Towards a Framework for Realizing Healthcare Management Benefits Through the Integration of Patient\u27s Information

Business Intelligence (BI) applications, including customer relationship management systems, decision support systems, analytical processing systems, and data mining systems, have captured the attention of practitioners and researchers for the last few years. Health care organizations, which are data driven and in which quality and integration of data is of paramount importance, have adopted BI applications to help and assist healthcare managers in improving the quality of the information input to the decision process. Based on preliminary data collection results, it is found that high quality data is essential to successful BI performance and that technological support for data acquisition, analysis and deployment are not widespread. Yet, business organizations are not investing in improving data quality and data integration. In this paper the authors propose a framework for evaluating the quality and integration of patient’s data for BI applications in healthcare organizations. In doing so, a range of potential benefits is highlighted. Even though this framework is in an early stage of development, it intends to present existing solutions for evaluating the above issues. The authors conclude that further research needs to be carried out to refine this framework, through model testing and case studies evaluation

Implementation Issues of Enterprise data Warehousing and Business Intelligence in the Healthcare Industry

The healthcare industry is following the lead of other industries and finding value in enterprise data warehousing (EDW) and business intelligence (BI) tools. Healthcare organizations are leveraging these tools to provide a plethora of benefits realized through enhanced business operations and performance. The EDW combines data from multiple source systems across an enterprise, and BI tools extract the data in meaningful ways to enable managers to make the best informed decisions. As with all management information systems, there are technical issues to be considered that impact the design, build, implementation, and support of the system. These benefits and challenges are explored, as well as special considerations necessary for the healthcare industry compared to other industries utilizing data warehousing and business intelligence. This paper investigates these critical issues and provides suggestions to harness the implementation of EDW and BI in the healthcare industry

Комбінація локальної порогової бінаризації та машинного навчання для класифікації пухлин молочної залози

Рання діагностика раку молочної залози має колосальне значення, оскільки дана патологія є одним із найбільш розповсюджених чинників летальності серед жінок по всьому світу. Чи не небезпечнішим підтипом раку молочної залози вважається інвазивна протокова карцинома. Зазвичай патологоанатоми фокусуються на областях з подібною карциномою, так як це дозволяє присвоїти оцінку агресивності усьому зразку монтування. Саме тому важливою задачею є автоматизоване виявлення карциноми при діагностиці ракових пухлин молочної залози. Мета даної роботи полягала у встановленні основних етапів побудови діагностичних алгоритмів класифікації типу ракової пухлини молочної залози на основі аналізу гістологічних знімків. Для цього було запропоновано алгоритм на основі методу локальної порогової бінаризації (для вилучення інформативних ознак з медичних зображень) та машинного навчання для (побудови моделей розпізнавання типу ракової пухлини молочної залози за допомогою методів класифікації). База гістологічних знімків, яка використовувалась для дослідження, була взята з відкритого джерела Kaggle, що є онлайн ресурсом для проведення змагань з машинного навчання. Перед виконанням першого етапу дослідження, який полягав у застосуванні алгоритму локальної порогової бінаризації, вибірку зображень було розбито на робочу (75%), для навчання моделей, та екзаменаційну (25%), яка не приймала жодної участі в експериментах аж до отримання результуючої моделі. Другий етап дослідження полягав у отриманні таких інформативних ознак як дуети (комбінації із двох пікселей) і тріо (комбінації із трьох пікселей). Вони розраховуються після застосування запропонованого методу бінаризації. На основі даних ознак були побудовані моделі наступних алгоритмів класифікації: метод групового урахування аргументів, логістична регресія, наївний Байєсівський класифікатор, метод k найближчих сусідів, а також метод випадкового лісу. Результатом моделювання є 10 моделей класифікації, найкращою з яких стала модель метода k найближчих сусідів, навчена на дуетах бінаризованих пікселей. Ця модель дала на екзаменаційній вибірці 78.5% точності класифікації, значення чутливості становило 0.803, специфічності – 0.767.Early diagnosis of breast cancer is of great importance, as this pathology is one of the most common causes of mortality among women around the world. Invasive ductal carcinoma is the most dangerous subtype of breast cancer. Typically, pathologists focus on areas with similar carcinoma, as this allows an aggressiveness score to be assigned to the entire mount specimen. That is why the automated detection of carcinoma in the diagnosis of cancerous tumors of the mammary gland is an important task. The purpose of this work was to establish the main stages of building diagnostic algorithms for the classification of the type of breast cancer tumor based on the analysis of histological images. For this, an algorithm was proposed based on the local threshold binarization method (for extracting informative features from medical images) and machine learning (building breast cancer tumor type recognition models using classification methods). The database of histological images used for the study was taken from the open-source Kaggle, an online resource for running machine learning competitions. Before performing the first stage of the research, which consisted of the application of the local threshold binarization algorithm, the sample of images was divided into working (75%), for model training, and examination (25%), which did not participate in any experiments until obtaining the resulting model. The second stage of the research consisted in obtaining such informative features as duets (combinations of two pixels) and trios (combinations of three pixels). They are calculated after applying the proposed binarization method. Models of the following classification algorithms were built based on these features: group method of data handling, logistic regression, naive Bayesian classifier, the method of k nearest neighbors, and random forest method. The result of the modeling is 10 classification models, the best of which was the k-nearest neighbors model, trained on binarized pixel pairs. This model gave 78.5% classification accuracy on the exam sample, the sensitivity value was 0.803, and the specificity value was 0.767

Model Reka Bentuk Konseptual Operasian Storan Data Bagi Aplikasi Kepintaran Perniagaan

The development of business intelligence (BI) applications, involving of data sources, Data Warehouse (DW), Data Mart (DM) and Operational Data Store (ODS), imposes a major challenge to BI developers. This is mainly due to the lack of established models, guidelines and techniques in the development process as compared to system development in the discipline of software engineering. Furthermore, the present BI applications emphasize on the development of strategic information in contrast to operational and tactical. Therefore, the main aim of this study is to propose a conceptual design model for BI applications using ODS (CoDMODS). Through expert validation, the proposed conceptual design model that was developed by means of design science research approach, was found to satisfy nine quality model dimensions, which are, easy to understand, covers clear steps, is relevant and timeless, demonstrates flexibility, scalability, accuracy, completeness and consistency. Additionally, the two prototypes that were developed based on CoDMODS for water supply service (iUBIS) and telecommunication maintenance (iPMS) recorded a high usability average min value of 5.912 using Computer System Usability Questionnaire (CSUQ) instrument. The outcomes of this study, particularly the proposed model, contribute to the analysis and design method for the development of the operational and tactical information in BI applications. The model can be referred as guidelines by BI developers. Furthermore, the prototypes that were developed in the case studies can assist the organizations in using quality information for business operations

Business Intelligence e os fatores de adesão nas empresas de saúde

Orientador : Márcia Ramos MayProjeto Técnico (especialização) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Curso de Especialização MBA em Inteligência de NegóciosInclui referênciasResumo : As organizações hospitalares têm utilizado a inteligência de negócios como uns dos mecanismos modernos para aumentar a vantagem competitiva e superar concorrentes, como também uma oportunidade para melhorar sua gestão. A combinação dos dados de aplicações clínicas, financeiras e outras, que são essenciais para os hospitais, obtidos com auxílio de sistemas de inteligência, são uns dos benefícios que podem gerar o ponto de partida para a concepção de gestão adequada e que as estratégias sejam alcançadas. Este trabalho analisa os fatores organizacionais que dificultam a adesão aos softwares de business intelligence por empresas de saúde. Através de uma pesquisa do tipo exploratória, experimental, com utilização de survey, com abordagem quantitativa, utilizando questionário online e presencial, realizada com profissionais do setor de tecnologia de hospitais, procurou-se identificar aspectos envolvidos na implantação e na adesão para os sistemas de business intelligence. Na análise realizada, identificou-se que fator humano e alto custo de software de business intelligence ainda são obstáculos à aderência dessas ferramentas nos hospitais pesquisados

Business Intelligence para apoio à gestão das listas de inscritos para cirurgia em Portugal continental

Em Portugal Continental a problemática das listas de inscritos para cirurgia e os seus tempos de espera são matérias que preocupam a sociedade portuguesa desde o início da década de noventa, do século XX. Atualmente as ferramentas de business intelligence ganham cada vez maior importância nas organizações inseridas num contexto mais complexo, competitivo e que exige respostas rápidas, adequadas e em constante mudança. O projeto desenvolvido consiste na implementação de uma aplicação de business intelligence, na Unidade Central de Gestão de Inscritos para Cirurgia, sedeada na Administração Central do Sistema de Saúde, I.P., que apoie a gestão das listas de inscritos para cirurgia de forma mais atempada, com maior qualidade e rigor, e com benefícios inquestionáveis para os utentes. Este projeto visa a monitorização de indicadores basilares; melhoria do controlo do desempenho dos hospitais; comparação entre os valores estabelecidos para determinados indicadores e os desvios verificados; simulação do impacto de algumas medidas, na lista de inscritos para cirurgia, antes da sua implementação; e facultar informação que permita adequar, a todo o momento, a oferta à procura, em determinadas patologias cirúrgicas. Os objetivos do projeto, definidos à priori, foram concretizados na sua totalidade, tendo sido a aplicação concluída com sucesso. Sugere-se, como ações futuras, acrescer novos indicadores e mais dimensões de análise à aplicação desenvolvida no âmbito deste projeto, alargando a capacidade de análise da Unidade Central de Gestão de Inscritos para Cirurgia, com inerente aumento da sua competência de gestão da Lista de Inscritos para Cirurgia em Portugal Continental

Business intelligence for sustainable competitive advantage: the case of telecommunications companies in Malaysia

The concept of Business Intelligence (BI) as an essential competitive tool has been widely emphasized in the strategic management literature. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well explained. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage.Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.This research uses combination of theoretical foundation of resource-based theory and diffusion of innovation theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. Qualitative field study then is carried out to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. A survey is carried out with sample of business analysts and decision makers in telecommunications firms and is analyzed by Partial Least Square-based Structural Equation Modeling.The findings revealed that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management will have better chance in realizing their dreams of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility and observability are also significant in ensuring BI success. It thus implied that the executives’ positive perceptions towards BI initiatives are deemed necessary. Moreover, the most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social and environmental issues.The BI model well explained how BI was deployed in Malaysian telecommunications companies. This study thus contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts

Data and the city – accessibility and openness. a cybersalon paper on open data

This paper showcases examples of bottom–up open data and smart city applications and identifies lessons for future such efforts. Examples include Changify, a neighbourhood-based platform for residents, businesses, and companies; Open Sensors, which provides APIs to help businesses, startups, and individuals develop applications for the Internet of Things; and Cybersalon’s Hackney Treasures. a location-based mobile app that uses Wikipedia entries geolocated in Hackney borough to map notable local residents. Other experiments with sensors and open data by Cybersalon members include Ilze Black and Nanda Khaorapapong's The Breather, a "breathing" balloon that uses high-end, sophisticated sensors to make air quality visible; and James Moulding's AirPublic, which measures pollution levels. Based on Cybersalon's experience to date, getting data to the people is difficult, circuitous, and slow, requiring an intricate process of leadership, public relations, and perseverance. Although there are myriad tools and initiatives, there is no one solution for the actual transfer of that data

Business Intelligence Maturity Model voor ziekenhuizen

In de loop der tijd zijn meerdere Business Intelligence Maturity Models (BIMMs) ontwikkeld. Deze scriptie onderzoekt welke goed toepasbaar zijn voor non-profitorganisaties, waaronder ziekenhuizen. Eerst werd onderzocht welke BIMMs bestaan en uit welke facetten zij zijn opgebouwd. Vervolgens zijn de gevonden BIMMs beoordeeld op hun theoretische basis en op de volledigheid en duidelijkheid van het model zelf. Daarna is onderzocht aan welke uitgangspunten BIMMs voor met name ziekenhuizen moeten voldoen. Verder zijn er eisen geformuleerd waaraan huidige BIMMs niet voldoen, maar waaraan ze wel zouden moeten voldoen. Deze uitgangspunten werden afgezet tegen de overgebleven BIMMs, wat resulteerde in een selectie. Op basis van deze modellen is een samengesteld conceptueel BIMM voor ziekenhuizen ontwikkeld, een model dat alle gevonden uitgangspunten zou moeten afdekken. Dit conceptueel BIMM is vervolgens getoetst bij BI-deskundigen van vijf ziekenhuizen. Hieruit is gebleken dat enkele facetten als overbodig beschouwd kunnen worden. Enkele punten waarvan de empirie leert dat ze meegenomen dienen te worden in het BIMM, zijn in het nieuwe conceptuele BIMM voor ziekenhuizen gealloceerd