Related Experiment Video
Updated: Jul 27, 2026
08:50
In Vitro Rearing of Solitary Bees: A Tool for Assessing Larval Risk Factors
Published on: July 16, 2018
New ventures require accurate risk analyses and adjustments
1George Washington University, Washington, D.C., USA.
Summary
Healthcare leaders must analyze risks and returns for business success. Applying investment portfolio principles can optimize departmental strategies and manage organizational risk effectively.
Area of Science:
- Healthcare Management
- Business Strategy
- Risk Analysis
Background:
- Successful new business ventures require robust risk analysis and strategic approaches to risk-return balancing.
- Risk analysis encompasses both objective and subjective risk factors.
- Principles from investment portfolio management can be adapted for organizational units.
Purpose of the Study:
- To explore the application of risk analysis and portfolio management principles in healthcare business strategy.
- To guide healthcare executives in balancing risk and return for optimal decision-making.
Main Methods:
- Examination of objective and subjective risk factors in business ventures.
- Application of mathematical principles from investment portfolio theory to organizational units.
- Analysis of conservative and speculative strategies for service line determination.
Main Results:
- The ideal business investment offers high expected return with low standard deviation.
- A balanced approach considering both conservative and speculative strategies is crucial.
- Portfolio management concepts can be effectively applied to a portfolio of healthcare departments or strategic business units.
Conclusions:
- Healthcare executives should employ comprehensive risk analyses for strategic planning.
- Adapting investment portfolio principles aids in optimizing service lines and managing organizational risk.
- Considering diverse strategies is essential for achieving a favorable risk-return balance in healthcare ventures.
Related Concept Videos
01:25The Anchoring-and-Adjustment Heuristic
In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000...
00:59Uncertainty: Overview
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
00:59Propagation of Uncertainty from Random Error
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
01:13Accuracy and Errors in Hypothesis Testing
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5% chance...
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast, controlled...
01:12Relative Risk
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...