show, refers to patients who do not show up in the scheduled appoint...
Introduction: Understanding the causes of low engagement in healthcare is a pre-requisite for improv...
Objective Patients that do not show up for scheduled clinic appointments affect the quality of healt...
Missed appointment is a common problem in ambulatory settings that has serious clinical and economic...
Objectives: Using predictive modeling techniques, we developed and compared appointment no-show pred...
A significant number of people with type 1 diabetes do not attend their clinic appointments. This st...
In the chronic care model, a missed appointment decreases continuity, adversely affects practice eff...
Purpose Missed appointments within the National Health Service (NHS) are a drain on resources, as...
Introduction Understanding the causes of low engagement in health care is a prerequisite for improvi...
Health care systems across the world generate large volumes of data about patients including informa...
Purpose/ObjectivesBroken appointments are an important cause of waste in health care. Patients who f...
From PubMed via Jisc Publications RouterPublication status: epublishMissed hospital appointments pos...
This study sought to explore the root causes of missed appointments in the health care safety net by...
Healthcare systems across the world generate large volumes of data about patients including informat...
© 2018 IEEE. The rate at which people miss hospital appointments has decreased but remains a big con...
Abstract Background and aim: No-show, refers to patients who do not show up in the scheduled appoint...
Introduction: Understanding the causes of low engagement in healthcare is a pre-requisite for improv...
Objective Patients that do not show up for scheduled clinic appointments affect the quality of healt...
Missed appointment is a common problem in ambulatory settings that has serious clinical and economic...
Objectives: Using predictive modeling techniques, we developed and compared appointment no-show pred...
A significant number of people with type 1 diabetes do not attend their clinic appointments. This st...
In the chronic care model, a missed appointment decreases continuity, adversely affects practice eff...
Purpose Missed appointments within the National Health Service (NHS) are a drain on resources, as...
Introduction Understanding the causes of low engagement in health care is a prerequisite for improvi...
Health care systems across the world generate large volumes of data about patients including informa...
Purpose/ObjectivesBroken appointments are an important cause of waste in health care. Patients who f...
From PubMed via Jisc Publications RouterPublication status: epublishMissed hospital appointments pos...
This study sought to explore the root causes of missed appointments in the health care safety net by...
Healthcare systems across the world generate large volumes of data about patients including informat...