Comparaci´ on con Resultados de Otras Investigaciones

Introduction

This chapter presents a review of the literature surrounding the use of simulation in healthcare education and training. A full narrative synthesis of the literature relating to virtual patient use in

pharmacy is provided in Chapter 3.

The chapter begins with a discussion of the literature search in section 2.2, including the aim of the

review (section 2.2.1), the search strategy (2.2.2), the eligibility criteria for study inclusion (section 2.2.3) and the appraisal of the literature (section 2.2.4). A classification of the simulation tools used in healthcare education and training is then provided in section 2.3. These tools are then discussed in turn; section 2.4 discusses standardised and simulated patients, section 2.5 discusses human-patient

simulation and section 2.6 discusses computer-aided instruction. Virtual patients are introduced in section 2.7; their typology is presented in section 2.7.1 and their design is presented in 2.7.2. The literature surrounding virtual patient use in medicine and nursing is reviewed in section 2.7.3. Considerations of clinical simulation are then discussed in section 2.8, starting with the benefits (section 2.8.1), the limitations (section 2.8.2) and the current integration of simulation in healthcare

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Literature Review: Background

Aim

The aim of the literature review was to identify the range of literature and define the different types of clinical simulation and their uses in healthcare education and training.

Search Strategy

A literature search was performed in September 2013 and repeated at regular intervals throughout the duration of the PhD (until December 2017) to identify appropriate literature. A number of electronic databases appropriate for locating articles on clinical simulation were searched (see Table

2-1). Both recent, up-to-date literature and historical work was sought and considered for inclusion in the literature review.

Electronic Databases Searched in the Literature Review Web of Science (1960 – 2017)

PubMed (1960 – 2017) MedLine (1960 – 2017) Science Direct (1960 – 2017) BioMed Central (1960 – 2017)

Cumulative Index to Nursing and Allied Health (CINAHL) (1960 – 2017) Google Scholar (1960 – 2017)

Table 2-1 Electronic databases and their associated dates for literature searching.

The search strategy involved using keywords in combination with Boolean operators (e.g. AND, OR, NOT) to search the electronic databases. The keywords included: ‘simulation’, ‘simulate’, ‘education’, ‘training’, ‘learning’, ‘pharmacy’, ‘medicine’, ‘nursing’, ‘healthcare’, ‘technology’, ‘computer’, ‘e-

learning’. Thesaurus or MeSH terms were used within databases to help expand the search strategy. Truncation was used to search for all terms which belonged to a particular string of letters (e.g. using the word ‘simulat*’ to find ‘simulation’, ‘simulate’, ‘simulators’).

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In addition to searching the above electronic databases, the archives of particularly relevant journals were accessed and searched directly. These included: Pharmacy Education, American Journal of

Pharmaceutical Education and The Pharmaceutical Journal. Literature obtained from these search strategies allowed for ‘snowballing’ of other, relevant literature from their bibliographies (Greenhalgh and Peacock, 2005). In addition, books which related to simulation or technology and learning were identified via the Keele University library search function and included in the review (Keele University, no date). Grey literature was identified from Government and regulatory bodies (e.g. Department of

Health and The General Pharmaceutical Council).

Initially, the literature surrounding the use of virtual patients in pharmacy education and training was searched. There were few recorded uses of virtual patients in pharmacy, thus a systematic approach to the review of this literature was adopted; the results of which are presented in Chapter 3. As a result, the search strategy for this general review was expanded to include the use of virtual patients

in the education and training of all healthcare professions. This identified that the term ‘virtual patient’ was used for a wide variety of tools (see section 2.7.1). Few studies reported generalisable evidence of the pedagogical benefits of virtual patients which led to a more inclusive search of the literature relating to technology-enhanced learning and clinical simulation, as research in these fields had shaped that of virtual patient simulation.

Inclusion and Exclusion Criteria

Wide inclusion criteria were employed during the early stages of the literature review due to the

variety of keywords, healthcare professions and uses of simulation. This allowed for a wide range of literature to be retrieved and reduced the potential of missing relevant literature. As this produced more ‘hits’ during the literature search, titles and abstracts of potential literature were briefly appraised to determine those which were relevant; full papers were examined where required.

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Papers which related to simulation in areas other than healthcare education and training were excluded because of the differences in uses and outcome measures.

Searches included the keywords presented in section 2.2.2 and were limited to include only articles in

the English language. Date limitations were also applied, particularly in the latter stages of the research, to ensure the most recent literature was identified. Short conference abstracts (less than one page), conference proceedings without full-text available online, conference presentations or posters and articles where no full text was available were not included in the review.

Literature Appraisal

A great breadth of literature was identified, so in line with the principles laid out by Bryman (2012), relating to the variety of methods used in the literature, an exhaustive coverage of all literature associated with simulation in healthcare education and training was deemed beyond the scope of this research. The literature review revealed more research relating to simulation use in other healthcare professions, particularly medicine and nursing, rather than in pharmacy. As this was relevant and

could serve to inform the benefits and disadvantages associated with the implementation of computer-based simulation as a training tool, these papers were included in the review.

A critical approach to appraising the literature was adopted, but a systematic review was not conducted in relation to the use of simulation and technology-enhanced learning in healthcare education and training. An in-depth appraisal of all the literature regarding clinical simulation in healthcare was not the aim of the literature review, instead an overview of the main tools, their uses and outcomes was sought; thus a systematic review was not required.

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A literature referencing library was created using Mendeley®. This was updated continuously throughout the duration of the PhD and duplicates were removed periodically. Literature was categorised based on the type of clinical simulation and the professional area.

The findings from the literature review are discussed in the following sections: classification of clinical simulation (section 2.3), standardised and simulated patients (section 2.4), human-patient simulation (section 2.5), computer-aided instruction (section 2.6) and virtual patients (section 2.7).

Classification of Clinical Simulation

Simulation in healthcare has been defined as:

“…a technique, not a technology, to replace or amplify real experiences with guided experiences, often immersive in nature, that evoke or replicate substantial aspects of the real world in a fully interactive fashion” (Gaba, 2004).

The term ‘simulation’ encompasses a variety of tools which can be classified in a number of ways; the most common is based on the fidelity of the tools. Fidelity refers to “the degree of exactness with which something is copied or reproduced” (Oxford Dictionaries, 2017c) and thus refers to the accurate, real-life portrayal of the simulation. Simulation can be split into three categories on the basis of fidelity:

1. Low fidelity – part-task trainers, non-computerised simulation

2. Medium fidelity - standardised patients, computer programs, video games, human-patient simulation

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(Seropian et al., 2004; Bradley, 2006; AAMC, 2007; Scalese et al., 2007; Harder, 2010; Ker and Bradley, 2010).

Although simulations can be categorised, it is possible for a simulation to span the range of fidelity based on factors including: the creation of the tool, the utility of the tool and expected learning outcomes (Seropian et al., 2004). Within healthcare the optimum simulation attempts to achieve a

high enough fidelity to convince users they are, in fact, using something that resembles what they would encounter in real life but should not be too realistic and fall into the ‘Uncanny Valley’ (Mori, 2012). This hypothesis suggests that objects which portray human characteristics are viewed

positively, up to a certain point when the degree of visual similarity to real humans becomes

unsettling and triggers negative thoughts (Macdorman et al., 2009; Cheetham et al., 2003; Lay et al., 2016). Thus, clinical simulations should mimic the clinical environment and allow the learner to apply cognitive, affective, and psychomotor skills in a realistic scenario, without inducing negative

emotions. All types and fidelities of simulation are effective learning tools; the type of simulation

required will ultimately depend on the intended learning outcomes (Seropian et al., 2004). Throughout the literature, there is variability in the depth of descriptions provided regarding simulation tools, thus it can be difficult to determine study quality and understand the predictors of simulation which lead to effective learning.

A systematic review carried out by Issenberg et al. (2005) identified ten features of high-fidelity medical simulations that lead to effective learning: feedback, repetitive practice, curriculum integration, range of difficulties, multiple learning strategies, clinical variation, controlled

environment, individualised learning, defined outcomes, simulator validity or authenticity.

Throughout this thesis, the effectiveness of the various simulation tools will be discussed in relation to these ten features.

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The definitions of the different simulation tools vary but those most commonly implemented in healthcare education and training can be classified as: standardised or simulated patients, human-

patient simulators, computer-aided instructions and virtual patients (Gaba, 2004; Seropian et al., 2004; Bradley, 2006; AAMC, 2007; Rosen, 2008; Rosen et al., 2009; Chapman, 2012). Each of these will be discussed in turn below.

Standardised and Simulated Patients

Standardised patients can either be actors playing the role of a patient, or they can be real patients who have been trained to present specific emotional, verbal and behavioural responses (Barrows, 1993; Adamo, 2003).

The process of using standardised patients in education began in 1963 (Barrows, 1993), but they were considered expensive and unrealistic due to their inability to portray abnormal physiological signs or symptoms, thus were not utilised as fully as expected (Rosen, 2008). They have since become

commonplace, especially in healthcare professions education as the benefits have been found to outweigh the limitations. Standardised patients offer the benefit of ‘real-human’ interaction which may be essential for the development of affective skills which are integral to all healthcare

professions, such as communication or consultation skills (Lust and Moore, 2006). Standardised

patients provide a safe environment for students to practice and develop their affective skills and knowledge before transitioning to real patients, which can only be beneficial to improve their confidence and competence (Barrows, 1993; Schultz and Marks, 2007; Ker and Bradley, 2010; Basheti, 2014).

Standardised patients have been found to ensure consistency for specific learning outcomes (Adamo, 2003). They are required to adopt certain behaviours or characteristics to provide standardisation of a learning experience (Barrows, 1993). Standardised patients are different to simulated patients

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which refer to individuals involved in a medical simulation that is conducted purely for educational purposes, during which, the ‘patients’ may act and respond as they would do during any real-life consultation; thus there is less standardisation of the learning experience (Adamo, 2003; Ziv et al.,

2005). Simulated patients (SPs) are also a useful training tool as they provide ‘real human’ benefit, which other types of simulation may not. The terms ‘standardised patients’ and ‘simulated patients’ are used interchangeably within the literature, which can make it difficult to ascertain where both tools are best placed to be used in education, but these two types of simulation do differ and should

be recognised as such1 _{(Wallace et al., 2002; Adamo, 2003; Wind et al., 2004; Ker and Bradley, 2010).}

Both have been identified as useful educational simulation tools and it may be appropriate to ‘mix and match’ between the two to allow students to meet different learning or assessment outcomes. They could also be used in different stages of education, for example, standardised patients could be used in lower years to develop students’ communication skills and confidence and the integration of

simulated patients could come when students are more confident and able to deal with more unpredictable consultations.

Simulated patients may be preferred over standardised patients in healthcare education “due to the authenticity of role play and the quality of feedback [which] take precedence over uniformity and consistency of role play” (Wind et al., 2004). Authenticity has been established as one of the factors of high-fidelity medical simulations that lead to effective learning (Issenberg et al., 2005). SPs are used in the training of medics and nurses all over the world and their reliability, validity and feasibility

have been confirmed in several studies, however this may have led to a reduction in more current, up-to-date literature evaluating their usefulness. With the growing pool of more interactive forms of simulation, it may be appropriate for researchers to evaluate current students’ perspectives of SPs with or without a comparison to other forms of simulation (Wallace et al., 2002; Adamo, 2003; Wind

1 _{For the purpose of this review, the abbreviation ‘SP’ will be used when referring to either standardised or}

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et al., 2004; McGaghie et al., 2010). SPs have also been studied in pharmacy education, but to a lesser extent, which may be due to the perceived less clinical nature of pharmacists. However, with the increasing clinical aspect of a pharmacist’s role and focus on consultation skills, SPs are a prime tool

for training pharmacy students to improve their confidence and competence before qualification. A large proportion of SP literature, like all simulation literature, has focused on their effectiveness and usability with emphasis on student satisfaction (reported by questionnaires or interviews). These are suitable methods of data collection, but are associated with limitations, including bias,

misunderstanding of questions or the Hawthorne effect. When assessing an educational intervention which is a core part of a curriculum, there is the risk of students responding in a positive way or how they anticipate they should, which can therefore affect the reliability and validity of results obtained. These methods of data collection also don’t allow for the actual effectiveness of SPs in the

development of knowledge or skills to be evaluated and, as such, more formal checklists of

competence could be used.

SPs are played by human beings, and as such, there is a risk of variation due to fatigue, memorisation and bias (Austin et al., 2006; Lurie et al., 2008; Schwartzman et al., 2011). Wind et al. (2004) created an instrument to evaluate the quality of SP performances and provide more rigour when SPs are used (especially in assessments), however there has been little discussion regarding utilisation of this tool in the literature. The authors found that simulation authenticity and SP feedback were the two most important components of a high-quality SP interaction. These findings have also been noted by

Issenberg et al. (2005) as important factors of high-fidelity medical simulation that lead to effective learning. The literature has specified that the majority of SP consultations end with the provision of individualised feedback to the students, which can therefore aid their learning.

SPs require organisation by faculty members and the cost associated with their use and the time required to train the SPs themselves and the associated assessors have been identified as barriers to

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their use (Barrows, 1993; Hubal et al., 2000; Ker and Bradley, 2010). SPs may provide benefits when used in addition to, and not instead of, real-patients as they offer a complementary learning

experience for students (Chapman, 2012). There have been few instances where SPs have been evaluated against another learning tool, however those studies which have included this as an outcome have found little difference regarding knowledge or skill development, but have identified that users respond with greater empathy towards the SPs (Cook and Triola, 2009). Thus, there are benefits for different simulation tools to be used in combination depending on the intended learning

outcomes. Over the past few decades, the use of standardised and simulated patients in education has become utilised to such an extent that they are now often included in undergraduate exams and licensing examinations of healthcare professionals, as well as postgraduate performance reviews, but complete utilisation may be limited by the factors discussed above (Adamo, 2003; Munoz et al., 2005; Austin et al., 2006; Watson, Norris, et al., 2006; Rosen, 2008; Mesquita et al., 2010).

Human-Patient Simulation

Human-patient simulation (HPS) includes the use of mannequins or part-task trainers to simulate patient care experiences (Gaba, 2004; AAMC, 2007; Rosen, 2008; Ker and Bradley, 2010).

Part-task trainers can be anatomical models of specific human body parts in their normal or diseased

states or surgical task trainers which simulate specific surgical procedures (Gaba, 2004; Bradley, 2006; Scalese et al., 2007). They include those technologies that replicate a portion of a complete process or system. They are designed for students to practice and perfect specific technical, procedural or psychomotor skills, so when faced with a real patient they are competent and confident at

completing the task (Bradley, 2006; Ker and Bradley, 2010). Part-task trainers are routinely used and

are associated with high satisfaction scores for the development of basic skills, such as catheterisation or venepuncture, and more complicated surgical and diagnostic skills which may be difficult to attain

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through traditional learning, placements or initial practice (Rosen et al., 2009; Ker and Bradley, 2010). However, their use is limited, as more difficult practical skills or ‘affective’ skills cannot be developed via the use of part-task trainers, thus educational centres would have to invest in additional

simulation tools for individuals’ more rounded development.

A second HPS tool is mannequins. These are anatomical models of the human body which can vary in

their fidelity based on the model used and the associated technology (Cooper and Taqueti, 2004; Seropian et al., 2004; Bradley, 2006; Lapkin et al., 2010). They can be non-responsive, voiced by an individual or able to portray specific physiological signs and symptoms; all have been successful at

developing skills, knowledge and are associated with high user satisfaction (Cooper and Taqueti, 2004; Seropian et al., 2004; Rosen, 2008). The fidelity of mannequins can affect the realism of a simulation and ultimately students’ skill development, with those that are higher-fidelity being reported as more realistic and thus more effective at skill development. They are also likely to be more expensive and may require increased staff or student training to use, therefore institutions

would have to weigh up the costs:benefits to make an informed decision regarding students’ training needs and therefore the most appropriate simulation tool (Seropian et al., 2004; Lapkin et al., 2010).

The literature search revealed that mannequin simulations were not a new concept in medical education. They were first used in the 1500’s when Hieronymus Fabricius described a mannequin used to teach the reduction of joint dislocations (Cooper and Taqueti, 2004). The first latter day mannequin in widespread use was Resuci-Anne, a low-fidelity mannequin developed in 1960 by Asmund Laerdal for medical students to practice cardiopulmonary resuscitation (CPR). Resuci-Anne

has been established as a successful teaching tool that it is currently used in the training of a wide range of healthcare professions and general first aid training (Cooper and Taqueti, 2004; Bradley, 2006; Fritz et al., 2008; Rosen, 2008). It has been associated with measurable improvements in

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students’ performance and high satisfaction of use; both of which are important considerations when evaluating learning tools.

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