Search

Objective: To confirm described dimensions of making care fit and explore how patients and clinicians collaborate to make care fit in clinical practice. Methods: As part of an ongoing study, we audiotaped and transcribed patient-clinician consultations in diabetes care. We purposively selected consultations based on participants’ demographical, biomedical and biographical characteristics. We analysed transcripts using reflexive thematic analysis. We combined a deductive and inductive approach, using the pre-described dimensions of making care fit and adding new (sub-)dimensions when pertinent. Results: We analysed 24 clinical consultations. Our data confirmed eight previously described dimensions and provided new sub-dimensions of making care fit with examples from clinical practice (problematic situation, influence of devices, sense of options, shared agenda setting, clinician context, adapting to changing organization of care, and possibility to reconsider). Conclusion: Our study confirmed, specified and enriched the conceptualization of making care fit through practice examples. We observed patient-clinician collaboration in exploration of patients’ context, and by responsively changing, adapting or maintaining care plans. Practice implications: Our findings support clinicians and researchers with insights in important aspects of patient-clinician collaboration. Ultimately, this would lead to optimal design of care plans that fit well in each patient life.

Background:: Patients with cancer often have to make complex decisions about treatment, with the options varying in risk profiles and effects on survival and quality of life. Moreover, inefficient care paths make it hard for patients to participate in shared decision-making. Data-driven decision-support tools have the potential to empower patients, support personalized care, improve health outcomes and promote health equity. However, decision-support tools currently seldom consider quality of life or individual preferences, and their use in clinical practice remains limited, partly because they are not well integrated in patients’ care paths. Aim and objectives:: The central aim of the 4D PICTURE project is to redesign patients’ care paths and develop and integrate evidence-based decision-support tools to improve decision-making processes in cancer care delivery. This article presents an overview of this international, interdisciplinary project. Design, methods and analysis:: In co-creation with patients and other stakeholders, we will develop data-driven decision-support tools for patients with breast cancer, prostate cancer and melanoma. We will support treatment decisions by using large, high-quality datasets with state-of-the-art prognostic algorithms. We will further develop a conversation tool, the Metaphor Menu, using text mining combined with citizen science techniques and linguistics, incorporating large datasets of patient experiences, values and preferences. We will further develop a promising methodology, MetroMapping, to redesign care paths. We will evaluate MetroMapping and these integrated decision-support tools, and ensure their sustainability using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework. We will explore the generalizability of MetroMapping and the decision-support tools for other types of cancer and across other EU member states. Ethics:: Through an embedded ethics approach, we will address social and et

The authors regret to inform readers that there were errors in the numbering of some citations in Table 1 and in one sentence in section 3.3.2. The corrections (indicated in bold) are: Table 1, Alameddine, 2020, Dubai [64]; Baig, 2020, Pakistan [65]; Becher, 2021, Germany [40]; Carrotte, 2021, Australia [38]; Finlay, 2020, Canada [81]; Gibson, 2020, England [41]; Haugom, 2020, Norway [80]; House, 2021, USA [79]; Huang, 2021, China [45]; Jiang, 2021, China [54]; Keij, 2021, the Netherlands [9]; Moleman, 2021, the Netherlands [67]; Pan, 2022, China [26]; Rodenburg-Vandenbussche, 2020, the Netherlands [36], Sumpton, 2021, Australia [35], Van Beek-Peeters, 2021, the Netherlands [21]; Vedasto, 2021, Tanzania [25]; Whitney, 2021, USA [77]; Windon, 2021, USA [34]; Wubben, 2021, the Netherlands [66]. The corrected table can be found below. Self-efficacy and self-confidence in being involved in the SDM process were reported to benefit patient involvement [20, 35, 40, 54], and such self-efficacy may grow over time [35]. The authors would like to apologise for any inconvenience caused [Table presented]. DOI of original article: doi: 10.1016/j.pec.2023.107677.

Treatment decision-making can be complex, notably when there are multiple treatments available, with different (probabilities of) benefits and harms, for example, survival and side effects.1 It is precisely in these complex situations that the preferences of the patient are of utmost importance, as the trade-offs of benefits and harms are subjective and concern patients' lives.2 In such trade-offs, shared decision making (SDM) has gained momentum as a strategy to include both the best available evidence and the patient's preferences.3, Methodologie en Organisatie van Design, Applied Ergonomics and Design

Treatment decision-making can be complex, notably when there are multiple treatments available, with different (probabilities of) benefits and harms, for example, survival and side effects.1 It is precisely in these complex situations that the preferences of the patient are of utmost importance, as the trade-offs of benefits and harms are subjective and concern patients' lives.2 In such trade-offs, shared decision making (SDM) has gained momentum as a strategy to include both the best available evidence and the patient's preferences.3

Methodologie en Organisatie van Design

For too many people, their care plans are designed without fully accounting for who they are, the lives they live, what matters to them or what they aspire to achieve. In other words, these care plans are designed for ‘patients like this’ rather than for ‘this patient’. To improve this situation, investigators often propose interventions, such as patient decision aids or patient-reported experience measures, which may disrupt clinical practice and increase the work patients must do. These interventions ‘target’ patients, or rather the images, biomarkers, or numbers that represent their disease, rather than the person behind their patient role. Success becomes getting patients to use the interventions or to report high experience scores as they become overwhelmed by the work of accessing and using healthcare while completing self-care tasks

Introduction: For too many people, their care plans are designed without fully accounting for who they are, the lives they live, what matters to them or what they aspire to achieve. We aimed to summarize instruments capable of measuring dimensions of patient–clinician collaboration to make care fit. Methods: We systematically searched several databases (Medline, Embase, Cochrane, Scopus and Web of Science) from inception to September 2021 for studies using quantitative measures to assess, evaluate or rate the work of making care fit by any participant in real-life clinical encounters. Eligibility was assessed in duplicate. After extracting all items from relevant instruments, we coded them deductively on dimensions relevant to making care fit (as presented in a recent Making Care Fit Manifesto), and inductively on the main action described. Results: We included 189 papers, mostly from North America (N = 83, 44%) and in the context of primary care (N = 54, 29%). Half of the papers (N = 88, 47%) were published in the last 5 years. We found 1243 relevant items to assess efforts of making care fit, included within 151 instruments. Most items related to the dimensions ‘Patient-clinician collaboration: content’ (N = 396, 32%) and ‘Patient-clinician collaboration: manner’ (N = 382, 31%) and the least related to ‘Ongoing and iterative process’ (N = 22, 2%) and in ‘Minimally disruptive of patient lives’ (N = 29, 2%). The items referred to 27 specific actions. Most items referred to ‘Informing’ (N = 308, 25%) and ‘Exploring’ (N = 93, 8%), the fewest items referred to ‘Following up’, ‘Comforting’ and ‘Praising’ (each N = 3, 0.2%). Discussion: Measures of the work that patients and clinicians do together to make care fit focus heavily on the content of their collaborations, particularly on exchanging information. Other dimensions and actions previously identified as crucial to making care fit are assessed infrequently o

Objective: To identify patient-related characteristics considered to affect patient involvement in shared decision making (SDM) about treatment. Methods: We conducted a scoping review of qualitative studies. We searched for literature across seven databases until March 2022, and included qualitative studies that focused on associations between patient-related characteristics and SDM about treatment in adults. We analyzed studies using an inductive thematic approach. Results: The search yielded 5948 articles, of which 70 were included. We identified many different patient-related characteristics, which we grouped into four categories related to: (1) the individual who is facing the decision, (2) the decision, (3) the relationship between the patient and the clinician and others involved in the decision, and (4) the healthcare context. Conclusions: Studies report a variety of patient-related characteristics that may affect patient involvement in SDM. Amongst others, patients may need to feel informed, to understand their role in SDM, and be able to communicate. Involvement may be challenging with characteristics such as perceived time pressure, poor patient-clinician relationships, emotional distress, and severe illness. Practice implications: In order to truly involve patients in SDM, we might need to focus on characteristics such as patient emotions and relationship building, besides information provision and values clarification.