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عنوان
Two-Step Predictive Model for Missed Appointments at Outpatient Primary Care Settings Serving Rural Areas

پدید آورنده
Abu Lekham, Laith

موضوع
Health care management,Industrial engineering,Information technology,Public health

رده

کتابخانه
کتابخانه مطالعات اسلامی به زبان های اروپایی

محل استقرار
استان: قم ـ شهر: قم

کتابخانه مطالعات اسلامی به زبان های اروپایی

تماس با کتابخانه : 32910706-025

TL53736

انگلیسی

Two-Step Predictive Model for Missed Appointments at Outpatient Primary Care Settings Serving Rural Areas
[Thesis]
Abu Lekham, Laith
Khasawneh, Mohammad T.

State University of New York at Binghamton
2020

145 p.

M.S.
State University of New York at Binghamton
2020

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.

Health care management
Industrial engineering
Information technology
Public health

Abu Lekham, Laith

Khasawneh, Mohammad T.

State University of New York at Binghamton

 مطالعه متن کتاب 

p

[Thesis]
276903

a
Y

الاقتراح / اعلان الخلل

تحذیر! دقق في تسجیل المعلومات
ارسال عودة
تتم إدارة هذا الموقع عبر مؤسسة دار الحديث العلمية - الثقافية ومركز البحوث الكمبيوترية للعلوم الإسلامية (نور)
المكتبات هي المسؤولة عن صحة المعلومات كما أن الحقوق المعنوية للمعلومات متعلقة بها
برترین جستجوگر - پنجمین جشنواره رسانه های دیجیتال