...EHR/CPOE Implementation Executive Summary This thesis follows the implementation of Computerized Patient Order Entry/Electronic Health Record (CPOE/EHR) system implemented by Partners Healthcare System (PHS) during 2002-2003 for all its constituent practitioners. It looks at the problems faced during implementation of the system and identifies new potential problems that the system may encounter. Particularly in consideration is the effort it takes to convince healthcare professionals to switch to CPOE/EHR, the cost of installing the system, the potential of automating redundancies in the system and the potential of healthcare professionals getting skewed data out of the system suggestions. It looks at the management challenges faced by the administration when bringing about CPOE/EHR to PHS and divulges in some techniques that were used for tackling these issues. It defines ways in which the system is being used to improve patient healthcare and save millions of dollars for the government, healthcare facilities and patients alike. This thesis also finds ways to combat the potential problems that may arise later and the system and looks at related government policies and statutes which apply to the implementation. Finally some metrics of success are discussed their effectiveness in driving a result. Problem Definition CPOE/EHR Implementation can face a host of problems that can hinder the process flow and the acceptability of the system by the people involved. The initial...
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... In the world of healthcare, there are many advantages to improving the quality of healthcare while keeping the costs of healthcare down. One system is known as the “Pay for performance” system. This system is aimed at improving healthcare by offering a financial reward. This type of program usually pays a bonus if the providers or hospital meet on an agreed upon performance measure, for example, reducing the hemoglobin A1c in diabetic patients. Hemoglobin A1c is a test that shows the average level of blood sugar in a patient within the past 2-3 months. This is important for diabetic patients to see whether their blood sugar levels have been on target or not. These incentives can be paid directly to physicians, hospitals, and other healthcare providers to ensure that their facility carries out such improvements and to achieve the most optimal quality of care for their patients. Another system is called “pay for performance”, where paying providers for resulting in better outcomes for patients, who in return will improve these outcomes. While both of these programs are a great way to improve the quality of healthcare by giving providers some type of motivation to improve their services, it can also come with some penalties. Hospitals and nursing homes can face financial penalties if their patient becomes ill while in their care, for example, a patient having a Urinary Tract Infection from that patient having to use a catheter. o Data Analysis Procedures One...
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...T ackling Fraud, Waste, and Abuse in the M edicare and Medicaid Programs: R esponse to the May 2 Open Letter to the Healthcare Community Dan Olson, CFE June 2012 Tackling Fraud, Waste, and Abuse in the Medicare and Medicaid Programs White Paper C ontents I. Introduction ............................................................................................................ 1 II. Recommendations................................................................................................... 3 Recommendation 1 – Expand the Medicare Fraud Strike Force Model....................................... 3 Potential Savings .......................................................................................................................... 4 Recommendation 2 – Expand Integrated Data Repository .......................................................... 4 Potential Savings .......................................................................................................................... 5 Recommendation 3 – Expand “Do Not Pay List” .......................................................................... 5 Potential Savings .......................................................................................................................... 6 Recommendation 4 – Publicize Drug Expiration Dates ................................................................ 6 Potential Savings ....................................................................
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...Corporate Capability Statement Quality Review of EMS Performance Measure Data Solicitation No.: 2012-0100 Submitted To: U.S. Department of Transportation National Highway Traffic Safety Administration Attn: Lloyd S. Blackwell 1200 New Jersey Avenue, SE Washington, DC 20590 lloyd.blackwell@nhtsa.dot.gov Submitted By: Econometrica, Inc. 4416 East-West Highway Suite 215 Bethesda, Maryland 20814 DUNS Number: 196693170 TIN: 52-2108043 November 30, 2011 November 30, 2011 Lloyd S. Blackwell U.S. Department of Transportation National Highway Traffic Safety Administration 1200 New Jersey Avenue, SE Washington, DC 20590 Reference: Sources Sought Notice; Solicitation No. 2012–0100; Quality Review of EMS Performance Measure Data. Dear Mr. Blackwell: Econometrica in partnership with Traffic Safety Analysis Systems & Services (TSASS), Inc., is pleased to submit a Corporate Capability Statement in response to the above-referenced Sources Sought Notice. We believe we offer the Department of Transportation (DOT) highly qualified expertise and capabilities that will benefit DOT in future work projects. Econometrica and TSASS are small businesses, and TSASS is veteran-owned. If you wish to discuss any aspect of this submission, please feel free to contact me at (301) 657-8311. Thank you for your consideration of Econometrica. Sincerely, Econometrica, Inc. Cyrus Baghelai President/CEO Table of Contents Introduction...
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...HCS/465 October 3, 2013 Results The procedure of collecting data was appropriate because the data was collected in the most efficient and logical way of collecting data for obtaining the desired outcome. The data for this study was collected by a web-enabled database for error reporting system in a hospital setting. Nurses, physicians, and pharmacists had access to the system at any time to report any type of medication error. The rights of the subjects were protected because the patients information was never used on the error reporting database and the error reports made by the staff were anonymous, other than tracking what job title the error reporter held. Reporting any error was completely voluntary to the staff and staff was fully aware of what the system was for. The data collection tool in this instance was a web-enabled database for error reporting. This system supports the reliability of the study by keeping accurate percentage tallies of the types of errors, who is reporting the errors, and the suggestions to prevent the errors. Data Analysis Procedures The data analysis procedures in this study were appropriate for the data collected because the procedures used to analyze the data were as follows: two experts, the investigator, and the pharmacist worked together to analyze the material. The two experts agreed on the categories of errors and the one year time limit on data collection. The data analysis procedures are appropriate for answering the research question...
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...The importance of identity in falls prevention. Falls are frequently identified as one of the elderly giants. It mostly generates diagnostic and rehabilitative dilemmas in today's healthcare practice. The importance of a fall lies in its effect on the older person's health, function and independence. A fall can be the first indication of an undiagnosed disease and repeated falls often indicate a change in an older person's functional ability. The prevention of falls in the older population is the most important area in clinical practice which reduces fall associated mortality and morbidity. Fear of falling is commonly identified although poorly defined phenomena among older people (close, 2005). Approximately one third of adults over 65 years of age fall each year. Many will have some psychological consequences after a fall. A frequently expressed psychological factor is loss of confidence in self functioning. This may lead to physical deconditioning, depression, isolation from the society and a low quality of life. Low fall-related efficacy is associated with the number and severity of future falls in older adults with balance disorders (Bishop 2010). A critique of the qualitative study. The importance of identity in falls prevention is a qualitative study where author clearly offers a picture of the occurrence of falls, its prevention and the interventions in older adults who lives in our community. Each individual dreams to have a community where older adults can...
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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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...Introduction to Big data Every day, 2.5 quintillion bytes of complex, every changing data are generated. (IBM) Data comes from social sites, digital images, transaction records, and countless unknown resources. The amount of data we generate daily is enormous, and the rate it is being generated is accelerating. As we head into a future where technology dominates the global market, this pace will only continue accelerate. Businesses and other entities are aware of this data and its power. In a survey taken by Capgemini and the Economist, over 600 global business leaders identified their companies as data driven and identified data analytics as an integral part of their business. Big Data solutions are considered the answer for handling this data converting it into useful information. According to the O'Reilly Radar Team (Big Data Now), Big Data consists of three variables – size, velocity and variety. Data is considered big if conventional systems cannot handle its size. It is not only that size of Big Data that matters, but also the volume of transactions that come with it. The second issue is how fast the data is generated and how fast if it changes (velocity). New data and updated data is constantly generated, and it must be processed and analyzed quickly to create real value for an organization. The final issue is data structure (variety). Data is typically collected in raw form, unstructured, from a variety of sources. To acquire useful information, data needs to be processed...
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...Big data analytics is projected to change the way companies manage and analyze large information set and how people produce massive amounts of data. A recent findings produced by few Internet and Online Business Degree looked at the future of this trend sweeping through the IT industry. This concept is up-growing one as the current data storage pattern utilized by the companies is not as productive as plotted. It is refers to following type of data 1) Traditional Enterprise Data:- includes customer related data ERP, CRM, web transaction 2) Machine Generated Data:- weblogs, Trading Systems etc 3) Social Data: - data of facebook, twitter, google etc. Big Data can be seen in the finance and business where enormous amount of stock exchange, banking, online and onsite purchasing data flows through computerized systems every day and are then captured and stored for inventory monitoring, customer behavior and market behavior. Day by day the capacity of data is increasing & many of industries are not able to manage it efficiently. By 2020, a total of 35 zeta-bytes of data will be produced as the average annual generation of information grows 43,000 percent, according to Computer Sciences Corporation. Big data may still be a relatively new phenomenon, but its impact is already being felt throughout various industries. Organizations that can effectively store, manage and analyze this information may set themselves apart from their competitors or, even better, make key advancements...
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...IS5111:Integration of IS & Business Business Intelligence and Analytics Abhishek Kumar Singh Anbarasan Thangapalam R Deepak Chattani Naadiya Danapal Ram Vibhakar S [A0120022] [A0119959] [A0119975] [A0119961] [A0120054] A Case study on Business Intelligent and Analytics Systems in NTUC FairPrice Table of Contents Executive Summary ............................................................................................................................. 3 Acknowledgments ............................................................................................................................... 4 1. Introduction ...................................................................................................................................... 5 2. Business Intelligence in Retail Industry: ................................................................................. 6 3. Combining BI in CRM ...................................................................................................................... 7 3.1 Analytical CRM ..........................
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... In the video, the narrator said “The data is considered as the greatest source of power in the 21st Century”. Discuss this by using several examples from real life. Big data is a Term that have been used a lot in the recent years also there been various studies dedicated into extracting those type of Data. and if we search for the meaning of the word data we find that its defined as pieces of information that is needed to understand the universe. and now its the age is Big Data. as we go through the documentary movie we learned a lot about why Big Data became the new generation of Data. the police force has recognized its value and adapted a new program which apply mathematical algorithms to predict when and where crime will be committed by simple going through the database of previous crime and then highlight areas of recurrent crimes. shifted from another area data can make us rich which as we witnessed inn London Metal Exchange they hired elite staff of scientist, and analysis to monitor the data and give almost accurate prediction of stocks rate which can assist brokers to understand the market and avoid some type of risks. Big data didn't stop at only this two fields but spread to reach healthcare as we saw how they have started into understanding causes of diseases by examining the DNA by implementing the Big Data into it, also consumers now are targeted by personalized advertisement which is now possible by the use of big data which helps in giving clear and accurate...
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...Introduction Predictive analytics is an advanced analytics technique. Predective analytics uses both new and historical data to foresee the result, activity, behavior and trends. Statistics is branch of mathematics, mainly concerns about collection, analysis, interpretation and presentation of tons of numerical facts. Statistics is used in almost every field of research. Head to Head Comparisons Predictive Analytics Statistics Definition Predictive analytics is branch of the data analytics to predict the future events. Statistics in simpler terms is collection of numerical facts. It is the science of collecting, classifying and representing the numerical data. Why it matters? Predictive analytics can identify the risks and opportunities...
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...the need to collect and analyze data? Organization | Drivers for Data Collection and Analysis | Amazon.com | * Customer information * Website personalization * Supply chain management * Employee training and incentivization * Email/newsletter personalization * Website traffic patterns to improve customer experience * Demographic information for marketing * Information about supplier and for supplier about their products * Error rates and how to fix errors * Tracking new and popular items that amazon might want to stock * Warehouse data and error rates * Shipping data, time it takes to complete orders * Tracking app orders and glitches * Improving relationships with suppliers | Wal-Mart | * Supply chain management for internal and external (supplier) use * Shelf stocking information, were shelves stocked in time? * Sales data information to improve shopping experience * Forecasting demand for products * Competitor pricing information * Optimizing factory production for suppliers * Error rates * Time it takes to complete and ship online orders * Employee information such as payroll * New product information and forecasting * Employee training * Demographic information for marketing * Website data for optimization * Tracking app purchases and orders and glitches * Improving relationships with suppliers | Brose | * Raw material procurement and forecasting * Manufacturing data and error rates * Making sure...
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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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...distributing food and food-related products and services to restaurants, healthcare, and educational facilities in the United States and Canada. It is the world’s largest broadline food distributor with more than 400,000 clients in a wide array of fields. • SYSCO is belong to food wholesale industry, crowding the market along with its main competitors such as Ben E. Keith Company, Bunzl plc, and Edward Don & Company. What specific company or industry situation(s) represented the key rationale/reason (Business Drivers) for this initiative? • Information Technology is the critical business driver for SYSCO to drive the cost & time efficiency (by organizing a large number of datasets) and understand well the business pattern such as sales tracking by customers and business forecasting (by analyzing and monitoring selected datasets). Initiative Objectives/Benefits What were the key business objectives of this initiative (I.e. what specific problems were they trying to solve)? • The effort to make a better use of the large number of datasets and information generated by its 100 operating companies in order to serve its customers better. • SYSCO has already had an ERP systems and Data Warehouse to integrate business process across its operating companies and to organize large number of datas into one single repository. However, SYSCO still need to address the needs of specific data analysis, monitoring, and accurate business forecasting. • To address these...
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