Remote sensing approaches can complement NPS monitoring, while expanding the spatial scale and increasing the temporal frequency for more rapid detection. Science Explorer. Mission Areas. Unified Interior Regions. Science Centers. Frequently Asked Questions. Educational Resources. Multimedia Gallery. Web Tools. Board on Geographic Names.
The National Map. USGS Library. USGS Store. Park Passes. News Releases. Featured Stories. Science Snippets. For example, a DSS may be used to project a company's revenue over the upcoming six months based on new assumptions about product sales. Due to a large number of factors that surround projected revenue figures, this is not a straightforward calculation that can be done manually. However, a DSS can integrate all the multiple variables and generate an outcome and alternate outcomes, all based on the company's past product sales data and current variables.
A DSS can be tailored for any industry, profession, or domain including the medical field, government agencies, agricultural concerns, and corporate operations. The primary purpose of using a DSS is to present information to the customer in an easy-to-understand way. A DSS system is beneficial because it can be programmed to generate many types of reports, all based on user specifications. For example, the DSS can generate information and output its information graphically, as in a bar chart that represents projected revenue or as a written report.
As technology continues to advance, data analysis is no longer limited to large, bulky mainframe computers. Since a DSS is essentially an application, it can be loaded on most computer systems, whether on desktops or laptops. Certain DSS applications are also available through mobile devices. The flexibility of the DSS is extremely beneficial for users who travel frequently.
This gives them the opportunity to be well-informed at all times, providing them the ability to make the best decisions for their company and customers on the go or even on the spot.
In organizations, a decision support system DSS analyzes and synthesizes vast amounts of data to assist in decision-making. With this information, it produces reports that may project revenue, sales, or manage inventory. Many different industries, from medicine to agriculture, use decision support systems. To help diagnose a patient, a medical clinician may use a computerized decision support system for diagnostics and prescriptions.
Combining clinician inputs and previous electronic health records, a decision support system may assist a doctor in diagnosing a patient. Broadly speaking, decision support systems help in making more informed decisions. Often used by upper and mid-level management, decision support systems are used to make actionable decisions, or produce multiple possible outcomes based on current and historical company data.
At the same time, decision support systems can be used to produce reports for customers that are easily digestible and can be adjusted based on user specifications. Centers for Disease Control and Prevention. Evidence shows that CDSS can be tied to lower blood pressure and cholesterol levels, but the findings on this association are inconsistent.
The ability of CDSS to reduce health disparities is understudied, and several researchers have suggested that further work is needed to directly examine this issue. Some have noted that providers working with underserved communities typically lag behind in the uptake of electronic health records EHRs and CDSS, and evidence exists that CDSS leads to successful health outcomes when used in underserved communities.
Economic factors related to the implementation and maintenance of CDSS have not been well-documented. A review by the Community Preventive Services Task Force was inconclusive because of a lack of available data. The Task Force found that current studies are extremely heterogeneous in the range of CDSS functions and CVD risk factors studied and in the completeness or inclusion of major cost factors. Thus, the ability to determine an overall estimate of the cost or economic benefit of CDSS is limited.
Of the studies available, health care costs appear to be more likely to decrease than increase after CDSS implementation, but the usefulness of this evidence is limited by incomplete and inconsistent data.
It has a higher percentage of low-income patients than clinics in surrounding areas. As a result of this assessment, the clinic increased its use of EHRs and implemented systems to better identify patients with undiagnosed hypertension, increase use and monitoring of clinical quality measures, and increase use of clinically supported self-measured blood pressure monitoring.
CDPCProgram nebraska. Note: The web version has been updated in an effort to keep the linked resources current, and for this reason some of the content may differ with the PDF version. Skip directly to site content Skip directly to page options Skip directly to A-Z link. On the other hand, optimization models are more adaptable , can handle more complex issues and deal with multiple constraints and tradeoffs. The most appropriate DSS depends upon organizational maturity, complexity and, to a certain extent, size.
In small organizations, hybrid systems may suffice. If the organization is new to analytics, historical DSS systems would be a good place to start, while those involved in activities such as trading and commodities may benefit more from a predictive decision support system example. Without a doubt, the greatest benefit lies with selecting a prescriptive analytics derived decision management system that models the business and provides the ability to determine the most advantageous decision based on certain criteria, such as revenue and profitability.
While entailing a greater investment in resources and money, such a solution has a greater probability of exceeding expectations and achieving a greater ROI. Additionally, it takes the guesswork out of decision-making, and because the model replicates the business, this type of decision support system example is more likely to offer feasible and rational solutions.
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The Use of DSS to Guide Decision-Making While some balk at the idea of trusting complex computer software solutions to make decisions for them, most are comfortable using computer-generated statistics to understand key trends. The three key elements of DSS include: Organizational data: Relevant information and knowledge A model: Mathematical and statistical formulae that represent the business and analyze data A user interface: Dashboards or other interfaces allowing users to interact with and view results 1.
Common Day-to-Day Decision Support System Examples Decision support systems operate at many levels, and there are many examples in common day-to-day use. Decision Support System Examples That Use Historical Data Historical data analysis, used in every facet of business and life, is well-developed and mature.
Some examples include: Descriptive analytics: Metrics such as sales results, inventory turnover and revenue growth. Diagnostic analytics: Diagnostic information that digs a bit deeper to reveal results and explains reasons for past performance as measured by descriptive analytics.
Business intelligence BI : Although largely based on historical data, BI solutions allow users to develop and run queries that are used to guide and support decision-making. ERP dashboards: User-configurable dashboards that allow managers to monitor a variety of performance indicators.
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