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On Tue, Sep 20, 2016 at 1:38 AM, kavneet kaur <[email protected]> wrote: PPTs pls Sent from my Windows PhoneFrom: Talented Essay Writers Sent: ‎19-‎09-‎2016 08:26 AM To: kavneet kaur Subject: Re: Urgent Assignment Sorry, we forgot to communicate to you that we will be submitting to you the PPT tomorrow. Meanwhile, feel free to ask for any corrections On Sun, Sep 18, 2016 at 11:14 PM, kavneet kaur <[email protected]> wrote: Hi there. I cudnt find the presentation part. I mean PPT slides for Task 3. Can u Pls send the slides? Sent from my Windows PhoneFrom: Talented Essay Writers Sent: ‎18-‎09-‎2016 11:45 PM To: kavneet kaur Subject: Re: Urgent Assignment Kavneet, your order is done. We have attached the word document, the excel file and the plagiarism report. The plagiarism, as er the report, shows that it is at 39\%. However, the plagiarized parts are the questions and calculations. Those parts couldnt of course be changed. Kindly go through it and be free to ask for corrections Analysis  ​​​​​​​​             (40 marks)   Food and Beverages at Southwestern University football games   PLEASE READ THE CASE ENTITLED “FOOD AND BEVERAGES AT SOUTHWESTERN UNIVERSITY FOOTBALL GAMES” AND PREPARE A REPORT AS PER SUGGESTED FORMAT. IN SOME OF THE TABLES, AN EXAMPLE OF COMPUTATION HAS BEEN SHOWN BUT YOU HAVE TO COMPLETE THE REMAINING SPACES FOR OTHER ITEMS IN THE RELEVANT TABLES.   REQUIRED: a) The total fixed cost per game includes salaries, rental fees, and cost of the workers in the six booths. Based on the data in the case, complete the following Table 1: ​​(3 marks) Table 1 Items $ Salaries    Rental fees    Booth worker wages    Total fixed cost per game      b)  Allocate the total fixed cost to each food item as shown in the Table 2: ​​(9 marks) ​​​​​​Table 2 Item Percent revenue Allocated fixed cost Soft drink 25\%   Coffee     Hot dogs     Hamburgers     Misc. snacks       c) Compute the break-even points for each of these items and complete the Table 3: (9 marks) ​​​​​​Table 3 Item Sellingprice Var.cost Contributionmargin Percentrevenue Allocatedfixed cost Break evenvolume Soft drink $1.50 $0.75 $0.75 25\% $6,515 8686.67 Coffee             Hot dogs             Hamburgers             Misc. snacks             d) Determine the total sales for each item that is required to break even, and show them in       Table 4: ​​​​​​​​​​    (9marks) ​​​​​​Table 4 Item Selling price Break even volume Dollar volume of sales Soft drink $1.50 8686.67 $13,030.00 Coffee       Hot dogs       Hamburgers       Misc. snacks       Total       e) Write a brief report with your comments for Dr. Starr for his next meeting. Also comment critically on the assumptions and shortcomings of the decision based on break-even analysis (200 words). ​​​​​​​​(10 marks)   TASK 2:   2. Solving Numerical Problems (Break-Even Analysis) ​​​                       (30 marks)   Question 2.1 A group of students at State University decided to put their education into practice by developing a tutoring company for business students. While private tutoring was offered, it was determined that group tutoring before tests in the large statistics classes would be most beneficial. The students rented a room close to campus for $300 for 3 hours. They developed handouts based on past tests, and these handouts (including colour graphs) cost $5 each. The tutor was paid $25 per hour, for a total of $75 for each tutoring session.   Required: a) If students are charged $20 to attend the session, how many students must enroll for the company to break even? b) A somewhat smaller room is available for $200 for 3 hours. The company is considering this possibility. How would this affect the break-even point?   Question 2.2 Zoe Garcia is the manager of a small office support business that supplies copying, binding, and other services for local companies. Zoe must replace a worn-out copy machine that is used for black and white copying. Two machines are being considered and each of these has a monthly lease cost plus a cost for each page that is copied. Machine 1 has a monthly lease cost of $600, and there is a cost of $0.010 per page copied. Machine 2 has a monthly lease cost of $400, and there is a cost of $0.015 per page copied. Customers are charged $0.05 per page for copies.   Required: a) What is the break-even point for each machine? b) If Zoe expects to make 10,000 copies per month, then what would be the cost for each machine? c) If Zoe expects to make 30,000 copies per month, then what would be the cost for each machine? d) At what volume (the number of copies) would the two machines have the same monthly cost? What would be the total revenue for this number of copies?     Key Equations • Profit = (Selling price per unit) × (No. of units)–(Fixed cost)–(Variable cost per unit) × (No. of units) • BEP = Fixed cost / (Selling price per unit - Variable cost per unit) • Break-even point in dollars (BEP$) = Fixed cost + (Variable costs × BEP)             TASK 3   Paper Review Report + Presentation  ​​​​                        ​ (30 marks)   This is an individual task where you have to identify and download a journal article (or paper) in the field of decision making , and do the following two parts:   PART A: REPORT PREPARATION​​​​​​(20 marks) You will need to read this article thoroughly, and prepare a REPORT which should include:  o Abstract o Introduction  o Review methodology  o Key issues in the papers (summarized under suitable headings or subheadings)  o Conclusions o References (you should read and cite a maximum of three more relevant papers in your report).   Marking Criteria for Part A (see Appendix 1) Your report will be assessed on the basis of the relevance of the paper to the • Learning outcome 1 (i.e. evaluate organisational decision making in a specific organisational environment). • Learning outcome 2 (compare and contrast a range of decision making models)     Part B: REPORT PRESENTATION ​(10 marks) You will need to identify the important points from your report and present them using PP slides (10 to 12 pp slides)   Marking Criteria for Part B (see Appendix 2) You will be assessed on the basis of • Identified points from the report • Quality of PP slides • Presentation style  • Q & A at the end of the presentation.     1 Running head: DECISION ANALYSIS PAGE 5 DECISION ANALYSIS Decision Analysis Name: Institution: Course: Tutor: Date: Task 1 Food and Beverages at Southwestern University football games a) The total fixed cost per game includes salaries, rental fees, and cost of the workers in the six booths. Based on the data in the case, complete the following Table 1:  South-western University Salaries $20,000 Rental fees $4,800 Booth worker wages $1,260 Total fixed cost per game $26,060 b) Allocate the total fixed cost to each food item as shown in the Table 2 Item Percentage revenue Fixed cost allocated Soft drink 25\% $6,515 Coffee 25\% $6,515 Hot dogs 20\% $5,212 Hamburgers 20\% $5,212 Misc. snacks 10\% $2,606 c) Compute the break-even points for each of these items and complete the Table 3 Item Selling Price Var. Cost Profit margin \% Revenue Allocated Fixed Cost Break even volume Soft drink $1.50 $0.75 $0.75 25\% 6515 $8,686.67 Coffee $2.00 $0.50 $1.50 25\% 6515 $4,343.33 Hot dogs $2.00 $0.80 $1.20 20\% 5212 $4,343.33 Hamburgers $2.50 $1.00 $1.50 20\% 5212 $3,474.67 Msc. Snacks $1.00 $0.40 $0.60 10\% 2606 $4,343.33 d) Determine the total sales for each item that is required to break even, and show them in  Table 4:  Item Selling Price Break even volume Dollar volume of sales Soft drink $1.50 $8,686.67 $13,030.00 Coffee $2.00 $4,343.33 $8,686.67 Hot dogs $2.00 $4,343.33 $8,686.67 Hamburgers $2.50 $3,474.67 $8,686.67 Msc. Snacks $1.00 $4,343.33 $4,343.33 Total     $43,433.33 e) Write a brief report with your comments for Dr. Starr for his next meeting. Also comment critically on the assumptions and shortcomings of the decision based on break-even analysis (200 words).  From the above computation, break even the total sales must be equal to $43,433.31. In the event, a total of 35,000 people attend Task 2 Question 2.1 a) If students are charged $20 to attend the session, how many students must enroll for the company to break even? · The fixed costs entails the cost of the room and the cost for the tutor to get = $300 + $75 = $375. · For the learning materials, cost per student is $5 · Thus the costs are 375 + 5s. · For each student, we get $20 · We can only start making money when 20s > 375 + 5s · We solve by putting likes terms together as; = (20 – 5) s = 375. · This gives = 15s = 375 · To have s = 25 · Thus to break even, a total of 25 students must enroll. b) A somewhat smaller room is available for $200 for 3 hours. The company is considering this possibility. How would this affect the break-even point? · With a cheaper room of $200 for three hours, the fixed cost drops to ($200 +$75= $275). · We compute break even as follows to determine how it has affected it. · From 20s > 275 + 5s · We solve by putting likes terms together as; = (20 – 5) s = 275. · This gives = 15s = 275 · To have s = 18.33 · Thus to break even, a total of 18.33 students must enroll. · Since there is 0.33 of students, a total of 19 students need to enroll so as to break even. Question 2.2 Machine 1 Machine 2 Monthly lease cost $600 $400 Cost per page copied $0.010 $0.015 Charges per page for copies $0.05 a) What is the break-even point for each machine? Break-even point for each machine Using the formula, BEP = Fixed cost / (Selling price per unit - Variable cost per unit)                   Machine 1 Machine 2 Fixed Coast $600 $400 Selling price per unit- Variable cost per unit $0.040 $0.035       BEP in units 15000 11428.57       Break-even point in dollars (BEP$) = Fixed cost + (Variable costs × BEP) $750.000 $571.429 b) If Zoe expects to make 10,000 copies per month, then what would be the cost for each machine? Cost for each machine         Machine 1 Machine 2 Monthly lease cost $600 $400 Cost per page copied $0.010 $0.015 Copies made 6,000 4,000       Cost per machine $660.000 $460.000 c) If Zoe expects to make 30,000 copies per month, then what would be the cost for each machine? Cost for each machine         Machine 1 Machine 2 Monthly lease cost $600 $400 Cost per page copied $0.010 $0.015 Copies made 18,000 12,000       Cost per machine $780.000 $580.000 d) At what volume (the number of copies) would the two machines have the same monthly cost? What would be the total revenue for this number of copies? · At 150,000,000 copies, the two machines will each produce 75,000 copies per month. · The total revenue would be $7,500 Task 3 References Choudhary, P, Patnaik, S, Singh, M & Kaushal, G., (2013). Break-Even Analysis in Healthcare Setup. Int J Res Foundation Hosp Healthc Adm 2013;1(1): 29 – 32. Kucey, D., (1999). Decision analysis for the surgeon. World J Surg. 1999 Dec;23(12):1227-31. Sears, E. D., & Chung, K. C. (2010). Decision Analysis in Plastic Surgery: A Primer. Plastic and Reconstructive Surgery, 126(4), 1373–1380. http://doi.org/10.1097/PRS.0b013e3181ead10a World J. Surg. 23, 1227–1231, 1999 WORLD Journal of SURGERY © 1999 by the Société Internationale de Chirurgie Decision Analysis for the Surgeon Daryl S. Kucey, M.D., M.Sc. Department of Surgery, University of Toronto, The Banting Institute, 100 College Street, Toronto, Ontario M5G 1L5, Canada Abstract. Surgical practice, by nature, is full of important decision mak- ing scenarios. Surgeons have begun to utilize the decision sciences as a methodology of approaching clinically relevant surgical problems. This article provides a brief overview of some of the important concepts of the decision sciences as they apply to practicing surgeons. Concepts discussed include the basic principles behind decision trees, valuing outcomes, and Markov modeling as well as the pros and cons of the decision analytic approach. Decision analysis is a valuable aid in determining answers to clinical scenarios, and understanding the principles behind this method- ology is an important addition to the armamentarium of all practicing surgeons. Decision making is a crucial component of the daily practice of surgery. Uncertainty arises from many sources, and in most cir- cumstances surgeons formulate answers to clinical problems by utilizing the store of knowledge and clinical experience they have accumulated over time. If uncertainty regarding the decision problem remains, a surgeon may the seek the experience of senior colleagues or the published experience of peers at other centers. Occasionally, one is able to find a randomized controlled clinical trial that offers a definitive answer to the clinical question. Despite this standard approach to clinical uncertainties, there remain innumerable situations in clinical surgery that are fraught with doubt and quandary. In recent years, surgeons have begun to turn to statistical methodologies to assist in this decision-making process. The de- cision sciences have evolved from techniques largely limited to business applications and have become a popular methodology to assess a large variety of clinical scenarios. The recent emphasis on cost-effective medical care has served to increase the interest in decision analysis. Although decision analysis does not provide definitive answers for all clinical scenarios, it is an important addition to the surgeon’s armamentarium and one about which all surgeons should have basic knowledge. In addition, an under- standing of the benefits and limitations of this technique aid the surgeon in deciphering the current medical literature. This article provides an overview of the decision sciences from the perspective of a practicing surgeon. What Is Decision Analysis? Decision analysis is a mathematic tool that attempts to emulate the human decision-making process. Although we may not be aware of it, each decision we make is based on an evaluation of the options at hand followed by a choice based on the perceived outcomes derived from that choice. Decision analysis seeks to provide a systematic approach to decision making under condi- tions of uncertainty by providing an intuitive framework through which complex problems can be studied. The technique can be summarized as four basic steps [1]. Step 1: Identification, definition, and bounding of the decision problem. The first and most important consideration is to identify and define exactly what the decision problem is. To do this one must have a disease state and an outcome goal in mind. Once this is done in as concise a manner as possible, the problem must then be looked at objectively and critically from many viewpoints to identify all of the possible alternative options available to solve the problem or treat the clinical situation. One must also consider the consequences of each treatment option and what ramifications they have to the outcome of the patient. For example, if one chooses to treat a patient surgically, the operation may go as planned or there may be expected or unexpected complications of this treatment path. Therefore it is essential to realize that each decision action has many possible predictable reactions. One must also consider what possible clinical information could be obtained from the physical examination and biochemical or imaging testing and how the results of these tests or examinations would affect the decision-making process and the outcome of the patient. Lastly, one must consider the possible clinical states a patient may pass through over time and how these differing levels of health would affect the decision-making and patient outcome. Step 2: Structuring the decision problem. The decision tree is the fundamental analytic tool for decision analysis. It is a tech- nique that displays the proper temporal and logical sequence of events in a clinical decision problem. Commonly, a problem we might think to be simple or straightforward becomes complex when all the possible options and consequences are explored. Independent of its size, each decision tree has four basic structural components: (1) the clinical starting point (the trunk of the tree); (2) the alternative actions that are available to the decision maker (the major branches of the tree); (3) the events that follow from Correspondence to: D.S. Kucey, M.D., Sunnybrook Health Science Cen- tre, 2075 Bayview Avenue, H-185, Toronto, Ontario M4N 3M5, Canada. and affect these actions, such as clinical information obtained or the clinical consequences revealed (subbranches of the tree); and (4) the outcomes for the patient that are associated with each possible scenario of actions and consequences (the foliage of the tree). Step 3: Characterizing the information needed to solve the problem. The type of information that must be sought includes probabilistic chances of certain choices and outcomes occurring. For example, one might need to know the chance that a female patient with right lower quadrant pain, nausea, an elevated white blood cell count, and signs of peritonitis has acute appendicitis, in contrast to other right lower quadrant pathology. Another exam- ple is determining the probability of death, stroke, or myocardial infarction after carotid endarterectomy in a certain clinical situa- tion. The chance of these events occurring can be established using information that has been previously published in the liter- ature, new primary data from observational or experimental stud- ies, or consensus polling of experts in the area. Some probabilities may have to be calculated using the probabilistic theory (Baye’s theorem) [2]. Once the probability of all of the branch points in the tree is established, the outcome of interest must be quantified. The outcome may vary from life versus death to number of days of disease-free survival to quality-adjusted life expectancy to length of stay to number of dollars spent. The value assigned to this outcome again must be established using existing data or by objectively valuing the outcome states. Step 4: Choosing a preferred course of action. Decision analysis maximizes quantities as a means to an end. Therefore the treat- ment strategy that maximizes life expectancy or minimizes death or cost is the preferred strategy for solving the problem. Because this type of analysis relies on a number of probabilities and outcome values that could change depending on the individual patient, a sensitivity analysis of the conclusions should be per- formed. A sensitivity analysis is merely a method of varying one or a number of variables at once to determine the threshold values for choosing one decision pathway preferentially over another. A practical example of significant threshold values is illustrated by the large carotid endarterectomy trials [3–9]. If the operating surgeon’s perioperative morbidity and mortality rates are less than 3\%, patients with both symptomatic and asymptomatic carotid lesions might benefit from endarterectomy. However, if the rate is 6\%, asymptomatic patients no longer benefit but symptomatic patients still do. If the rate is greater than 10\%, no patients benefit from this type of procedure. Changes in the value of key proba- bilities in a decision tree therefore may have a profound influence on the outcome of the decision analysis. Figure 1 illustrates a simple decision tree with hypothetic prob- abilities of certain events happening. There are two possible strategies to answer the clinical question. In this case, treatment A maximizes the chance of a good outcome (in terms of quality- adjusted life years) and therefore would be termed the dominant or preferred treatment plan. Valuing Outcomes To determine what value to place on nonmonetary outcomes, decision analysts rely on a variety of tools to help define objective value for often subjective clinical states. For example, if we rank quality of life on a scale of 0 to 1, with 0 being death and 1 being perfect health, where does claudication at two blocks walking distance fit? To value intermediate health states, the surgeon should be familiar with two methodologies: the standard gamble and multiattribute utility models. Standard Gamble The standard gamble is one of the most basic tools utilized for valuing outcomes. Figure 2 illustrates a simple lottery where the candidate is faced with making a choice. On one hand is a decision pathway that offers a certain result (probability 5 1) with no risk involved; but we do not know the value associated with this sure-fire outcome, and indeed it is not a perfect health state (perfect health or death). Therefore the value of this state is between 0 and 1. On the other hand the best possible outcome is offered (perfect health: probability of this happening is 1 2 p). Unfortunately, to achieve this outcome some risk is involved (death; probability of death 5 p). The patient therefore faces a decision choice: At what point would I be willing to take the risk of achieving a perfect outcome? If the patient would never gamble at a chance of perfect health he or she is considered “risk ad- verse,” which suggests that this patient considers any quality-of- life highly (close to 1). On the other hand, some patients are “risk takers,” which suggests that the intermediate state, in their per- ception, is valued lowly (close to 0). The point at which equilib- rium is reached between the two decision arms (neither is more desirous) is considered the value of the intermediate health state. For example, if patients are faced with a choice between certain chronic pain and a lottery that offered a chance at perfect health but was associated with a risk of death, the risk of death (p) they would accept to achieve that state of perfect health would be related to the value they associate with the health state of chronic pain (1 2 p). Fig. 1. Basic decision tree. The utilities for the health states have been empirically designated as cure 5 1.0, improved 5 0.75, no benefit 5 0.50, worse 5 0.25, death 5 0.00. Treatment A (TxA) is the dominant pathway and would be deemed the preferred decision pathway. TxB: treatment B. 1228 World J. Surg. Vol. 23, No. 12, December 1999 Multiattribute Utility Models Health states are complex entities and usually cannot be ad- dressed with a single standard gamble. There are many factors that contribute to a patient’s holistic well-being, including not only physical pain and suffering but mental health, social skills, and perception of self-worth. To address the value of health states from a more comprehen- sive point of view, a number of general and disease-specific utility models have been designed. Examples of multiattribute utility models [10, 11] are the short form 36 (SF-36), the Health Utilities Index, and the injury severity score (ISS). The commonality of all of these models is that the patient is assessed by a number of viewpoints or domains, and a value is then placed on their overall well-being. These scales and guidelines have been based on re- petitive studies of large groups of individuals using basic tech- niques such as the standard gamble in each of the domains of the model. All of these domain values are then combined to give the overall health score. Although they provide a more holistic measurement of overall health, the difficulty with multiattribute scoring mechanisms is that they may dilute disease-specific functional outcomes. For example, details of the impact of an amputation in a patient with occlusive vascular disease from a functional point of view is not closely assessed. On the other hand, if one merely uses a disease- specific score, the impact of an amputation on the patient’s life in general is underassessed. Therefore analyses using these scoring mechanisms often utilize a general measurement tool (e.g., SF-36) combined with a disease-specific score. From a practitioner’s point-of-view, the generalizability of util- ity models to an individual patient in an actual medical practice can be difficult: Is my patient the same as the average person? The answer to this dilemma is that utility values must always be interpreted with caution and in the presence of sensitivity analysis calculations before important decisions are made regarding indi- vidual patients. Therefore despite the use of advanced math- ematic tools, the interpretation still requires what would termed “the art of medicine.” Markov Modeling Another technique that is useful to understand is Markov mod- eling [12]. This mathematic tool is helpful when one applies decision analysis to help calculate life expectancy or quality- adjusted life years (QALYs) following treatment interventions in the management of a chronic disease. A simple Markov model is illustrated in Figure 3. In uncomplicated terms, a Markov model consists of a number of health states that may range from perfect health to death. These health states are all related to the disease process in question and serve to model the natural history of the disease. For example, if the disease we are studying is lower limb occlusive vascular disease, the health states a patient might pass through would include an asymptomatic phase, intermittent clau- dication, night pain, rest pain, ulceration, gangrene, limb loss, and death. For each time period t (typically 1 year but could be any measure of time) the patient has disease x there is a chance that the patient may die, a chance he or she may be cured, and chances that the patient may improve or worsen slightly or stay the same. A Markov cycle is a mechanism that allows the patient to reenter the same probability tree over and over again as time progresses and allows chronic diseases to evolve as they would in a real life scenario. The Markov process continues to cycle over and over until the chance of death is equal to 1 and the probability of the patient existing in the other health states is zero. Death is called “an absorbing state” because you cannot leave this state. Once death is achieved, you can then add up the time spent in each of the nonabsorbing states to calculate overall life expectancy, or you can quality-adjust the value you assign to each of the nonabsorb- ing states (possibly via the standard gamble) to determine QALYs. Pros and Cons of Decision Analysis Understanding the basic concepts involved in the decision sci- ences offers advantages to every practicing surgeon. The most compelling benefit is an enhanced ability to structure decision problems in a logical, stepwise fashion. Many surgeons already ponder problems decision-analytically even though they do not Fig. 2. Standard gamble. The utility of the intermediate state is estimated when indifference exists between selecting the pathway leading to chronic pain and the lottery between cure and death. For example, if indifference exists at p 5 0.50, chronic pain would be given a utility of 0.50. Fig. 3. Basic Markov model. t: interval of time; pdeath: probability of death occurring during one turn of the time cycle; pbetter: probability of getting better; pworse: probability of getting worse; psame: chance of staying the same; pcure: probability of being cured during the time inter- val. Death is an absorbing state. Kucey: Decision Analysis for the Surgeon 1229 realize it. Formal introduction to analytic techniques makes prac- titioners more aware of the consequences and outcomes related to different treatment strategies and hopefully helps make the deci- sion process easier for the surgeon. Introduction to the terminol- ogy used for decision analysis may make communication between physicians easier by applying a more objective value to subjective circumstances. For example, the use of quantities such as proba- bilities may help avoid ambiguous terms such as rarely, some- times, or often. If medical controversies are structured in a decision-analysis framework, the disagreements contributing to the controversy may be understood more clearly. Medical problems are typically complex, with a number of contributing small controversies. If the problem is structured logically, the small issues that contribute to the overall problem can then be recognized. If these small dis- agreements are then studied and resolved in a logical fashion, the larger controversy may then be closer to resolution. Another incentive to familiarize oneself with decision analysis is the grow- ing concern over health care expenditures. The physician, who is the primary advocate for patient care and the gatekeeper for access to health services, must be familiar with the methodologies used for the assessment of health care resource utilization, such as cost-effectiveness analysis and cost-benefit analysis, both of which are products of the decision sciences [13]. An understanding of the methodologies utilized in the management side of health care delivery continues to ensure an influential position for surgeons in health care policy development and administration. Unfortunately, one of the biggest disadvantages of the decision sciences stems from the fact that clinical decision problems are complex. As a result of this complexity it is may be difficult to model the problem realistically: There are just too many potential branches and subbranches of the decision tree. Therefore to simplify the technical aspects of decision analysis, often assump- tions regarding the treatment protocol, the patient, or the disease process may have to be made. Assumptions might be looked on as concessions made in the model to simplify the issue. Unfortu- nately, the more concessions that are made, the less realistic and generalizable the model becomes. For example, to assess the utility of infrainguinal revascularization in someone with intermit- tent claudication, one might make the assumption that the prob- lem is unilateral only at a single point in time. This is clearly not realistic in most patients, but to simplify the decision model it may be necessary to model the problem in this fashion. Another drawback is the difficulty of determining the utility of specific patients and the fact that the value of utility may change over time and depend on the circumstance in which the patient finds himself or herself. For example, prior to amputation, a patient may say that the utility of the amputated state is zero; but after it has occurred and the patient has dealt with the situation emotionally, the utility of the amputated state in the patient’s mind increases. This enigma underlines the basic human instinct that life of any sort is precious. Lastly, thinking probabilistically from the physician’s point of view may be a difficult concept to accept. Many physicians prefer generally applicable clinical max- ims and clear-cut choices, rather than dealing with chance or probability. Decision analysis is merely an aid for clinical reasoning, not a substitution for sound clinical judgment, and it must always be used and interpreted with this in mind. Despite the obvious shortcomings of the decision sciences, familiarity with these meth- odologies provides surgeons with tools that can serve to improve their approach to the unique challenges present in the practice of clinical medicine. Résumé Le propre de la pratique chirurgicale est de devoir prendre des décisions importantes dans un certain nombre de scénarios. Depuis peu, les chirurgiens ont commencé à utiliser une méthodologie décisionnelle pour résoudre les problèmes de clinique chirurgicale. Dans cet article, on revoit brièvement les concepts importants de la démarche décisionnelle tels qu’ils s’appliquent aux chirurgiens dans la pratique. Les concepts discutés ici comprennent les principes de base des arbres de décision, l’évaluation des résultats et le modèle de Markov, ainsi que les pour et les contres de l’approche analytique de la démarche décisionnelle. L’analyse décisionnelle est une aide importante dans la détermination des réponses aux scénarios cliniques. Comprendre les principes derrière cette méthodologie est un plus pour tous les chirurgiens dans leur pratique. Resumen La práctica quirúrgica, por naturaleza, está plena de importantes escenarios para la toma de decisiones. Recientemente los cirujanos han comenzado a utilizar las ciencias de la decisión como metodologı́a para enfrentar problemas quirúrgicos de relevancia clı́nica. El presente artı́culo revisa la aplicación de los conceptos importantes provenientes de las ciencias de la decisión a la práctica quirúrgica. Se discuten los principios básicos en que se fundamentan los árboles de decisión, la valoración de resultados finales, y la modelación markoviana, ası́ como los pros y los contras del enfoque analı́tico de decisión. El análisis de decisión es un instrumento de valor para determinar escenarios clı́nicos y comprender los principios que sustentan esta metodologı́a y, como tal, representa una importante adición al armamentario de la práctica quirúrgica. References 1. Weinstein, M.C., Fineberg, H.V., Elstein, A.S., Frazier, H.S., Neu- hauser, D., Neutra, R.R., McNeil, B.J.: Clinical Decision Analysis. Philadelphia, W.B. Saunders, 1980, pp. 1–50 2. Cornfeild, J.: Baye’s Theorem: Proceedings of the Sixth IBM Medical Symposium. New York, International Business Machines Corp., 1964, pp. 163–196 3. North American Symptomatic Carotid Endarterectomy Trial Collab- orators: Beneficial effect of carotid endarterectomy in symptomatic patients with high-grade carotid stenoses. N. Engl. J. Med. 325:445, 1991 4. European Carotid Surgery Trialists’ Collaborative Group: MRC Eu- ropean Carotid Surgery Trial: interim results for symptomatic steno- ses with severe (70 –99\%) or with mild (0 –29\%) carotid stenosis. Lancet 337:1235, 1991 5. Asymptomatic Study Executive Committee: Endarterectomy for asymptomatic carotid artery stenosis. J.A.M.A. 273:1421, 1995 6. Hobson, R.W., Weiss, D.G., Fields, W.S., Goldstone, J., Moore, W.S., Towne, J.B., Veterans Affairs Cooperative Study Group: Efficacy of carotid endarterectomy for asymptomatic carotid stenosis. N. Engl. J. Med. 328:221, 1993 7. European Carotid Surgery Trialists’ Collaborative Group: Endarter- ectomy for moderate symptomatic carotis stenosis: interim results from the MRC European Carotid Suregry Trial. Lancet 347:1591, 1996 1230 World J. Surg. Vol. 23, No. 12, December 1999 8. CASANOVA Study Group: Carotid surgery versus medical therapy in asymptomatic carotid stenosis. Stroke 22:1229, 1991 9. Mayo Asymptomatic Carotid Endarterectomy Group: Results of a randomized controlled trial of carotid endarterectomy for asymptom- atic carotid stenosis. Mayo Clin. Proc. 67:513, 1992 10. Beck, J.R., Pauker, S.G.: The Markov process in medical prognosis. Med. Decis. Making 3:419, 1983 11. Torrance, G.W.: Measurement of health state utilities for economic appraisal: a review. J. Health Econ. 5:1, 1986 12. Patrick, D.L., Deyo, R.A.: Generic and disease-specific measures in assessing health status and quality of life. Med. Care 27(Suppl. 3): s217, 1989 13. Johannesson, M., Jönsson, B.: Economic evaluation in health care: is there a role for cost-benefit analysis? Health Policy 17:1, 1991 Kucey: Decision Analysis for the Surgeon 1231 Reproduced with permission of the copyright owner. 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