Disagreement Between Clinicians and Score in Decision-Making Capacity of Critically Ill Patients

Emergency department and intensive care unit health professionals' knowledge and application of the law that applies to end-of-life decision-making for adults: A scoping review of the literature

BACKGROUND

Laws that regulate healthcare practice at the end of life reflect the values of the society where they apply. Traditionally, healthcare professionals rely on their clinical knowledge to inform treatment decisions, but the extent to which the law also informs health professionals' decision-making at the end of life is uncertain.

OBJECTIVE

The objective of this study was to describe what healthcare professionals working in emergency departments and intensive care units know about the law that relates to end-of-life decision-making for hospitalised adults and what affects its application.

REVIEW METHOD

This review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews.

DATA SOURCES

Data were sourced by searching the following databases: Cumulative Index to Nursing and Allied Health Literature (CINAHL [via EBSCOhost]), Nursing and Allied Health and Health and Medical Collection (via ProQuest Central), Excerpta Medica dataBASE (Embase), PubMed, PsycINFO, and HeinOnline.

RESULTS

Systematic screening of the search results and application of inclusion criteria resulted in the identification of 18 quantitative and three qualitative articles that were reviewed, summarised, and reported. Ten of the quantitative studies assessed knowledge and attitudes to law or end-of-life decision-making using hypothetical scenarios or vignettes. Qualitative studies focussed on how the law was applied when end-of-life decisions were made. End-of-life decision-making is mostly based on the clinical needs of the patient, with the law having a secondary role.

CONCLUSION

Around the world, there are significant gaps in healthcare professionals' legal knowledge. Clinical factors are considered more important to end-of-life decision-making than legal factors. End-of-life decision-making is perceived to carry legal risk, and this results in the provision of nonbeneficial end-of-life care. Further qualitative research is needed to ascertain the clinician-related factors that affect the integration of law with end-of-life decision-making.

A survey on clinical natural language processing in the United Kingdom from 2007 to 2022

Much of the knowledge and information needed for enabling high-quality clinical research is stored in free-text format. Natural language processing (NLP) has been used to extract information from these sources at scale for several decades. This paper aims to present a comprehensive review of clinical NLP for the past 15 years in the UK to identify the community, depict its evolution, analyse methodologies and applications, and identify the main barriers. We collect a dataset of clinical NLP projects (n = 94; £ = 41.97 m) funded by UK funders or the European Union's funding programmes. Additionally, we extract details on 9 funders, 137 organisations, 139 persons and 431 research papers. Networks are created from timestamped data interlinking all entities, and network analysis is subsequently applied to generate insights. 431 publications are identified as part of a literature review, of which 107 are eligible for final analysis. Results show, not surprisingly, clinical NLP in the UK has increased substantially in the last 15 years: the total budget in the period of 2019-2022 was 80 times that of 2007-2010. However, the effort is required to deepen areas such as disease (sub-)phenotyping and broaden application domains. There is also a need to improve links between academia and industry and enable deployments in real-world settings for the realisation of clinical NLP's great potential in care delivery. The major barriers include research and development access to hospital data, lack of capable computational resources in the right places, the scarcity of labelled data and barriers to sharing of pretrained models.

Decoding health status transitions of over 200 000 patients with traumatic brain injury from preceding injury to the injury event

For centuries, the study of traumatic brain injury (TBI) has been centred on historical observation and analyses of personal, social, and environmental processes, which have been examined separately. Today, computation implementation and vast patient data repositories can enable a concurrent analysis of personal, social, and environmental processes, providing insight into changes in health status transitions over time. We applied computational and data visualization techniques to categorize decade-long health records of 235,003 patients with TBI in Canada, from preceding injury to the injury event itself. Our results highlighted that health status transition patterns in TBI emerged along with the projection of comorbidity where many disorders, social and environmental adversities preceding injury are reflected in external causes of injury and injury severity. The strongest associations between health status preceding TBI and health status at the injury event were between multiple body system pathology and advanced age-related brain pathology networks. The interwoven aspects of health status on a time continuum can influence post-injury trajectories and should be considered in TBI risk analysis to improve prevention, diagnosis, and care.

Activities of daily living and psychiatric symptoms after intensive care unit discharge among critically ill patients with or without tracheostomy: a single center longitudinal study

Aim

Tracheostomy is widely performed in critically ill patients who require prolonged mechanical ventilation. Long-term morbidity (post-intensive care syndrome) in tracheostomized patients is not widely reported, however, so we evaluate it here.

Methods

This is a sub-analysis of a single center prospective longitudinal study, which assessed activities of daily living (ADL) and psychiatric symptoms in adult patients emergently admitted to the intensive care unit (ICU). We evaluated association between these symptoms and tracheostomy by posting questionnaires at 3 and 12 months after ICU discharge.

Results

We analyzed 107 patients (15 patients with tracheostomy) at 3 months and 74 patients (13 patients with tracheostomy) at 12 months after ICU discharge. ADL tended to be lower in patients with tracheostomy than in those without tracheostomy at 3 months after ICU discharge (65 [10-100] versus 95 [59-100]; P = 0.28, 7/15 [47%] versus 30/102 [30%] Barthel Index scored ≤ 60; P = 0.23), however there were no significant differences. Psychiatric symptoms were not different between the groups at 3 months and again at 12 months.

Conclusion

Activities of daily living disability and psychiatric symptoms were not significantly worse in patients with tracheostomy at 3 and 12 months from ICU discharge compared with patients without tracheostomy. Despite the limited number in our cohort, our study may inform shared decision making concerning tracheostomy for critically ill patients and their families.

Values and self-perception of behaviour among critical care nurses

BACKGROUND

Moral distress has various adverse effects on nurses working in critical care. Differences in personal values, and between values and self-perception of behaviour are factors that may cause moral distress.

RESEARCH AIMS

The aims of this study were (1) to identify ethical values and self-perception of behaviour of critical care nurses in Japan and (2) to determine the items with a large difference between value and behaviour and the items with a large difference in value from others.

RESEARCH DESIGN

A nationwide, cross-sectional study was conducted.

PARTICIPANTS AND RESEARCH CONTEXT

We developed a self-administered questionnaire with 28 items, which was completed by 1014 critical care nurses in Japan. The difference between value and self-perception of behaviour was calculated from the score of each value item minus the score of each self-perception of behaviour item. The size of the difference in value from the others was judged by the standard deviation of each item.

ETHICAL CONSIDERATIONS

The study was approved by the Ethics Committee of the Tokyo Medical and Dental University (approval nos. M2018-214, M2019-045).

RESULTS

The items with a large difference between value and behaviour sources were related to the working environment and decision-making support. The items with a large difference in value from others were related to hospital management and disclosure of information to patients.

DISCUSSION

Improving the working environment for nurses is important for reducing moral distress. Nurses are faced with a variety of choices, including advocating for patients and protecting the fair distribution of medical resources, and each nurse's priorities might diverge from those of other team members, which can lead to conflict within the team.

CONCLUSION

This study revealed items with particularly high risks of moral distress for nurses. The results provide foundational information that can guide the development of strategies to mitigate moral distress.

Mental Capacity Assessments for COVID-19 Patients: Emergency Admissions and the CARD Approach

The doctrine of consent (or informed consent, as it is called in North America) is built upon presumptions of mental capacity. Those presumptions must be tested according to legal rules that may be difficult to apply to COVID-19 patients during emergency presentations. We examine the principles of mental capacity and make recommendations on how to assess the capacity of COVID-19 patients to consent to emergency medical treatment. We term this the CARD approach (Comprehend, Appreciate, Reason, and Decide).

Artificial Intelligence and Surgical Decision-making

Importance

Surgeons make complex, high-stakes decisions under time constraints and uncertainty, with significant effect on patient outcomes. This review describes the weaknesses of traditional clinical decision-support systems and proposes that artificial intelligence should be used to augment surgical decision-making.

Observations

Surgical decision-making is dominated by hypothetical-deductive reasoning, individual judgment, and heuristics. These factors can lead to bias, error, and preventable harm. Traditional predictive analytics and clinical decision-support systems are intended to augment surgical decision-making, but their clinical utility is compromised by time-consuming manual data management and suboptimal accuracy. These challenges can be overcome by automated artificial intelligence models fed by livestreaming electronic health record data with mobile device outputs. This approach would require data standardization, advances in model interpretability, careful implementation and monitoring, attention to ethical challenges involving algorithm bias and accountability for errors, and preservation of bedside assessment and human intuition in the decision-making process.

Conclusions and Relevance

Integration of artificial intelligence with surgical decision-making has the potential to transform care by augmenting the decision to operate, informed consent process, identification and mitigation of modifiable risk factors, decisions regarding postoperative management, and shared decisions regarding resource use.

Decision analysis and reinforcement learning in surgical decision-making

BACKGROUND

Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.

METHODS

Cochrane, EMBASE, and PubMed databases were searched from their inception to June 2019. Included were 41 articles about cognitive and diagnostic errors, decision-making, decision analysis, and machine-learning. The articles were assimilated into relevant categories according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines.

RESULTS

Requirements for time-consuming manual data entry and crude representations of individual patients and clinical context compromise many traditional decision-support tools. Decision analysis methods for calculating probability thresholds can inform population-based recommendations that jointly consider risks, benefits, costs, and patient values but lack precision for individual patient-centered decisions. Reinforcement learning, a machine-learning method that mimics human learning, can use a large set of patient-specific input data to identify actions yielding the greatest probability of achieving a goal. This methodology follows a sequence of events with uncertain conditions, offering potential advantages for personalized, patient-centered decision-making. Clinical application would require secure integration of multiple data sources and attention to ethical considerations regarding liability for errors and individual patient preferences.

CONCLUSION

Traditional decision-support tools are ill-equipped to accommodate time constraints and uncertainty regarding diagnoses and the predicted response to treatment, both of which often impair surgical decision-making. Decision analysis and reinforcement learning have the potential to play complementary roles in delivering high-value surgical care through sound judgment and optimal decision-making.

Inter-ICU transfer of patients with ventilator dependent respiratory failure: Qualitative analysis of family and physician perspectives

OBJECTIVES

Ventilator dependent respiratory failure (VDRF) patients are seriously ill and often transferred between ICUs. Our objective was to obtain multi-stakeholder insights into the experiences of families during inter-ICU transfer.

METHODS

We conducted a qualitative study using semi-structured interviews with family members of VDRF patients as well as clinicians that have received or transferred VDRF patients to our hospital. Interviews were transcribed and template analysis was used to identify themes within/across stakeholder groups.

RESULTS

Patient, family, clinician and systems-level factors were identified as key themes during inter-ICU transfer. The main findings highlight that family members were rarely engaged in the decision to transfer as well as a lack of standardized communication between clinicians during care transitions. Family members were reassured with the care after transfer in spite of practical and financial challenges. Clinicians acknowledged the lack of a systematic approach for meeting the needs of families and suggested various resources.

CONCLUSIONS

This is one of the first qualitative studies to gather a multi-stakeholder perspective and identify problems faced by families during inter-ICU transfer of VDRF patients.

PRACTICE IMPLICATIONS

Our results provide a starting point for the development of family-centered support interventions which will need to be tested in future studies.