Evaluation of feedback modalities and preferences regarding feedback on decision-making in a pediatric emergency department

Diagnosis ◽  
2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Jessica M.K. Graham ◽  
Lilliam Ambroggio ◽  
Jan E. Leonard ◽  
Sonja I. Ziniel ◽  
Joseph A. Grubenhoff
Keyword(s):  
Decision Making ◽  
Emotional Support ◽  
Pediatric Emergency ◽  
Practice Change ◽  
Supporting Evidence ◽  
Case Review ◽  
Feedback Preferences ◽  
Performance Reports ◽  
Feedback Modalities ◽  
Case Based

Abstract Objectives To compare pediatric emergency clinicians’ attitudes toward three feedback modalities and assess clinicians’ case-based feedback preferences. Methods Electronic survey sent to pediatric emergency medicine (PEM) physicians and fellows; general pediatricians; and advanced practice providers (APPs) with nine questions exploring effectiveness and emotional impact of three feedback modalities: case-based feedback, bounce-back notifications, and biannual performance reports. Additional questions used a four-point ordinal agreement response scale and assessed clinicians’ attitudes toward case review notification, case-based feedback preferences, and emotional support. Survey responses were compared by feedback modality using Pearson’s chi-squared. Results Of 165 eligible providers, 93 (56%) responded. Respondents agreed that case-based feedback was timely (81%), actionable (75%), prompted reflection on decision-making (92%), prompted research on current clinical practice (53%), and encouraged practice change (58%). Pediatric Emergency Care Applied Research Network (PECARN) performance reports scored the lowest on all metrics except positive feedback. No more than 40% of providers indicated that any feedback modality provided emotional support. Regarding case-based feedback, 88% of respondents desired email notification before case review and 88% desired feedback after case review. Clinicians prefer receiving feedback from someone with similar or more experience/training. Clinicians receiving feedback desire succinctness, supporting evidence, consistency, and sensitive delivery. Conclusions Case-based feedback scored highest of the three modalities and is perceived to be the most likely to improve decision-making and promote practice change. Most providers did not perceive emotional support from any feedback modality. Emotional safety warrants purposeful attention in feedback delivery. Critical components of case-based feedback include succinctness, supporting evidence, consistency, and sensitive delivery.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

Exploring the Differences in Macrocognition Between Experts and Non-CHD Experts Managing Congenital Heart Disease (CHD)

2021 ◽  
Vol 10 (1) ◽  
pp. 335-339
Author(s):  
Azadeh Assadi ◽  
Peter C. Laussen ◽  
Patricia Trbovich
Keyword(s):  
Decision Making ◽  
Emergency Medicine ◽  
Congenital Heart Disease ◽  
Heart Disease ◽  
Congenital Heart ◽  
Pediatric Emergency ◽  
Acute Management ◽  
Naturalistic Decision Making ◽  
The Face

Background and aims: Children with congenital heart disease (CHD) are at risk of deterioration in the face of common childhood illnesses, and their resuscitation and acute management is often best achieved with the guidance of CHD experts. Access to such expertise may be limited outside specialty heart centers and the fragility of these patients is cause for discomfort among many emergency medicine physicians. An understanding of the differences in macrocognition of these clinicians could shed light on some of the causes of discomfort and facilitate the development of a sociotechnological solution to this problem. Methods: Cardiac intensivists (CHD experts) and pediatric emergency medicine physicians (non-CHD experts) in a major academic cardiac center were interviewed using the critical decision method. Interview transcripts were coded deductively based on Klein’s macrocognitive framework and inductively to allow for new or modified characterization of dimensions. Results: While both CHD-experts and non-CHD experts relied on the macrocognitive functions of sensemaking, naturalistic decision making and detecting problems, the specific data and mental models used to understand the patients and course of therapy differed between CHD-experts and non-CHD experts. Conclusion: Characterization of differences between the macrocognitive processes of CHD experts and non-CHD experts can inform development of sociotechnological solutions to augment decision making pertaining to the acute management of pediatric CHD patients.

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