Predictive analytics

Table of Contents Executive Summary 3 Facts about Alzheimer’s: 3 Usage of Data analytics in the healthcare sector 3 Internal Processes 4 1. Clinical Trials 4 2. Systems Biology 4 3. Simulation of processes: 4 4. Better disease prediction: 4 5. Risk and compliance management: 4 External Processes 4 1. Real-time health tracking 4 2. Electronic health records and Electronic medical records 4 3. Cross-selling applications 5 4. Credit services to medical practitioners 5 Internal Process – Clinical…

AML software is used by financial institutions to detect suspicious transactions and analyze customer data. AML software operates under various categories including currency transaction report, customer identity management, transaction monitoring systems, and compliance software. AML solutions provide real-time alerts and tools for financial enterprises to automatically report suspicious events to maximize security and operational efficiency. It is becoming essential for financial enterprises to…

(Willans). George Orwell prophesied the use of horrific techniques to implement governmental control in his novel, 1984, regarding a totalitarian government that prohibits any ideas outside their own, through the use of the Thought Police’s predictive analytic technology, audio detection software, and constant surveillance. Companies in today’s society are reaping the benefits from utilizing analytical data to archive their customers’ psychological desires and purchases, to track what they are…

An analytical competitor is defined as “an organization that uses analytics extensively and systematically to outthink and out execute the competition.” (Davenport, p23) According to Davenport and Harris, the measure of a successful analytical competitor lies in its ability to meet the following: 1) must possess the four key characteristics that form the pillars of analytical competiton and 2) transition through the five stages of development of the analytical competitor. In order to…

application of healthcare analytics will require integrating each of these platforms into a single data warehouse (Groenfeldt, 2012). The EHR may capture the various supplies used on a patient, but the accounting system knows the cost of each…

rise of predictive analytics of Amazon, Netflix, and Google, CEOs are asking all functional departments to deliver better business outcomes (Roberts, 2015). HR is no exception. Strategic analytics in HR falls under many different names: Human Capital Management, Human Capital Analytics, Workforce Analytics, Talent Management, People Management, Talent Supply Chain, and most recently, People Analytics or People Science. Regardless of its name, several examples of strategic analytics occur…

Noting the promise and potential big data analytics offers in the healthcare industry, it is equivalently important to consider the potential technical and socio-political challenges it will face during its development and in the wake of its implementation. Firstly some of the biggest technical challenges big data analytics must face include driving the collection of health data in real-time and minimizing lag between data collection and processing (W. Raghupathi and V. Raghupathi). Additionally…

According to Winters-Miner (2014), PA “uses technology and statistical methods to search through massive amounts of information, analyzing it to predict outcomes for individual patients” (para 4). Many hospitals have turned to utilizing predictive analytics to assist in reducing the number of patients that are readmitted back into the hospital. Physicians need to be able to determine when the appropriate time is to discharge patients and basing this solely on what brought them into the…

The decision model used for my topic is the top-down decision tree model. This decision tree measures the optimum recovery rate for the non-compliant patients. There isn’t really a threshold values that would cause the tree to flip. In this case, my decision tree would flip if I have too many variables in addition to the three types of non-compliant patients, or the outcome of these non-compliant patients in addition to the nursing home or hospitalization. Other possible patients’ category could…

for the issues(such as diseases and various different treatments methods). With technology advancement, the risk can be measured through the emerging concepts such as data from wearable devices, genetic profiling, and driving behaviors, predictive modeling analytics and some mortality indexes. This information along with the latest inventions in solving the underlying issues can be used to perform an evidence based underwriting. With large of amount of data from all different sources, an…