Predicting Hospital Safety: A Comparison of Four Models May 5, 2010

نویسندگان

  • J. Scott Ashwood
  • H. John Heinz
  • Scott Ashwood
چکیده

Choosing the best hospitals or avoiding the worst hospitals is important to patients, physicians, and insurers. There is a wealth of publicly reported quality data to use in making those choices, but that data is presented in ways that may conceal important information. We develop three models of how individuals might use the available data in choosing a hospital and present a model that relies on a group-based alternative to the current reporting methods. We compare these four models on their ability to both predict hospital performance on quality and to identify the best and worst hospitals using three measures of patient safety calculated for California hospitals 1997-2006. We find that there are no clear winners in predicting the levels of quality, but we do find evidence that groupbased models may be better at identifying the worst hospitals.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Predicting Hospital safety: A Comparison of Four Models

Choosing the best hospitals or avoiding the worst hospitals is important to patients, physicians, and insurers. There is a wealth of publicly reported quality data to use in making those choices, but that data is presented in ways that may conceal important information. We develop three models of how individuals might use the available data in choosing a hospital and present a model that relies...

متن کامل

Comparison of Four Data Mining Algorithms for Predicting Colorectal Cancer Risk

Background and Objective: Colorectal cancer (CRC) is one of the most prevalent malignancies in the world. The early detection of CRC is not only a simple process, but it is also the key to its treatment. Given that data mining algorithms could be potentially useful in cancer prognosis, diagnosis, and treatment, the main focus of this study is to measure the performance of some data mining class...

متن کامل

Comparison of Three Decision-Making Models in Differentiating Five Types of Heart Disease: A Case Study in Ghaem Sub-Specialty Hospital

Introduction: cardiovascular diseases are becoming the main cause of mortality and morbidity in most countries. This research goal was to predict the types of heart diseases for more accurate diagnosis by data mining and neural network technics. Method: This research was an applied-survey study and after data preprocessing, three approaches of neural network, decision making tree and Bayes simp...

متن کامل

Comparative study of predictive ability of AIDS incidence in HIV positive people using Markov model according to two criteria, WHO and CDC in CD4 cell categorization

Background: The Multi state Markov models have extensively application with categorization of laboratory marker of CD4 cells for evaluation of HIV disease progression. These models with different states result in different effects of covariates and prediction of HIV disease trend. The main purpose of this study was comparison of four and five states models with the three- state in order to sele...

متن کامل

Comparison of Gestational Diabetes Prediction Between Logistic Regression, Discriminant Analysis, Decision Tree and Artificial Neural Network Models

Background and Objectives: Gestational Diabetes Mellitus (GDM) is the most common metabolic disorder in pregnancy. In case of early detection, some of its complications can be prevented. The aim of this study was to investigate early prediction of GDM by logistic regression (LR), discriminant analysis (DA), decision tree (DT) and perceptron artificial neural network (ANN) and to compare these m...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2010