Week of 10/05/09

Content:

The two lectures this week were on the basics of decision science.  The first lecture taught how decision science includes statistical approaches to decision making.  One of the models that is used to represent those statistic approaches is a decision tree.  Expected values in decision trees help medical practitioners to decide on tests or treatments to administer.  An example of a lottery was used to explain estimated values in the first lecture.  Folding back nodes is a technique that is used to evaluate combined expected values of a decision tree.  Decision trees can be folded back from the right to the left through determinations of the product sums of series of chance/terminal nodes.  Sensitivity analyses can be preformed to evaluate how robust the probabilities are on a decision tree.  A probability may not be robust if a small change in the parameters of the calculation for that probability results in a large change in the probability.  Models in decision science can help medical practitioners to compare the possible outcomes of actions that they may take and also compare the inter-relations between variables effecting those outcomes.  Through those comparisons the models can help medical practitioners to make efficient and accurate predictions of the outcomes of actions that they can take.   

The second lecture included information about using decision science to decide whether to do more tests on or to treat a patient.  Elements of economics, statistics, and psychology are all often used for those decisions.  The consequences of those decisions can involve long term benefits, quality of life, and economic costs.  Some of the values of outcomes can be morbid events averted, life years saved, and quality of future life years.  Conditional probabilities were discussed in the class.  An example of a conditional probability is the probability that a disease is present when a test result is positive.  Sensitivity and specificity that are used in Bayes' theorem are conditional probabilities.

The lectures caused me to look at medical decision making in a variety of ways.  One element of the decision making systems that was interesting to me was the measures of utility that the decision analyses use.  Even if surgery can result on average of a higher net gain of quality adjusted life years, the risks might be more than some people would want to endure.  It could be a particularly hard choice to face if evidence was presented to someone that included death by surgery to be a possibility.  Perhaps someone would choose an option with a lower number of quality adjusted life years but virtually no death risk instead of an option with a higher number of quality adjusted life years with a death risk.  I think that decision analysis models can be especially useful for providing a visual representation of what tests and treatments can be preformed for a patient.  Additionally, I think that those models can be highly useful for sorting through the statistical analyses that have been made for those tests and treatments.

An article that discussed a study that evaluated the performance of the Quick Medical Reference (QMR) decision support tool is available here:
http://www.pubmedcentral.nih.gov.ezproxy1.lib.asu.edu/picrender.fcgi?artid=1230623&blobtype=pdf&tool=pmcentrez
The article describes a study done by two physicians using 154 cases of illnesses.  The article includes information about specific cases that were evaluated in it.  The article also includes information about some of the disease that were found in QMR's records of over 600 diseases and some that were not in QMR's records.

Another study that evaluated the performance of QMR and the Illiad decision support tool is available here:
http://www.pubmedcentral.nih.gov.ezproxy1.lib.asu.edu/picrender.fcgi?artid=1726199&blobtype=pdf&tool=pmcentrez
That study showed both QMR and Illiad results in an emergency department in a tertiary care academic medical centre.

Posted by:

Nate