Risk Factor Model to Predict a Missed Clinic Appointment in an Urban, Academic, and Underserved Setting
| العنوان: | Risk Factor Model to Predict a Missed Clinic Appointment in an Urban, Academic, and Underserved Setting |
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| المؤلفون: | Michael B. Rothberg, Orlando Torres Md, Owolabi Ogunneye, Thomas L. Higgins, Judepatricks Onyema, Jane Garb |
| المصدر: | Population Health Management. 18:131-136 |
| بيانات النشر: | Mary Ann Liebert Inc, 2015. |
| سنة النشر: | 2015 |
| مصطلحات موضوعية: | Adult, Male, medicine.medical_specialty, Leadership and Management, Names of the days of the week, education, MEDLINE, Medically Underserved Area, Sample (statistics), Logistic regression, Health Services Accessibility, Risk factor model, Appointments and Schedules, Urban Health Services, Humans, Medicine, Language proficiency, health care economics and organizations, Retrospective Studies, Chronic care, Academic Medical Centers, Risk Management, business.industry, Health Policy, Public Health, Environmental and Occupational Health, Retrospective cohort study, United States, humanities, Family medicine, Female, business, Follow-Up Studies |
| الوصف: | In the chronic care model, a missed appointment decreases continuity, adversely affects practice efficiency, and can harm quality of care. The aim of this study was to identify predictors of a missed appointment and develop a model to predict an individual's likelihood of missing an appointment. The research team performed a retrospective study in an urban, academic, underserved outpatient internal medicine clinic from January 2008 to June 2011. A missed appointment was defined as either a "no-show" or cancellation within 24 hours of the appointment time. Both patient and visit variables were considered. The patient population was randomly divided into derivation and validation sets (70/30). A logistic model from the derivation set was applied in the validation set. During the period of study, 11,546 patients generated 163,554 encounters; 45% of appointments in the derivation sample were missed. In the logistic model, percent previously missed appointments, wait time from booking to appointment, season, day of the week, provider type, and patient age, sex, and language proficiency were all associated with a missed appointment. The strongest predictors were percentage of previously missed appointments and wait time. Older age and non-English proficiency both decreased the likelihood of missing an appointment. In the validation set, the model had a c-statistic of 0.71, and showed no gross lack of fit (P=0.63), indicating acceptable calibration. A simple risk factor model can assist in predicting the likelihood that an individual patient will miss an appointment. |
| تدمد: | 1942-7905 1942-7891 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8e0501da07b57b52373f79e57514ef27 https://doi.org/10.1089/pop.2014.0047 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi.dedup.....8e0501da07b57b52373f79e57514ef27 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19427905 19427891 |
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