Throughout history, technological advancements have appeared for one purpose before finding applications elsewhere that lead to spikes in its usage and development. The internet, for example, was originally developed to share research before becoming a staple of work and entertainment. But technology—new and repurposed—will undoubtedly continue to be a driver of healthcare information. Informaticists often stay tuned to trends to monitor what the next new technology will be or how the next new idea for applying existing technology can benefit outcomes.
In this Discussion, you will reflect on your healthcare organization’s use of technology and offer a technology trend you observe in your environment.
Reflect on the Resources related to digital information tools and technologies.
Consider your healthcare organization’s use of healthcare technologies to manage and distribute information.
Reflect on current and potential future trends, such as use of social media and mobile applications/telehealth, Internet of Things (IoT)-enabled asset tracking, or expert systems/artificial intelligence, and how they may impact nursing practice and healthcare delivery.
By Day 3 of Week 6
Post a brief description of general healthcare technology trends, particularly related to data/information you have observed in use in your healthcare organization or nursing practice. Describe any potential challenges or risks that may be inherent in the technologies associated with these trends you described. Then, describe at least one potential benefit and one potential risk associated with data safety, legislation, and patient care for the technologies you described. Next, explain which healthcare technology trends you believe are most promising for impacting healthcare technology in nursing practice and explain why. Describe whether this promise will contribute to improvements in patient care outcomes, efficiencies, or data management. Be specific and provide examples.
Well we got 2 informational indexes to examination utilizing SPSS PASW 1) Wine Quality Data Set and 2) The Poker Hand Data Set. We can do this utilizing CRISP system. Give us a chance to look what is CRISP by wikipedia "Fresh DM represents Cross Industry Standard Process for Data Mining It is an information mining procedure model that portrays normally utilized methodologies that master information diggers use to handle issues." PASW Modeler is an information mining workbench that empowers you to rapidly create prescient models utilizing business aptitude and convey them into business activities to improve basic leadership. Structured around the business standard CRISP-DM model, IBM SPSS PASW Modeler bolsters the whole information mining process, from information to better business results. Fresh DM, Clementine's own "lightweight" technique of 5 phases Business Understanding, Data Understanding, Data Preparation Displaying, Evaluation and Deployment. Fresh Methodology Business Understanding: Understanding the venture necessities and destinations from a business point of view, and after that changing over this learning into an information mining issue definition Information understanding In this progression following exercises are going on, Data understanding, Collecting Initial Data at that point depicting Data, Exploring Data and in conclusion confirming Data Quality The information planning stage Undertakings incorporate table, record, and quality choice just as change and cleaning of information for demonstrating tools.Cleaning Data utilizing proper cleaning and purging systems at that point Integrating Data into a solitary point. Demonstrating: Choice and utilization of different demonstrating strategies done in this stage, and their parameters are changed in accordance with ideal qualities. Essentially, there are more than one procedure for similar information mining issue type. A few procedures have explicit necessities on the type of information. Subsequently, venturing back to the information planning stage is frequently required. Steps comprise of Generating a Test Design, Building the Models surveying the Model Assessment Working of model (or models) happens in this stage. Prior to continuing to definite arrangement of the model, it is imperative to all the more completely assess the model, and survey the means executed to develop the model. Sending In the last arrange Knowledge picked up is sorted out introduced with the goal that an end client can without much of a stretch use it. According to the prerequisites this can be a report or a mind boggling information mining process. Regularly Customers do the arrangement step Wine quality informational index Wine quality is demonstrated under characterization and relapse draws near, which jam the request for the evaluations. Illustrative learning is given as far as an affectability investigation, which estimates the reaction changes when a given information variable is differed through its area The red wine informational collection contains 1600 examples out of which I have chosen 200 irregular examples and doing the analysis("Data mining can't find designs that might be available in the bigger assortment of information if those examples are absent in the example being "mined" ") .So I chose the informational collection remembering. The informational index I have chosen has high certainty. With estimations of 13 substance constituents (for example liquor, Mg) and the objective is to locate the nature of red and white wine. Information factors 1 – fixed corrosiveness 2 – unpredictable causticity 3 – citrus extract 4 – remaining sugar 5 – chlorides 6 – free sulfur dioxide 7 – complete sulfur dioxide 8 – thickness 9 – pH 10 – sulfates 11 – liquor Yield variable is quality (score somewhere in the range of 0 and 10) Fresh system has been finished out the stage .By checking the site and assets found out about the wine area .the following stage was to check whether erroneous, absent or "anomalous" values in the informational collection end guarantee the information quality. Information nature of the informational collection is generally excellent. PASW Data stream grouping of red and white wines Grouping for Red and White wine 2 informational collections red wine and white wine have been imported utilizing variable record hubs Use of sort hub here is to depict the qualities of information. . The Classification and Regression (C&R) Tree hub is a tree-based characterization and forecast technique. Like C5.0, this strategy utilizes recursive apportioning to part the preparation records into sections with comparative yield field esteems. The C&R Tree hub begins by inspecting the info fields to locate the best split, estimated by the decrease in a debasement record that outcomes from the split. The split characterizes two subgroups, every one of which is along these lines part into two additional subgroups, etc, until one of the halting criteria is activated. All parts are twofold (just two subgroups) Red Wine's variable significance White wine variable significance From variable significance chart we can say that significant ascribe to decide Red wine quality is pH. The variable significance is in the request pH, citrus extract, chloride as appeared in the figure1. In any case, for deciding White wine's quality the most contributing characteristic is chloride and second trait is Alcohol. Examination and end The above created tree comprises of hubs and its youngsters. The top hub speak to the all out number of wine tests and what number of number has a place with various categories(1 to 9).The initially split is on chloride. This infers the vast majority of the wine has a place with chloride level<0.041 and some has a place with chloride level>0.041.We see that great quality wine has chloride level<0.041.If we stop after one split we would state cl<0.041 are great wine. Second split from LHS hub is based on fixed acridity shifts most between great quality and terrible quality wine. In RHS wine quality contrast in citrus extract. It has been found from tally Vs Quality diagram that what number of has a place with great quality classes. Alcoholic grouping of white wine tests is more than that of red wine test. Great wines typically have high fixation. So we can infer that White wine tests are great. In the white wine chloride level is typically high that suggests it has got great Aroma. Where as in red wine the citrus level is between specific levels that demonstrates the red wine is scrumptious!! PASW has various 2-D and 3-D diagrams like bar, pie, histogram, dissipate and so forth for time being I am utilizing direct chart and 3-d disperse chart. You can utilize any of the diagram according to the prerequisites. A few charts are anything but difficult to translate .Let us consider a 2-D diagram between most contributing variable pH and quality from the diagram unmistakably the connection send among pH and quality is so that if pH is in the middle of 3.23 and 3.27 quality is great. Quality is low for 3.38 and 3.50.We can plot comparable chart among quality and citrus extract or towards what regularly contributing variable at that point discover the connection send between them Give us a chance to plot a chart among chloride and Quality for the white wine. In the underneath figure it demonstrates the quality is excellent when chloride level beneath 0.036.And quality in the range 5 to 6 when chloride level is over .048. Like this if plot a diagram among quality and liquor we will see the quality is excessively great if alcoholic fixation in the middle of 12.5 and 13(as per the example I have broke down) 3D chart which demonstrates the connection transport between liquor, quality and chloride level of white wine from the 2d examination it was indicated how the quality is being influenced by single variable. In the event that the one variable does not tell about how quality being connected we can check connection dispatch between 3 factors utilizing a 3d diagram. It is having 3 tomahawks. How Regression is valuable In this different relapse ,Predictors, for example, (Constant), liquor, fixed sharpness, lingering sugar, chlorides, unstable corrosiveness, free sulfur dioxide, sulfates, pH, all out sulfur dioxide, citrus extract, thickness decide the estimation of value. Beneath gave a Pasw stream for relapse. Each by changing the autonomous variable's worth we can get estimation of ward variable quality. With the assistance of a theory we have to comprehend and manufacture a connection dispatch among the factors. To anticipate the mean quality incentive for a given autonomous variable (state unstable causticity) we need a line which goes between the mean estimation of both quality and unpredictable sharpness and which limit the total of separation between every one of the focuses and prescient line. This fits into a line. The Poker Hand Data Set Each record is a case of a hand comprising of five playing cards drawn from a standard deck of 52. Each card is depicted utilizing two traits (suit and rank), for an aggregate of 10 prescient characteristics. There is one Class quality that depicts the "Poker Hand". The request for cards is significant and there are 480 conceivable Royal Flush hands. Beneath examining about how to decide poker hands utilizing information mining. I am thinking about grouping as it were. In the event that we think about grouping/Regression it doesn't bode well PASW MODEL CLASSIFICATION USING CRT ALGORITHAM We got preparing and testing informational collection .First applying a model on preparing informational collection. Source document is a Comma isolated record (CSV) with 1 million columns. It is hard to do break down on this info informational collection so chose test informational index and doing the examination. Issue confronted The given source information was not in a significance full position so I have given important property name and Values by utilizing Vlookup work in MS exceed expectations, presently the information has turned out to be all the more significance full and it would appear that beneath. Information purging is significant and goes under information readiness period of the system Exactness of prescient model The exactness of prescient model is checked by examination hub. It has been discovered that exactness is 90%. Utilizing the Algorithm need to foresee any of these: 0: Nothing close by; 1: One pair;2: Two pairs;3: Three of a kind;4: Straight;5: Flush; 6: Full house;7: Four of a kind;8: Straight flush;9: Royal flush; Give me a chance to state what did I comprehended from the chart. Rank2 (rank of card2) is generally cont>GET ANSWER