# Decision Making in Healthcare

In the health care field, the steps of making a decision may be remembered with the mnemonic BRAND, which includes

• Benefits of the action

• Risks in the action

• Alternatives to the prospective action

• Nothing: that is, doing nothing at all

• Decision

# Decision Making in Business and Management

In general, business and management systems should be set up to allow decision making at the lowest possible level.

Several decision making models or practices for business include:

SWOT Analysis: Evaluation by the decision making individual or organization of Strengths, Weaknesses, Opportunities and Threats with respect to desired end state or objective.

Analytic hierarchy process: Widely-used procedure for group decision making.

Buyer decision processes: Transaction before, during and after a purchase.

Complex systems: Common behavioural and structural features that can be modeled.

Corporate finance

- The investment decision

- The financing decision

- The dividend decision

- Working capital management decisions

- Cost-benefit analysis: process of weighing the total expected costs vs. the total expected benefits

• Control-Ethics, a decision making framework that balances the tensions of accountability and 'best' outcome.

Decision trees

- Decision analysis: the discipline devoted to prescriptive modeling for decision making under conditions of uncertainty.

- Program Evaluation and Review Technique (PERT)

- Critical path analysis

- critical chain analysis.

Force field analysis: Analyzing forces that either drive or hinder movement toward a goal

Game theory: The branch of mathematics that models decision strategies for rational agents under conditions of competition, conflict and cooperation.

Grid Analysis: Analysis done by comparing the weighted averages of ranked criteria to options. A way of comparing both objective and subjective data.

Hope and fear (or colloquially greed and fear) as emotions that motivate business and financial players and often bear a higher weight that the rational analysis of fundamentals, as discovered by neuroeconomics research.

Linear programming: Optimization problems in which the objective function and the constraints are all linear.

Min-max criterion.

Model (economics): Theoretical construct of economic processes of variables and their relationships.

Monte Carlo method: Class of computational algorithms for simulating systems.

Morphological analysis: All possible solutions to a multi-dimensional problem complex.

Optimization

- Constrained optimization.

Paired Comparison Analysis: Paired choice analysis.

Pareto Analysis: Selection of a limited of number of tasks that produce significant overall effect.

Robust decision: Making the best possible choice when information is incomplete, uncertain, evolving and inconsistent.

Satisficing: In decision-making, satisfying explains the tendency to select the first option that meets a given need or select the option that seems to address most needs rather than seeking the "optimal" solution.

Scenario analysis: Process of analyzing possible future events.

Six Thinking Hats: Symbolic process for parallel thinking.

Strategic planning process: Applying the objectives, SWOTs, strategies, programs process.

Trend following and other imitations of what other business deciders do, or of the current fashions among consultants.

# Decision-makers and Influencers

In the context of marketing, there is much theory and even more opinion, expressed about how the various 'decision-makers' and 'influencers' (those who can only influence, not decide, the final decision) interact. Large purchasing decisions are frequently taken by groups, rather than individuals and the official buyer often does not have authority to make the decision.

# Decision Support Systems

Decision making software is essential for autonomous robots and for different forms of active decision support for industrial operators, designers and managers.

Due to the large number of considerations involved in many decisions, computer-based decision support systems (DSS) have been developed to assist decision makers in considering the implications of various courses of thinking. They can help reduce the risk of human errors. DSSs which try to realize some human/cognitive decision making functions are called Intelligent Decision Support Systems (IDSS), see for ex. "An Approach to the Intelligent Decision Advisor (IDA) for Emergency Managers, 1999". On the other hand, an active/intelligent DSS is an important tool for the design of complex engineering systems and the management of large technological and business projects, see also: "Decision engineering, an approach to Business Process Reengineering (BPR) in a strained industrial and business environment".