What's behind the white coat: Potential mechanisms of physician-attributable variation in critical care

Effect of death and dying elective on student empathy and attitudes toward mortality

INTRODUCTION

Pharmacy graduates should be equipped for one inevitable aspect of health care, mortality, yet only 10% of United States pharmacy curricula courses cover end-of-life (EoL) with limited evidence of effectiveness. This study's objective was to evaluate the impact of an EoL elective on student pharmacists' empathy and attitudes toward mortality and caring for terminally ill persons.

METHODS

First- through third-year student pharmacists enrolled in an EoL elective. Students completed pre- and post-course surveys on self-perceptions of empathy and mortality. Surveys included the following: Kiersma-Chen Empathy Scale (KCES), which assesses empathy of pharmacy and nursing students; revised Collett-Lester Fear of Death and Dying Scale (CL-FODS), which measures fear related to death; and Frommelt Attitudes Toward Care of the Dying Scale Form B (FATCOD-B), which measures health care professionals' attitudes toward EoL care. Anonymous identifiers were used to link pre- and post-course surveys and were collected with an online survey software. Data were analyzed using two-sided paired t-tests.

RESULTS

Twenty-seven student pharmacists completed the elective. The change in overall mean scores for KCES, CL-FODS, and FATCOD-B correlated with increased empathy, reduced fear of death, and increased positive attitudes toward caring for terminal patients (KCES pre-course = 86.15 vs. post-course = 90.37; CL-FODS pre-course = 93.70 vs. post-course = 75.15; FATCOD-B pre-course = 115.89 vs. post-course = 124.04).

CONCLUSIONS

After the EoL elective, student empathy and attitudes toward mortality and caring for terminally ill persons improved. Implementing EoL concepts in pharmacy curricula should be explored to better prepare graduates in patient care.

The burden they bear: A scoping review of physician empathy in the intensive care unit

PURPOSE

Research shows that physician empathy can improve patients' reporting of symptoms, participation in care, compliance, and satisfaction; however, success in harnessing these advantages in the ICU hinges on a myriad of contextual factors. This study describes the current state of knowledge about intensivists' empathy.

METHODS

A scoping review was conducted across six databases and grey literature to clarify intensivists' experiences of empathy and identify directions of future inquiries. The search had no date limits and was specific to empathy, intensivists, and ICU environments. Results were blindly and independently reviewed by authors.

RESULTS

The search yielded 628 manuscripts; 45 met inclusion criteria. Three overarching themes connected the manuscripts: (1) the risks and benefits of empathy, (2) the spectrum of connection and distance of intensivists from patients/families, and (3) the facilitators and barriers to empathy's development.

CONCLUSION

Empathy among intensivists is not a dichotomous phenomenon. It instead exists on continua. Four steps are recommended for optimizing empathy in the ICU: clearly defining empathy, addressing risks and benefits transparently, providing education regarding reflective practice, and developing supportive environments. Overall, this review revealed that the state of knowledge about empathy as experienced by intensivists still has room to grow and be further explored.

A model for occupational stress amongst paediatric and adult critical care staff during COVID-19 pandemic

Purpose

The coronavirus 2019 pandemic has placed all intensive care unit (ICU) staff at increased risk of psychological distress. To date, measurement of this distress has largely been by means of validated assessment tools. We believe that qualitative data may provide a richer view of staff experiences during this pandemic.

Methods

We conducted a cross-sectional, observational study using online and written questionnaires to all ICU staff which consisted of validated tools to measure psychological distress (quantitative findings) and open-ended questions with free-text boxes (qualitative findings). Here, we report our qualitative findings. We asked four questions to explore causes of stress, need for supports and barriers to accessing supports. A conventional content analysis was undertaken.

Results

In total, 269 of the 408 respondents (65.9%) gave at least one response to a free-text question. Seven overarching themes were found, which contribute to our proposed model for occupational stress amongst critical care staff. The work environment played an important role in influencing the perceived psychological impact on healthcare workers. Extra-organisational factors, which we termed the “home-work interface” and uncertainty about the future, manifested as anticipatory anxiety, had a proportionally larger influence on worker well-being than would be expected in non-pandemic conditions.

Conclusion

Our findings have important implications for appropriate allocation of resources and ensuring well-being of the ICU multidisciplinary team for this and future pandemics.

Modeling physician variability to prioritize relevant medical record information

Objective

Patient information can be retrieved more efficiently in electronic medical record (EMR) systems by using machine learning models that predict which information a physician will seek in a clinical context. However, information-seeking behavior varies across EMR users. To explicitly account for this variability, we derived hierarchical models and compared their performance to nonhierarchical models in identifying relevant patient information in intensive care unit (ICU) cases.

Materials and methods

Critical care physicians reviewed ICU patient cases and selected data items relevant for presenting at morning rounds. Using patient EMR data as predictors, we derived hierarchical logistic regression (HLR) and standard logistic regression (LR) models to predict their relevance.

Results

In 73 pairs of HLR and LR models, the HLR models achieved an area under the receiver operating characteristic curve of 0.81, 95% confidence interval (CI) [0.80-0.82], which was statistically significantly higher than that of LR models (0.75, 95% CI [0.74-0.76]). Further, the HLR models achieved statistically significantly lower expected calibration error (0.07, 95% CI [0.06-0.08]) than LR models (0.16, 95% CI [0.14-0.17]).

Discussion

The physician reviewers demonstrated variability in selecting relevant data. Our results show that HLR models perform significantly better than LR models with respect to both discrimination and calibration. This is likely due to explicitly modeling physician-related variability.

Conclusion

Hierarchical models can yield better performance when there is physician-related variability as in the case of identifying relevant information in the EMR.