5,416 research outputs found

    Data Mining in Health-Care: Issues and a Research Agenda

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    While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

    Barriers to the adoption of health information technology

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    Information Technology (IT) is successfully applied in a diverse range of fields. Though, the field of Medical Informatics is more than three decades old, it shows a very slow progress compared to many other fields in which the application of IT is growing rapidly. The spending on IT in health care is shooting up but the road to successful use of IT in health care has not been easy. This paper discusses about the barriers to the successful adoption of information technology in clinical environments and outlines the different approaches used by various countries and organisations to tackle the issues successfully. Investing financial and other resources to overcome the barriers for successful adoption of HIT is highly important to realise the dream of a future healthcare system with each customer having secure, private Electronic Health Record (EHR) that is available whenever and wherever needed, enabling the highest degree of coordinated medical care based on the latest medical knowledge and evidence. Arguably, the paper reviews barriers to HIT from organisations’ alignment in respect to the leadership; with their stated values when accepting or willingness to consider the HIT as a determinant factor on their decision-making processes. However, the review concludes that there are many aspects of the organisational accountability and readiness to agree to the technology implementation

    CLINICAL DATA WAREHOUSE: A REVIEW

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    Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW

    Electronic medical records concepts and data management

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    Healthcare information (Clinical Data) is associated with every individual, young or old, rich or poor, belonging to any country. Clinical data is very extensive. Everyday some new diseases and new symptoms are being seen and the human race is struggling to find cures. There are many diseases whose diagnosis, symptoms, and possible treatment are known but unfortunately that rare knowledge is not available to every individual in the world. This initiates all the vision behind presenting a paper on EMR/ EHR and its Data Management. The thesis reviews the concept of EMR/ EHR thus explaining its concepts, importance, market need etc. Thesis will also explain privacy and security related to clinical data in electronic format which is a very important as any electronic data is prone to hacks and data loss. To manage and utilize such amount of data, there is need of extensive data management and so the thesis explains the concepts of Datawarehouse, its importance, ETL, Schemas etc. As part of explaining these concepts a mini EMR/EHR Datawarehouse is designed which explains various subject areas possible in any EMR Datawarehouse. Last but not the least, the thesis comments on the Future of EMR/ EHR and the World Vision on this revolutionary change

    Healthcare Data Analytics on the Cloud

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    Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution
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