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Abstract

This study synthesized the available evidence of simulation-based electronic health records (EHRs) training in educational and clinical environments for healthcare providers in the literature. The Arksey and O’Malley methodological framework was employed. A systematic search was carried out in relevant databases from inception to January 2020, identifying 24 studies for inclusion. Three themes emerged: (a) role of simulation-based EHR training in evaluating improvement interventions, (b) debriefing and feedback methods used, and (c) challenges of evaluating simulation-based EHR training. The majority of the studies aimed to emphasize the practical skills of individual medical trainees and employed post-simulation feedback as the feedback method. Future research should focus on (a) using simulation-based EHR training to achieve specific learning goals, (b) investigating aspects of clinical performance that are susceptible to skill decay, and (c) examining the influence of simulation-based EHR training on team dynamics.

Keywords

Electronic health records healthcare professional training simulation-based training

Introduction

The Health Information for Economic and Clinical Health Act of 2009 encouraged the implementation of electronic health records (EHRs) in healthcare organizations, and by 2017, more than 96% of hospitals and 86% of physicians’ offices in the United States reported having access to EHRs.^1,2 EHRs have emerged as “meaningful use” to improve the quality and efficiency of healthcare, and health disparities in population health.³ Thus, EHR use has become an essential component of digitally connected health care.⁴ Associated with EHR use are not only benefits (e.g. improved quality of care, reduced medical errors),^5–7 but also unintended consequences caused, in part, by inadequate education and training.^8–11 These unintended consequences are adverse events that result from either an EHR that functions in a manner that the vendor did not intend or from inappropriate use of EHRs by healthcare professionals.¹² Unintended consequences include disrupted workflows, adverse drug effects, and medication errors that impair patient safety and outcomes.^13,14 Innovative educational and training interventions can equip the current and future health care workforce with the requisite knowledge and skills to prevent the unintended consequences associated with EHR use.^15–17

Simulation-based training (SBT), which mimics real-world clinical conditions without the risk of harming real patients,^17,18 can lead to substantial improvements during providers’ interactions with EHRs.^15,16 It has been used as an effective educational tool for medical students,^19,20 and has been shown to enhance skills training of residents,^21,22 surgeons,^23,24 nurses,^25,26 and radiation oncology professionals.^27,28 SBT can be presented in different forms including partial task trainers, standardized patient actors, simulated clinical environment, virtual reality simulators, and EHRs.^29,30

Of particular interest in the present study is simulation-based EHR training, which has been associated with improved EHR use, improved clinician training, and enhanced patient safety.^31,32 Simulation-based EHR training has the propensity to reduce unintended consequences associated with EHR use, bridge the gap between classroom learning and real clinical experiences, and enhance skills training.^15,18 There are limitations in two previous literature reviews^33,34 on simulation-based EHR training. The study by Rajaram et al.³³ reviewed educational interventions in EHR use.³³ They found that all the interventions involved data entry into a simulated EHR with none requiring extraction or aggregation of clinical data. This study was limited to only medical students and residents.³³ The study by Wilbanks et al.³⁴ synthesized the literature on simulated-based EHR training in education to provide guidance for future educational and research practices.³⁴ They identified properties of an ideal educational EHR system (e.g. useable and modifiable graphical user interfaces), and outlined facilitators (e.g. faculty support and student support, acceptance) and barriers (e.g. high cost, decreased functionality compared with EHR in clinical use) for implementing educational EHRs. Wilbanks et al.³⁴’s study searched only two databases (PubMed and CINAHL), and was limited to pre-professional students only, not clinicians.³⁴ Both literature reviews did not include the evidence of the use of simulated-based training to train and/or improve the skills of practicing clinicians.

As far as we know, no study has identified and mapped the available evidence of simulation-based EHR training in both educational and clinical environments. The overarching goal of this study was to synthesize the current state of the literature on simulation based EHR training for healthcare providers in both educational and clinical environments. A scoping review, which can be a precursor to a systematic review, is an approach to evidence synthesis in which the available evidence is identified and mapped.³⁵ Since we were interested in identifying and mapping the available evidence, we chose to conduct a scoping review with these specific objectives: (1) to map the literature on the major dimensions of simulation-based EHR training in healthcare, (2) to synthesize EHR functionalities across prior simulation-based EHR training studies, and (3) to outline areas where further research is needed.

Method

A detailed description of our scoping review protocol has been published in BMJ Open.³⁶ We followed the framework proposed by Arksey and O’Malley.³⁷ This is a methodological framework for conducting scoping studies. Stages in the framework include (1) identifying the research question, (2) identifying relevant studies, (3) study selection, (4) charting the data, and (5) collating, summarizing, and reporting the results. We provide a brief overview below.

Stage 1: Identifying the research question

Our research question, what is known from the existing literature about EHR simulation-based training?, was developed by consulting a group of health care educators to determine attributes of simulation-based EHR training. These health care educators were working with the authors on a grant funded by the Agency of Healthcare Research and Quality (AHRQ) to assess the impact of SBT on health care professionals’ mental workload, situational awareness, and performance during quality assurance and treatment delivery process. They have 15 years of experience on average in radiation therapy and medical dosimetry education. We defined simulation-based EHR training as computer screen-based simulations intended for learners to acquire knowledge or assess learners’ competency of knowledge attainment and/or provide learner feedback related to clinical knowledge and critical-thinking skills.¹⁵

Stage 2: Identifying relevant studies

Five electronic databases (CINAHL, Embase, ProQuest, PubMed, Scopus) were searched from inception to 29 January 2020. Furthermore, Google Scholar was searched to identify additional potential papers and conference proceedings. Our search was conducted with the help of a health sciences librarian. Citations were downloaded and duplications were removed using the Zotero reference management software.³⁸

Stage 3: Study selection

Article selection was conducted in two stages. First, two reviewers (J.N and K.A) independently screened titles and abstracts against inclusion (i.e. computer screen-based simulations, English, full text) and exclusion (non-English, abstract only, animal research study) criteria with the aid of Covidence,³⁹ a web-based screening and data extraction tool. A third reviewer (L.M) assisted in resolving disagreements regarding article eligibility. In the second stage, two reviewers (J.N and K.A) independently reviewed the selected full-text articles to determine eligibility. Again, a third reviewer (L.M) assisted in resolving disagreements regarding article eligibility in this stage.

Stage 4: Data Items and Data Collection Process

We developed a data abstraction form with relevant characteristics to address our research question. Data abstraction was based on (1) study characteristics (i.e. authors, year, objectives, metrics, assessment methods, findings), (2) dimensions of simulation proposed by Gaba,¹⁵ and (3) EHR core functionalities from the IOM.⁴⁰ Our data abstraction form is shown in Table 1. The dimensions of simulation by Gaba¹⁵ is a widely used framework to categorize simulations along 11 dimensions (see Table 2). According to the Institute of Medicine (IOM), every EHR should be capable of performing eight core functionalities (see Table 3). We used Microsoft Excel 2010 to chart the data. Together, these data formed the basis of our analysis.

Table 1.

Data abstraction form.

Category	Variable	Classification method
Research	Year of publication	Metadata
	Geolocation	Metadata
	Number of participants	Metadata
	Participants’ age group	Metadata
	Specialty	Metadata
Simulation study	Aim of simulation	Gaba¹⁵
	Unit of participation in simulation
	Experience level of simulation participants
	Knowledge, skill, and attitudes addressed
	Site of simulation participation
	Extent of direct participation in simulation
	Feedback accompanying simulation
EHR	Health information and data	Institute of medicine⁴⁰
	Results management
	Order entry/management
	Decision support management
	Electronic communication and connectivity
	Patient support
	Administrative processes
	Reporting and population health management

Table 2.

Dimensions of simulation.¹⁵

Dimension	Item	Explanation
Aim of simulation	Education	Emphasizes theoretical knowledge and basic skills
	Training	Emphasizes practical skills
	Performance assessment	Evaluates competency
	Clinical rehearsal	Adjunct to actual clinical practice
	Research	Investigates human factors
Unit of participation	Individual	Single participant
	Crew	Single-discipline group of individuals
	Team	Multi-discipline crew
	Work unit	Team, in an organization, with specific training target
	Organization	Nonclinical personnel and work units
Experience level of participants	School	Early learners, primary and secondary school students
	University	Students
	Initial professional education	Medical students, interns
	Residency or on-the-job training	Residents, fellows
	Continuing education and training	Attending physicians, physicians, surgeons
Health care domain	Imaging	Radiology, pathology
	Primary care	Psychiatry, family medicine, internal medicine
	In-hospital ward-based	Medicine/pediatrics
	Procedural	Surgery, obstetrics, and gynecology
	Dynamic high hazard	Operating room, intensive care unit (ICU), emergency department (ED), anesthesia, critical care
Health care discipline of participants
	Nurses	Nurses including advanced practice nurses
	Physicians	Doctor of medicine
	Managers	Executives, trustees
	Regulators Aids, clerks, allied health	Legislators Technicians
Type of knowledge, skill, attitudes, or behavior addressed	Conceptual understanding	Basic skills and knowledge required to carry out professional functions
	Technical skills	Practical demonstration of accumulated knowledge
	Decision-making skills	Metacognition –critical thinking, problem-solving, self-assessment
	Attitudes and behaviors	Teamwork, professionalism
Age of patient being simulated	Neonates	Less than 4 weeks old
	Infants	Children less than 1 year of age
	Children Teens	Age ranges from 1–11 yearsAge ranging from 12–17 years
	Adults	Age ranging from 18–60
	Elderly	Age of 65 years or older
Technology	Verbal, roleplaying	“What if” discussions
	Standardized patients
	Part-task trainer	Physical, virtual reality
	Computer patient	Computer screen, screen-based
	Electronic patient	Replica of the clinical site, mannequin based, full virtual reality
Site of simulation	Home or office	Multimedia screen-only simulations
	School or library	Multimedia screen-only simulations
	Dedicated laboratory	Physical part-task trainers, virtual reality part-task trainers
	Replica clinical environment	Replica clinical sites, patient simulation systems, full video capture
	Actual work unit	In situ simulation, mobile simulation
Extent of direct participation	Remote viewing only
	Remote viewing with verbal interaction
	Remote viewing with hands-on interaction
	Direct on-site hands-on participation
	Immersive participation
Feedback method accompanying simulation	None	No feedback provided
	Automatic critique by simulator	Real-time, delayed
	Instructor critique of prior simulation sessions	Post simulation feedback
	Real-time critique	Pause/restart, real-time mentoring
	Video-based post-hoc debriefing	Individual/group

Table 3.

EHR functionalities.⁴⁰

Functionality	Explanation
Health information and data	Process and store patients' diagnoses, allergies, laboratory results, and medications in a way that can be easily retrieved, analyzed, and transmitted
Results management	Allow providers to access new and past test results
Order entry/order management	Allow providers to enter and store orders for prescriptions, tests, and other services
Decision support management	Allow for the use of notifications and decision-support systems to improve compliance with clinical practices
Electronic communication and connectivity	Enable efficient and secure communication among providers and patients
Patient support	Allow patients access to their health records
Administrative processes and reporting	Have tools that can improve efficiency and provide timely services to patients
Reporting and population health	Store and transmit clinical data to provide public health organizations

Stage 5: Collating, summarizing, and reporting the results

We did not evaluate the robustness or generalizability of our findings since the purpose of a scoping review is not to assess the quality of evidence. Rather, we collated, summarized, and reported our findings using descriptive numerical analysis. Two reviewers (J.N and K.A) conducted the analysis to present a summary of the nature and distribution of the studies included in the review. We produced tables mapping the distribution of studies. Further, we used inductive content analysis to identify themes in the included articles. Two reviewers (J.N and K.A) independently read and reread each of the 24 included articles to identify emerging themes. Then, all reviewers met and iteratively structured emergent themes.

Results

Search results

A total of 24 articles were included in the final review. Figure 1 describes details of the screening process.

Figure 1.

PRISMA Flow diagram for the article selection process.

The results presented hereafter are based on categories from our data abstraction: study characteristics (i.e. authors, year, objectives, metrics, assessment methods, findings), dimensions of simulation proposed by Gaba,¹⁵ and EHR core functionalities from the IOM.⁴⁰

Study characteristics

The articles in this review included a variety of metrics, assessment methods, and publication dates ranging from 2012 to 2019 (Table 4). 19 out of 24 articles were published from 2015 to 2019.

Table 4.

Study characteristics.

Year	Authors	Objective(s)	Metric	Assessment Method(s)	Findings
2012	Ammenwerth et al.⁴³	Assess the impact of EHR prototype on the appropriateness of prescriptions and drug-related activities	1. Performance against gold standard 2. Perceived impact of EHR on patient safety	The gold standard consisted of a list of expected clinical activities	No improvement in performance. Simulated-based EHR can provide important insight into potentially harmful technologies
				Performance was determined by fulfillment of the gold standard
				A semi-structured interview was used to elicit the perceived impact of EHR
2013	Mar et al.⁴⁴	Assess efficacy and safety of EHR within the intensive care unit environment	1. Error recognition rate	Participants were scored based on error recognition and screen utilization	Simulation helps determine gaps in identifying dangerous medical management issues as well as foster EHR-specific training
2013	Mar et al.⁴⁴		2. Number of screens used
2013	Sperl-hillen et al. ⁴⁵	Develop and implement a virtual model to teach cognitive skills of type 1 and type 2 diabetes management	1. Self-assessed knowledge	Knowledge assessment questions, the question on confidence (both at baseline and follow-up), online evaluation survey of satisfaction after simulation	Self-assessed knowledge and confidence in diabetes management were improved
			2. Self-confidence
			3. Satisfaction
2014	Milano et al.⁶²	Assess performance on common EHR tasks and the ability to order prescriptions	1. Perceived simulation-based EHR effectiveness	Electronic survey used to gather perceptions of simulation-based EHR effectiveness	Simulation-based EHR is an effective, interactive method for providing learners with EHR skills education
2014	Stephenson et al.⁴	Evaluate the use of EHR by medical trainees	Percentage of errors recognized	Two different cases with three common themes: Recognition of inappropriate medication dosing, recognition of significant changes in hemodynamics, recognition of oversedation	Enhanced EHR use and detection of patient safety issues
2015	Ben-assuli et al.⁴⁶	Assess EHR use and examine whether it improves medical decision-making during emergency department triage	1. Differential diagnosis (based on diagnostic standards of the ICD10)	Questionnaire was used to collect initial and final diagnoses	Enhanced medical decision-making in terms of accuracy of diagnosis and correct admission decisions
			2. Admission decisions	Open-ended questions were used to collect admission decisions and a likert scale was used to assess confidence level
			3. Confidence level
2015	Gold et al.⁴⁷	Assess the effect of integrating EHR on patient safety, team dynamics, and clinical decision making in the intensive care unit	Recognition rate of patient safety issues	EHR logs and interviews were used	Present for all members of the interprofessional team were alert fatigue, communication errors, and selective data gathering errors
2015	Metzger et al.⁵⁶	Evaluate medication reconciliation and order verification activity using simulated-based EHR	Perceptions of medication reconciliation and order verification simulation activity	Open-ended questions and likert-scale survey used to gather perceptions of simulation activity	Enhanced learning, improved clinical decision-making skills, stimulated interest in institutional pharmacy
2015	Shachak et al.⁴⁸	Examine the impact of simulated-based EHR training on family medicine residents’ self-reported competencies and attitudes	1. Competencies	1. Self-reported competencies measured on a 4-point scale	Simulated-based EHR would be useful for teaching how to better use EHRs in clinical encounters
			2. Attitudes	2. Attitude measured on 5-point likert scale
			3. Perceived usefulness	3. Perceived usefulness, perceived ease of use, and intention to use measured with technology acceptance model questionnaire
			4. Perceived ease of use
			5. Intention to use the system	4. Overall evaluation of simulation measured with a post-simulation questionnaire
			6. Overall evaluation of simulation
2015	Vuk et al.⁴⁹	Examine whether simulation-based EHR training enhanced providers’ self-efficacy and perceptions	1. Overall self-efficacy to use pre- and post-simulation	Confidence and preparedness were measured with pre- and post-simulation questionnaires	Enhanced self-efficacy and preparedness to use EHRs
2015	Vuk et al.⁴⁹		2. Perceptions about the importance of EHRs in helping patients and improving patients’ safety and confidence and preparedness level to use EHRs		Enhanced self-efficacy and preparedness to use EHRs
2016	Ben-assuli et al.⁵⁰	Assess the effect of EHR use on medical decision-making	1. Admission decision rates	A questionnaire was used to collect initial and final diagnoses, level of confidence, and admission decision	Enhanced decision-making performance and diagnostic accuracy
2016	Ben-assuli et al.⁵⁰	Assess the effect of EHR use on medical decision-making	2. Diagnosis accuracy
2016	Mar et al.⁵⁰	Evaluate patterns of EHR utilization and note writing skills	1. Utilization of macros	Daily progress notes created from simulated chart; notes graded for utilization of macros, copy-paste, macros, recognition of patient safety issues	High variability in EHR utilization and note writing process
			2. Utilization of copy-paste
			3. Recognition rate of patient safety issues		There must be a balance between documentation habits that foster efficient clinical data retrieval/notation and the promotion of patient safety
2016	Gold et al.⁶⁰	Investigate practicality of integrating commercial eye tracker into high-fidelity ICU EHR simulation exercise	1. Recognition and verbalization of patient safety issues	Correlation between eye-tracking metrics and performance (recognition and verbalization of patient safety issues)	Eye tracking can be integrated into simulation-based EHR training and provides a surrogate measure of cognitive decision making and EHR usability
2016	Gold et al.⁶⁰		2. Metrics derived from eye-tracking: Composite eye tracking score, number of mouse clicks, number of unique screens, number of total screens, number of fixations, number of saccades
2017	Biagioli et al.⁵³	Understand EHR-related communication and data management skills	1. EHR-related communication skills	A questionnaire consisting of a list of EHR competencies was used	Improvements in EHR-related communication tasks but deficiencies in EHR-related communication and data management skills
2017	Biagioli et al.⁵³		2. EHR-related data management skills
2017	Horsky et al.⁵²	Investigate behavior that may contribute to inaccurate international classification of diseases, tenth Revision (ICD-10) coding	1. Number of entered diagnoses	Interactions were recorded into audio-video files; variability in search strategies and resulting sets of entered codes were analyzed	Wide variation in search terms used to query EHR for the same diagnosis
			2. Number of entered search terms		Enhanced design of EHR query modules may decrease the rate of inappropriate and omitted codes
			3. Number of returned search items
2017	Lee et al.⁶³	Use simulated-EHR to teach about the international classification of diseases, tenth	1. Knowledge assessment test score	1. Multiple-choice test administered before and after educational	Significant improvement in knowledge
			1. Knowledge assessment test score	Activities
		Revision, clinical modification (ICD-10-cm) coding system	2. Engagement	2. Engagement survey administered at the end of the exercise
2017	Pranaat et al.⁵⁴	Evaluate and quantify variability and accuracy of medical scribes’ transcribed notes	1. Comparison of structural elements (note length, word economy, data elements, copy and paste blocks, pended orders, and attestations) between scribes	Transcribed notes in simulation-based EHR environment were analyzed for interscribe variability and compared with gold standard note for accuracy	Significant interscribe variability both in terms of structure and accuracy
2017	Pranaat et al.⁵⁴		2. Comparison of structural elements compared with gold standard note		EHR-based simulation can be used to develop a scribe curriculum and assess competency
2018	Coon et al.⁵⁸	Evaluate the impact of virtual EHR on learning efficiency, perceptions of clinical skills, communication, and satisfaction	1. Time required to provide the best therapeutic recommendation	1. Pharmacotherapy rubric used for clinical assessment	Clinical skills, communication, and learning satisfaction were improved
2018	Coon et al.⁵⁸		2. Perceived clinical skills, communication, and learning satisfaction	2. Survey administered to evaluate perceptions of clinical skills, communication, and learning satisfaction
2018	Elliott et al.⁵⁵	Evaluate the value and impact of SBT-EHR on student engagement	Degree of student engagement	Electronic	SBT-EHR was perceived as useful and valuable
2018	Elliott et al.⁵⁵		Degree of student engagement	Likert-style survey used to gather degree impact of simulation activity	SBT-EHR was perceived as useful and valuable
2018	Mohan et al.⁶¹	Use high-fidelity EHR-based simulation to understand learners struggling with clinical efficiency and decision making	1. Number of safety issues identified	Participants were assessed for the number of safety issues identified. Screen use and eye-tracking patterns were matched with talk aloud sessions to identify participants' thought processes	EHR-based simulation allowed for differentiating between learners with difficulty in data processing from those with difficulty in data acquisition
			2. Gaze fixations
			3. Post-intervention survey
2018	Orenstein et al.⁴¹	Investigate association between simulation-based EHR training and EHR use patterns in real clinical settings	1. Utilization of data visualization tool	EHR audit logs were used to assess the frequency with which data visualization and information retrieval tools were utilized	Simulation-based EHR training is associated with continual changes to EHR use patterns
2018	Orenstein et al.⁴¹		2. Utilization of information retrieval tool
2018	Skelley et al.⁵⁷	Evaluate a simulation-based EHR activity within an ambulatory care elective course	1. Comprehension of pharmacist patient care process (PPCP) steps	16-Item survey focused on understanding the pharmacist patient care process steps and confidence in used EHRs was administered before and after simulation activity	Participants expressed the benefit of simulated-based EHR training and a desire for expanded opportunities to use it
2018	Skelley et al.⁵⁷		2. Confidence in ability to implement PPCP in practice
2018	Zoghbi et al.⁴²	Assess interns' performance before and after having viewed “how to” videos on EHR tasks and simulations	1. Task performance	1. Times for performing tasks were recorded	Enhanced performance and increased confidence
			2. Time to task completion	2. Performance on perioperative emergency simulation was scored on a scale from 0 to 8
			3. Clinical scores on emergency simulations	3. Confidence in performing tasks was recorded using a 5-point likert scale
			4. Confidence
2019	Vyas et al.⁵⁹	Assess impact of simulated-based EHR training on student knowledge and skills	1. Performance on computerized provider order entry activities	Instructor rubric was used to assess performance	Simulated-based EHR training improved student understanding of the PPCP
			2. Performance on case work-ups	Pre- and post-attitude surveys were used to self-perceived abilities
			3. Self-perceived abilities to perform select tasks related to implementation phase of the pharmacist patient care process (PPCP)

The objective of the majority (14/24) of the studies was either to improve learning skills of medical trainees and clinicians or to teach technical and non-technical skills.^{4,41–46,46–52} Of these studies seven had the objective of showing that simulation-based EHR training can enhance the teaching of students.^53–59 One study investigated the practicality of integrating an eye tracker into an EHR simulation environment,⁶⁰ one study sought to understand EHR-related communication and data management skills,⁵³ and one study used high-fidelity EHR-based simulation to understand learners struggling with clinical efficiency and decision making.⁶¹ Furthermore, 16 studies used subjective methods^{42,43,45,46,48,51,55–59,61–63} while five studies used both subjective and objective assessment methods^{42,43,58,59,61} to track, assess, and/or compare satisfaction, confidence, and/or performance on simulation-based EHR training (Table 3).

Simulation-based EHR training was found to enhance medical decision making,^46,50,64 improve detection of patient safety issues,^4,47 enhance EHR use,^63,65–68 enhance understanding of EHR usage pattern,^69–72 and facilitate student learning.^49,59,73,74 Also, simulation-based EHR training enabled researchers to differentiate between learners with difficulty in data processing from those with difficulty in data acquisition,⁶¹ and improve EHR-related communication skills.^58,75

Dimensions of simulation-based training

Table 5 provides a summary of dimensions of simulation-based training based on Gaba’s dimensions of simulation .

Table 5.

Dimensions of simulated-based training.

Dimension	Item	Number of articles	References
Aim of simulation	Education	7	^{53,55–59,62}
	Training	8	^{4,41,42,44,45,48,50,63}
	Performance assessment	7
	Research	2	^60,61
Unit of participation	Individual	20	^{4,42–46,48–55,58–62}
	Crew	2	^56,57
	Team	2	^47,63
Experience level of participants	University	5	^56–59,65
	Initial professional education	5	^{50,54,61,62,75}
	Residency or on-the-job training	8	^{4,41,42,44,45,48,60,63}
	Continuing education and training	6	^{43,46,47,49,51,52}
Health care domain	Pharmacy	4	^56–59
	Primary care and ambulatory care	10	^{41,43,45,48–50,52,53,62,63}
	Nursing	1	⁵⁵
	Procedural	2	^42,54
	Dynamic high hazard	7	^{4,44,47,60,61}
Health care discipline of participants	University students	7	^53–59
	Trainees and interns	12	^{4,41,42,44–46,48,50,60–63}
	Physicians	6	^{43,46,47,49,51,52}
	Nurses	1	⁵⁵
Knowledge, skill, attitudes or behavior addressed	Conceptual understanding	7	^{53,55–59,63}
	Technical skills	11	^{4,41,44,45,48,50,54,60–62,67}
	Decision-making skills	4	^43,46,49,51
	Attitudes and behaviors	2	^47,52
Age of patient being simulated	Infants	1	⁴¹
Age of patient being simulated	Elderly	2	^44,47
Technology	Computer screen only	18	^{4,41,43–46,48,50–52,55–57,59–63}
	Computer screen and standardized patients	2	^49,53
	Computer-screen based and patient simulator	2	^47,58
	Computer-screen based and video	2	^42,54
Site of simulation	University	2	^41,54
	Dedicated laboratory	5	^{42–44,49,53}
	Replica clinical environment	2	^56,58
	Actual work unit	1	⁶⁰
Feedback method accompanying simulation	Instructor critique of prior simulation sessions	16	^{41,43–46,48,49,53,55–62}

Aim of simulation

Twenty-nine percent (7/24) of the articles aimed at educating participants,^{53,55–59,62} 33 percent (8/24) emphasized practical skills,^{4,41,42,44,45,48,50,63} and 29 percent (7/24) evaluated the competency of clinicians.^{4,41,42,44,45,48,50,63} Two articles investigated human factors^60,61; one study investigated objective EHR usability measures that can be anchored to markers of successful EHR use,⁶⁰ and the other study used high-fidelity EHR-based simulation to understand the etiology of learners struggling with clinical decision making.⁶¹

EHR Functionalities

Figure 2 shows simulated EHR functionalities. No article reported simulation of electronic communication and connectivity, patient support, administrative processes, reporting, and population health.

Figure 2.

EHR functionality simulated. Note: EHR: Electronic Health Records.

Health information and data

All articles simulated the health information and data functionality of EHR.^4,41–63 Studies simulated diagnoses, laboratory results, and medications of elderly ICU patients,^44,47 and infants.⁴¹

Results management

All articles simulated the results management EHR functionality.^4,41–63 Simulations were created such that participants could access past laboratory test results of ICU,^44,60 and ED patients.⁵¹

Order entry/order management

Ten articles simulated order entry/order management.^{42,43,45,47,51,53,56,59,60,62} One study improved pharmacy students’ understanding of the Pharmacist Patient Care Process by allowing them to enter orders.⁵⁹ In another study, internal medicine physicians were allowed to enter orders in a simulated EHR.⁴³

Decision support management

Two articles simulated the decision support management functionality of EHR;^43,56 one study simulated the influence of an automatic alert system on decision-making,⁴³ and the other simulated the effect of decision support alerts on order verification and medication reconciliation.⁵⁶

Discussion

The present study synthesized the evidence of the literature on simulation-based EHR training. We found considerable variation in the number of published articles from the year 2012 to the year 2019, with observational checklists and questionnaires as the most employed assessment method. Categorization of articles by Gaba’s dimensions revealed that the majority of simulation-based EHR training was aimed at emphasizing practical skills of individual primary care and ambulatory care medical trainees, and employed post-simulation debriefing as feedback method.

Few studies simulated the decision support management functionality of EHRs. This review found no study that simulated electronic communication and connectivity, patient support, administrative processes, and reporting, and population health.

Outcome evaluation of simulation-based EHR training based on Gaba’s dimensions of simulation

Consistent with Henriksen et al.,⁷⁶ the majority of included articles used pre- and post-intervention self-report questionnaires for subjective assessment of confidence, experience, and satisfaction. This is in part due to the effectiveness of self-report measures to assess the affective states of participants.⁷⁷ However, these measures suffer from recall bias,⁷⁸ and fail to provide useful insights into the etiology of learning and training issues associated with simulation-based EHR training. Physiological measures, including eye-tracking measures, have been used to measure cognitive load⁷⁹ and assess performance⁸⁰ during SBT. In this review, only one study⁶¹ employed eye-tracking measures to understand the etiology of learners struggling with clinical decision making.

Debriefing and feedback are important components of healthcare simulation.⁸¹ Most of the included studies reported facilitator-guided post-event debriefing, known to improve individual and team performance^82,83 as the debriefing method, a finding consistent with Sawyer et al.⁸¹ This is an important finding, given that post-simulation debriefing is a very effective component in the process of learning through healthcare simulation.^81,84,85 Post-simulation debriefing has been found to facilitate performance modification, and is best applied when participants have little clinical and simulation experience.^81,86

Our results suggest that simulation-based EHR training is applicable throughout the health care system. Medical trainees formed the highest number of simulation participants in the articles under review, underscoring the increasing role of simulation-based EHR as an innovative pedagogy to promote the acquisition of technical and non-technical skills needed to use EHRs in a safe environment before encounters with real patients.^4,87,88 Without such training, there is a risk of undermining preparedness for future independent practice.^89,90 Medical trainees have themselves recognized the importance of EHR training,⁹¹ which is known to enhance workflow and improve communication skills.^92,93 We were surprised by a single study targeting nurses and no study targeting managers and regulators. This could be due to a shortage of health professional faculty with health informatics competencies and experiences capable of designing curricula and teaching participants from different healthcare disciplines.⁹⁴ Also, as EHRs are often managed by departments within hospitals or health networks whose primary focus is patient care and not education, tailoring systems often requires time and money for reconfiguration, authorization, and maintenance thus limiting the training opportunities for diverse healthcare participants.⁹⁵ Thus, there is a need to explore other barriers for developing simulation based EHR training for participants of other healthcare disciplines.

The high number of articles targeted at individuals rather than teams suggests that most EHR simulations were designed for single persons.³¹ This may, in part, be because the current EHR structure does not facilitate interprofessional healthcare teams.^96,97 The EHR environment is not conducive for conducting team-based training, since EHR platforms focus more on single users than multiple users.⁹⁶ However, in real clinical environments teams of healthcare providers work collaboratively to accomplish shared goals.⁹⁸

Outcome evaluation of simulation-based EHR training based on EHR core functionalities

Mohan et al assert that simulations should replicate a significant proportion of data complexity expected in direct patient care.¹⁷ The health information and data functionality, which forms the basis for other EHR functionalities, was simulated by all articles in this review. Our results suggest a paucity of evidence documenting simulation of electronic communication and connectivity, patient support, administrative processes, and reporting and population health. This may be, in part, because simulations have focused on individuals and not communication among health care team members and other care partners.

Challenges of evaluating simulation-based EHR training

EHR-SBT affords researchers the opportunity to introduce an intervention and take repeated measures in the same simulated environment thereby enabling researchers to assess to what extent a phenomenon of interest can be explained by the intervention in question.⁹⁹ Furthermore, EHR-SBT can be used to evaluate how an intervention is implemented and received by study participants.¹⁰⁰ However, these are not without challenge. Studies with a small sample size are quick to conduct, especially if the research seeks to test a new hypothesis, however, findings can be difficult to interpret.¹⁰¹ Further, the overall number of studies with small sample size makes it challenging to assess the impact of any intervention.¹⁰² Integrating contextual factors into EHR-SBT to create realistic scenarios is challenging.³⁴ This requires a critical resource of healthcare information technology personnel who have an understanding of EHR functionality within the context of real-world patient care events.^34,103 Institutional policies are needed to ensure that all students in healthcare receive adequate EHR-SBT.⁷⁵ Not providing student support is a major barrier to implementing and using EHR-SBT.³⁴ Finally, it is difficult to use the evidence provided in the included articles to support the interpretations and uses of the results. This is, in part, because of heterogeneity of included studies and the lack of generalizable assessment methods to the effectiveness of EHR-SBT and the learning that they produce.¹⁰⁴

Future research

The majority of the studies in this scoping review had the primary aim of demonstrating the effectiveness of simulation-based EHR training as an intervention to enhance teaching, training, and to improve clinical skills. However, the body of knowledge would gain more value if future research aims at enhancing the effectiveness of EHR-SBT.^76,105 Task analysis, the decomposition of tasks into subtasks and then into human actions, allows researchers to identify potential actions and errors. To make simulation-based EHR training more effective, researchers need to perform task analysis to assess the skills and knowledge of entering participants.⁵⁸

Mohan et al.³¹ posited that since clinical care tends to rely on teamwork, simulations should be more team-focused than individual-focused.³¹ Consequently, there is a need to investigate the influence of simulation-based EHR training on team dynamics. Also, further research is needed in the use of simulation-based EHR for clinical rehearsals, to address teamwork, and to target non-physician clinicians like nurse practitioners and physician assistants.

Skill decay, “the loss or decay of acquired skills (or knowledge) after periods of nonuse”,¹⁰⁶ is an important issue that needs to be addressed.¹⁰⁷ It is not known how skill decays following a simulation-based EHR training. Furthermore, it is worth investigating what aspects of clinical performance are susceptible to skill decay.⁵⁸

The multiplicity of EHR vendors and the continuous development and modification of EHR interfaces make the training of students and residents quite challenging. Future research should investigate novel ways of catering for different vendor interfaces.

Study Limitations

There are some limitations in this review study that warrant discussion. First, we did not categorize simulations in the included studies by fidelity because it is a complex ill-defined concept.^108,109 Second, the quality of each identified article was not assessed. Consequently, the robustness or generalizability of findings was not evaluated. Third, we did not further categorize the included studies that employed post-event facilitator-guided debriefing. This was beyond the scope of our study. Fourth, our search strategy was designed to be rigorous and comprehensive, however, limitations such as not including simulation training as MeSH term has possibly impacted retrieving all possible studies.

Conclusion

Simulation-based EHRs enable healthcare providers to acquire skills needed to use EHRs in a safe environment before encounters with real patients. This scoping review aimed to map the literature on the major dimensions of simulation-based EHR training in healthcare, synthesize EHR functionalities across prior simulation-based EHR training studies, and outline areas where further research is needed. We identified that the majority of the included studies evaluated the effectiveness of simulation based EHR training as an intervention to enhance teaching, training, and improving clinical skills of individual clinicians rather than teams of clinicians. The body of knowledge will gain more value if future research aims to enhance the effectiveness of simulation-based EHR. Future research should focus on using simulation-based EHR training to achieve specific learning goals, investigating aspects of clinical performance that are susceptible to skill decay, and examining the influence of simulation-based EHR training on team dynamics.

Footnotes

Acknowledgment

The authors are grateful to the University of North Carolina (UNC) Health System and the Lineberger Comprehensive Cancer Center for their support.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research was supported in part by grant R18HS023458 from the Agency for Healthcare Research and Quality (AHRQ). The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

ORCID iDs

Karthik Adapa

Lukasz M Mazur

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State of the evidence on simulation-based electronic health records training: A scoping review

Abstract

Keywords

Introduction

Method

Stage 1: Identifying the research question

Stage 2: Identifying relevant studies

Stage 3: Study selection

Stage 4: Data Items and Data Collection Process

Stage 5: Collating, summarizing, and reporting the results

Results

Search results

Study characteristics

Aim of simulation

EHR Functionalities

Health information and data

Results management

Order entry/order management

Decision support management

Discussion

Outcome evaluation of simulation-based EHR training based on Gaba’s dimensions of simulation

Outcome evaluation of simulation-based EHR training based on EHR core functionalities

Challenges of evaluating simulation-based EHR training

Future research

Study Limitations

Conclusion

Footnotes

Acknowledgment

Declaration of conflicting interests

Funding

ORCID iDs

References