HIMSS13 - What everyone is talking about

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.

In the visualization below, you can see some of the terms that people are talking about on Twitter regarding HIMMS13. The data below was pulled from Twitter on 2/7/2013 at 11:50pm using the twitteR package for R. Data from approximately 500 tweets was pulled into a corpus. Various stopwords and transformations were done on the corpus was assembled into a Term Document Matrix using the tm library. Finally interesting colors were selected using the RColorBrewer library, and the wordcloud was put together with the wordcloud library. Just a small example of how you can use social media to get an understanding of what people are talking about. Text mining will no doubt be a topic of discussion at HIMSS. Imagine taking all of your patient survey data and performing a sentiment analysis on it to discover what patients feelings are about the care they are being provided? What sort of wordcloud would exist for your institution if you scraped the online news media sites and social networks on a daily basis to see how you are being perceived? Not surprising that BYOD is a hot topic. Everyone wants to be mobile. Its more productive to be mobile and not held captive by a centralized system or terminal. More productivity translates to better care, which everyone wants. The key is rolling out your applications in a secure manner, keeping the data safe, but at the same time making the systems easy to use for its authorized users. Data is also a hot topic. Data has to be accurate, secure, organized and available. Combine Data and BYOD and you have a pretty powerful combination to address the needs of patients.