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Chapter 3. Decision Making in Healthcare Facilities

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## Chapter 3. Decision Making in Healthcare Facilities

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**What should I do?**Chapter 3. Decision Making in Healthcare Facilities Yasar A. Ozcan**Outline**• Decision Process • What Causes Poor Decisions? • The Decision Level & Decision Milieu • Decision Making under Uncertainty • Payoff Table • Methods • Decision Making under Risk • EVM, EOL, EVPI • Decision Tree Approach • Rollback Procedure • What if Payoff Values are Cost? • Decision Analysis with Non-Monetary Values & Multiple-Attributes • Dominance • Minimum Attribute Satisfaction • Most Important Attribute Yasar A. Ozcan**The Decision Process**• Identification of the problem and its nature • Specification of objectives and decision criteria • Development of alternatives • Analysis and comparison of alternatives • Selection of the best alternative • Implementation of the choice • Controlling and monitoring the results Yasar A. Ozcan**What Causes Poor Decisions?**• Mistakes in the Decision Process • Bounded Rationality: is the limits imposed on decision making by costs, human abilities and errors, time, technology, and the tractability of data. • Suboptimization: is characterized by Decisions are often departmentalized as separate organizational units compete for scarce resources. Individual departments often seek solutions that benefit their own department, but not necessarily the healthcare organization as a whole. Yasar A. Ozcan**Decision Theory**represents a general approach to decision making which is suitable for a wide range of operations management decisions, including: capacity, service design, location planning, equipment selection, etc. Yasar A. Ozcan**The Decision Level & Decision Milieu**• Certainty-- Known values for parameters of interest • Uncertainty-- Impossible to assess the likelihood of various possible future events • Risk-- Certain parameters have probabilistic outcomes Yasar A. Ozcan**Certainty**• Certainty rarely exists, especially in health care decisions. But if it does, simply choose the best available option (highest profit/least cost). Yasar A. Ozcan**Decision Making under Uncertainty**• Maximin-- best of the worst (pessimist) • Maximax-- best of the best (optimist) • Hurwitz-- allows you to adjust the probabilities/weighing between maximin and maximax or pessimist vs. optimist • Laplace-- best average payoff • Minimax Regret-- best of the worst regrets Yasar A. Ozcan**Decision Tools**Table 3.1 Payoff Table Yasar A. Ozcan**Example 3.1: A major imaging center is not able to meet the**increased demand from patients for MRIs. The administration is willing to explore the possibilities by evaluating such alternatives as adding one or two additional units or out sourcing to other image centers and earning a commission of $30.00 per MRI. A feasibility analysis showed that three major demand chunks could occur in the future, summarized as 500, 750 and 1000 additional MRI requests. The financial analysis of the potential business summarizes profits/losses under additional MRI demand chunks in a payoff table shown in Table below. * in $ 000s Yasar A. Ozcan**Maximin Solution*** in $ 000s Yasar A. Ozcan**Maximax Solution*** in $ 000s Yasar A. Ozcan**Hurwitz Solution**For optimism with α = 0.5. Then the HV value for the three alternatives would be: HV (Buy one MRI unit) = .5(300,000)+(.5)(-15,000) = 142,500. HV (Buy two MRI units)= .5(725,000)+(.5)(-150,000)= 287,500. HV (Outsource) = .5(40,000)+(.5)(15,000) = 27,500. Yasar A. Ozcan**Opportunity Losses (Regrets)*** in $ 000s Yasar A. Ozcan**Laplace Strategy*** in $ 000s Yasar A. Ozcan**Expected Value Model**Once the healthcare manager has assessed the probability distribution, computation of the expected values for each alternative is straightforward, as follows: EMV(Ai) = Σj pj Oij Yasar A. Ozcan**Payoff Table for EMV*** in $ 000s Yasar A. Ozcan**Expected Opportunity Loss**The probabilities can also be incorporated into the regrets (or opportunity losses) calculated earlier. In this way the healthcare manager can assess the expected losses and try to minimize them with proper decision. Calculations of expected opportunity loss follow the formula: EOL(Ai) = Σj pj Rij Yasar A. Ozcan**Expected Opportunity Loss*** in $ 000s Yasar A. Ozcan**Expected Value of Perfect Information (EVPI)**EVPI = EVUC-EMV EVUC = Σj pj (Best Oij given Sj) * in $ 000s Yasar A. Ozcan**Expected Value of Perfect Information (EVPI)**EVPI = EVUC-EMV EVUC = Σj pj (Best Oij given Sj) EVUC = (.2*15000) + (.6*200000) + (.2*725000) = 268000. EMV = $177,000 EVPI = $268,000 – $177,000 = $91,000 Yasar A. Ozcan**What if Payoffs are Costs?*** in $ 000s Yasar A. Ozcan**Regret Table Using Costs*** in $ 000s Yasar A. Ozcan**1**Actions Event Node 2 3 Action A 1 Decision Node Event Node Action B 2 3 1 Action C Event Node 2 3 Decision Tools-- The Decision Tree Outcomes Events Outcome 1 Outcome 4 Outcome 7 Outcome 2 Outcome 5 Outcome 8 Outcome 3 Outcome 6 Outcome 9 Yasar A. Ozcan**Figure 3.1 Decision Tree**-$15* 500 Cases, p=.2 750 Cases, p=.6 $200 1000 Cases, p=.2 $300 Buy One MRI Unit -$150 500 Cases, p=.2 Buy Two MRI Units 750 Cases, p=.6 $100 1000 Cases, p=.2 $725 Outsource $15 500 Cases, p=.2 750 Cases, p=.6 $22.5 * in $000 1000 Cases, p=.2 Yasar A. Ozcan $40**Analysis of the Decision Tree: Rollback Procedure**-$15* 500 Cases, p=.2 177 750 Cases, p=.6 $200 1000 Cases, p=.2 $300 Buy One MRI Unit -$150 500 Cases, p=.2 Buy Two MRI Units 750 Cases, p=.6 175 $100 177 ║ 1000 Cases, p=.2 $725 ║ Outsource $15 500 Cases, p=.2 750 Cases, p=.6 24.5 * in $000 $22.5 1000 Cases, p=.2 Yasar A. Ozcan $40**Multi-attribute Decisions**• Dominance Procedure: compares a pair of alternatives attribute by attribute. • Minimum Attribute Satisfaction Procedure:satisfactory levels are set for each alternative • Most Important Attribute Procedure: attributes are ranked in order of importance • Combination: combines two or more of the above procedures. Yasar A. Ozcan**Decision Analysis with Non-Monetary Values and Multiple**Attributes *Attributes are scored on a 1-10 scale (with the exception of those associated with costs and on-time-delivery percentage), score of 10 being most favorable. Yasar A. Ozcan**The End**Yasar A. Ozcan