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 We employed user-centered design to adapt the DA in a two-phase study during which we recruited patients, family members, intensivists and other allied health professionals from a closed medical and surgical ICU. During phase 1, we conducted three weeks of ethnography of the decision-making context in our ICU to identify clinician and patient needs for a decision aid. During this time, we observed five dyads of intensivists and patients discussing goals of care. We also conducted semi-structured interviews with the attending intensivists in this ICU. During phase 2, we conducted three rounds of rapid prototyping involving 15 patients and 11 other allied health professionals. We recorded discussions between intensivists and patients and used a standardised observation grid to collect patients’ comments and sociodemographic data. We applied content analysis to field notes, verbatim transcripts and the completed observation grids. Each round of observations and rapid prototyping iteratively informed the design of the next prototype. We also used the programming architecture of a wiki platform to embed the GO-FAR prediction rule programming code to create an online tailored CPR outcome prediction calculator. We also linked the calculator to a risk graphics software to better illustrate outcome risks calculated by the GO-FAR rule.  We employed user-centered design to adapt the DA in a two-phase study during which we recruited patients, family members, intensivists and other allied health professionals from a closed medical and surgical ICU. During phase 1, we conducted three weeks of ethnography of the decision-making context in our ICU to identify clinician and patient needs for a decision aid. During this time, we observed five dyads of intensivists and patients discussing goals of care. We also conducted semi-structured interviews with the attending intensivists in this ICU. During phase 2, we conducted three rounds of rapid prototyping involving 15 patients and 11 other allied health professionals. We recorded discussions between intensivists and patients and used a standardised observation grid to collect patients’ comments and sociodemographic data. We applied content analysis to field notes, verbatim transcripts and the completed observation grids. Each round of observations and rapid prototyping iteratively informed the design of the next prototype. We also used the programming architecture of a wiki platform to embed the GO-FAR prediction rule programming code to create an online tailored CPR outcome prediction calculator. We also linked the calculator to a risk graphics software to better illustrate outcome risks calculated by the GO-FAR rule. 
- During phase I, we identified the need to add a section in our DA concerning invasive mechanical ventilation. During phase II, we produced a context-adapted decision aid about CPR and mechanical ventilation (see "DA in English" section) that includes a values clarification section, questions about the patient’s functional autonomy prior to admission to the ICU and the functional decline that they would judge acceptable upon hospital discharge, risks and benefits of CPR and invasive mechanical ventilation, population-level statistics about CPR, a synthesis section to help patients come to a final decision and an online calculator based on the GO-FAR prediction rule (see "GO-FAR in English" section). + During phase I, we identified the need to add a section in our DA concerning invasive mechanical ventilation. During phase II, we produced a context-adapted decision aid about CPR and mechanical ventilation (see **[[English:paperDA|Decision Aid in English]]**) that includes a values clarification section, questions about the patient’s functional autonomy prior to admission to the ICU and the functional decline that they would judge acceptable upon hospital discharge, risks and benefits of CPR and invasive mechanical ventilation, population-level statistics about CPR, a synthesis section to help patients come to a final decision and an online calculator based on the GO-FAR prediction rule (see **[[:go_far_calculator|Good Outcome Following Attempted Resuscitation (GO-FAR) Score to Predict Neurologically Intact Survival After In-Hospital Cardiopulmonary Resuscitation]]**.
  
 Our results inform producers of decision aids on the use of wikis and user-centered design to develop DAs that are better adapted to users’ needs. Free and open access to our wiki platform could allow other centers to adapt our tools to their local contexts thus reducing duplication and accelerating the scale-up of such DAs. Further work is needed on the creation of a video version of our DA. Physicians will also need training to use our DA and to develop shared decision making skills about goals of care.  Our results inform producers of decision aids on the use of wikis and user-centered design to develop DAs that are better adapted to users’ needs. Free and open access to our wiki platform could allow other centers to adapt our tools to their local contexts thus reducing duplication and accelerating the scale-up of such DAs. Further work is needed on the creation of a video version of our DA. Physicians will also need training to use our DA and to develop shared decision making skills about goals of care. 
  
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-** Patient decision aid in English** 
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-Patient Decision Aid  on Powerpoint  
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- {{:final_da_english.pptx|}} 
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-Patient Decision Aid in google slide adaptable  
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-https://docs.google.com/presentation/d/1oOWHDY4nZjIOgGhi23dmhRZuRITpSsBxo1VMXUiWWvA/edit?usp=sharing ** 
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-You are free to modify, use, propagate this Patient Decision Aid without asking permission of the authors. However, we would like to be kept informed of its use.  
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-To do so please contact Patrick Archambault patrick.m.archambault@gmail.com 
  
  
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/home/wikidecision/public_html/data/attic/english/project.1488161757.txt.gz · Dernière modification : 2017/02/27 03:15 (modification externe)

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