Evidence map›Paper›PMID 26869137›Full record

ArticleJMIR research protocols2016

Development of a Decision Aid for Cardiopulmonary Resuscitation Involving Intensive Care Unit Patients' and Health Professionals' Participation Using User-Centered Design and a Wiki Platform for Rapid Prototyping: A Research Protocol.

Ariane Plaisance, Holly O Witteman, Daren Keith Heyland, Mark H Ebell, Audrey Dupuis, Carole-Anne Lavoie-Bérard, France Légaré, Patrick Michel Archambault

Registry-linked trialAbstract read
In one paragraph

Article in JMIR research protocols, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04093245 (Supporting the Creation of a LEARNing INteGrated Health System to Mobilize Context-adapted Knowledge With a Wiki Platform to Improve the Transitions of Frail Seniors From From Hospitals and Emergency Departments to the cOMmunity), which is not on this map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT04093245 naunknown statusnot on this mapstarted 2019, after this paper: background citation

Supporting the Creation of a LEARNing INteGrated Health System to Mobilize Context-adapted Knowledge With a Wiki Platform to Improve the Transitions of Frail Seniors From From Hospitals and Emergency Departments to the cOMmunity: Phase II

TypeinterventionalSponsorLaval UniversityRan2019 to 2022Enrolled4,000ConditionsTransition, Emergencies, Health Care Utilization, FrailtyArmsGEM nurse, pre- and post-hospitalization medication list reconciliation, systematic discharge summaries, medical follow-up appointment, follow-up phone call
3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Designing for impact: identifying stakeholder-driven interventions to support recovery after major cancer surgery.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2018
    Article
  4. Article
  5. Development and Testing of Shared Decision Making Interventions for Use in Emergency Care: A Research Agenda.Academic emergency medicine : official journal of the Society for Academic Emergency Medicine · 2016
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Ariane PlaisanceDépartement de médecine sociale et préventive, Faculté de médecine, Université Laval, Québec, QC, Canada. arianeplaisance@gmail.com.ORCID http://orcid.org/0000-0003-4683-7650

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiopulmonary resuscitation (CPR) is an intervention used in cases of cardiac arrest to revive patients whose heart has stopped. Because cardiac arrest can have potentially devastating outcomes such as severe neurological deficits even if CPR is performed, patients must be involved in determining in advance if they want CPR in the case of an unexpected arrest. Shared decision making (SDM) facilitates discussions about goals of care regarding CPR in intensive care units (ICUs). Patient decision aids (DAs) are proven to support the implementation of SDM. Many patient DAs about CPR exist, but they are not universally implemented in ICUs in part due to lack of context and cultural adaptation. Adaptation to local context is an important phase of implementing any type of knowledge tool such as patient DAs. User-centered design supported by a wiki platform to perform rapid prototyping has previously been successful in creating knowledge tools adapted to the needs of patients and health professionals (eg, asthma action plans). This project aims to explore how user-centered design and a wiki platform can support the adaptation of an existing DA for CPR to the local context.

objectiveThe primary objective is to use an existing DA about CPR to create a wiki-based DA that is adapted to the context of a single ICU and tailorable to individual patient's risk factors while employing user-centered design. The secondary objective is to document the use of a wiki platform for the adaptation of patient DAs.

methodsThis study will be conducted in a mixed surgical and medical ICU at Hôtel-Dieu de Lévis, Quebec, Canada. We plan to involve all 5 intensivists and recruit at least 20 alert and oriented patients admitted to the ICU and their family members if available. In the first phase of this study, we will observe 3 weeks of daily interactions between patients, families, intensivists, and other allied health professionals. We will specifically observe 5 dyads of attending intensivists and alert and oriented patients discussing goals of care concerning CPR to understand how a patient DA could support this decision. We will also conduct individual interviews with the 5 intensivists to identify their needs concerning the implementation of a DA. In the second phase of the study, we will build a first prototype based on the needs identified in Phase I. We will start by translating an existing DA entitled "Cardiopulmonary resuscitation: a decision aid for patients and their families." We will then adapt this tool to the needs we identified in Phase I and archive this first prototype in a wiki. Building on the wiki's programming architecture, we intend to integrate the Good Outcome Following Attempted Resuscitation risk calculator into our DA to determine personal risks and benefits of CPR for each patient. We will then present the first prototype to 5 new patient-intensivist dyads. Feedback about content and visual presentation will be collected from the intensivists through short interviews while longer interviews will be conducted with patients and their family members to inform the visual design and content of the next prototype. After each rapid prototyping cycle, 2 researchers will perform qualitative content analysis of data collected through interviews and direct observations. We will attempt to solve all content and visual design issues identified before moving to the next round of prototyping. In all, we will conduct 3 prototyping cycles with a total of 15 patient-intensivist dyads.

resultsWe expect to develop a multimedia wiki-based DA to support goals of care discussions about CPR adapted to the local needs of patients, their family members, and intensivists and tailorable to individual patient risk factors. The final version of the DA as well as the development process will be housed in an open-access wiki and free to be adapted and used in other contexts.

conclusionsThis study will shed new light on the development of DAs adapted to local context and tailorable to individual patient risk factors employing user-centered design and a wiki to support rapid prototyping of content and visual design issues.

Indexed as

cardiopulmonary resuscitationend-of-life planninggoals of care discussionsintensive care medicinemedical informaticsshared decision makinguser-centered designwikis

Identifiers

PMID26869137
PMCPMC4768044

What OpenQuestion holds

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LicenceCC BY
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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.