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Patient. 2018 Feb;11(1):97-106. doi: 10.1007/s40271-017-0268-2.

Serious Choices: A Protocol for an Environmental Scan of Patient Decision Aids for Seriously Ill People at Risk of Death Facing Choices about Life-Sustaining Treatments.

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The Dartmouth Institute for Health Policy and Clinical Practice, 1 Medical Center Drive, Lebanon, NH, 03766, USA.
The Dartmouth Institute for Health Policy and Clinical Practice, 1 Medical Center Drive, Lebanon, NH, 03766, USA.
Dartmouth-Hitchcock Medical Center, Lebanon, NH, USA.
Geisel School of Medicine, Hanover, NH, USA.



Seriously ill people at high risk of death face difficult decisions, especially concerning the extent of medical intervention. Given the inherent difficulty and complexity of these decisions, the care they receive often does not align with their preferences. Patient decision aids that educate individuals about options and help them construct preferences about life-sustaining care may reduce the mismatch between the care people say they want and the care they receive. The quantity and quality of patient decision aids for those at high risk of death, however, are unknown.


This protocol describes an approach for conducting an environmental scan of life-sustaining treatment patient decision aids for seriously ill patients, identified online and through informant analysis. We intend for the outcome to be an inventory of all life-sustaining treatment patient decision aids for seriously ill patients currently available (either publicly or proprietarily) along with information about their content, quality, and known use.


We will identify patient decision aids in a three-step approach (1) mining previously published systematic reviews; (2) systematically searching online and in two popular app stores; and (3) undertaking a key informant survey. We will screen and assess the quality of each patient decision aid identified using the latest published draft of the U.S. National Quality Forum National Standards for the Certification of Patient Decision Aids. Additionally, we will evaluate readability via and content via inductive content analysis. We will also use natural language processing to assess the content of the decision aids.


Researchers increasingly recognize the environmental scan as an optimal method for studying real-world interventions, such as patient decision aids. This study will advance our understanding of the availability, quality, and use of decision aids for life-sustaining interventions targeted at seriously ill patients. We also aim to provide patients, their families, and friends, along with their clinicians, a broad set of resources for making life-sustaining treatment decisions. Although we intend to capture all patient decision aids for the seriously ill in our review, we anticipate the possibility that we may miss some decision aids. In addition to publishing our findings in an academic journal, we plan to post our inventory online in an easy-to-read format for public and clinical consumption.


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