...Original Contributions Data Mining Applications in Healthcare Hian Chye Koh and Gerald Tan A B S T R A C T Data mining has been used intensively and extensively by many organizations. In healthcare, data mining is becoming increasingly popular, if not increasingly essential. Data mining applications can greatly benefit all parties involved in the healthcare industry. For example, data mining can help healthcare insurers detect fraud and abuse, healthcare organizations make customer relationship management decisions, physicians identify effective treatments and best practices, and patients receive better and more affordable healthcare services. The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. This article explores data mining applications in healthcare. In particular, it discusses data mining and its applications within healthcare in major areas such as the evaluation of treatment effectiveness, management of healthcare, customer relationship management, and the detection of fraud and abuse. It also gives an illustrative example of a healthcare data mining application involving the identification of risk factors associated with the onset of diabetes. Finally, the article highlights the limitations of data mining and discusses some future directions....
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...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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...The increasing use of data mining by corporations and advertisers obviously creates apprehension from the perspective of the individual consumer due to privacy, security and the potential use of inaccurate information. The idea that there are data warehouses that contain customers’ personal information can be rather frightening. However, the use of data mining by these organizations can also lead to numerous benefits for consumers they otherwise would not have realized. Besides the obvious benefit of guiding consumers to products or services they’d be more interested in purchasing, the use of data mining by companies has also benefitted individuals’ health and financial safety. Not long after the use of data mining came into prominence the use of data mining consumer information vs. consumer privacy became a major issue in early 1998 after CVS and Giant entered into an agreement with Elensys, a Massachusetts direct marketing company, to send reminders to customers who had not renewed their prescriptions. However, neither CVS nor Giant explained how the program would work or received their customers' permission to transfer their prescription records to a third party (Pyatt Jr.). Giant and CVS’s rationale for entering into this agreement was “to develop a positive program to benefit consumers, many of whom don't take their medication properly,” (Pyatt Jr.). Even though their primary intention was good, Giant and CVS were not transparent about their agreement with Elensys...
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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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...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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...A BIG DATA APPROACH FOR HEALTH CARE APPLICATION DEVELOPMENT G.Sravya [1], A.Shalini [2], K.Raghava Rao [3] @ B.Tech Students, dept. of Electronics and Computers. K L University, Guntur, AP. *.Professor, dept. of Electronics and Computers. K L University, Guntur, AP sravyagunturi93@gmail.com , shaliniaramandla@gmail.com, raghavarao@kluniversity.in ABSTRACT: Big data is playing a vital role in present scenario. big data has become the buzzword in every field of research. big data is collection of many sets of data which contains large amount of information and little ambiguity where other traditional technologies are lagging and cannot compete with it .big data helps to manipulate and update the large amount of data which is used by every organization in any fields The main aim of this paper is to address the impact of these big data issues on health care application development but health care industry is lagging behind other sectors in using big data .although it is in early stages in health care it provides the researches to accesses what type of treatment should be taken that are more preferable for particular diseases, type of drugs required and patients records I. Introduction Health care is one of the most prominent problems faced by the society now a day. Every day a new disease is take birth which leads to illness of millions of people. Every disease has its own and unique medicine for its cure. Maintaining all the data related to...
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...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 words. Please use complete sentences to write 25 to 50 words describing each...
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...Data Mining Teresa M. Tidwell Dr. Sergey Samoilenko Information Systems for Decision Making September 2, 2012 Data Mining The use of data mining by companies assists them with identifying information and knowledge from databases and data warehouses that would be beneficial for the company. The information is often buried in databases, records, and files. With the use of tools such as queries and algorithms, companies can access data, analyze it, and use it to increase their profit. The benefits of using data mining, its reliability, and privacy concerns will be discussed. Benefits of Data Mining 1. Predictive Analytics: This type of analysis uses the customer’s data to make a specific model for the business. Existing information is used such as a customer’s recent purchases and their income, to create a prediction of future purchases and how much or what type of item would be purchased. The more variables used the more likely that the prediction will be correct. Such variables include the customer ranking, based on the number of and most recent purchases and the average profit made per customer purchase. Without data made available through web access and purchases by the customer, predictive analysis would be difficult to perform. The company, therefore, would not be able to plan nor predict how well they are performing. 2. Associations Discovery: This part of data mining helps the company to discover the “relationships hidden in larger data sets” (Pang-Ning...
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...Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology, if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining. Keywords: data mining, benefits, privacy concerns Data Mining Benefits of Data Mining for a Business Data mining can be explained as the process of a business collecting data on their customers or potential customers to increase customer business. A business will collect data on their customers or potential customers and use that data to give them coupons, promote sells, and analyze buying and selling trends. Data mining can benefit the customer as well as the business. Data mining can be used in the retail industry, the finance industry, and the healthcare industry. Any industry can benefit from data mining but those are the top three (Turban & Volonino, 2011). Data mining is a way for large businesses to get to know their customers. The information gathered from data mining can let a large company learn what their customers want and how they want it. It can also benefit large companies get to know their employees, the company can learn how to satisfy their...
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...1. Define data mining. Why are there many different names and definitions for data mining? Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models. Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining. What recent factors have increased the popularity of data mining? Following are some of most pronounced reasons: * More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace. * General recognition of the untapped value hidden in large data sources. * Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc. * Consolidation of databases and other data repositories into a single location in the form of a data warehouse. * The exponential increase...
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...Cloud Computing in Healthcare |Ayesha Asif |………. |Noorulain Ashraf | |Department of Software Engineering |Department of Software Engineering |Department of Software Engineering | |Fatima Jinnah Women University |Fatima Jinnah Women University |Fatima Jinnah Women University | |Rawalpindi, Pakistan |Rawalpindi, Pakistan |Rawalpindi, Pakistan | |Ayesh_15_94@yahoo.com |abc@xyz.com |noorulainkhaan@gmail.com | Abstract – Healthcare Delivery Organizations (HDOs) are facing a number of problems due to their mode of operations. Amongst the problems are improper modes of data storage, insecurity of medical records, difficulty in accessing healthcare services, high cost, and inaccurate diagnosis. Recently research has identified Cloud Computing (CC) as a new and substantial business model capable of providing efficient services that can benefit the healthcare industry. The aim of this paper is to study the existing cloud computing applications in healthcare industry and propose optimal techniques and solution on the basis of analysis. Keywords – Cloud Computing (CC); Healthcare Information Technology (HIT); Healthcare Organizations (HCOs);...
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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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...AND ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics, Web 2.0 Introduction...
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...ANALYTICS: FROM BIG DATA TO BIG IMPACT Hsinchun Chen Eller College of Management, University of Arizona, Tucson, AZ 85721 U.S.A. {hchen@eller.arizona.edu} Roger H. L. Chiang Carl H. Lindner College of Business, University of Cincinnati, Cincinnati, OH 45221-0211 U.S.A. {chianghl@ucmail.uc.edu} Veda C. Storey J. Mack Robinson College of Business, Georgia State University, Atlanta, GA 30302-4015 U.S.A. {vstorey@gsu.edu} Business intelligence and analytics (BI&A) has emerged as an important area of study for both practitioners and researchers, reflecting the magnitude and impact of data-related problems to be solved in contemporary business organizations. This introduction to the MIS Quarterly Special Issue on Business Intelligence Research first provides a framework that identifies the evolution, applications, and emerging research areas of BI&A. BI&A 1.0, BI&A 2.0, and BI&A 3.0 are defined and described in terms of their key characteristics and capabilities. Current research in BI&A is analyzed and challenges and opportunities associated with BI&A research and education are identified. We also report a bibliometric study of critical BI&A publications, researchers, and research topics based on more than a decade of related academic and industry publications. Finally, the six articles that comprise this special issue are introduced and characterized in terms of the proposed BI&A research framework. Keywords: Business intelligence and analytics, big data analytics...
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