Cognitive biases, environmental, patient and personal factors associated with critical care decision making: A scoping review

Variation in Hospital Performance for General Surgery in Younger and Older Adults: A Retrospective Cohort Study

OBJECTIVE

To compare hospital surgical performance in older and younger patients.

BACKGROUND

In-hospital mortality after surgical procedures varies widely among hospitals. Prior studies suggest that failure-to-rescue rates drive this variation for older adults, but the generalizability of these findings to younger patients remains unknown.

METHODS

We performed a retrospective cohort study of patients ≥18 years undergoing one of 10 common and complex general surgery operations in 16 states using the Healthcare Cost and Utilization Projects State Inpatient Databases (2016-2018). Patients were split into 2 populations: patients with Medicare ≥65 (older adult) and non-Medicare <65 (younger adult). Hospitals were sorted into quintiles using risk-adjusted in-hospital mortality rates for each age population. Correlations between hospitals in each mortality quintile across age populations were calculated. Complication and failure-to-rescue rates were compared across the highest and lowest mortality quintiles in each age population.

RESULTS

We identified 579,582 patients treated in 732 hospitals. The mortality rate was 3.6% among older adults and 0.7% among younger adults. Among older adults, high- relative to low-mortality hospitals had similar complication rates (32.0% vs 29.8%; P = 0.059) and significantly higher failure-to-rescue rates (16.0% vs 4.0%; P < 0.001). Among younger adults, high-relative to low-mortality hospitals had higher complications (15.4% vs 12.1%; P < 0.001) and failure-to-rescue rates (8.3% vs 0.7%; P < 0.001). The correlation between observed-to-expected mortality ratios in each age group was 0.385 ( P < 0.001).

CONCLUSIONS

High surgical mortality rates in younger patients may be driven by both complication and failure-to-rescue rates. There is little overlap between low-mortality hospitals in the older and younger adult populations. Future work must delve into the root causes of this age-based difference in hospital-level surgical outcomes.

Examining how goals of care communication are conducted between doctors and patients with severe acute illness in hospital settings: A realist systematic review

BACKGROUND

Patient involvement in goals of care decision-making has shown to enhance satisfaction, affective-cognitive outcomes, allocative efficiency, and reduce unwarranted clinical variation. However, the involvement of patients in goals of care planning within hospitals remains limited, particularly where mismatches in shared understanding between doctors and patients are present.

AIM

To identify and critically examine factors influencing goals of care conversations between doctors and patients during acute hospital illness.

DESIGN

Realist systematic review following the RAMESES standards. A protocol has been published in PROSPERO (CRD42021297410). The review utilised realist synthesis methodology, including a scoping literature search to generate initial theories, theory refinement through stakeholder consultation, and a systematic literature search to support program theory.

DATA SOURCES

Data were collected from Medline, PubMed, Embase, CINAHL, PsychINFO, Scopus databases (1946 to 14 July 2023), citation tracking, and Google Scholar. Open-Grey was utilized to identify relevant grey literature. Studies were selected based on relevance and rigor to support theory development.

RESULTS

Our analysis included 52 papers, supporting seven context-mechanism-output (CMO) hypotheses. Findings suggest that shared doctor-patient understanding relies on doctors being confident, competent, and personable to foster trusting relationships with patients. Low doctor confidence often leads to avoidance of discussions. Moreover, information provided to patients is often inconsistent, biased, procedure-focused, and lacks personalisation. Acute illness, medical jargon, poor health literacy, and high emotional states further hinder patient understanding.

CONCLUSIONS

Goals of care conversations in hospitals are nuanced and often suboptimal. To improve patient experiences and outcome of care interventions should be personalised and tailored to individual needs, emphasizing effective communication and trusting relationships among patients, families, doctors, and healthcare teams. Inclusion of caregivers and acknowledgment at the service level are crucial for achieving desired outcomes. Implications for policy, research, and clinical practice, including further training and skills development for doctors, are discussed.

Limiting life-sustaining treatment for very old ICU patients: cultural challenges and diverse practices

BACKGROUND

Decisions about life-sustaining therapy (LST) in the intensive care unit (ICU) depend on predictions of survival as well as the expected functional capacity and self-perceived quality of life after discharge, especially in very old patients. However, prognostication for individual patients in this cohort is hampered by substantial uncertainty which can lead to a large variability of opinions and, eventually, decisions about LST. Moreover, decision-making processes are often embedded in a framework of ethical and legal recommendations which may vary between countries resulting in divergent management strategies.

METHODS

Based on a vignette scenario of a multi-morbid 87-year-old patient, this article illustrates the spectrum of opinions about LST among intensivsts with a special interest in very old patients, from ten countries/regions, representing diverse cultures and healthcare systems.

RESULTS

This survey of expert opinions and national recommendations demonstrates shared principles in the management of very old ICU patients. Some guidelines also acknowledge cultural differences between population groups. Although consensus with families should be sought, shared decision-making is not formally required or practised in all countries.

CONCLUSIONS

This article shows similarities and differences in the decision-making for LST in very old ICU patients and recommends strategies to deal with prognostic uncertainty. Conflicts should be anticipated in situations where stakeholders have different cultural beliefs. There is a need for more collaborative research and training in this field.

Imaging Artificial Intelligence: A Framework for Radiologists to Address Health Equity, From the AJR Special Series on DEI

Artificial intelligence (AI) holds promise for helping patients access new and individualized health care pathways while increasing efficiencies for health care practitioners. Radiology has been at the forefront of this technology in medicine; many radiology practices are implementing and trialing AI-focused products. AI also holds great promise for reducing health disparities and promoting health equity. Radiology is ideally positioned to help reduce disparities given its central and critical role in patient care. The purposes of this article are to discuss the potential benefits and pitfalls of deploying AI algorithms in radiology, specifically highlighting the impact of AI on health equity; to explore ways to mitigate drivers of inequity; and to enhance pathways for creating better health care for all individuals, centering on a practical framework that helps radiologists address health equity during deployment of new tools.

Cognitive biases and moral characteristics of healthcare workers and their treatment approach for persons with advanced dementia in acute care settings

Introduction

Palliative care (PC) delivery for persons with advanced dementia (AD) remains low, particularly in acute-care settings. Studies have shown that cognitive biases and moral characteristics can influence patient care through their effect on the thinking patterns of healthcare workers (HCWs). This study aimed to determine whether cognitive biases, including representativeness, availability, and anchoring, are associated with treatment approaches, ranging from palliative to aggressive care in acute medical situations, for persons with AD.

Methods

Three hundred fifteen HCWs participated in this study: 159 physicians and 156 nurses from medical and surgical wards in two hospitals. The following questionnaires were administered: a socio-demographic questionnaire; the Moral Sensitivity Questionnaire; the Professional Moral Courage Scale; a case scenario of a person with AD presenting with pneumonia, with six possible interventions ranging from PC to aggressive care (referring to life-prolonging interventions), each given a score from (-1) (palliative) to 3 (aggressive), the sum of which is the "Treatment Approach Score;" and 12 items assessing perceptions regarding PC for dementia. Those items, the moral scores, and professional orientation (medical/surgical) were classified into the three cognitive biases.

Results

The following aspects of cognitive biases were associated with the Treatment Approach Score: representativeness-agreement with the definition of dementia as a terminal disease and appropriateness of PC for dementia; availability-perceived organizational support for PC decisions, apprehension regarding response to PC decisions by seniors or family, and apprehension regarding a lawsuit following PC; and anchoring-perceived PC appropriateness by colleagues, comfort with end-of-life conversations, guilt feelings following the death of a patient, stress, and avoidance accompanying care. No association was found between moral characteristics and the treatment approach. In a multivariate analysis, the predictors of the care approach were: guilt feelings about the death of a patient, apprehension regarding senior-level response, and PC appropriateness for dementia.

Conclusion

Cognitive biases were associated with the care decisions for persons with AD in acute medical conditions. These findings provide insight into the potential effects of cognitive biases on clinical decisions, which may explain the disparity between treatment guidelines and the deficiency in the implementation of palliation for this population.

The role of clinical phenotypes in decisions to limit life-sustaining treatment for very old patients in the ICU

BACKGROUND

Limiting life-sustaining treatment (LST) in the intensive care unit (ICU) by withholding or withdrawing interventional therapies is considered appropriate if there is no expectation of beneficial outcome. Prognostication for very old patients is challenging due to the substantial biological and functional heterogeneity in that group. We have previously identified seven phenotypes in that cohort with distinct patterns of acute and geriatric characteristics. This study investigates the relationship between these phenotypes and decisions to limit LST in the ICU.

METHODS

This study is a post hoc analysis of the prospective observational VIP2 study in patients aged 80 years or older admitted to ICUs in 22 countries. The VIP2 study documented demographic, acute and geriatric characteristics as well as organ support and decisions to limit LST in the ICU. Phenotypes were identified by clustering analysis of admission characteristics. Patients who were assigned to one of seven phenotypes (n = 1268) were analysed with regard to limitations of LST.

RESULTS

The incidence of decisions to withhold or withdraw LST was 26.5% and 8.1%, respectively. The two phenotypes describing patients with prominent geriatric features and a phenotype representing the oldest old patients with low severity of the critical condition had the largest odds for withholding decisions. The discriminatory performance of logistic regression models in predicting limitations of LST after admission to the ICU was the best after combining phenotype, ventilatory support and country as independent variables.

CONCLUSIONS

Clinical phenotypes on ICU admission predict limitations of LST in the context of cultural norms (country). These findings can guide further research into biases and preferences involved in the decision-making about LST. Trial registration Clinical Trials NCT03370692 registered on 12 December 2017.

Development and External Validation of a Prediction Model for Quality of Life of ICU Survivors: A Subanalysis of the MONITOR-IC Prospective Cohort Study

OBJECTIVES

To develop and externally validate a prediction model for ICU survivors' change in quality of life 1 year after ICU admission that can support ICU physicians in preparing patients for life after ICU and managing their expectations.

DESIGN

Data from a prospective multicenter cohort study (MONITOR-IC) were used.

SETTING

Seven hospitals in the Netherlands.

PATIENTS

ICU survivors greater than or equal to 16 years old.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Outcome was defined as change in quality of life, measured using the EuroQol 5D questionnaire. The developed model was based on data from an academic hospital, using multivariable linear regression analysis. To assist usability, variables were selected using the least absolute shrinkage and selection operator method. External validation was executed using data of six nonacademic hospitals. Of 1,804 patients included in analysis, 1,057 patients (58.6%) were admitted to the academic hospital, and 747 patients (41.4%) were admitted to a nonacademic hospital. Forty-nine variables were entered into a linear regression model, resulting in an explained variance ( R2 ) of 56.6%. Only three variables, baseline quality of life, admission type, and Glasgow Coma Scale, were selected for the final model ( R2 = 52.5%). External validation showed good predictive power ( R2 = 53.2%).

CONCLUSIONS

This study developed and externally validated a prediction model for change in quality of life 1 year after ICU admission. Due to the small number of predictors, the model is appealing for use in clinical practice, where it can be implemented to prepare patients for life after ICU. The next step is to evaluate the impact of this prediction model on outcomes and experiences of patients.

Factors Associated With Image-Guided Radiation Therapy Image Rejection in a Multisite Institution

PURPOSE

Nonclinical factors and cognitive biases have been shown to significantly affect clinical decision making. In this study, we aimed to identify clinical and environmental factors that might influence the decision to approve or reject image-guided radiation therapy (IGRT) images in a large multisite institution.

METHODS

We identified all IGRT image approval and rejection decisions recorded within an electronic imaging system from July 1, 2016, to June 30, 2018. For each decision, we tabulated the following parameters: the attending physician of the patient, the physician reviewing the image, total images reviewed by the physician that day, time of day, day of week, treatment site, and imaging modality (kilovoltage or cone beam computed tomography [CBCT]). We created a binary multivariable logistic regression model to identify factors associated with IGRT image rejection.

RESULTS

Overall, of 51,797 total image records evaluated, 881 (1.70%) were rejected and 50,916 (98.30%) were approved. Univariable analysis revealed that images reviewed by physicians with high rejection rates (odds ratio [OR], 3.16; P < .001) and by physicians reviewing fewer IGRT images (OR, 0.99; P = .024), images from various anatomic sites (particularly skin, breast, and head and neck), and CBCT imaging compared with kilovoltage imaging (OR, 1.49; P < .001) were associated with the increased rate of rejection. On multivariable analysis, images reviewed by physicians with high rejection rates (OR, 3.28; P < .001), images from specific anatomic sites including breast (P < .001), and CBCT imaging (P < .001) persisted as independent predictors of image rejection.

CONCLUSION

These data provide important insight into the clinical, cognitive, and environmental factors that might influence the routine clinical decision of IGRT image approval. Recognition of these factors may not only improve the quality of individual decisions but also identify opportunities for systems-based quality improvement in IGRT.

Improved Task Performance, Low Workload, and User-Centered Design in Medical Diagnostic Equipment Enhance Decision Confidence of Anesthesia Providers: A Meta-Analysis and a Multicenter Online Survey

Decision confidence—the subjective belief to have made the right decision—is central in planning actions in a complex environment such as the medical field. It is unclear by which factors it is influenced. We analyzed a pooled data set of eight studies and performed a multicenter online survey assessing anesthesiologists’ opinions on decision confidence. By applying mixed models and using multiple imputation to determine the effect of missing values from the dataset on the results, we investigated how task performance, perceived workload, the utilization of user-centered medical diagnostic devices, job, work experience, and gender affected decision confidence. The odds of being confident increased with better task performance (OR: 1.27, 95% CI: 0.94 to 1.7; p = 0.12; after multiple imputation OR: 3.19, 95% CI: 2.29 to 4.45; p < 0.001) and when user-centered medical devices were used (OR: 5.01, 95% CI: 3.67 to 6.85; p < 0.001; after multiple imputation OR: 3.58, 95% CI: 2.65 to 4.85; p < 0.001). The odds of being confident decreased with higher perceived workload (OR: 0.94, 95% CI: 0.93 to 0.95; p < 0.001; after multiple imputation, OR: 0.94, 95% CI: 0.93 to 0.95; p < 0.001). Other factors, such as gender, job, or professional experience, did not affect decision confidence. Most anesthesiologists who participated in the online survey agreed that task performance (25 of 30; 83%), perceived workload (24 of 30; 80%), work experience (28 of 30; 93%), and job (21 of 30; 70%) influence decision confidence. Improved task performance, lower perceived workload, and user-centered design in medical equipment enhanced the decision confidence of anesthesia providers.

Dynamic prediction of mortality after traumatic brain injury using a machine learning algorithm

Intensive care for patients with traumatic brain injury (TBI) aims to optimize intracranial pressure (ICP) and cerebral perfusion pressure (CPP). The transformation of ICP and CPP time-series data into a dynamic prediction model could aid clinicians to make more data-driven treatment decisions. We retrained and externally validated a machine learning model to dynamically predict the risk of mortality in patients with TBI. Retraining was done in 686 patients with 62,000 h of data and validation was done in two international cohorts including 638 patients with 60,000 h of data. The area under the receiver operating characteristic curve increased with time to 0.79 and 0.73 and the precision recall curve increased with time to 0.57 and 0.64 in the Swedish and American validation cohorts, respectively. The rate of false positives decreased to ≤2.5%. The algorithm provides dynamic mortality predictions during intensive care that improved with increasing data and may have a role as a clinical decision support tool.

The simple observational critical care studies: estimations by students, nurses, and physicians of in-hospital and 6-month mortality

Background

Prognostic assessments of the mortality of critically ill patients are frequently performed in daily clinical practice and provide prognostic guidance in treatment decisions. In contrast to several sophisticated tools, prognostic estimations made by healthcare providers are always available and accessible, are performed daily, and might have an additive value to guide clinical decision-making. The aim of this study was to evaluate the accuracy of students’, nurses’, and physicians’ estimations and the association of their combined estimations with in-hospital mortality and 6-month follow-up.

Methods

The Simple Observational Critical Care Studies is a prospective observational single-center study in a tertiary teaching hospital in the Netherlands. All patients acutely admitted to the intensive care unit were included. Within 3 h of admission to the intensive care unit, a medical or nursing student, a nurse, and a physician independently predicted in-hospital and 6-month mortality. Logistic regression was used to assess the associations between predictions and the actual outcome; the area under the receiver operating characteristics (AUROC) was calculated to estimate the discriminative accuracy of the students, nurses, and physicians.

Results

In 827 out of 1,010 patients, in-hospital mortality rates were predicted to be 11%, 15%, and 17% by medical students, nurses, and physicians, respectively. The estimations of students, nurses, and physicians were all associated with in-hospital mortality (OR 5.8, 95% CI [3.7, 9.2], OR 4.7, 95% CI [3.0, 7.3], and OR 7.7 95% CI [4.7, 12.8], respectively). Discriminative accuracy was moderate for all students, nurses, and physicians (between 0.58 and 0.68). When more estimations were of non-survival, the odds of non-survival increased (OR 2.4 95% CI [1.9, 3.1]) per additional estimate, AUROC 0.70 (0.65, 0.76). For 6-month mortality predictions, similar results were observed.

Conclusions

Based on the initial examination, students, nurses, and physicians can only moderately predict in-hospital and 6-month mortality in critically ill patients. Combined estimations led to more accurate predictions and may serve as an example of the benefit of multidisciplinary clinical care and future research efforts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03809-w.