Interprofessional simulated learning: short-term associations between simulation and interprofessional collaboration
TL;DR: Results from this study indicate that focusing interprofessional simulation education on shared leadership may provide the most leverage to improve interprofessional care.
Abstract: Background: Health professions education programs use simulation for teaching and maintaining clinical procedural skills. Simulated learning activities are also becoming useful methods of instruction for interprofessional education. The simulation environment for interprofessional training allows participants to explore collaborative ways of improving communicative aspects of clinical care. Simulation has shown communication improvement within and between health care professions, but the impacts of teamwork simulation on perceptions of others’ interprofessional practices and one’s own attitudes toward teamwork are largely unknown. Methods: A single-arm intervention study tested the association between simulated team practice and measures of interprofessional collaboration, nurse-physician relationships, and attitudes toward health care teams. Participants were 154 post-licensure nurses, allied health professionals, and physicians. Self- and proxy-report survey measurements were taken before simulation training and two and six weeks after. Results: Multilevel modeling revealed little change over the study period. Variation in interprofessional collaboration and attitudes was largely attributable to between-person characteristics. A constructed categorical variable indexing ‘leadership capacity’ found that participants with highest and lowest values were more likely to endorse shared team leadership over physician centrality. Conclusion: Results from this study indicate that focusing interprofessional simulation education on shared leadership may provide the most leverage to improve interprofessional care.
Citations
More filters
TL;DR: In this article, the authors used a phenomenological approach to explore the potential for hospital-based interdisciplinary care provided by physicians, nurses, and unlicensed assistive personnel (UAPs) using a purposive nonprobability, criterion-based, convenience sample from a metropolitan hospital.
Abstract: Purpose
Historically, health care has primarily focused on physician, nurse, and allied healthcare provider triads. Using a phenomenological approach, this study explores the potential for hospital-based interdisciplinary care provided by physicians, nurses, and unlicensed assistive personnel (UAPs).
Design
This phenomenological study used a purposive nonprobability, criterion-based, convenience sample from a metropolitan hospital.
Theoretical Foundation
Malhotra's (1981) Schutzian lifeworld phenomenological orchestra study provided the theoretical basis for the conductorless orchestra model, which guided this study. In an orchestra, each member sees and hears the musical score from a different vantage point or perspective and has a different stock of knowledge or talent; however, members work together to produce a cohesive performance. Like the orchestra, individual talents and perspectives of physicians, nurses, and UAPs can be collaboratively blended to create a symphony: enhanced patient-centered care.
Methods
Qualitative semistructured face-to-face, individual interviews were carefully transcribed and coded with the aid of NVivo 9, a qualitative data analysis software program, to discover emergent patterns and themes.
Findings
The study suggests that most of the time physicians, nurses, and UAPs operate as separate healthcare providers who barely speak to each other. Physicians see themselves as the primary patient care decision makers. Many physicians acknowledge the importance of nurses’ knowledge and expertise. On the other hand, the study indicates a hierarchical, subservient relationship among nurses and UAPs. Physicians and nurses tend to work together or consult each other at times, but UAPs are rarely included in any type of meaningful patient discussion.
Conclusions
Since physicians, nurses, and UAPs each provide portions of patient care, coordination of the various treatments and interventions provided is critical to prevent errors and fragmentation of care. Tensions, misunderstandings, and conflicts caused by differences of opinions and interests can interfere with effective interdisciplinary communications and collaboration. Improving patient safety in the hospital requires addressing the current hierarchical professional structure inherent in healthcare delivery. A hospital patient care model based on the conductorless orchestra model would mitigate hierarchy; recognize physician, nurse, and UAP contributions to care; promote improved communication and collaboration; and enhance patient safety.
Clinical Relevance
Study results provide additional information supporting interdisciplinary communication and collaboration education and training among physicians, nurses, and UAPs to support positive patient care outcomes.
159 citations
TL;DR: A descriptive review that covers the HCS and IPE literature, indicating factors that led to the use of HCS in IPE and a number of benefits in using HCS to address common challenges to IPE are outlined.
Abstract: This article explores the evolution and history of interprofessional education (IPE) using healthcare simulation (HCS). The evolution described here demonstrates an achievement of patient safety efforts as a consequence of the historical roots of healthcare and highlights HCS as a progressive method synergistic with IPE. This paper presents a descriptive review that covers the HCS and IPE literature, indicating factors that led to the use of HCS in IPE. Understanding the history of simulation-enhanced IPE provides healthcare educators with fertile ground to support future IPE. A number of benefits in using HCS to address common challenges to IPE are outlined, including natural relevance and engagement for learners, faculty attraction to its use, and the opportunity to explore socio-historical issues in teams. Several promising directions for future research are suggested.
133 citations
TL;DR: Five key themes emerged from the data analysis: enthusiasm and motivation, professional role assignment, scenario realism, facilitator style and background and team facilitation, which suggest that program developers need to be mindful of these five themes when using role-plays in an interprofessional context.
Abstract: Simulated learning activities are increasingly being used in health professions and interprofessional education (IPE). Specifically, IPE programs are frequently adopting role-play simulations as a key learning approach. Despite this widespread adoption, there is little empirical evidence exploring the teaching and learning processes embedded within this type of simulation. This exploratory study provides insight into the nature of these processes through the use of qualitative methods. A total of 152 clinicians, 101 students and 9 facilitators representing a range of health professions, participated in video-recorded role-plays and debrief sessions. Videotapes were analyzed to explore emerging issues and themes related to teaching and learning processes related to this type of interprofessional simulated learning experience. In addition, three focus groups were conducted with a subset of participants to explore perceptions of their educational experiences. Five key themes emerged from the data analysis: enthusiasm and motivation, professional role assignment, scenario realism, facilitator style and background and team facilitation. Our findings suggest that program developers need to be mindful of these five themes when using role-plays in an interprofessional context and point to the importance of deliberate and skilled facilitation in meeting desired learning outcomes.
119 citations
TL;DR: Specific challenges are described and strategies to improve advanced practice nursing clinical education in order to ensure that a sufficient number of APRNs are available to work in educational, practice, and research settings.
Abstract: Nursing education programs may face significant difficulty as they struggle to prepare sufficient numbers of advanced practice registered nurses to fulfill the vision of helping to design an improved US healthcare system as described in the Institute of Medicine's “Future of nursing” report. This paper describes specific challenges and provides strategies to improve advanced practice nursing clinical education in order to ensure that a sufficient number of APRNs are available to work in educational, practice, and research settings. Best practices are identified through a review of classic and current nursing literature. Strategies include intensive interprofessional collaborations and radical curriculum revisions such as increased use of simulation and domestic and international service work. Nurse educators must work with all stakeholders to create effective and lasting change.
88 citations
Cites background from "Interprofessional simulated learnin..."
...Clinical simulation activities can add greater value by linking APRN students with medicine, pharmacy, and rehabilitation students across the health sciences [43]....
[...]
TL;DR: A simulation-enhanced IPE curriculum was successful in improving participant attitudes toward teamwork and components of patient safety culture related to teamwork and communication in the emergency department setting.
Abstract: IntroductionHealth care providers must effectively function in highly skilled teams in a collaborative manner, but there are few interprofessional training strategies in place. Interprofessional education (IPE) using simulation technology has gained popularity to address this need because of its inh
80 citations
Cites background from "Interprofessional simulated learnin..."
...effecting successful improvements in trainees’ perceptions and attitudes.(15,16) Because health care simulation immerses learners together in an experiential process through a realistic practice environment while facilitating participants’ self-reflection of internal frames and perceptions through the debriefing process, simulation-enhanced IPE may be the ideal tool to change clinicians’ attitudes toward teamwork and interprofessional communication....
[...]
References
More filters
Book•
01 Jan 1999
TL;DR: In this paper, the authors proposed a multilevel regression model to estimate within-and between-group correlations using a combination of within-group correlation and cross-group evidence.
Abstract: Preface second edition Preface to first edition Introduction Multilevel analysis Probability models This book Prerequisites Notation Multilevel Theories, Multi-Stage Sampling and Multilevel Models Dependence as a nuisance Dependence as an interesting phenomenon Macro-level, micro-level, and cross-level relations Glommary Statistical Treatment of Clustered Data Aggregation Disaggregation The intraclass correlation Within-group and between group variance Testing for group differences Design effects in two-stage samples Reliability of aggregated variables Within-and between group relations Regressions Correlations Estimation of within-and between-group correlations Combination of within-group evidence Glommary The Random Intercept Model Terminology and notation A regression model: fixed effects only Variable intercepts: fixed or random parameters? When to use random coefficient models Definition of the random intercept model More explanatory variables Within-and between-group regressions Parameter estimation 'Estimating' random group effects: posterior means Posterior confidence intervals Three-level random intercept models Glommary The Hierarchical Linear Model Random slopes Heteroscedasticity Do not force ?01 to be 0! Interpretation of random slope variances Explanation of random intercepts and slopes Cross-level interaction effects A general formulation of fixed and random parts Specification of random slope models Centering variables with random slopes? Estimation Three or more levels Glommary Testing and Model Specification Tests for fixed parameters Multiparameter tests for fixed effects Deviance tests More powerful tests for variance parameters Other tests for parameters in the random part Confidence intervals for parameters in the random part Model specification Working upward from level one Joint consideration of level-one and level-two variables Concluding remarks on model specification Glommary How Much Does the Model Explain? Explained variance Negative values of R2? Definition of the proportion of explained variance in two-level models Explained variance in three-level models Explained variance in models with random slopes Components of variance Random intercept models Random slope models Glommary Heteroscedasticity Heteroscedasticity at level one Linear variance functions Quadratic variance functions Heteroscedasticity at level two Glommary Missing Data General issues for missing data Implications for design Missing values of the dependent variable Full maximum likelihood Imputation The imputation method Putting together the multiple results Multiple imputations by chained equations Choice of the imputation model Glommary Assumptions of the Hierarchical Linear Model Assumptions of the hierarchical linear model Following the logic of the hierarchical linear model Include contextual effects Check whether variables have random effects Explained variance Specification of the fixed part Specification of the random part Testing for heteroscedasticity What to do in case of heteroscedasticity Inspection of level-one residuals Residuals at level two Influence of level-two units More general distributional assumptions Glommary Designing Multilevel Studies Some introductory notes on power Estimating a population mean Measurement of subjects Estimating association between variables Cross-level interaction effects Allocating treatment to groups or individuals Exploring the variance structure The intraclass correlation Variance parameters Glommary Other Methods and Models Bayesian inference Sandwich estimators for standard errors Latent class models Glommary Imperfect Hierarchies A two-level model with a crossed random factor Crossed random effects in three-level models Multiple membership models Multiple membership multiple classification models Glommary Survey Weights Model-based and design-based inference Descriptive and analytic use of surveys Two kinds of weights Choosing between model-based and design-based analysis Inclusion probabilities and two-level weights Exploring the informativeness of the sampling design Example: Metacognitive strategies as measured in the PISA study Sampling design Model-based analysis of data divided into parts Inclusion of weights in the model How to assign weights in multilevel models Appendix. Matrix expressions for the single-level estimators Glommary Longitudinal Data Fixed occasions The compound symmetry models Random slopes The fully multivariate model Multivariate regression analysis Explained variance Variable occasion designs Populations of curves Random functions Explaining the functions 27415.2.4 Changing covariates Autocorrelated residuals Glommary Multivariate Multilevel Models Why analyze multiple dependent variables simultaneously? The multivariate random intercept model Multivariate random slope models Glommary Discrete Dependent Variables Hierarchical generalized linear models Introduction to multilevel logistic regression Heterogeneous proportions The logit function: Log-odds The empty model The random intercept model Estimation Aggregation Further topics on multilevel logistic regression Random slope model Representation as a threshold model Residual intraclass correlation coefficient Explained variance Consequences of adding effects to the model Ordered categorical variables Multilevel event history analysis Multilevel Poisson regression Glommary Software Special software for multilevel modeling HLM MLwiN The MIXOR suite and SuperMix Modules in general-purpose software packages SAS procedures VARCOMP, MIXED, GLIMMIX, and NLMIXED R Stata SPSS, commands VARCOMP and MIXED Other multilevel software PinT Optimal Design MLPowSim Mplus Latent Gold REALCOM WinBUGS References Index
9,578 citations
Book•
01 Jan 2003TL;DR: In this paper, a framework for investigating change over time is presented, where the multilevel model for change is introduced and a framework is presented for investigating event occurrence over time.
Abstract: PART I 1. A framework for investigating change over time 2. Exploring Longitudinal Data on Change 3. Introducing the multilevel model for change 4. Doing data analysis with the multilevel mode for change 5. Treating TIME more flexibly 6. Modelling discontinuous and nonlinear change 7. Examining the multilevel model's error covariance structure 8. Modelling change using covariance structure analysis PART II 9. A Framework for Investigating Event Occurrence 10. Describing discrete-time event occurrence data 11. Fitting basic Discrete-Time Hazard Models 12. Extending the Discrete-Time Hazard Model 13. Describing Continuous-Time Event Occurrence Data 14. Fitting Cox Regression Models 15. Extending the Cox Regression Model
8,435 citations
TL;DR: The results indicated that feedback may be more effective when baseline performance is low, the source is a supervisor or colleague, it is provided more than once, and the role of context and the targeted clinical behaviour was assessed.
Abstract: Background Audit and feedback continues to be widely used as a strategy to improve professional practice. It appears logical that healthcare professionals would be prompted to modify their practice if given feedback that their clinical practice was inconsistent with that of their peers or accepted guidelines. Yet, audit and feedback has not been found to be consistently effective. Objectives To assess the effects of audit and feedback on the practice of healthcare professionals and patient outcomes. Search strategy We searched the Cochrane Effective Practice and Organisation of Care Group's register up to January 2001. This was supplemented with searches of MEDLINE and reference lists, which did not yield additional relevant studies. Selection criteria Randomised trials of audit and feedback (defined as any summary of clinical performance over a specified period of time) that reported objectively measured professional practice in a healthcare setting or healthcare outcomes. Data collection and analysis Two reviewers independently extracted data and assessed study quality. Quantitative (meta-regression), visual and qualitative analyses were undertaken. Main results We included 85 studies, 48 of which have been added to the previous version of this review. There were 52 comparisons of dichotomous outcomes from 47 trials with over 3500 health professionals that compared audit and feedback to no intervention. The adjusted RDs of non-compliance with desired practice varied from 0.09 (a 9% absolute increase in non-compliance) to 0.71 (a 71% decrease in non-compliance) (median = 0.07, inter-quartile range = 0.02 to 0.11). The one factor that appeared to predict the effectiveness of audit and feedback across studies was baseline non-compliance with recommended practice. Reviewer's conclusions Audit and feedback can be effective in improving professional practice. When it is effective, the effects are generally small to moderate. The absolute effects of audit and feedback are more likely to be larger when baseline adherence to recommended practice is low.
4,946 citations
TL;DR: While research in this field needs improvement in terms of rigor and quality, high-fidelity medical simulations are educationally effective and simulation-based education complements medical education in patient care settings.
Abstract: SUMMARY Review date: 1969 to 2003, 34 years. Background and context: Simulations are now in widespread use in medical education and medical personnel evaluation. Outcomes research on the use and effectiveness of simulation technology in medical education is scattered, inconsistent and varies widely in methodological rigor and substantive focus. Objectives: Review and synthesize existing evidence in educational science that addresses the question, ‘What are the features and uses of high-fidelity medical simulations that lead to most effective learning?’. Search strategy: The search covered five literature databases (ERIC, MEDLINE, PsycINFO, Web of Science and Timelit) and employed 91 single search terms and concepts and their Boolean combinations. Hand searching, Internet searches and attention to the ‘grey literature’ were also used. The aim was to perform the most thorough literature search possible of peer-reviewed publications and reports in the unpublished literature that have been judged for academic quality. Inclusion and exclusion criteria: Four screening criteria were used to reduce the initial pool of 670 journal articles to a focused set of 109 studies: (a) elimination of review articles in favor of empirical studies; (b) use of a simulator as an educational assessment or intervention with learner outcomes measured quantitatively; (c) comparative research, either experimental or quasi-experimental; and (d) research that involves simulation as an educational intervention. Data extraction: Data were extracted systematically from the 109 eligible journal articles by independent coders. Each coder used a standardized data extraction protocol. Data synthesis: Qualitative data synthesis and tabular presentation of research methods and outcomes were used. Heterogeneity of research designs, educational interventions, outcome measures and timeframe precluded data synthesis using meta-analysis. Headline results: Coding accuracy for features of the journal articles is high. The extant quality of the published research is generally weak. The weight of the best available evidence suggests that high-fidelity medical simulations facilitate learning under the right conditions. These include the following:
3,176 citations
"Interprofessional simulated learnin..." refers background in this paper
...The simulation environment provides participants the freedom to make mistakes, correct them and improve communication and processes of care [18]....
[...]