عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
شماره کتابشناسی ملی
شماره
TL53736
زبان اثر
زبان متن نوشتاري يا گفتاري و مانند آن
انگلیسی
عنوان و نام پديدآور
عنوان اصلي
Two-Step Predictive Model for Missed Appointments at Outpatient Primary Care Settings Serving Rural Areas
نام عام مواد
[Thesis]
نام نخستين پديدآور
Abu Lekham, Laith
نام ساير پديدآوران
Khasawneh, Mohammad T.
وضعیت نشر و پخش و غیره
نام ناشر، پخش کننده و غيره
State University of New York at Binghamton
تاریخ نشرو بخش و غیره
2020
يادداشت کلی
متن يادداشت
145 p.
یادداشتهای مربوط به پایان نامه ها
جزئيات پايان نامه و نوع درجه آن
M.S.
کسي که مدرک را اعطا کرده
State University of New York at Binghamton
امتياز متن
2020
یادداشتهای مربوط به خلاصه یا چکیده
متن يادداشت
Missed appointments are a significant cause of inefficiency in the healthcare industry. Many researchers have studied this problem in various healthcare settings. However, limited research has been conducted that is concerned with predicting missed appointments at outpatient primary care settings serving rural areas. This study holistically investigates the factors behind two types of missed appointments - no shows and cancellations - at an outpatient primary care medical center serving rural areas and develops a predictive model to reduce their incidence. The study was carried out in three main phases. First, exploratory data analysis was conducted to discover the patterns related to the missed appointments. Also, a text mining framework was developed to conduct a root cause analysis (RCA) and Pareto analysis. Second, the association between some attributes and appointment status was analyzed using Chi-square and Welch's t-test. Third, a two-step predictive model for the missed appointments was built using machine learning classifiers. The first step of the model is to predict the missed appointments in more than one-week notice without using the weather forecasts. The second step of the model is to predict the missed appointments in less than a one-week notice by incorporating the weather forecasts in the model. It was found that appointment lead time is a key driver for missed appointments. The longer the lead time, the more likely a patient is to miss an appointment. Also, the missed appointment rate decreases significantly as the day progresses. Based on the text mining framework for RCA and Pareto analysis, it was found that most of the missed appointments are either related to the patient or processes. The ensemble classifiers performed the best among all the classifiers after tuning their hyperparameters with an average accuracy of more than 93%. Also, most of the classifiers showed moderate variability in their performance. Incorporating the weather as an external variable improved the performance of the model significantly with an average best accuracy of 99%. Based on this analysis, some interventions were proposed to reduce the missed appointments rate such as reducing appointment lead time and using the predictive model in scheduling patients.
اصطلاحهای موضوعی کنترل نشده
اصطلاح موضوعی
Health care management
اصطلاح موضوعی
Industrial engineering
اصطلاح موضوعی
Information technology
اصطلاح موضوعی
Public health
نام شخص به منزله سر شناسه - (مسئولیت معنوی درجه اول )
مستند نام اشخاص تاييد نشده
Abu Lekham, Laith
نام شخص - ( مسئولیت معنوی درجه دوم )
مستند نام اشخاص تاييد نشده
Khasawneh, Mohammad T.
شناسه افزوده (تنالگان)
مستند نام تنالگان تاييد نشده
State University of New York at Binghamton
دسترسی و محل الکترونیکی
نام الکترونيکي
مطالعه متن کتاب وضعیت انتشار
فرمت انتشار
p
اطلاعات رکورد کتابشناسی
نوع ماده
[Thesis]
کد کاربرگه
276903
اطلاعات دسترسی رکورد
سطح دسترسي
a
تكميل شده
Y
