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Can your spreadsheets:
- Quantify the risks associated with operations and financial
costs?
- Accurately forecast demand of supplies and services?
- Predict operational bottlenecks before they occur?
- Give you confidence in the accuracy of your financial
projections?
- Pinpoint which budget items are causing the most uncertainty?
No? Then you need Crystal
Ball!
Crystal Ball is a Microsoft® Excel®-based suite of analytical tools that includes Monte Carlo simulation, optimization, forecasting, and predictive management. With little effort, you can apply these advanced analytical techniques to your new or existing spreadsheets to create more accurate cost and financial predictions and better informed business decisions.
Today, Crystal
Ball is the tool chosen by more than 85% of the Fortune 500.
In health care and medicine, organizations as diverse as Beckman
Coulter, Abbott Laboratories, Pfizer, Roche Diagnostics, and
UCLA Medical Center, all rely on Crystal Ball to manage risk
and make more informed business and strategic decisions.
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With
increased competition, reduced resources, staff cutbacks, and
often drastically lowered budgets, this is a challenging time
for health care and medical companies and organizations. Whether
you're focused on cost estimation, human health risk assessment,
strategic resource allocation, or production forecasts, your
knowledge and your toolset will make the difference between
whether your work succeeds or fails.
Low-cost software and improved computing power
can enable you to better calculate the risks in your strategy
or process. Crystal Ball can help you better assess your alternatives,
increase the confidence you have in planning details, and make
more informed decisions despite a lack of data or an uncertain. Primary health care and medical Crystal Ball applications
include human health risk assessment, return on investment analysis,
demand forecasting, business development planning, capital budgeting,
and process improvement.
Key
risk analysis features include sensitivity
analysis, historical data fitting, and optimization. The sensitivity
analysis helps you to understand which of the uncertain input variables
are most critical and drive the uncertainty of your cost
model. If historical data
does exist, the data fitting feature will compare the data to
the distribution algorithms and calculate the best possible fit
and parameters for your data. Optimization allows you to account for uncertainty and risk in simulations but still select the best possible settings (e.g., staffing levels, investment amounts, product prices) to achieve success.
With Crystal Ball, you can:
- Replace min/max estimates with more accurate range of all possible outcomes
- Reduce the time required to produce forecasts,
- Eliminate multiple manual “what if” estimates,
- Mitigate your cost and schedule risks,
- Gain immediate insight to the driving inputs and output variations,
- Make knowledgeable decisions on where to focus resources, and
- Provide decision-makers with factual data that shows the risk associated with each choice.
LEARN MORE ABOUT CRYSTAL BALL FOR HEALTHCARE APPLICATIONS
This page offers links to a growing number of resources, including recorded Web seminars, articles, white papers, case studies, and example models. Additionally, you can view a list of common uses and examples reported directly from customers using Crystal Ball. You can also download a free trial version of Crystal Ball to see how it can help improve your business forecasts and decisions!
"I found the software very versatile - whether
developing patient treatment models or performing cost analyses."
-- Dr. Brian Murphy, Director, Clinical Studies Program,
St. Vincents Hospital and Medical Center |
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RECORDED WEB SEMINARS
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Estimating waiting targets for NHS Inpatients, Outpatients and Diagnostic
This seminar presents a powerful planning tool which was developed using stochastic modelling to assess which waiting time targets (Inpatient, Outpatient and Diagnostic) are needed in order to meet the Department of Health 18 weeks target and identify bottlenecks in patient pathways.
Presented by Jorge Villacampa-Ortega from Barts and The London NHS Trust
Recorded June 21, 2007
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View recording
 Download files
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WHITE PAPERS
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CASE STUDIES
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Cycle Time Reduction
Misys Healthcare Systems used simulation to validate that a new process for software
implementation will result in a 50% cycle time reduction and accelerated revenue
recognition. |
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EXAMPLE MODELS
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Financial Impact of Process Risk - Medical Claim Payments
From: This model is discussed in the textbook Lean Six Sigma Statistics, written by Dr. Alastair Muir, and is available from Dr. Muir's Web site.
Detail: We are on the project team directed towards reducing the variation in time for processing medical claim payments. As part of the Measure phase, we must assess the financial risk associated with the existing process. We have surveyed the customers and know that errors causing delays in payment are a common complaint and require a great deal of time on the part of the business to identify and correct.
The belief from management is that the error rate is relatively low, on the order of 1-2 percent. Our job is to estimate the span of the problem in financial terms to baseline the process and get buy-in from management. A Monte Carlo simulation of the process is conducted using Crystal Ball. The results show the range of expected cycle times and financial impact on the customer. They also identify the key process steps influencing the wide range of cycle times and financial impact. |
Download
For:
Crystal Ball
Level:
Moderate |
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COMMON USES & EXAMPLES
The following examples were provided by our customers and represent
only some of the potential health care applications for Crystal
Ball.
- Analysis of costs of state health care program that purchases
health insurance for low-income children
- Analyze profit potential of expanding business segments, evaluate strategic opportunities, optimizing utilization of resources, compare what-if scenarios/sensitivity analysis
- Assessing uncertainty in human health risk assessment
- Budget and scientific forecasts
- Business development planning
- Cost effectiveness analyses
- Cost effectiveness analyses for medical outcomes research
- Cost-effectiveness analysis of health care interventions
- Cost effectiveness models of health care technologies
- Demands of supplies and appointments
- Designing and implementing health care strategies and processes
- Determine future of academic medical centers
- Dose reconstruction / retrospective exposure assessment
- Estimating hospital use within a community
- Financial predictions for various startup operations including
private medical practices and a medical billing company
- Forecast health care contracts
- Forecast service line volumes
- Forecast workload in an IT support center
- Forecasting sales incentive payments
- Medical outcomes research
- Modeling of market environments and product usage parameters
- Predict probabilities of events related to use of antibiotics
- Projecting departmental revenue streams
- Projecting Medicaid enrollment and expenditures
- Quantifying sources of uncertainty in epidemiological studies
- Optimizing Designs within DFSS-based methodology
- Return on investment analysis
- Risk analysis and project appraisal
- Risk analyses associated with the preparation of business cases
- Time-series analysis of cancer cases
- Use to forecast health care contracts and forecast workload
in an IT support center
- Valuing potential acquisitions
- Valuing potential company acquisitions (i.e., product lines)
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