結合專題導向式學習與線上社群以融入社會責任教育之行動研究

[[abstract]]由於環境、文化、社會和技術的變化，期待⾼等教育機構加強社區參與，並培育具社會責任感的現代公⺠，近來受到了許 多的關注。然⽽，社會責任教育是⼀件深具挑戰性的⼯作，設計合適的教學⽅法融入社會責任教育，成為重要且具挑戰性 的教學實踐研究問題。本計畫以彰化師範⼤學「社頭織襪，永續經營」⼤學社會責任計畫中多元課程「互動式網⾴設計」 為⽬標，探討如何將社會責任教育融入原有課程。具體⽽⾔，本計畫結合專題導向式學習與線上社群，帶領學⽣了解彰化 社頭織襪產業現況與問題，使學⽣統整運⽤所學網⾴設計能⼒，結合地⽅特⾊，嚐試構思設計織襪廠商網⾴，以新的教學 ⽅式將社會責任教育融入原有課程。本計畫以系統開發法擴充「社頭織襪，永續經營」產官學社區溝通平台功能，以提供 線上社群討論空間，並以教育⾏動研究法，透過每⼀個循環的規劃、⾏動、觀察與省思，精進實務教學。[[abstract]]Due to the changes of environment, culture, society and technology, the call for increased community engagement of the higher education institutions has received attention recently. However, designing appropriate methods to teach social responsibility is challenging. This study aims to investigate a new method to teach social responsibility in the IWD (Interactive Webpage Design) course of the STUSR (She-Tou University Social Responsibility) project. Specifically, this study proposes to combine project-based learning and online community to improve students’ learning effectiveness in social responsibility. By doing so, motivated students can apply the web design skills they have learned to participate in the related website tasks of the STUSR project. This study expanded the related functions of the communication platform used in the STUSR project to provide online discussion space, and refined the proposed teaching method by performing action research

Epidemiology and Clinical Peculiarities of Norovirus and Rotavirus Infection in Hospitalized Young Children with Acute Diarrhea in Taiwan, 2009

Background/PurposeAcute diarrhea is one of the most common morbidities in pediatrics worldwide. We conducted a study to investigate the incidence of norovirus in young children hospitalized with acute diarrhea in Taiwan and its clinical peculiarity compared with rotavirus gastroenteritis.MethodsBetween January and December, 2009, patients younger than 5 years and admitted to hospital with acute diarrhea were randomly selected; and their stool samples were collected and tested for presence of rotavirus and norovirus by enzyme immunoassay and reverse transcription-polymerase chain reaction, respectively. The clinical manifestations and laboratory findings of the enrolled patients were analyzed.ResultsA total of 989 cases were enrolled with a mean age of 21.6 ± 13.7 months and a male proportion of 56.0%. Rotavirus and norovirus was detected in 20.2% and 14.6% of all patients, respectively. Genogroup II was the predominant strain of norovirus (80.6%). Children aged 6-36 months accounted for the majority of patients positive for rotavirus and norovirus (73.0% and 81.3%, respectively). The incidences of norovirus and rotavirus infection were higher during winter and early spring. Most patients with rotavirus and norovirus diarrhea experienced vomiting (74.9% vs. 74.8%, respectively) and fever (94.7% vs. 71.3%, respectively).ConclusionMost young diarrheal patients presenting with vomiting were likely to have norovirus or rotavirus infection. Patients with norovirus diarrhea experienced an absence of, or low-grade fever and longer duration of vomiting compared with those positive for rotavirus infection. A family history of current gastroenteritis may suggest the possibility of norovirus infection

A Process-Mining Framework for the Detection of Healthcare Fraud and Abuse

[[abstract]]People rely on government-managed health insurance systems, private health insurance systems, or both to share the expensive healthcare costs. With such an intensive need for health insurances, however, health care service providers’ fraudulent and abusive behavior has become a serious problem. In this research, we propose a data-mining framework that utilizes the concept of clinical pathways to facilitate automatic and systematic construction of an adaptable and extensible detection model. The proposed approaches have been evaluated objectively by a real-world data set gathered from the National Health Insurance (NHI) program in Taiwan. The empirical experiments show that our detection model

Selecting Structural Patterns for Classification

[[abstract]]Many techniques have recently been proposed for discovering structural patterns. Using the discovered structural patterns as features for classification has shown success in some application domains. However, the efficiency and effectiveness of such a classification algorithm is often impeded by the huge number of structural patterns discovered by the associated structural pattern mining algorithm. In this paper, we focus on the feature selection problem of structural patterns. The goal is to develop a scheme that effectively selects a subset of structural patterns as the features for the following induction algorithm. We show how to make use of the downward closure property inherent in the structural patterns to design a novel feature selection algorithm. We also evaluate our algorithm by applying the real-world health insurance data for building a classification model to detect health care fraud and abuse. The experimental results show that a great extent of redundant features can be eliminated by our feature selection algorithm, resulting in both accuracy improvement and computation cost reduction

流程萃取以偵測醫療詐欺及濫用之研究

[[abstract]]With the intensive need for health insurances, health care service providers’ fraud and abuse have become a serious problem. The practices, such as billing services that were never rendered, performing medically unnecessary services, and misrepresenting non-covered treatments as medically necessary covered treatments, etc, not only contribute to the problem of rising health care expenditure but also affect the health of patients. We are therefore motivated to investigate the detection of service providers’ fraudulent and abusive behavior. In this research, we introduce the concept of clinical pathways and thereby propose a framework that facilitates automatic and systematic construction of adaptable and extensible detection systems. For the purposes of building such detection systems, we study the problems of mining frequent patterns from clinical instances, selecting features that have more discriminating power and revising detection model to have higher accuracy with less labeled instances. The performance of the proposed approaches has been evaluated objectively by synthetic data set and real-world data set. Using the real-world data set gathered from the National Health Insurance (NHI) program in Taiwan, the experiments show that our detection model has fairly good prediction power. Comparing to traditional expense driven approach, more importantly, our detection model tends to capture different fraudulent scenarios.[[abstract]]隨著生活品質的改善與醫療資訊的普及，民眾愈來愈重視身體健康，對醫療資源的使用也日益頻繁，因此，對醫療保險的需求日益高漲。在各國不同的制度下，民眾或透過私人保險的購買，或透過國家整體醫療保險的參與，分擔高額醫療費用的風險，以取得醫療服務。在不同的醫療保險制度中，按量計酬(Fee for Service)是一種常見的費用給付方式。在按量計酬的方式下，病人於醫療機構先取得醫療服務，醫療機構再依據所提供的各項診斷、治療服務，逐項向保險機構提出費用申請。因此，醫療機構如果申報較多的醫療服務，便可能取得較多的給付，而使得按量計酬常成為醫療機構浪費、謊報醫療服務的誘因。面對可能的浪費、詐欺行為，保險機構因而常聘請專家以審查醫療案例。然而，專家審查的方式，耗費大量的時間、人力成本，對於大量的保險案例(例如，國家整體醫療保險)，往往無法負荷。本研究著眼於此，引入流程分析的概念，提出整體分析架構與方法，透過系統化、自動化的方式，偵測可能的醫療浪費、詐欺行為