Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey

ObjectiveTo assess barriers and facilitators to de-implementation.DesignA qualitative evidence synthesis with a framework analysis.Data sourcesMedline, Embase, Cochrane Library and Rx for Change databases until September 2018 were searched.Eligibility criteriaWe included studies that primarily focused on identifying factors influencing de-implementation or the continuation of low-value care, and studies describing influencing factors related to the effect of a de-implementation strategy.Data extraction and synthesisThe factors were classified on five levels: individual provider, individual patient, social context, organisational context, economic/political context.ResultsWe identified 333 factors in 81 articles. Factors related to the individual provider (n=131; 74% barriers, 17% facilitators, 9% both barrier/facilitator) were associated with their attitude (n=72; 55%), knowledge/skills (n=43; 33%), behaviour (n=11; 8%) and provider characteristics (n=5; 4%). Individual patient factors (n=58; 72% barriers, 9% facilitators, 19% both barrier/facilitator) were mainly related to knowledge (n=33; 56%) and attitude (n=13; 22%). Factors related to the social context (n=46; 41% barriers, 48% facilitators, 11% both barrier/facilitator) included mainly professional teams (n=23; 50%) and professional development (n=12; 26%). Frequent factors in the organisational context (n=67; 67% barriers, 25% facilitators, 8% both barrier/facilitator) were available resources (n=28; 41%) and organisational structures and work routines (n=24; 36%). Under the category of economic and political context (n=31; 71% barriers, 13% facilitators, 16% both barrier/facilitator), financial incentives were most common (n=27; 87%).ConclusionsThis study provides in-depth insight into the factors within the different (sub)categories that are important in reducing low-value care. This can be used to identify barriers and facilitators in low-value care practices or to stimulate development of strategies that need further refinement. We conclude that multifaceted de-implementation strategies are often necessary for effective reduction of low-value care. Situation-specific knowledge of impeding or facilitating factors across all levels is important for designing tailored de-implementation strategies.

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“… 40 When faced with an uncertain outcome, clinicians prefer to avoid a greater unlikely loss than to incur a certain, but lesser, cost. 41 …”

Section: Discussionmentioning

confidence: 99%

Barriers and facilitators to reduce low-value care: a qualitative evidence synthesis

Dulmen¹,

Naaktgeboren

Heus

et al. 2020

BMJ Open

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“…Alternatively, and in a type of 'loss aversion' (where people focussing on avoiding 'losses' -viz. adverse consequences), 33 participants may not have perceived there was a decision to be made, and thus focussed on avoiding moral distress, time-consuming complaints and legal disputes.…”

Section: Discussionmentioning

confidence: 99%

At the end: A vignette-based investigation of strategies for managing end-of-life decisions in the intensive care unit

Reader

Dayal

Brett

2020

Journal of the Intensive Care Society

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Background Decision-making on end-of-life is an inevitable, yet highly complex, aspect of intensive care decision-making. End-of-life decisions can be challenging both in terms of clinical judgement and social interaction with families, and these two processes often become intertwined. This is especially apparent at times when clinicians are required to seek the views of surrogate decision makers (i.e., family members) when considering palliative care. Methods Using a vignette-based interview methodology, we explored how interactions with family members influence end-of-life decisions by intensive care unit clinicians ( n = 24), and identified strategies for reaching consensus with families during this highly emotional phase of care. Results We found that the enactment of end-of-life decisions were reported as being affected by a form of loss aversion, whereby concerns over the consequences of not reaching a consensus with families weighed heavily in the minds of clinicians. Fear of conflict with families tended to arise from anticipated unrealistic family expectations of care, family normalization of patient incapacity, and belief systems that prohibit end-of-life decision-making. Conclusions To support decision makers in reaching consensus, various strategies for effective, coherent, and targeted communication (e.g., on patient deterioration and limits of clinical treatment) were suggested as ways to effectively consult with families on end-of-life decision-making.

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“…As artefacts from production, genome information processed for clinical use may pose a likelihood of misinterpretation due to information distortion, omissions, and fragmented senses. Furthermore, information reliability is a critical factor determining the ability of clinicians to utilise the genomic information 43 . Thus, our approach in developing this cGDM for focussed on the multi-dimensional scope of information, including procedural factors, derived from NGS technology.…”

Section: Methodsmentioning

confidence: 99%

Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine

Kim

Park

et al. 2020

Sci Rep

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in light of recent developments in genomic technology and the rapid accumulation of genomic information, a major transition toward precision medicine is anticipated. However, the clinical applications of genomic information remain limited. This lag can be attributed to several complex factors, including the knowledge gap between medical experts and bioinformaticians, the distance between bioinformatics workflows and clinical practice, and the unique characteristics of genomic data, which can make interpretation difficult. Here we present a novel genomic data model that allows for more interactive support in clinical decision-making. Informational modelling was used as a basis to design a communication scheme between sophisticated bioinformatics predictions and the representative data relevant to a clinical decision. This study was conducted by a multidisciplinary working group who carried out clinico-genomic workflow analysis and attribute extraction, through Failure Mode and Effects Analysis (FMEA). Based on those results, a clinical genome data model (cGDM) was developed with 8 entities and 46 attributes. The cGDM integrates reliability-related factors that enable clinicians to access the reliability problem of each individual genetic test result as clinical evidence. The proposed cGDM provides a data-layer infrastructure supporting the intellectual interplay between medical experts and informed decision-making.

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Understanding clinical and non-clinical decisions under uncertainty: a scenario-based survey

Cited by 17 publications

References 46 publications

Barriers and facilitators to reduce low-value care: a qualitative evidence synthesis

Barriers and facilitators to reduce low-value care: a qualitative evidence synthesis

At the end: A vignette-based investigation of strategies for managing end-of-life decisions in the intensive care unit

Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine

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