Clinical decision-making at the end of life: a mixed-methods study

ObjectivesTo improve the ability of clinical staff to recognise end of life in hospital inpatients dying as a result of cancer and heart failure, and to generate new hypotheses for further research.MethodsThis mixed-methods study used decision theory as a theoretical basis. It involved a parallel databases-convergent design, incorporating findings from previously published research, with equal priority to study groups and synthesis by triangulation. The individual arms were (1) a retrospective cohort study of 102 patients with cancer and 81 patients with heart failure in an acute trust in the North of England, and(2) a semistructured interview study of 19 healthcare professionals caring for the same patient groups.ResultsThe synthesis of findings demonstrated areas of agreement, partial agreement, silence and dissonance when comparing the cohort findings with the interview findings. Trajectories of change are identified as associated with poor prognosis in both approaches, but based on different parameters. Management of patients has a significant impact on decision-making. The decision process requires repeated, iterative assessments and may benefit from a multidisciplinary approach. Uncertainty is a defining characteristic of the overall process, and objective parameters only have a limited role in predicting end of life.ConclusionsThe role of uncertainty is important as a trigger for discussions and a defined stage in a patient’s illness journey. This is consistent with current approaches to recognising irreversible deterioration in those with serious illness. This study contributes ongoing evidence that these concepts are vital for decision-making.

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Understanding the factors that influence clinical decision-making - a sequential explanatory mixed methods study protocol

European Journal for Person Centered Healthcare ◽

10.5750/ejpch.v6i2.1510 ◽

2018 ◽

Vol 6 (2) ◽

pp. 329 ◽

Cited By ~ 3

Author(s):

Veena Manja ◽

Sandra Monterio ◽

Gordon Guyatt ◽

John You ◽

Satyan Lakshminrusimha ◽

...

Keyword(s):

Decision Making ◽

Mixed Methods ◽

Patient Outcomes ◽

Healthcare Costs ◽

Clinical Decision Making ◽

Clinical Decision ◽

Mixed Methods Study ◽

Explanatory Mixed Methods ◽

Survey Results ◽

Case Based

Background: Despite soaring healthcare costs, patient outcomes are suboptimal in the USA. Efforts to limit healthcare costs and improve quality of care have had limited success. An improved understanding of factors that influence clinical decision-making may provide insight into optimizing the quality and costs of care. The process of healthcare decision-making is contextual, complex and poorly understood. This study aims to explore the factors that influence clinical decision-making in the setting of limited evidence of effectiveness, limited or conflicting guidance, significant resource burden and variation in values and preferences.Rationale for study design: This sequential explanatory mixed methods study includes a case-based survey (quantitative phase). The results of the survey will guide the sampling and questions for the semi-structured interviews (qualitative phase). The interviews will provide an in-depth explanation of the survey results. Combining the two methods provides complementary information and deeper understanding of the phenomenon of clinical decision-making.Methods: The quantitative strand will consist of case-based surveys in the fields of neonatology and cardiology. Participants are asked to pick the best management choice for each question followed by a rating of the influence of different factors on a 7-point Likert scale. Follow-up questions explore knowledge and influence of evidence, guideline recommendations and costs on decision-making. Analysis of the survey results will inform sampling and the focus of qualitative interviews. The interviews will be analyzed using qualitative description.Discussion: To our knowledge, this is the first study using a mixed methods approach including a case-based survey of physicians practicing in diverse settings to explore the factors that influence clinical decision-making. The results of this study may assist with strategies to implement high value care resulting in improved patient outcomes and limiting costs.

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Clinical decision making in managing the ?difficult? patient with chronic heart failure: who, when, how, where?

European Heart Journal Supplements ◽

10.1016/s1520-765x(03)80014-7 ◽

2003 ◽

Vol 5 ◽

pp. 88-96

Author(s):

H KRUM

Keyword(s):

Heart Failure ◽

Decision Making ◽

Chronic Heart Failure ◽

Clinical Decision Making ◽

Clinical Decision ◽

Difficult Patient

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Contemporary Heart Failure Treatment Based on Improved Knowledge and Personalized Care of Comorbidities

Healthcare Policy and Reform ◽

10.4018/978-1-5225-6915-2.ch071 ◽

2019 ◽

pp. 1565-1579

Author(s):

Kostas Giokas ◽

Charalampos Tsirmpas ◽

Athanasios Anastasiou ◽

Dimitra Iliopoulou ◽

Vassilia Costarides ◽

...

Keyword(s):

Heart Failure ◽

Decision Making ◽

Chronic Diseases ◽

Clinical Decision Making ◽

Medical Condition ◽

Collective Intelligence ◽

Clinical Decision ◽

Mortality And Morbidity ◽

Personalized Care ◽

Care Approach

Chronic diseases are the leading cause of mortality and morbidity. A significant contribution to the burden of chronic diseases is the concurrence of co-morbidities. Heart failure (HF) is a complex, chronic medical condition frequently associated with co-morbidities. The current care approach for HF patients with co-morbidities is neither capable to deliver personalised care nor to halt the on-going increase of its socio-economic burden. Our approach aims to improve the complete care process for HF patients and related co-morbidities to improve outcome and quality of life. This will be achieved by the proposed standardised yet personalised patient-oriented ICT system that supports evidence-based clinical decision making as well as interaction and communication between all stakeholders with focus on the patients and their relatives to improve self-management. We propose that such a system should be build upon a novel European-wide data standard for clinical input and outcome and that it should facilitate decision making and outcome tracking by new collective intelligence algorithms.

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