...Introduction 2 Assumptions 3 Data Availability 3 Overnight processing window 3 Business sponsor 4 Source system knowledge 4 Significance 5 Data warehouse 6 ETL: (Extract, Transform, Load) 6 Data Mining 6 Data Mining Techniques 7 Data Warehousing 8 Data Mining 8 Technology in Health Care 9 Diseases Analysis 9 Treatment strategies 9 Healthcare Resource Management 10 Customer Relationship Management 10 Recommended Solution 11 Corporate Solution 11 Technological Solution 11 Justification and Conclusion 12 References 14 Health Authority Data (Appendix A) 16 Data Warehousing Implementation (Appendix B) 19 Data Mining Implementation (Appendix B) 22 Technological Scenarios in Health Authorities (Appendix C) 26 Technology Tools 27 Data Management Technology Introduction The amount of information offered to us is literally astonishing, and the worthiness of data as an organizational asset is widely acknowledged. Nonetheless the failure to manage this enormous amount of data, and to swiftly acquire the information that is relevant to any particular question, as the volume of information rises, demonstrates to be a distraction and a liability, rather than an asset. This paradox energies the need for increasingly powerful and flexible data management systems. To achieve efficiency and a great level of productivity out of large and complex datasets, operators need have tools that streamline the tasks of managing the data and extracting valuable...
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...2. Data mining search parameters A data mining algorithm is a set of heuristics and calculations that creates a data mining model from data. To create a model, the algorithm first analyzes the data you provide, looking for specific types of patterns or trends. The algorithm uses the results of this analysis to define the optimal parameters for creating the mining model. These parameters are then applied across the entire data set to extract actionable patterns and detailed statistics. The mining model that an algorithm creates from your data can take various forms, including: * A set of clusters that describe how the cases in a dataset are related. * A decision tree that predicts an outcome, and describes how different criteria affect that outcome. * A mathematical model that forecasts sales. * A set of rules that describe how products are grouped together in a transaction, and the probabilities that products are purchased together. Microsoft SQL Server Analysis Services provides multiple algorithms for use in your data mining solutions. These algorithms are implementations of some of the most popular methodologies used in data mining. All of the Microsoft data mining algorithms can be customized and are fully programmable using the provided APIs, or by using the data mining components in SQL Server Integration Services. You can also use third-party algorithms that comply with the OLE DB for Data Mining specification, or develop custom algorithms that can be...
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...Key fields are an essential part of system design since they facilitate effective organization, access, and maintenance of data structures within the system (Scott, and Rosenblatt, 2017). Primary and foreign keys are examples of the various key fields that can be utilized when designing the system. The primary keys that will be used for the patient data set include patient identification number, name, and date of birth while the secondary keys will be the provider ID number. Provider ID no will be the primary key for the primary physician data set while the patient ID, name, and date of birth will be the secondary keys. The recommended approach is advantageous over the others since it provides the patients with the flexibility to choose various...
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...Case Study 2 1. Yes, I do believe that a program of HR metrics and workforce analytics would be useful in Regional Hospital because it will help them improve organizational effectiveness. By having a HR metric and workforce analytics program put into place Regional Hospital will be able to solve their staffing problem. 2. The opportunities I see regarding “where” and “how” metrics and analytics might be applied is by gathering data on the number of employees and patients during each shift (morning, evening, and night). With this data I would then use workforce analytics to help identify ways to match optimal skills and number of employees to the appropriate shifts at Regional Hospital. 3. Three analysis and associated metrics I think maybe useful are data mining, predictive analysis and operational experiments. Data mining will help Regional Hospital’s staffing issue by enhancing decision making through the use of correlations and multiple regression methods to identify patterns in the hospital’s data set. This in turn will help identify unrecognized causal mechanisms. Productive analysis will help Regional Hospital develop models of organizational systems to better help predict the future outcomes and gain an understanding of the consequences of hypothetical changes within the organization. Lastly, operational experiments will allow the hospital to implement their new HR metrics and workforce analytics program in the organization to see if it is successful or not....
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...University of Phoenix Material A Research Plan Part I – Research Problem Think about something in psychology that you would be interesting in studying. You can select a topic from what you’ll be studying in this course or another topic that seems intriguing to you. Respond to the following in 50 to 75 words: |Identify your research problem and describe it. What about this topic interests you? | |Healthcare is occupied, directly otherwise indirectly, with the condition of health services to personalities. These services can happen in | |a variety of effort settings, as well as hospitals, hospitals, dental place of works, out-patient operation centers, birthing middles, | |emergency checkup care, house healthcare, as well as nursing homes. We choose this topic because health staff, big healthcare facilities | |uses a wide variety of deals that have health as well as safety hazards related with them. These comprise mechanical preservation, medical | |tools maintenance, food service, building as well as grounds maintenance, and administrative staff. (What is healthcare?, 2014) | | | Part II – The Scientific Method Consider the scientific method and complete the following table explaining each step of the scientific method in your own...
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...identify and evaluate data mining algorithms which are commonly implemented in modern Medical Decision Support Systems (MDSS). They are used in various healthcare units all over the world. These institutions store large amounts of medical data. This data may contain relevant medical information hidden in various patterns buried among the records. Within the research several popular MDSS’s are analysed in order to determine the most common data mining algorithms utilized by them. Three algorithms have been identified: Naïve Bayes, Multilayer Perceptron and C4.5. Prior to the very analyses the algorithms are calibrated. Several testing configurations are tested in order to determine the best setting for the algorithms. Afterwards, an ultimate comparison of the algorithms orders them with respect to their performance. The evaluation is based on a set of performance metrics. The analyses are conducted in WEKA on five UCI medical datasets: breast cancer, hepatitis, heart disease, dermatology disease, diabetes. The analyses have shown that it is very difficult to name a single data mining algorithm to be the most suitable for the medical data. The results gained for the algorithms were very similar. However, the final evaluation of the outcomes allowed singling out the Naïve Bayes to be the best classifier for the given domain. It was followed by the Multilayer Perceptron and the C4.5. Keywords: Naïve Bayes, Multilayer Perceptron, C4.5, medical data mining, medical decision...
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...Data Mining Data mining began with the advent of databases. Databases are warehouses full of computer data. Computer scientists began to realize that this data contains patterns and relationship to other sets of data. As computer technology emerged, data was extracted into useful information. Often, hidden relationships began to appear. Once this data became known and useful, industries grew around data mining. Data mining is a million dollar business aimed at improving marketing, research, criminal apprehension, fraud detection and other applications. History of Data Mining Computers began to be more widely used in the 1960’s. Computers were used to collect and store data. The data was stored on tapes and disks. The companies and organizations began to wonder about the data that was stored. They wanted to know about past sales, past performances and other pertinent information that was stored on these tapes and disks. The next step was to find an accurate way to retrieve the needed information without manually reading all the data. The next step in this quest came in the 1980’s with relational databases and structured queries. Query language could be used to find out more of what was in the data. The companies and organizations could now identify what has happened in the past. They also wanted to know how to apply this knowledge to future predictions based on past performances. In 1989, the first knowledge discovery workshop was held in Detroit (SQL Data Mining, 2012)...
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...Knowledge about users and their information needs can contribute to better user interface design and organization of information in clinical information systems. This can lead to quicker access to desired information, which may facilitate the decision-making process. Qualitative methods such as interviews, observations and surveys have been commonly used to gain an understanding of clinician information needs. We introduce clinical information system (CIS) log analysis as a method for identifying patient-specific information needs and CIS log mining as an automated technique for discovering such needs in CIS log files. We have applied this method to WebCIS (Web-based Clinical Information System) log files to discover patterns of usage. The results can be used to guide design and development of relevant clinical information systems. This paper discusses the motivation behind the development of this method, describes CIS log analysis and mining, presents preliminary results and summarizes how the results can be applied. Go to: INTRODUCTION The availability of clinical information to the clinician at the point of care is essential to the health care process. Inability to locate needed information can be costly in terms of time and quality of care. Clinical information systems have been developed to assist clinicians with their decisions; however, these systems need to ensure that they provide the information in optimal ways. In order to develop a useful and effective system, the...
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...Data Mining Prepared by: Kirsten Sullivan Strayer University CIS 500 Dr. Baab September 9, 2012 Data mining is a concept that companies use to gain new customers or clients in an effort to make their business and profits grow. The ability to use data mining can result in the accrual of new customers by taking the new information and advertising to customers who are either not currently utilizing the business's product or also in winning additional customers that may be purchasing from the competitor. Generally, data are any “facts, numbers, or text that can be processed by a computer.”1 Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes operational or transactional data such as, sales, cost, inventory, payroll, and accounting. Data mining also known as “knowledge discovery”, is the process of analyzing data from different perspectives and summarizing it into useful information- information that can then be used to increase revenue, cuts costs, and continue the goals outlined for the company. Data mining consists of five major elements: “Extract, transform, and load transaction data onto the data warehouse system, store and manage the data in a multidimensional database system, provide data access to business analysts and information technology professionals, analyze the data by application software, present the data in a useful format, such as a graph or table.”2...
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...500 Complete ClasCIS 500 Complete Class Assignments and Term Paper Click link Below To Download Entire Class: http://strtutorials.com/CIS-500-Complete-Class-Assignments-and-Term-Paper-CIS5006.htm CIS 500 Complete Class Assignments and Term Paper CIS 500 Assignment 1 Predictive Policing CIS 500 Assignment 2: 4G Wireless Networks CIS 500 Assignment 3 Mobile Computing and Social Networking CIS 500 Assignment 4 Data Mining CIS 500 Term Paper Mobile Computing and Social Networks CIS 500 Assignment 1 Predictive Policing Click link Below To Download: http://strtutorials.com/CIS-500-Assignment-1-Predictive-Policing-CIS5001.htm In 1994, the New York City Police Department adopted a law enforcement crime fighting strategy known as COMPSTAT (COMPuter STATistics). COMPSTAT uses Geographic Information Systems (GIS) to map the locations of where crimes occur, identify “hotspots”, and map problem areas. COMPSTAT has amassed a wealth of historical crime data. Mathematicians have designed and developed algorithms that run against the historical data to predict future crimes for police departments. This is known as predictive policing. Predictive policing has led to a drop in burglaries, automobile thefts, and other crimes in some cities. Write a four to five (45) page paper in which you Compare and contrast the application of information technology (IT) to optimize police departments’ performance to reduce crime versus random patrols of the streets. Describe...
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...CHAPTER ONE INTRODUCTION 1.1 Background of the Study The mining industry in Ghana, is an important sector for the socio economies of countries that have substantial gold deposits. To achieve rapid economic development, many countries resort to various activities to exploit natural resources. One of such activities is mining. Consequently, mining is an important economic activity which has the potential of contributing to the development of areas endowed with the resource. In North America, raw mineral production in 1998 was valued at approximately US$ 70 billion. The industry employs approximately 1 million people (Mbendi Profile, 2005). In Peru, the mining sector accounts for 50% of the country’s annual export earnings. During 1993, the mining industry’s contribution to the Peruvian economy was represented by $240m paid in taxes; $400m spent on local purchases; $280m in imported goods and accounted for over 11% of GDP (Acheampong, 2003). In South Africa, where gold is the largest mineral foreign income earner, gold mining alone contributes 27.4% in mineral revenues. The gold industry is also responsible for 56% of South Africa’s mine labour force (Mbendi, 2005). In Ghana, the sector plays a vital role in the development of the economy. In 2000, minerals accounted for 38.96% of total export earnings, followed by cocoa (22.51%) and timber (9.03%) (ISSER, 2001). The mining sector now contributes 41% to the country’s foreign exchange and is the leading foreign exchange earner. Of...
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...development methodology, and examples in public health. Results: The paper provides a broad overview of health analytics for researchers and practitioners. Conclusions: Health analytics is rapidly emerging as a key and distinct application of health information technology. The key objective of health analytics is to gain insight for making informed healthcare decisions. Keywords: Data warehousing; ETL; Descriptive analytics; Discovery Introduction analytics; Health analytics; Informed decision; Insight; Predictive analytics; Prescriptive analytics management, and preventive care. In one scenario, for example, the use of health analytics technologies can ensure that emergency room doctors are briefed and ready to treat patients prior to their arrival by ambulance. Diagnostic and current health data can be downloaded by hospital staff from a wide variety of systems to develop a patient profile that includes past illnesses, chronic conditions, allergies, blood and tissue typing. With this information as well as a constant stream of vital sign data fed directly by paramedics en route to the hospital, receiving doctors can make better...
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...Running Head: Fraud and Abuse Fraud and Abuse in the U.S. Healthcare System Tenisha Howard Keller Graduate Professor Cutspec June 12, 2011 Background People can be affected by healthcare fraud and abuse directly and indirectly. Fraud is defined as an intentional deception, false statement or misrepresentation made by a person with the knowledge that the deception could result in unauthorized benefit to oneself or another person. It includes any act that constitutes fraud under applicable federal or state law. Abuse is defined as practices that are inconsistent with professional standards of care; medical necessity; or sound fiscal, business, or medical practices. Intent is the key distinction between Fraud and Abuse. An allegation of waste and abuse can escalate into a fraud investigation if a pattern of intent is determined (B, Tom). Both, fraud and abuse can be committed by physicians, patients, and private insurers. Situations of fraud and abuse that occur in our healthcare system are billing for services that have not been provided, overbilling for services provided, and misdiagnosing health conditions in order to avoid financial responsibility for the proper treatment of illnesses. Define the Problem What can decrease the high costs of premiums and co payments? With the decrease of fraud and abuse, premiums and co payments would not be high. Who pays for fraud and abuse healthcare bill? Medicaid and Medicare are the two federal programs that are...
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...to buy the same EHR product as their hospital it’s trying to accomplish how to make process writing order from their practices to the hospital. The physicians would have better access to the information that is needed to monitor the patients. The provider would be able to tap into the other providers’ through EHR systems when they are covering the emergency room. The pros of EHR are: It provides accurate and up to date information about the patients at the point of care. It also enables quick access to patient records. It has secure sharing information that is electronic and also helps provider to be more effect when diagnosing patients that leads to a minimally errors in the medical field. It’s a convenience and a source of communication because patient and provider. It’s a more reliable way to prescribing and also help streamline data to the billing department that would enable them to complete the coding process. The cons of EHR are: A lack of security which is one of the biggest problems. The information that stored in the database can be access without authorized authority. This leads to more medical records breeches. Hospitals cannot emerge the systems overnight so this means that a whole lot of time would be needed to make sure the paper information is inputted correctly. Not everyone is computer savvy so that means training needs to be done as well, more money needed and more time. This can also have a loss of data. If the system crash or do not back...
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...eTourism – ICT usage in tourism Definition and history of eTourism Facts & trends Reasons, advantages and implications of ICT usage ECCA – eTourism Competence Center Austria 3 ICT in Tourism © copyright IFITT 1.2 eTourism – ICT usage in tourism Definition and history of eTourism Facts & trends Reasons, advantages and implications of ICT usage ECCA – eTourism Competence Center Austria 4 ICT in Tourism © copyright IFITT Introduction - ICT ICT - Information and Communication Technologies Umbrella term for technological developments for the Production, analysis, storage, search, distribution and use of information ICT includes a combination of hardware software telecommunications netware groupware humanware ICT enables effective data processing & communication for organisational benefit opportunities & challenges for suppliers in all industries Provide enormous capabilities for consumers 5 ICT in Tourism © copyright IFITT Introduction - ICT Development phases of ICT Time period 1945-1960 1960-1975 1975-1990 Technology Early electronic computers Mainframes, databases Host systems, PC, networks, communication protocols Distributed systems,...
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