The Healthcare Information And Management Systems Society Essay

SHC mission was to care, to educate, and to discover for the benefit of patients and larger community. Multiple problems and opportunities were present within the organization’s IT infrastructure that needed to be resolved before implementing an EMR system. The case stated, “In the early 2000s, SHC was in no shape to support an EMR system comparable to other healthcare groups” (Denend & Zenios, 2010). They needed to fix their existing IT infrastructure in order to resolve network, security, and regulatory compliance (HIPPA) issues. After addressing these concerns, they could focus on a solution for an EMR system. The strategic motivation behind implementing an EMR system was to reduce cost, meet competitive (internal and external) pressures, improve

Change is never easy and is often met with resistence. HIT is essential to the medical needs of our changing healthcare system. Most industry such as retail would fail if they did not incorporate information technology within their business strategy. Walmart is able to offer good products at an affordable rate because they depend on information they receive to analyze consumer behavior and product positioning. The healthcare system is far beyond most industries that capitalize on information technology. However, the impact of HIT is undeniable and the potential to save billions of dollars to the healthcare system is receiving attention from providers. At 90 percent adoption of EMR the efficiency saving could be $77 billion annually by reducing hospital bed stays, reducing nurse work hours, reducing drug and radiology usage (Hillestad et al, 2005). More importantly HIT has the opportunity to improve the quality of care of patients with chronic disease. Patients suffering from cancer, heart disease, and other chronic illness are undermanaged or mismanaged. HIT can eliminate redundant testing and provide relevant patient information so the provider can make better medical decisions for the

According to the Department of Health and Human Services, by 2009, EMR adoption rate for a freestanding ambulatory surgery center (ASC) was 22.3 percent versus 62.4 percent for a larger hospital-based ASC. Further, the likelihood of installing EMR in freestanding ASCs was 63.6 percent versus 87.7 percent in hospital-based ASCs. These differences in the EMR adoption and installation rates were reported due to limited availability of capital funds and the number of practitioners. Usually, it is difficult for smaller centers to generate enough revenue for installing fully-integrated EMR systems. Moreover, solo or fewer practitioners practicing in small ambulatory care settings could not afford to support such expensive EMR installations. (“Factors Influencing..”, 2010). Hence, hospital affiliation can be a key contributing determinant towards EMR adoption

One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.

This includes assessment of organizational motivation, awareness, and support, a needs assessment (how EHR technology can meet clinic needs by improving efficiency and improving patient care), and an assessment of existing barriers to change and technology adoption. The organizational environment is crucial to adoption and implementation of meaningful use. We must understand and help support staff understand how implementation of meaningful use fits with clinic goals and patient care goals. (Ford, 2010). This includes selecting an EHR that is usable and meets the needs of the clinic. Usability is one of the major factors hindering widespread adoption of EMRs. Usability has a strong, often direct relationship with clinical productivity, error rate, user fatigue, and user satisfaction–critical factors for EMR adoption. Clinicians lose productivity during the training days and for months afterward as they adapt to new tools and workflow.

From this diagram we find that there is no country which can reach 100% EMR implementation. Some of European countries, including Norway, Netherlands, United Kingdom and New Zealand have high EMR adoption rate which is almost 100 percent. And Australia has adoption rate of 92 percent, which is also a high adoption rate. In comparison, Canada’s EMR adoption rate is relatively low, with only 56% adoption rate. Many physicians or health professionals are still resistant to use EMR in Canada. According to an international comparison, less than 30% of physician in Canada use electronic lab review, e-prescribing or some other eHealth technology, comparing with rates of greater than 90% in the

Data warehouses, in contrast, are targeted for decision support. Historical, summarized and consolidated data is more important than detailed, individual records. Since data warehouses contain consolidated data, perhaps from several operational databases, over potentially long periods of time, they tend to be orders of magnitude larger than operational databases; enterprise data warehouses are projected to be hundreds of gigabytes to terabytes in size. The workloads are query intensive with mostly ad hoc, complex queries that can access millions of records and perform a lot of scans, joins, and aggregates. Query throughput and response times are more important than transaction throughput.

Answer: The term data warehouse is often used to refer to a system that extracts data from one or more sources, in order to transform and store in a model suitable for presentation and analysis. It can also be used to refer to just the database used in the aforementioned type of system. There are two main approaches to building a data warehouse, the Kimball approach and the Inmon approach.

It helps an organization consolidate data from several sources by separating analysis workload from transactional workload. Additionally, a data warehouse environment includes ETL which is Extraction, Transportation and Loading solution, an OLAP which is Online Analytical Processing Engine, analysis tools and other tools so as to look over the process of gathering data and finally delivering it to business users. The data stored in these warehouses must be stored in a way which is reliable, secure, and easy to process and manage. The need for data warehousing arises as businesses become more complex and start generating and gathering huge amount of data which were difficult to manage in the traditional way.

Data warehouse is a central repository integrating data from various operating systems for validation of data, prediction etc .Data Warehouse is a relational database used for analysis and query rather than transactional database. It is used to collect historical data from various sources, integrate, analyze a particular subject, report. Data warehouse is time variant i.e one can retrieve any older data and once data enters data warehouse it cannot change [1]. According to Ralph Kimball Data warehouse is “copy of transaction data constructed for analysis and query”[5]. Data is taken from various sources like marketing, sales, ERP etc.

Data warehouse (DW of DWH) also called enterprise data warehouse (EDW) refers to the system utilized in the analysis and reporting of data. The can be described as the main component making up business intelligence. Normalized data warehousing describes the repositories containing integrated data form several dissimilar sources. It contains information which can be utilized in creating investigative reports for the various users within an organization. Examples of reports that can be retrieved from these repositories include annual and periodic trends of sales within the organization. The data contained in these sources is uploaded form the operational systems and hence can be utilized in making accurate reports regarding the operations. Before the data can be used for reporting purposes it could pass through operational data stores. This reports presents summaries of researches conducted in topics seeking to describe various normalized models of data warehousing. The research covers the topics indicated in the table below

Data warehouse is aggregation of subject-oriented, integrated databases, which is designed to confirm DSS support. Now days these repository has become a focal point for DSS in organisation. These data repository used for online analytical Processing (OLAP), data mining and support queries. Decisions which are pending from a long time get resolved by analysing data warehouses. Another benefit of data warehouse is it improves the productivity by redesigning business process and work. It is challenging and technical undertaking because data comes from different sources and systems. There are some other organisational issues like sponsorship maintenance, scope avoidance and political issues. Because of these reasons data warehouse project get

A Data WareHouse is a type of database normally used by large companies to store large amounts of data in and have the data be easily accessible. They are normally set up in one of three set-ups. The basic model that takes data straight from it sources, such as operational systems and flat files. The Staging Mode that has a staging area that takes the data, from the systems and files before moving it to data warehouse. The Final type adds data marts, a small database that takes specific information from the data warehouse, to the previous model between the data warehouse and the end users. Data Warehouses are also really useful because they make it easy to pull data from either queries or data mining. Data warehouses are a useful tool when dealing with large amounts of data.

Every organizations have their own independent Data Warehouse and due to increase in the number of transactions, the size of the data is also increasing. Data warehouse is the central repository of information for an organization. There are multiple data sources like OLTP, excel, csv, txt, xml, etc, that are generated from various systems and are populated to data warehouse by ETL and thus Data Warehouse stores the summarized integrated business data in a central repository. The Data Warehouse is used for the analytical applications (OLAP – On-Line Analytical Processing), decision making, data mining and user applications.

Data is the raw materials of any information system. With the revolution of Information Technology we are improving our decision making process more quick and smart. Data warehouse technology is the process of collection, sorting, structural formation, analysis, storing and presentation of data. So we say that data warehouse is the technology is overall data management system in the organization.