Bias in simulation training for healthcare professions: a scoping review

Selçuk Akturan,Amy Binns-Calvey,Christine Park

doi:10.54531/zynx5861

Bias in simulation training for healthcare professions: a scoping review

Selçuk Akturan, Amy Binns-Calvey, Christine Park

https://doi.org/10.54531/zynx5861, Pages: 1-19

Article Type: Original Research Article History

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Background
Methods
Results
Discussion
Conclusion
Declarations
References

Abstract

Background

Bias potentially affects simulation-based training (SBT) for healthcare professions. The role bias plays in SBT design, presentations, and in the experiences of learners should be understood. Dual process theory is a well-accepted framework for understanding types of bias.

Methods

The authors performed a scoping review to map ‘bias’ in SBT of health professions in the literature. Search terms were developed for a query in the PubMed database. Researchers reviewed abstracts, met ten times to discuss which papers’ full texts to read, and then analysed and categorized the articles. Researchers used the Arksey and O’Malley framework for scoping reviews.

Results

Three thousand six hundred and twenty abstracts were identified by a detailed query in the PubMed database of which, 115 full-text articles were identified for inclusion.

Discussion

Articles published about bias in SBT cover a broad range of topics, from addressing how bias affects patient care, to bias in raters’ scoring of medical students on exams. Researchers found that the prevalence of articles on bias in SBT increased over time and focused primarily on implicit bias. Specific types of bias in some instances were difficult to identify, and several biases mentioned in papers were unique to this review. The results showed that many SBT methodologies (i.e. manikins, videos, etc.) were referenced in the papers. The type of simulation training most prevalent in the articles was simulated patient (SP) methodology. The results show that biases can be explored in any type of simulation method, indicating that simulationsists should be aware of bias in training during all types of training methodolgy.

Akturan, Binns-Calvey, and Park: Bias in simulation training for healthcare professions: a scoping review

Background

Simulation-based training (SBT) for healthcare professions is increasingly used as an educational strategy and to improve patient safety [1–4]. SBT is an effective strategy to improve skills in healthcare professions [5]. Many different methodologies have been developed in SBT, and those methodologies have helped achieve learning outcomes, which leads to clinical competency [6]. Patient or human simulation is a well-known methodology involving human role players interacting with health professions’ education in a variety of experiential learning and assessment activities. The term simulated patient (SP) refers to a person trained to portray a role such as patients, clients, family members, healthcare professionals, etc. in realistic and repeatable method. The terms standardized patient and simulated patient are often used synonymously [7].

SBT should be developed and implemented to ensure that clinical competencies including technical, communication, decision-making and team dynamics, etc. are achieved [3]. Because SBT involves decision-making where learners must weigh different options to provide patient care, the role that bias plays in SBT design, presentations and in the experiences of learners should be understood [8].

Scoping reviews are useful when authors want to explore certain concepts in papers, and in the mapping, reporting or discussion of these concepts [9]. There are scoping reviews on SBT of healthcare professions exploring the types of professions engaged in interprofessional education, characterization of the types of simulations, effects of new technologies on SBT, effects of different methodologies on clinical competencies of healthcare professions and barriers to utilization different methodologies [10–12]. We did not find any scoping reviews on the topic of bias in SBT of healthcare professions.

In this review, we sought to explore bias in SBT of healthcare to: 1) identify which types of biases affect SBT for healthcare professionals, 2) categorize the types of bias explored and 3) note the prevalence of articles published on this topic.

Methods

We performed a scoping review to map ‘bias’ in the literature on SBT of health professions. Scoping reviews are used to examine the range and nature of the research activities, to determine the value of conducting a complete systematic review, to summarize and disseminate research findings, or to detect gaps in existing literature [13].

Review strategy

We used the Arksey and O’Malley framework for scoping reviews [13] which was developed and refined by Levac and colleagues [14]. This approach involves five steps:

1. Identifying the research question

SA and CP met to identify the focus of the scoping review: ‘How is the term “bias” in “simulation training” explored within the literature?’ After conducting background research, we discovered that the terms ‘cognitive bias’, ‘implicit bias’ and ‘decision-making’ are terms used in conjunction with ‘bias’, therefore it was decided to include these terms along with ‘simulation’ and ‘bias’ in the analysis.

2. Identifying relevant studies

After determining the scoping review goals and receiving assistance from a University of Illinois at Chicago-affiliated librarian, we decided to use a detailed query which included all potential MeSH terms and keywords that might be related to ‘simulation’ and ‘bias’ for a database search (Figure 1).

Figure 1:

Query for database search.

We searched the PubMed, Medline and CINAHL databases with the same terms, and compared the results. The PubMed results were the most comprehensive and included the results from the other databases, so we decided to focus only on the PubMed database. We limited the results by publication language (English).

3. Selecting the studies

As a first step, researchers SA and ABC conducted a pilot study to determine the method for analyzing the papers for this scoping review. We reviewed the first 100 papers found by a search using the detailed query to determine which articles should be included in the review. We then compared notes on the abstracts and full texts of the papers. We decided to include only primary research articles as it was too difficult to evaluate review papers based on the aims of this scoping review. After this pilot, we decided to select articles for study inclusion based on the following criteria:

(a) studies that investigated bias in simulation training of any health professions’ education program,

(b) studies that investigated the role of bias in simulation training,

(d) studies in which outcomes/assessment focused on decision-making.

The following exclusion criteria were also defined as:

(a) any type of reviews,

(b) studies written in a language other than English,

(d) studies including bias in simulation training, but, without any explanation for bias.

SA and ABC decided to analyze the papers’ abstracts for first reading because it was determined that papers might be selected based on their abstracts (without reading the full text) using the inclusion criteria. SA and ABC independently reviewed all abstracts published up to August 31, 2020. We then discussed any discrepancies and reached a consensus on which articles to include for the full review (second stage of scoping review).

Classification of bias

We referred to the papers’ descriptions of the type of bias they addressed, to identify if the bias was implicit or cognitive. In instances where the type of bias was not specified in the paper, we identified the type of bias from the content of the paper, including instances where both cognitive and implicit bias were explored. We then further classified the specific type of bias, again referring to the article’s content. In cases where the bias was the same, but terminology differed between papers (i.e. one paper used the term ‘race bias’, while another referred to is as ‘racial bias’), we standardized the naming of the bias by choosing one term for a similar type of bias.

4. Charting the data

We used Arksey and O’Malley’s ‘descriptive-analysis’ approach to data extraction, summarizing information from the selected articles and recording the data [13]. We also applied Levac and et al.’s recommendations for the data charting process and used an Excel sheet to analyze the selected articles [14]. By using this approach, the key information from the selected papers was charted under the headings: article name, author, journal, year, country, article type, population, details of simulation training and details of bias.

Results

5. Collating, summarizing and reporting the results

Three thousand six hundred and twenty abstracts were identified from PubMed. The first reading was conducted by SA and ABC from May 4, 2020 to August 31, 2020. During this first reading, we met 10 times to discuss which papers should be added for the second step (reading full texts). We reviewed 238 selected papers for the second step, and 125 full-text articles were selected to be analysed from October 23, 2020 to January 12, 2021. We independently read and reviewed the included articles, and reconvened at six online meetings to discuss individual findings (Figure 2).

Figure 2:

Results of search strategy and process of paper selection.

From 1985 until 2020, the number of articles published on the topic of bias in simulation in medical professional training increased dramatically (Figure 3).

Figure 3:

Classification by year.

We completed a review of articles published on bias in SBT for healthcare professionals. The articles reviewed cover a broad range of topics, from addressing how bias affects patient care, to bias in raters’ scoring of medical students on exams. We did not assess the methodological quality of the articles, but categorized them into four general themes: the type of healthcare profession, the method of simulation, whether the bias was cognitive or implicit, and the specific bias mentioned (Table 1).

Table 1:

The characteristics of papers decided at the end of the scoping review

Lead Author	Journal	Year	Country	Population (healthcare professions)	Method(s) of simulation mentioned in article	Bias types	Description of bias
Adamson, K. [70]	Nursing education perspectives	2016	USA	Simulation participant-raters (nurse)	Video-recorded simulations	Implicit bias	Race, ethnicity bias
Al-Moteri, M. [71]	Australian Critical Care	2019	Australia	Final-year undergraduate nurses, nurses enrolled in Masters or PhD programs.	Screen-based simulated scenario	Cognitive Bias	Perceptual, attention, confirmation biases
Altabbaa, G. [72]	Diagnosis	2019	Canada	Medical students, post-graduate year (PGY) 1 IM residents,	Simulated clinical environment	Cognitive Bias	Momentum, confirmation, playing-the-odds, order-effect biases
Arber, S. [17]	Social Science & Medicine	2006	USA	Primary care doctors	Video vignette	Implicit bias	Gender, age, SES, race biases
Barnato, A. [55]	Crit Care Med.	2011	USA	Emergency physicians, hospitalists, and intensivists	SPs	Implicit bias	Race bias
Barnato, A. [73]	Med Decis Making	2014	USA	EM physicians	Video-encounters	Implicit bias	Race bias
Bennett, P. [74]	Clin Teach	2016	Australia	Medical, nursing, allied health students	Immersive/wearable simulation	Implicit bias	Age bias
Berg, K. [18]	Acad Med	2015	USA	Medical students	OSCEs	Implicit bias	Gender, race and ethnicity biases
Boada, L. [19]	Comput Methods Programs Biomed	2018	Spain	Undergraduate nursing students	High fidelity simulators	Implicit bias	Gender bias
Bond, W. [75]	Acad Med	2004	USA	Emergency medicine residents	High fidelity simulators	Cognitive bias	Decision-making
Boulet, J. [20]	Adv Health Sci Educ Theory Pract	2005	USA	Medical students (CSA candidates)/physician note raters	SPs	Implicit and Cognitive Biases	Gender and rater bias
Braun, L. [76]	Diagnosis	2019	Germany	Medical students	Electronic case simulation platform	Cognitive Bias	Premature closure bias
Brown, S. [77]	Community Ment Health J	2010	USA	Undergraduate medical students	Simulation of auditory hallucinations	Implicit bias	Illness stigma
Brown, SA. [78]	Community Ment Health J	2010	USA	Undergraduate students	Simulation of auditory hallucinations	Implicit bias	Mental illness stigma
Bucknall, T. [79]	J Adv Nurs	2016	Australia	Nursing students (final year)	SPs	Cognitive bias	Premature closure and confirmation biases
Burgess, D. [80]	Soc Sci Med	2008	USA	Internal medicine physicians	Video vignettes	Implicit bias	Race bias
Cavalcanti, R. [56]	Acad Med	2014	Canada	Residents in internal medicine	OSCEs and High fidelity simulators	Cognitive bias	Not specified
Chen, A. [81]	Am J Pharm Educ	2011	USA	Pharmacy students	Geriatric medication game and SPs	Implicit bias	Age bias
Choi, H. [82]	Nurse Educ Today	2016	Korea	Undergraduate nursing students	SPs	Implicit bias	Mental illness stigma
Chugh, U. [83]	Med Teach	1993	Canada	Physicians/immigrant patients	SPs	Implicit bias	Race and Ethnicity bias
Cicero, M. [84]	Prehosp Emerg Care	2014	USA	SPs, high-fidelity manikins, and low-fidelity manikins	Disaster simulation scenarios using SPs, high-fidelity manikins, and low-fidelity manikins	Cognitive bias	Bias towards a specific pediatric disaster triage strategy
Claramita, M. [85]	Nurse Educ Today	2016	Indonesia	Nursing students	OSCE with SPs	Cognitive bias	SES bias
Clark, C.M. [86]	Nurse Educ	2019	USA	Undergraduate nursing students	Role play	Implicit bias	Uncivil behavior bias
Crapanzano, K. [87]	J Gen Intern Med	2018	USA	Internal medicine residents	SPs	Implicit bias	Mental illness stigma
Dearing, K. [88]	J Nurs Educ	2008	USA	Nursing students	Voice simulation mimics auditory hallucinations	Implicit bias	Mental illness stigma
Dedy, N. [45]	Surgery	2015	Canada	Surgery residents	OSCE	Implicit bias	Rater bias
Denney, M. [21]	Educ Prim Care	2016	UK	GPs	OSCE	Implicit bias	Ethnicity and gender bias
Doyle, K. [46]	J Grad Med Educ	2014	Canada	Faculties and residents of family medicine programs	Simulated a tri-college, on-site ER for internal review (IR) process	Implicit bias	Rater/reviewer bias
Eisenberg, E. [22]	J Gen Intern Med	2019	USA	First-year residents in the Internal Med-Residency Program	Simulation scenarios included interactions with SPs	Implicit bias	Race, ethnicity, nationality, religion, gender, sexual orientation, disability, physical appearance, SES biases
Eva, K. [89]	Acad Med	2010	Canada	Primary care physicians	Videotaped vignette	Cognitive bias	Confirmation, and premature closure bias
Evans. J. [90]	Issues Ment Health Nurs	2015	Australia	2nd year nursing students	Simulated auditory hallucinations for schizophrenics	Implicit bias	Mental illness stigma
Feldman, H. [23]	Health Serv Res	1997	USA	Physicians	Simulated scenarios on videotapes by professional actors	Implicit bias	Age, gender, race, and SES biases
Fitzgerald, S. [91]	MedEdPORTAL	2018	USA	Health professions students from multiple disciplines	SPs	Implicit bias	Ethnicity bias
Fletcher, G. [47]	Br J Anaesth	2003	UK	Anesthetists	SPs	Cognitive bias	Rater bias
Floyd, K. [92]	J Physician Assist Educ	2015	USA	Physician assistant students/2nd yr of MS degree and SPs	SPs	Cognitive bias	Inflation bias
Foster, K. [93]	Oral Surg Oral Med Oral Pathol Oral Radiol Endod	2008	USA	1st year dental students	Role play practice	Cognitive bias	Presentation bias
Galletly, C. [94]	Aust N Z J Psychiatry	2011	Australia/New Zealand	Medical students (final year)	Video presentation/Simulated auditory hallucinations	Implicit bias	Mental illness stigma
Gispert, R. [24]	Med Educ	1999	Spain	Undergraduate medical students	SPs	Implicit bias	Gender bias
Goddard, L. [95]	J Neurosci Nurs	1998	USA	Nursing students	Role play	Implicit bias	Disability bias
Gostlow, H. [96]	Ann Surg	2018	Australia	Surgical trainees and consultant surgeons	Video of operating theater sim – SPs	Implicit bias	Hierarchy bias
Gotlieb, R. [97]	Gynecologic Oncology Reports	2019	Canada	Medical doctors (staffs and residents)	Software (computer) simulation scenarios.	Cognitive Bias	Gender effects on cognition
Greene, R.E. [25]	Med Educ	2014	USA	Medicine residents	SPs	Implicit bias	Gender bias
Greene, R.E. [26]	J Grad Med Educ	2017	USA	Medicine students	SPs	Implicit bias	Gender bias
Hahn, T. [98]	J Man Manip Ther	2014	USA	Physical therapists	Video vignettes of SPs	Cognitive bias	Confirmation bias/ and training bias
Hales, C. [99]	Ostomy Wound Manage	2018	USA	Health care staff	Immersive/wearable simulation	Implicit bias	Weight bias
Haliko, S. [100]	Med Decis Making	2018	USA	Physicians	High fidelity simulator and SPs	Cognitive bias	Preference and comfort bias
Hanson, M. [101]	Acad Psychiatry	2008	Canada	Adolescent standardized patients	SPs	Implicit bias	Mental illness stigma
Hareli, S. [102]	Int J Psychol	2013	Israel	Undergraduate medical students	Video on computer screen simulation	Implicit bias	SES bias
Hermann-Werner, A. [103]	BMJ Open	2019	Germany	Medical students	SPs and immersive/wearable simulation	Cognitive Bias	Weight bias
Hillbrand, M. [104]	Psychiatr Rehabil	2008	USA	Nurses, psychiatric technicians, psychologists and social workers, rehabilitation therapist.	Role play	Implicit bias	Bias against prisoners
Hirsh, A. [27]	J Pain	2010	USA	Nurses	Virtual human (VH) videos	Implicit bias	Gender, race, age bias
Hu, Y. [105]	Adv Health Sci Educ Theory Pract	2015	USA	Undergraduate medical students	Simulation-based suturing task	Cognitive bias	Overestimation bias
Huber, M. [106]	J Adv Nurs	1992	USA	Healthcare care personnel	Simulated handicaps	Implicit bias	Age bias
Hunter, J. [107]	Nurse Educ Today	2018	UK	Nursing Students	Immersive/wearable simulation	Implicit bias	Weight bias
Jaeken, M. [108]	Front Psychol	2017	Belgium	Undergraduate psychology students	Role play	Cognitive bias	Self-enhancement bias
Jaworsky, D. [109]	AIDS Care	2017	Canada	Medical Students	SPs	Implicit bias	HIV stigma
Jensen, K. [48]	Surg Endosc	2019	Multi-centered	Medical students	Virtual reality simulator	Cognitive bias	Self-enhancement and self-diminishment bias
Junnola, T. [110]	J Clin Nurs	2002	Finland	Nurses	Screen-based computer simulated case	Cognitive bias	Confirmation bias
Kales, H. [28]	Psychiatr Serv	2005	USA	Psychiatrists	Video vignettes of SPs	Implicit bias	Race and gender bias
Kennedy, D. [29]	Nurse Educ Today	2020	Qatar	Male nursing students	Role-play, moderate and high fidelity simulators, SPs, simulated maternity clinic	Implicit bias	Gender bias
Khazadian-Figueroa, M. [111]	J Nurs Staff Dev	1997	USA	CNAs (certified nursing aids)	Simulation game	Implicit bias	Age bias
Kidd, L. [112]	Issues Ment Health Nurs	2015	USA	Undergraduate psychiatric nursing students	Hearing Distressing Voices Audio Simulation	Implicit bias	Mental illness stigma
Kim, M. [30]	Comput Inform Nurs	2016	Korea	Nursing students	Simulation-based learning/hybrid SP and Noelle human simulator	Implicit bias	Gender bias
Kumagai, A. [31]	Med Teach	2007	USA	Faculty	‘Forum Theater’ techniques; simulated classroom discussion	Implicit bias	Race, gender, sexual orientation, SES bias
Kushner, R. [113]	BMC Med Educ	2014	USA	Medical students	SPs	Implicit bias	Weight bias
LaRoche, K. [114]	Contraception	2015	Canada	Postabortion support team	Unannounced standardized patient	Implicit bias	Abortion stigma
Levett-Jones. T. [115]	Nurse Educ Pract	2011	Australia	Nursing Students	Videos w/SPs to train assessors	Implicit and Cognitive biases	Rater/Assessor bias Decision-making
Lewis, C. [116]	BMC Palliat Care	2016	UK	Nursing and medical students	High fidelity simlulator and SP	Implicit bias	Attitudes towards death
Li, L. [117]	Int J Epidemiol	2014	China	Hospital service providers	Unannounced standardized patient	Implicit bias	HIV stigma
Lockeman, K.S. [118]	Nurse Educ Today	2017	USA	Nursing and medical students	high fidelity – mannequins and SPs	Implicit Bias	Provider stereotypes
Lohman, P. [119]	Percept Mot Skills	2008	USA	Graduate students majoring in communications disorders	Peer role play	Implicit bias	Attitudes towards stutterers
Lorenzo, A. [120]	Fam Pract	2015	France	Practicing providers	SPs	Cognitive bias	Desirability bias/Hawthorne bias
Magpantay-Monroe, E. [121]	Nurse Educ Today	2017	USA	Nursing students	SPs	Implicit bias	Military and veteran bias
Marceau, L. [32]	J Eval Clin Pract	2011	USA	Primary care physicians	Video simulation	Implicit bias	Age, race, gender, SES biases
March, C. [122]	J Grad Med Educ	2018	USA	Pediatric residents	SPs	Implicit bias	Age, race and ethnicity stigma
Margolis, M. [33]	Acad Med	2002	USA	Medical doctors	Computer-based case simulation	Implicit bias	Gender and language bias
Maruca, A.T. [123]	Nurse Educ Perspect	2018	USA	Nursing students	High fidelity (manikin) simulation	Implicit bias	Gender bias
Maupome, G. [124]	Eur J Dent Educ	2002	Mexico	Senior dental students	SPs	Implicit bias	SES bias
McCave, E. [34]	MedEdPORTAL	2019	USA	Students’ of different health professions	SPs	Implicit bias	Gender bias
McGrath, J. [49]	West J Emerg Med	2015	USA	EM residents	High fidelity and virtual reality simulators	Implicit bias	Rater/observer bias
McNiel, P.L. [35]	J Nurse Educ	2018	USA	Nursing students	Role play	Implicit bias	Gender biases
Minehart, R. [125]	Anesthesiology	2014	USA	Anesthesia faculty	Role play/videos/SPs	Cognitive bias	Not specified
Mirza, A. [126]	MedEdPORTAL	2018	USA	Pediatric interns, upper-level residents (PGY-2 and PGY-3), and six fellows.	SPs	Cognitive Bias	Premature closure bias
Mohan, D. [127]	BMC Emerg Med	2016	USA	Emergency physicians	Virtual video games simulation	Cognitive bias	Poorly-calibrated heuristics
Nerup, N. [50]	Gastrointest Endosc	2015	Denmark	Physicians (10 experienced endoscopists and 11 trainees)	High fidelity simulator	Implicit bias	Rater/observer bias
Nicolai, J. [36]	Patient Educ Couns	2007	Germany	General practitioners	SPs	Implicit bias	Gender bias
Norman, R. [128]	J Nurs Educ	2001	Australia	RNs (nurses)	Simulation game, peer role playing	Implicit bias	Bias against illicit drug users
O’Lynn, C. [129]	J Nurse Educ	2014	USA	Male nursing students	Video and practice on manikins/debriefing	Implicit bias	Gender bias
Padilha, J. [57]	J Med Internet Res	2019	Portugal	Nursing students	Virtual reality simulator	Cognitive bias	Bias in clinical reasoning
Paige, J. [130]	J Surg Educ	2019	USA	General surgery residents/ emergency medicine residents/senior undergraduate nursing students	High fidelity simulation	Implicit bias	Hierarchy bias
Park, C. [58]	Simul Healthc	2014	USA	Residents, Anesthesiology (PG2)	Simulated operating room/simulated scenario	Cognitive bias	Not specified
Patterson, F. [131]	Med Educ	2018	UK	Medical students	High fidelity simulator	Implicit bias	Ethnicity bias
Pennaforte, T. [132]	JMIR Res Protoc	2016	Canada	General Pediatrics and Neonatal-Perinatal Medicine residents.	Simulation scenario and standardized health professionals	Cognitive bias	Not specified
Persky, S. [133]	Ann Behav Med	2011	USA	Undergraduate medical students	Immersive virtual environment/computer generated	Implicit bias	Weight bias
Prakash, S. [134]	BMC Med Educ	2017	Australia	Interns (medical students)	High-fidelity simulator and SPs	Cognitive bias	Search satisfying, premature closure, and anchoring bias
Raemer, D.B. [37]	Acad Med	2016	USA	Anesthesiologist	Simulated scenarios	Implicit bias	Hierarchy, gender and stereotypes bias
Richey Smith, C. [135]	Am J Pharm Educ	2016	USA	Pharmacy students	Simulation game	Implicit bias	SES bias
Richmond, A. [136]	MedEdPORTAL	2017	USA	Students/medicine, nurse, pharmacy	SPs	Implicit bias	Hierarchy bias
Ruparel, R. [137]	J Surg Educ	2014	USA	27 urology residents	Virtual reality simulator	Cognitive	Internal bias (experience w/simulator not translating to surgery affecting confidence)
Rutledge, C. [138]	Contemp Nurse	2008	USA	Nurses	Computer generated virtual learning platform, high performance simulators (HPS).	Implicit bias	Cultural bias
Sargeant, S. [38]	Adv Health Sci Educ Theory Pract	2017	Australia	Medical students/SPs	SPs	Implicit bias	Culture, age, gender biases
Schuler, S. [139]	Stud Fam Plann	1985	USA/Nepal	Family planning staff	SPs	Implicit bias	Hierarchy bias
Sidi, A. [140]	J Patient Saf	2017	USA	Residents	High fidelity simulator	Cognitive bias	Anchoring, availability bias, premature closer and confirmation bias
Siegelman, J.N. [39]	J Grad Med Educ	2018	USA	Emergency medicine residents	Simulated cases – SPs, nurses, and simulation operators	Implicit bias	Gender bias
Silverman, A.M. [141]	Disabil Rehabil	2018	USA	Masters of occupational therapy (1st year)	Impairment simulation (role play)	Implicit bias	Anti-disability and discriminatory bias
Stockmann, C. [40]	J Nurse Educ	2017	USA	Nursing students	Manikin	Implicit bias	Gender bias
Svendsen, M. [51]	World J Gastrointest Endosc	2014	Denmark	Ten consultants experienced in endoscopy (gastroenterologists, n = 2; colorectal surgeons, n = 8) and eleven fellows	Virtual reality simulator	Implicit and Cognitive biases	Rater/observer bias Decision-making
Theodossiades, J. [142]	Ophthalmic Physiol Opt	2012	UK	Optometrists	Unannounced standardized patients	Cognitive	Self-reporting bias
Thompson, C. [143]	J Adv Nurs	2012	UK	Nursing students, nurses	Low and high fidelity/paper cases and human simulation (manikins not actors)	Cognitive	Judgment bias
Tollison, A.C. [41]	J Nurse Educ	2018	USA	Male nursing students	Online simulation	Implicit bias	Gender bias
Underman, K. [42]	MedEdPORTAL	2016	USA	Undergraduate medical students	SPs	Implicit bias	Gender bias
Varas-Diaz, N. [144]	J Gay Lesbian Soc Serv	2019	USA	Physicians in training	SPs	Implicit bias	Gender and sexual orientation bias
Watson, M. C. [145]	Pharm World Sci	2004	UK	Emergency medicine residents	Simulation lab scenario/high fidelity simulation	Cognitive bias	Selection bias
Welch, L. [43]	J Health Sco Behav	2012	UK	Primary care physicians	Video vignettes of SPs	Implicit bias	Gender bias
Wijnen-Meijer, M. [52]	Adv Health Sci Educ Theory Pract	2013	Netherlands/Germany	Physicians	SPs	Cognitive bias	Rater bias Decision-making
Wiskin, C. [44]	Med Educ	2004	UK	Medical students	Role-play	Implicit bias	Gender bias
Woda, A. [146]	Nurs Educ Perspect	2019	USA	Nursing students	Simulated clinical environment	Cognitive bias	Bias in clinical reasoning
Worth-Dickstein, H. [147]	Teach Learn Med	2005	USA	Medical Students	SPs	Implicit bias	SP scoring, personal, race, ethnic, and age bias
Wu, B. [148]	BMC Med Educ	2016	Hong Kong	Medical students	Simulated cases – cognitive mapping	Cognitive bias	Bias in clinical reasoning
Yeates, P. [149]	BMC Med	2017	UK	Undergraduate medical students	SPs	Implicit bias and Cognitive bias	Race, ethnicity, and examiner, recollection bias
Yu, C. [150]	J Am Geriatr Soc	2012	Taiwan	Nursing assistants	SPs	Implicit bias	Age bias
Yuan, M. [151]	Interact J Med Res	2013	USA	Nurse evaluators	SPs	Cognitive bias	Premature closure, anchoring, confirmation, and framing bias
Yudkowsky, R. [152]	Acad Med	2015	USA	Medical students	SPs	Cognitive bias	Confirmation bias
Yule, S. [153]	World J Surg	2008	Scotland	Surgeons	Videos of SPs and High fidelity simulator	Cognitive bias	Competency bias
Zottmann, J.M. [154]	GMS J Med Educ	2018	Germany	Medical students	High fidelity simulator	Cognitive bias	Competency bias

Discussion

The exploration of types of biases and dual theory

Dual process theory is a well-accepted framework for understanding decision-making processes and bias. This theory explains our thinking processes as either type 1 or type 2. Type 1 thinking is a fast, intuitive, pattern recognition-focused problem-solving method that creates a low cognitive burden on the user and enables quick decisions. Type 2 thinking is a slower, more methodical, thoughtful process. Therefore, an optimal balance of type 1 and type 2 processes is required to prevent biases for optimal clinical practice [15].

In dual process theory, type 2 thinking can bring a higher cognitive strain on the user but allows them to evaluate data more critically and look beyond patterns, and may potentially be more appropriate for complex problem solving. The current opinion among psychologists is that we spend approximately 95% of our time in type 1 thinking [16]. Cognitive bias (and the resulting errors) are more likely during the type 1 process [15].

Optimal diagnostic approaches are likely to use both type 1 and type 2 thinking at appropriate times. Non-analytical (type 1) reasoning has been shown to be just as effective as reflective reasoning to diagnose routine clinical cases. Furthermore, not all biases are caused by type 1 processing, but it is believed that when bias occurs, it can only be solved by activating type 2 processing. The articles we reviewed showed that the biases explored in articles on SBT were related to both cognitive and implicit biases, both of which can be associated with the two types of dual theory.

Bias types in simulation training

In this scoping review, we looked for all types of cognitive and implicit biases in SBT of health professions. Implicit biases were explored more than cognitive biases (Figure 5). The most researched implicit bias in health professions’ SBT is ‘gender bias’ [17–44]. Gender bias was also explored in different types of health professions and with different levels of experience: residents, primary care physicians, medical students, nursing students, etc. The most researched cognitive bias in literature is ‘decision-making (premature closure)’ [20,45–52]. We noted some biases that were not found in other reviews: uncivil behavior bias; poorly calibrated heuristics; and selection bias of patient participants [53–56]. In several papers, the type of bias was not specified and in those instances, we classified the biases based on the article’s content [20,47,51,52]. We were unable to further classify the types of bias explored in a couple of papers [57,58]. Our review indicates the prevalence of undefined bias in simulation training, which supports the importance of educators’ awareness of bias. All biases explored were classified under cognitive and implicit biases.

Figure 4:

Types of biases.

Figure 5:

Simulation training methods.

Cognitive bias

Cognitive bias is defined as unconscious and automatically developed mental processing strategies. These strategies are developed as adaptive mechanisms to simplify the complex inflow of information ultimately leading to biased judgments and inferences [59].

Cognitive bias and its impact are an important parameter on decision-making processes [60,61]. Cognitive bias, also known as ‘heuristics’, are cognitive shortcuts to help us make decisions [62]. It is increasingly accepted that significant diagnostic error can result from cognitive bias [63]. Clinical decision-makers have a risk of error due to biases that are not associated with intelligence or any other measure of cognitive ability [64]. In addition, individuals lack awareness of how these biases can affect their perceptions as they are unaware that their judgments are biased. The doctors who describe themselves as ‘excellent’ decision-makers and ‘free from bias’, often lack insight into their own bias [65].

We explored papers on the effects of different simulation methodologies on clinical reasoning and decision-making, and we explored which types of biases affect clinical reasoning and decision-making in SBT.

Implicit bias

The natural tendency of the mind is to rely on type 1 thinking, interpret data through heuristic scanning, and establish quick connections with data and experiences already available. Beyond cognitive bias, which affects clinicians’ interpretation of clinical data, there are intuitive screening and systematic biases on how we perceive other people, including patients. The ways we perceive and classify other individuals based on their characteristics (i.e. social and cultural biases) are most likely shaped by the experiences we have been exposed to. In clinicians, these biases appear in parallel with the general population [66]. Implicit bias (sometimes called unconscious bias) affects interpersonal interactions in ways that we are not consciously aware of. The health and behavioral effects of these implicit attitudes can be important. Implicit bias has many dimensions. Some examples of implicit biases are: race or ethnicity, gender, age, weight, sexual orientation, education and socioeconomic status [67].

Meanwhile, experimental studies have repeatedly shown that these biases measurably affect clinical assessments and treatment decision-making [68]. This effect seems particularly significant in challenging or ambiguous situations, or under heavier cognitive loads.

In addition, we noted that the number of articles published on the topic of bias in simulation in healthcare professional training increased dramatically from 1985 until 2020. This increase could reflect the increasing attention paid to decision-making processes and bias in general. It could also be a snowball effect – the more papers published on a topic, the more authors become inspired to explore new data on biases in SBT.

Biases exposed in different simulation training methods

Biases were explored using different simulation methods (Figure 4). Most of the articles exploring biases in simulation training involved SP methodology. This may reflect the importance of SP methodology as a training approach, its prevalence, or the particular need for well-designed scenarios in SP methodology. While SP methodology was the modality most often referred to in the articles, other modalities were also present (i.e. manikins, videos, etc.)

All trainings can be subject to bias. SBT has enhanced learning, however, trainers and learners can benefit from understanding that biases might be present in SBT [58,69]. The results also show that biases can be explored in any type of training methods in simulation, indicating that simulationsists should be aware of bias in training during all types of training.

Limitations

One limitation of our review is that we only reviewed articles available in English. Additionally, there is no comprehensive classification guide for biases, especially implicit biases, so, we had some difficulties defining or naming some types of bias mentioned in the papers.

Another limitation is that we only reviewed articles found in one database, it is possible that some articles on bias in simulation training of healthcare professionals are included in a database other than PubMed. We also focused on peer-reviewed literature and therefore did not include literature produced outside of traditional academic publishing.

Conclusion

Understanding how bias affects SBT for healthcare professionals is important, as it affects not only how future providers are educated and develop their clinical decision-making skills, but also because of its impact on patient care and health outcomes. This review not only showed the depth of the types of bias examined in the literature, but also found some biases that had not been previously classified.

In future, researchers might explore how biases affect clinical reasoning and decision-making in SBT. Researchers might also explore how to avoid bias in simulation by looking at instructional design of SBT.

There are many opportunities for researchers to explore bias and its impact on SBT. Once SBT trainers become aware of the possible presence of bias in their methodology, they may adjust existing instructional design, better follow established best practices and create new best practices to help identify and address these biases.

Declarations

Acknowledgements

We acknowledge and thank Maureen Clark, Librarian at University of Illinois Chicago for her assistance in developing the search terms for this scoping review.

Authors’ contributions

Selçuk Akturan, MD, Christine Park, MD, and Amy Binns-Calvey all made substantial contributions to the conception or design of the work as well as acquisition and analysis of the data. All authors contributed to and provided final approval of the manuscript to be published and are accountable for the accuracy and integrity of the manuscript.

Funding

None to declare.

Availability of data and materials

Availability of data and materials: The datasets analyzed during the current review are available from the corresponding author on reasonable request.

Conflict of interest

This research does not contain any human subjects.

Ethics approval and consent to participate

The authors declare that they have no conflict of interest.

Disclaimer

None declared.

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Table of Contents

Background

Methods

Results

Discussion

Background

Methods

Review strategy

Classification of bias

Results

Discussion

The exploration of types of biases and dual theory

Bias types in simulation training

Cognitive bias

Implicit bias

Biases exposed in different simulation training methods

Limitations

Conclusion

Declarations

Acknowledgements

Authors’ contributions

Funding

Availability of data and materials

Conflict of interest

Ethics approval and consent to participate

Disclaimer

References