Benefits of data extraction for the healthcare system

Benefits of data extraction for the healthcare system

When people think of data extraction, they have to understand that is the process of information retrieval, which extract automatically structured information from semi-structured or unstructured web data sources. The companies that do data extraction provide for clients specific information available on different web pages. The Internet is a limitless source of information, and through this process, people from all domains can have access to useful knowledge. The same is with the healthcare system, which has to be concerned with providing patients quality services. They have to deal with poor documentation, and this has a huge impact on the way they provide services, so they have to do their best and try to obtain the needed information. If doctors confront with a lack of complete documentation in a case, they are not able to proper care the patients. The goal of data scraping in this situation is to provide accurate and sufficient information for correct billing and coding the services provided to patients.

The persons that are working in the healthcare system have to review in some situations hundred of pages long documents, for knowing how to deal with a case, and they have to be sure that the ones that contain useful information will be protected for being destroyed or lost in the future. A data mining company has the capability to automatically manage and capture the information from such documents. It helps doctors and healthcare specialists to reduce their dependency on manual data entry, and this helps them to become more efficient. If it is used a data scraping system, data is brought faster and doctors are able to make decisions more effectively. In addition, the healthcare system can collaborate with a company that is able to gather data from patients, to see how a certain type of drug reacts and what side effects it has.

Data mining companies can provide specific tools that can help specialists extract handwritten information. They are based on a character recognition technology that includes a continuously learning network that improves constantly. This assures people that they will obtain an increased level of accuracy. These tools transform the way clinics and hospitals manage and collect data. They are the key for the healthcare system to meet federal guidelines on patient privacy. When such a system is used by a hospital or clinic, it benefits from extraction, classification and management of the patient data. This classification makes the extraction process easier, because when a specialist needs information for a certain case he will have access to them in a fast and effective way. An important aspect in the healthcare system is that specialists have to be able to extract data from surveys. A data scraping company has all the tools needed for processing the information from a test or survey. The processing of this type of information is based on optical mark recognition technology and this helps at extracting the data from checkboxes more easily. The medical system has recorded an improved efficiency in providing quality services for patients since it began to use data scrapping.

Source: http://www.amazines.com/article_detail.cfm/6196290?articleid=6196290

Data Mining Basics

Data Mining Basics

Definition and Purpose of Data Mining:

Data mining is a relatively new term that refers to the process by which predictive patterns are extracted from information.

Data is often stored in large, relational databases and the amount of information stored can be substantial. But what does this data mean? How can a company or organization figure out patterns that are critical to its performance and then take action based on these patterns? To manually wade through the information stored in a large database and then figure out what is important to your organization can be next to impossible.

This is where data mining techniques come to the rescue! Data mining software analyzes huge quantities of data and then determines predictive patterns by examining relationships.

Data Mining Techniques:

There are numerous data mining (DM) techniques and the type of data being examined strongly influences the type of data mining technique used.

Note that the nature of data mining is constantly evolving and new DM techniques are being implemented all the time.

Generally speaking, there are several main techniques used by data mining software: clustering, classification, regression and association methods.

Clustering:

Clustering refers to the formation of data clusters that are grouped together by some sort of relationship that identifies that data as being similar. An example of this would be sales data that is clustered into specific markets.

Classification:

Data is grouped together by applying known structure to the data warehouse being examined. This method is great for categorical information and uses one or more algorithms such as decision tree learning, neural networks and "nearest neighbor" methods.

Regression:

Regression utilizes mathematical formulas and is superb for numerical information. It basically looks at the numerical data and then attempts to apply a formula that fits that data.

New data can then be plugged into the formula, which results in predictive analysis.

Association:

Often referred to as "association rule learning," this method is popular and entails the discovery of interesting relationships between variables in the data warehouse (where the data is stored for analysis). Once an association "rule" has been established, predictions can then be made and acted upon. An example of this is shopping: if people buy a particular item then there may be a high chance that they also buy another specific item (the store manager could then make sure these items are located near each other).

Data Mining and the Business Intelligence Stack:

Business intelligence refers to the gathering, storing and analyzing of data for the purpose of making intelligent business decisions. Business intelligence is commonly divided into several layers, all of which constitute the business intelligence "stack."

The BI (business intelligence) stack consists of: a data layer, analytics layer and presentation layer.

The analytics layer is responsible for data analysis and it is this layer where data mining occurs within the stack. Other elements that are part of the analytics layer are predictive analysis and KPI (key performance indicator) formation.

Data mining is a critical part of business intelligence, providing key relationships between groups of data that is then displayed to end users via data visualization (part of the BI stack's presentation layer). Individuals can then quickly view these relationships in a graphical manner and take some sort of action based on the data being displayed.

Source:http://ezinearticles.com/?Data-Mining-Basics&id=5120773