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Enregistrement W4395699430 · doi:10.1093/eurjcn/zvae044

Power to the people? Time to improve and implement patient decision aids to strengthen shared decision-making

2024· article· en· W4395699430 sur OpenAlexaff
Sandra Lauck, Krystina B. Lewis, Michelle Carter, Catriona Jennings

Notice bibliographique

RevueEuropean Journal of Cardiovascular Nursing · 2024
Typearticle
Langueen
DomaineHealth Professions
ThématiquePatient-Provider Communication in Healthcare
Établissements canadiensUniversity of OttawaSt. Paul's HospitalUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineDecision aidsMedical decision makingPower (physics)R-CASTDecision analysisBusiness decision mappingMedical emergencyAlternative medicine

Résumé

récupéré en direct d'OpenAlex

This invited commentary refers to ‘Patient decision aids for aortic stenosis and chronic coronary artery disease: a systematic review and meta-analysis’ by E. Harris et al., https://doi.org/10.1093/eurjcn/zvad138. For many people living with heart disease, the journey from their diagnosis to their consent for treatment and beyond is rarely a straight path.1,2 Barriers along the way include the maze of diagnostic testing and consultations, the steep learning curve to grasp what options might be appropriate and feasible, the evolving emotions accompanying the diagnosis and the need for treatment, and the diverse power dynamics that might be at play with health care providers.3 These challenges are often compounded by the need to manage their increasing burden of symptoms. In this complex context of care, patients must make decisions about their treatment, decisions that can have implications for their current and future health and quality of life. Recognizing the importance of engaging and supporting patients to achieve a high-quality treatment decision in partnership with their providers, multiple cardiovascular guidelines have endorsed shared decision-making (SDM) as a core principle of patient-centred communication in multidisciplinary team best practices.4 This bi-directional exchange of information is a mechanism to promote patient empowerment, consideration of patients’ preferences, values and priorities, and ultimately, the provision of true informed consent. Patient decision aids (PtDAs) are evidence-based, patient-facing interventions developed according to international standards and designed to facilitate SDM.5 Patient decision aids explicitly state the decision to be made, provide information about options (inclusive of the option of not actively intervening and choosing watchful waiting/active surveillance) and outcomes associated with each option, while helping patients clarify their values and preferences.6 Harris et al.7 provide important new evidence in a systematic review and meta-analysis of PtDAs developed for people with coronary artery disease (CAD) who require revascularization, or with severe symptomatic aortic stenosis (AS) whose diseased valve needs replacement. In a review of evidence published between 2006 and 2023, they identify a small body of research—including development or evaluation studies of 10 PtDAs related to the treatment of CAD and 11 for AS. After completing an assessment of the PtDAs, they conclude that there is a lack of contemporary, high-quality, publicly available PtDAs, especially for people with lower levels of health literacy, or from marginalized or under-served populations. These timely findings have important implications in closing the gap between the good intentions of SDM and the significant challenges to developing effective tools for all patients.8 Harris et al. call for research efforts to implement PtDAs that will promote a shift in the culture of health care in which patients are invited to be actively and meaningfully involved to participate in their treatment decisions, and in which their preferences, values, and goals of care are prioritized. In a recent updated Cochrane systematic review, a team of international experts identified 209 studies that examined the impact of PtDAs on 71 different decisions, including surgery, screening, genetic-testing, and long-term medication treatments, when compared to usual care.9 Cardiovascular treatments included the highest proportion of studies (n = 22, 31%), possibly attesting to the complexity of treatment decisions along the continuum of cardiac care, the accelerated inclusion of SDM in multiple cardiovascular guidelines across international regions, and the raised awareness of the pressing need for research to guide cardiovascular practice and policy. The investigators focused on important patient-reported outcomes, including decisional conflict, decision-making, and the ability to use strategies to make a high-quality decision. They concluded that there was moderate-certainty evidence that PtDAs increase the congruence between patients’ values and their treatment decisions, and high-certainty evidence that the use of PtDAs resulted in improved knowledge, and accuracy of patients’ perceptions of their risk(s), lower levels of decisional conflict related to feeling uninformed and indecision about personal values, and lower proportion of people who were passive not/less actively engaged in decision-making. Importantly, the review provides further evidence to address clinicians’ concerns that engaging in SDM and promoting the use of PtDAs creates a significant burden of time and effort on their already high workload: when PtDAs were used in the encounter, consultations were 1.5 min longer; when PtDAs were used in preparation for the encounter, there was no difference in consultation length. This high level of evidence, and the strong signals that SDM supported by the use of PtDAs is better for patients while feasible in clinical care encourages us to transition from the focus on ‘why use PtDAs?’ to ‘how should we implement PtDAs?’ What is now needed is knowledge translation and study of effective implementation strategies tailored to the diverse patients we aim to engage in care, and the unique contexts of health service delivery across regions. The production of good science does not always mean that users—clinicians, patients and families, policy-makers—will use this knowledge to its fullest potential, or at all. Although PtDAs are relatively simple tools, their production and implementation are complex endeavours.10 Harris et al. clearly demonstrate the uneven quality of PtDAs. Five of seven AS PtDAs and two of five CAD PtDAs did not meet the international standards’ six qualifying criteria, meaning that these tools did not even meet the definition of a PtDA. This fundamental flaw jeopardizes these tools’ subsequent activities and impact. The leadership of the International Patient Decision Aid Standards (IPDAS) collaboration has been instrumental in promoting gold standards for a careful development process, explicit inclusion of key information required for PtDAs, their evaluation and reporting. For PtDA development, IPDAS proposes an iterative user-centred design model to cycle through understanding users (e.g. needs, goals, strengths, limitations, and context), developing a refining prototype PtDA, prior to assessing uptake and impact (e.g. interactions and experiences with PtDA), and evaluating the development implementation process.11 For PtDA evaluation, IPDAS largely addresses measures of decision-making process quality and quality of the decision to establish their effectiveness. At present, the standards offer minimal guidance on adapting and evaluating existing PtDAs for diverse, equity seeking populations and contexts, other than the use of plain language. Finally, for PtDA reporting, the IPDAS collaboration developed the Standards for Universal reporting of patient Decision Aid Evaluations (SUNDAE) guidelines to address incomplete reporting of PtDA evaluation studies that pose challenges for clinicians and researchers wanting to identify PtDA content and use for clinical practice and/or future research. The SUNDAE reporting guidelines include 26-items and is part of the Equator Network (equator-network.org). The IPDAS collaboration is currently in the process of updating the standards, building on new evidence from Evidence Update 2.0 and the most recent Cochrane Systematic Review of PtDAs.9 Adherence to these standards is fundamental to advance this field of clinical care, research, and policy-setting. To move PtDAs ‘off the shelves, and onto the streets’,12 research and clinical efforts are needed to study the integration of these tools into practice to strengthen SDM. Implementation science refers to the scientific study of methods to promote the uptake of research findings into routine practice.13 Unlike research focused on the evaluation of an intervention (e.g. ‘do PtDAs improve outcomes?’), implementation research focuses on the close examination of questions such as ‘what strategies are effective to integrate PtDAs in patients’ journey of care?’ to overcome the complex barriers of making changes in practice.14 Recently, the Global Heart Hub—a non-profit alliance of cardiovascular patient organizations that aims to create a unified global voice for people living with or affected by heart disease—convened an expert panel of 19 international patients, leading multidisciplinary clinicians representative of the heart team, and researchers to refine, prioritize, and achieve consensus on tangible actions to support the implementation of SDM in the care of people with heart valve disease. The project was co-chaired by a person with lived experience and a clinician who sought to ensure recognition of the equal importance of diverse perspectives, illustrating the central importance of co-constructing evidence with diverse knowledge users to sustain meaningful change. This innovative initiative led to the development of a roadmap focused on global and local actions needed to (i) prepare and engage patients and families, (ii) train health care teams, and (iii) create supportive systems. Such advocacy-driven and evidence-informed strategies provide a template that is applicable to diverse areas of cardiovascular care and can advance health equity. This roadmap paves the way for putting PtDAs into the hands, digital screens, and consultation rooms across international regions to support the culture shift to SDM across the continuum of cardiac care. Efforts to move this agenda forward are further strengthened by recent innovative initiatives in the European Journal of Cardiovascular Nursing. The development of the EJCN KT Corner illustrates the objective of helping close the ‘know-do gap’ that too often hinders the adoption of evidence in clinical care and other health settings.15 Similarly, the launch of the EJCN Science for Patients aims to improve patients’ access to high-quality research while paying particular attention to strategies that address diverse information needs. One of the first initiatives reported on translating the findings of a systematic review and meta-analysis of PtDAs focused on the treatment of atrial fibrillation, including a proposed process to engage meaningfully in SDM.16 These two projects can provide a forum to accelerate the implementation of PtDAs across the continuum of cardiovascular care. The evidence presented by Harris et al.7 prompts us to collectively focus on scientific, clinical, and patient engagement efforts to improve the rigorous development, study strategies for uptake of PtDAs, and promote effective knowledge translation to narrow the gap between what we know and ought to do, and overcome/mitigate the barriers that are getting in the way. SB Lauck is suported by the St. Paul's Hospital Professorship in Cardiovascular Nursing at the University of British Columbia.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,049
score de la tête « metaresearch » (Gemma)0,173
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,049
Score d'incertitude au seuil0,258

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0490,173
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0100,015
Communication savante0,0220,044
Science ouverte0,0030,017
Intégrité de la recherche0,0080,020
Charge utile insuffisante (le modèle a refusé de juger)0,0320,006

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,050
Tête enseignante GPT0,362
Écart entre enseignants0,312 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2024
Routes d'admission1
Résumé présentnon

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Même revueEuropean Journal of Cardiovascular NursingMême sujetPatient-Provider Communication in HealthcareTravaux en français237 207