Institute: ONC | Component: 2 | Unit: 4 | Lecture: d | Slide: 7
Institute:Office of National Coordinator (ONC) Workforce Training Curriculum
Component:The Culture of Health Care
Unit:Health Care Processes and Decision Making
Lecture:Making a diagnosis Choosing therapy The impact of EHRs and technology on clinical decision-making
Slide content:Decision Analysis List options available List possible outcomes of each option Find probability of each possible outcome Ask patient for utility of each outcome (e.g., time trade off) Calculate expected utility of decision Toss ups Heuristics and biases 4.9 Chart: Decision analysis chart (Mills, 1991) 7
Slide notes:As mentioned earlier, a formal approach can be taken when making decisions about interventions using a technique called decision analysis . In most cases, this technique is too complex for use at the bedside or in individual decisions. Its more often used to determine health care policy or guidelines. In certain circumstances, however, if the problem is well formulated and the data is available, decision analysis can be applied to individual patients. This slide illustrates a conventional decision-analysis tree where three options are available for the treatment of a patient diagnosed as having anginal [an- jahyn -l] chest pain following a coronary artery bypass graft; the three choices are medical treatment, angioplasty [ an - jee -uh- plas -tee], or another bypass. For each of these choices, the patient may have similar outcomes of improvement, deterioration, or death. Each of these outcomes has a different probability depending on the treatment that is rendered and a different utility depending on the patients preferences. If we can determine these utilities (or dis-utilities) and probabilities with sufficient precision, we can actually calculate the expected value of each choice to determine the preferred choice. However, in some circumstances, known as toss ups , even decision analysis cant lead to a final determination because its too close to call. These scenarios are called preference-sensitive decisions , meaning that they depend more on the preferences of the patient than on the treatment results. Decision analysis has also been used to help clinicians understand their biases in cases where their decisions dont match what this formal decision-analytic procedure would predict. Another mathematical approach that is typically used to set policy and devise guidelines is cost-effectiveness analysis. For example, if the question is whether men at risk for gastric cancer should undergo early endoscopic [ en -duh- skop - ik ] evaluation, a model is created that reflects the options available and the possible outcomes for individuals who undergo these options. The calculation is performed based on the assumption that these choices are made for an entire population, and this calculation determines the cost effectiveness, or incremental cost effectiveness, of performing the procedure for differing age groups. Although this information is used mainly for setting policy, it can be used in some cases to help with individual treatment decisions. 7