Data mining (also known as knowledge discovery and data mining (KDD), knowledge discovery in data, and knowledge discovery in databases), refers to sorting through data to identify patterns and relationships in large relational databases in order to extract and utilize data. Data mining allows data to be extracted and transformed, stored in a data base, accessed, and analyzed by software programs. Additionally, the data retrieved can be presented in various forms, such as in graphs or tables. The types of relationships that are commonly found in data mining include classes, clusters, associations, and sequential patterns.
Data mining focuses on producing a solution that generates useful forecasting through a four-phase process:
- problem identification
- exploration of the data
- pattern discovery
- knowledge deployment, or application of knowledge to new data to forecast or generate predictions.
Continue reading “Data Mining / Nursing Informatics Reading and Sharing”
Humans acquire data and information in bits and pieces and then transform the information into knowledge. The information-processing functions of the brain are frequently compared to those of a computer, and vice versa. Humans can be thought of as organic information systems that are constantly acquiring, processing, and generating information of knowledge or knowledge in their professional and personal lives. they have an amazing ability to manage knowledge. This ability is learned and honed from birth as individuals make their way through life interacting with the environment and being inundated with data and information. Each person experiences the environment and learns by acquiring, processing, generating, and disseminating knowledge.
Continue reading “The Foundation of Knowledge Model / Reading and Sharing Nursing Informatics”
With the signing into law of the American Recovery and Reinvestment Act (ARRA) on February 17, 2009, a portion of the law created the Health Information Technology for Economic and Clinical Health (HITECH) Act. The HITECH Act provided stimulus money to increase the use of electronic health records across the country in what many felt were aggressive timelines. The term “Meaningful use” was created by both the Health Information Technology Policy and Standards Committees to determine how organizations would receive reimbursement for their implementation.
The term of system life cycle describes the ongoing process of developing and maintaining an information system.
- Needs assessment
- System selection
- The purpose of the needs assessment is to determine the gap between an organization’s current state and the overall needs of the organization with consideration to the strategic plan.
- evaluation of the strengths and weaknesses of the organization related to efficiency, quality, and financial strengths should be considered
- understanding an organization’s current state workflow process as well as long-term goals related to efficiency, quality, and financial out-comes by creating a gap analysis can assist the decision-making group
Continue reading “System Life Cycle: Needs Assessment / Reading and Sharing Nursing Informatics”
HIMSS Analytics, a not-for-profit subsidiary of the Healthcare Information and Management Systems Society (HIMSS) outlined the differences between the terms electronic medical record and electronic health record, defining the electronic medical record (EMR) as the “legal record created in hospitals and ambulatory environments that is the source of data for the EHR.” The EMR typically referred to a single encounter with no, or very limited, ability to carry information from one visit to another within a care delivery system. That situation has changed so that it is possible to bring information forward from prior visits within the organization or delivery system.
Basic EMR components:
- clinical messaging and email
- results reporting
- data repository
- decision support
- clinical documentation
- order entry
Continue reading “EMR vs. EHR /Reading & Sharing Nursing Informatics”