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Computer Predicts Wishes Of Incapacitated Patients Better Than Family Or Loved Ones

Date:
March 14, 2007
Source:
Public Library of Science
Summary:
When a person fails to complete an advance directive and becomes incapacitated by illness or injury, doctors typically ask the patient's loved one to predict what treatment the patient would have wanted. But a paper in PLoS Medicine reports that a computer-based decision tool can predict a patient's treatment wishes better than a loved one.
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When a person fails to complete an advance directive and becomes incapacitated by illness or injury, doctors typically ask the patient's loved one to predict what treatment the patient would have wanted. But a paper in PLoS Medicine reports that a computer-based decision tool can predict a patient's treatment wishes better than a loved one.

To use the decision tool, called a "population-based treatment indicator," the doctor first enters the incapacitated patient's circumstances and personal characteristics into a computer. Perhaps, for example, the patient has pneumonia and severe Alzheimer disease, and he is a 60 year old, well educated, Native American, male. The computer analyzes the treatment preferences of similar individuals and estimates the likelihood that the patient would want antibiotics to treat his pneumonia.

A finding that 90% of highly educated Native American men over the age of 50 do not want to receive antibiotics to treat pneumonia in the setting of advanced Alzheimer disease would provide strong evidence that this patient would not want antibiotics in these circumstances either.

David Wendler and colleagues (US National Institutes of Health), who devised the tool, analyzed how well the tool performs compared to asking a loved one (loved ones are known as "surrogates").

There is obviously no way to determine which medical treatments patients actually want at the time they are incapacitated, and so studies looking at whether surrogates accurately predict patients' treatment choices must use hypothetical scenarios. For example, one study used the following scenario:

"You recently suffered a major stroke leaving you in a coma and unable to breathe without a machine. After a few months, the doctor determines that it is unlikely that you will come out of the coma. If your doctor had asked whether to try to revive you if your heart stopped beating in this situation, what would you have told the doctor to do?"

Analysis of 16 such studies reveals that surrogates accurately predict patients' treatment preferences about 68% of the time. In comparison, Dr Wendler and colleagues found that a preliminary computer-based decision tool predicted the patient's treatment preferences with the same accuracy, and improved decisions tools undoubtedly would be more accurate than surrogates.

Important questions remain, say the authors, about how treatment decisions should be made on behalf of incapacitated patients: "Do patients care more about who makes decisions for them, or what decisions are made? Does making end-of-life treatment decisions benefit or burden families and loved ones overall?"

Reference:  http://medicine.plosjournals.org/perlserv/?request=get-document&doi=10.1371/journal.pmed.0040035

Citation: Shalowitz DI, Garrett-Mayer E, Wendler D (2007) How should treatment decisions be made for incapacitated patients, and why? PLoS Med 4(3): e35.


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Public Library of Science. "Computer Predicts Wishes Of Incapacitated Patients Better Than Family Or Loved Ones." ScienceDaily. ScienceDaily, 14 March 2007. <www.sciencedaily.com/releases/2007/03/070313114444.htm>.
Public Library of Science. (2007, March 14). Computer Predicts Wishes Of Incapacitated Patients Better Than Family Or Loved Ones. ScienceDaily. Retrieved April 19, 2024 from www.sciencedaily.com/releases/2007/03/070313114444.htm
Public Library of Science. "Computer Predicts Wishes Of Incapacitated Patients Better Than Family Or Loved Ones." ScienceDaily. www.sciencedaily.com/releases/2007/03/070313114444.htm (accessed April 19, 2024).

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