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. 2019 Nov 11;8(3):215–227. doi: 10.1080/20476965.2019.1687263

The emerging literature for the triad of health informatics, healthcare quality and safety, and healthcare simulation

Shikha Modi a, Bunyamin Ozaydin a, Ferhat Zengul a, Sue S Feldman a,✉
PMCID: PMC6896445  PMID: 31839933

ABSTRACT

The areas of health informatics, healthcare quality and safety, and healthcare simulation are often thought of as separate domains. The purpose of this position paper is to report on the interdependence that is emerging as an important triad across the healthcare/health system continuum. A qualitative review of 24 studies suggests the interdependence of health informatics, healthcare quality and safety, and healthcare simulation reaches much broader than traditional utilisation of simulation. We suggest ways that organisations can take advantage of the interdependence of this triad across a broader variety of healthcare environments, including teamwork, communication, and complex system relationships. In conclusion, the reviewed 24 studies suggest that the research in the triad focuses on simulation education and computerised simulation, and when coupled with health informatics, bears greater strength on quality improvement or patient safety.

KEYWORDS: Health, healthcare, health care, hospital, informatics, information technology, quality, patient safety, simulation

1. Introduction

One of the primary motivators behind the promotion of healthcare simulation is to support improved quality of care and patient safety by reducing medical errors and improving patient outcomes in a cost-effective manner (Qayumi et al., 2014). In 2011, the Institute of Medicine (IOM) released a report on Health Information Technology (health IT) and patient safety that provided a clear articulation of risks due to electronic health records (EHRs) to patients (Meyers, 2012). IOM is a well-respected US-based non-profit organisation that provides guidance on issues related to improving the quality of care. Two different polls of a national patient safety audience conducted by Texas Medical Institute of Technology confirmed that safety related to the electronic health record (EHR) system was the top hazard of concern in 2013 (Denham CRC, Swenson, Henderson, Zeltner, & Bates, 2013). The second potential hazard identified was related to interoperability failures of medical devices and IT systems (Denham CRC et al., 2013). We focus on literature that includes health informatics, healthcare quality and patient safety, and simulation.

Health informatics, healthcare quality and safety, and healthcare simulation definitions may change depending on the domain of use or the context of use. Health Informatics is defined as: “the interdisciplinary field that studies and pursues the effective uses of biomedical data, information, and knowledge for scientific inquiry, problem-solving and decision-making, motivated by efforts to improve human health (Kulikowski et al., 2012).” Healthcare quality and safety are defined separately, but operationalised jointly. “Quality [is] an optimal balance between possibilities realised and a framework of norms and values (Mitchell, 2008).” Safety is defined as, “the prevention of harm to patients (Erickson, Wolcott, Corrigan, & Aspden, 2003).” From the healthcare quality definition, one can conclude that how healthcare quality is defined changes from organisation to organisation dependent on the baseline standards (norms/values) and target metrics (possibilities realised) set by those at each organisation. Even though these baseline standards are set at each organisation, these standards might be influenced by national standards that are tied to the various reimbursement models. It is important to note that a variety of emerging payment models have set baseline standards and target metrics for healthcare quality (Song et al., 2012). Moreover, there are quality standards that are developed and recommended by organisations such as National Quality Forum, the Joint Commission, the Agency for Healthcare Quality and Research (AHRQ), Centers for Medicare & Medicaid Services (CMS), and American HealthCare Association (AHCA). The literature reviewed for this position paper does not separate out organisation set standards from payment model standards and other recommended standards.

We recognise that the definition of simulation may vary by domain (for instance engineering vs. healthcare). For the purpose of this paper, we define simulation through three different lenses: 1) as an interactive technique, rather than a technology (Gaba, 2004), 2) as a process used in education (Chisari et al., 2005; Decker, Sportsman, Puetz, & Billings, 2008), and 3) as activities and techniques closely mirroring a clinical environment (Jeffries, 2005). Each definition is described with detailed examples in the Healthcare Simulation section below.

Current literature tends to focus on health informatics, healthcare quality and safety, and healthcare simulation as separate entities, perhaps with only tangential relationships to one another. For example, one literature review examined health informatics for healthcare quality and safety, but did not consider the role of healthcare simulation (Feldman, Buchalter, & Hayes, 2018). Others examined healthcare simulation as it relates to healthcare quality and safety, but did not consider the role of health informatics (Ravindran, Thomas-Gibson, Murray, & Wood, 2019; Seaton et al., 2019). In the following three sections, we examine literature for health informatics, healthcare quality and safety, and healthcare simulation, respectively. Then, in the findings section, we illuminate the intersection of the three domains.

1.1. Health informatics

Health informatics in general, and health IT more specifically, has been regarded as that which will contribute to decreasing healthcare cost, increasing healthcare quality, and improving patient satisfaction; however, the literature is mixed in this regard (Buntin, Burke, Hoaglin, & Blumenthal, 2011; Goldzweig, Towfigh, Maglione, & Shekelle, 2009; Karsh, Weinger, Abbott, & Wears, 2010; Marmor, Oberlander, & White, 2009). Benefits of health informatics have been illustrated, though, in terms of the ability to capture data from disparate data sources and create visual composites (i.e. dashboards) that otherwise cannot be gleaned individually. EHRs involve dashboards that enable alerts as well as guidelines that are set of rules used to make diagnostic-related decisions. (Ancker, Kern, Abramson, & Kaushal, 2012; Colicchio et al., 2016; Gupta & Kaplan, 2017; Kothamasu, Huang, & VerDuin, 2006; Stusser 2013). Dashboards allow for standardisation of analytical processes and metrics and provide a simultaneous and shared viewing of the resulting visualisations across organisations (Stadler, Donlon, Siewert, Franken, & Lewis, 2016; Weiner, Balijepally, & Tanniru, 2015). Although dashboards can have alerts that signal when a certain metric is out of range, alerts more typically used in decision-making are those that are contained in the EHR. Such alerts have been shown to have a significant impact on the quality of care provided (Eslami, de Keizer, & Abu-Hanna, 2008; Fiks, Grundmeier, Biggs, Localio, & Alessandrini, 2007; Smith et al., 2006; Strom et al., 2010). Embedding clinical decision support system into the EHR helps with the diagnosis of a disease as well as monitoring the dynamically changing health status of the patient (Stusser RJD, 2013).

1.2. Healthcare quality and safety

Globally, metrics are often used as a “measure” of quality and safety; however, metrics may not tell the complete story of how they are enacted within the organisation (Martin, McKee, & Dixon-Woods, 2015).

Beyond metrics, quality and safety have been described as a cycle of event prevention, identification, and action (Feldman et al., 2018). Event prevention is defined as preventing an event from occurring, identification is defined as identifying that an event is about to occur, and action is defined as actions taken as a result of an event having already occurred (Feldman et al., 2018). Quality and safety cannot be singularly focused on metrics, but rather must include an understanding for where in the process the metrics are being operationalised so that an increasing amount of events are prevented than acted upon.

Specifically to healthcare, some have posited that various payment reform models and the attention to quality metrics may leave some vulnerable populations with different health outcomes when compared with other populations (DeCamp et al., 2014; Pollack & Armstrong, 2011), metrics have provided some very impactful organisational change in terms of quality of care, patient safety, and employee culture (Chassin, Loeb, Schmaltz, & Wachter, 2010; Smithback, Spector, Gabriela Dieguez, & Mirkin, 2012). This position paper focuses specifically on healthcare quality and safety.

1.3. Healthcare simulation

As previously mentioned, the literature situates simulation globally through three lenses: technique, education, and activities. In terms of technique, the literature suggests improved task proficiency can be gained from computer modelling (Schaaf, Funkat, Kasch, Josten, & Winter, 2014), teaching communication, interpersonal skills, decision-making abilities (Wiig SG et al., 2014), and performing traditional procedures in simulated environments (Fulton, Buethe, Gollamudi, & Robbin, 2016). Additional benefits have been seen in the areas of social accountability, health and gender equality, social justice, and human rights (Aronowitz 2017). Modelling can be creating a model or prototype of a technology or intervention in order to test it before implementing it. Simulation and modelling can be considered separately or modelling can also be considered as a type of simulation activity that is expressed in the literature as a way to understand the interactions and interdependencies of complex information and physical systems, business/workflow processes, people, and buildings (Eddy 2012; Gonzalez Bernaldo de Quiros, Dawidowski, & Figar, 2017; Schaaf et al., 2014)

Simulation, as a complement to traditional healthcare education, has been shown to increase students’ feeling of confidence (Heskin, Simms, Holland, Traynor, & Galvin, 2019; Kim, Fisher, Delman, Hinman, & Srinivasan, 2016; Samawi, Miller, & Haras, 2014; Wright et al., 2018), autonomy (Kim et al., 2016), and critical thinking skills (Samawi et al., 2014). Simulation-based education with deliberate practice is considered to be more useful when it comes to achieving healthcare quality and patient safety outcomes, compared to traditional clinical medical education (McGaghie, Issenberg, Cohen, Barsuk, & Wayne, 2011). Inclusion of clinical experiences through simulation in undergraduate training can replace traditional clinical placement hours (McGaghie et al., 2011). Furthermore, simulations allow education around the evaluation of the course, causes, and consequences of medical errors (Jones, Passos-Neto, & Braghiroli, 2015).

In terms of activities related to healthcare, simulation has been increasingly used to support, substitute, or extend research activities, such as randomised control trials (RCTs), in order to save time and cost (Coiera et al., 2007). Other activities carried out in a simulated environment are around user testing of health IT (Borycki et al., 2013), diagnostic reasoning with EHRs (Borycki et al., 2013), and clinical decision-making (Gold, Tutsch, Gorsuch, & Mohan, 2015). Simulated high-fidelity case scenarios offer a viable platform for teaching and evaluating learners relative to integration of technique, education, and activities (Carlson, Abel, Bridges, & Tomkowiak, 2011; Leigh, Stueben, Harrington, & Hetherman, 2016; Liaw et al., 2015). Additionally, when health IT products are modelled and tested in a simulated environment, the end-user can experience less frustration and potentially costly development errors are avoided (Haun et al., 2017; Pennathur et al., 2010).

Current literature discusses health informatics and healthcare simulation, as a single detailed scenario or a combination of scenarios that resemble reality (Jeffries, 2005). According to current literature, this resemblance to reality through healthcare simulation in conjunction with technology serves multiple purposes: replacement of traditional medical education, training employees, performance assessment of HIT or employees, rehearsing clinical scenarios, or research. The ultimate goal is to utilise healthcare simulation and technology to achieve healthcare quality outcomes and patient safety (Gaba, 2004). While current literature includes health informatics, healthcare quality and safety, and healthcare simulation, the emerging interdependence of these three components has not been discussed clearly. The paper synthesises the emerging peer-reviewed literature on the triad of health informatics, healthcare quality and safety, and healthcare simulation, to set an agenda for more attention to the triad, or more simply stated the intersection of all three.

2. Materials and methods

Multiple searches were performed in four well-established databases (CINAHL, Embase, PubMed, and Scopus) by using keywords health, healthcare, health care, hospital, informatics, information technology, quality, patient safety, and simulation. Given that these keywords are generic and some of them are among PubMed MeSH major topics, the initial searches generated very high numbers when these keywords searched separately.

Therefore, to optimise the chances of finding relevant studies on the triad of health informatics, quality and safety, and simulation, the following filters were applied into the searches: 1) keywords in the title or abstract, 2) published in English, and 3) presence of all three dimensions of the explored triad within the abstract or the title and the following search logic was used across each database: (((((((health[Title/Abstract]) OR healthcare[Title/Abstract]) OR hospital[Title/Abstract]) OR health care[Title/Abstract])) AND ((quality[Title/Abstract]) OR patient safety[Title/Abstract])) AND ((informatics[Title/Abstract]) OR information technology[Title/Abstract])) AND simulation[Title/Abstract]. As shown, these searches were combined with consistent use of AND and OR Boolean operators.

The search generated 232 articles including 50 from CINAHL, 40 from Embase, 32 from PubMed, and 110 from SCOPUS. The removal of 56 duplicates resulted in 176 for further review. Articles were then excluded if they met any of the following criteria: 1) not relevant to the explored triad, 2) non-peer reviewed, 3) non-empirical, 4) not a journal article, and 5) not in English. After application of exclusion criteria, 74 studies remained for a full-text review. After reviewing the full text of these 74 studies, 56 were excluded as they did not include all the components of the triad. Six additional studies were identified through manual reference searches, resulting in total of 24 studies for analysis. Figure 1 shows this process in a flow diagram.

Figure 1.

Figure 1.

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Flow diagram of included studies (adapted from (Liberati et al., 2009).

The 24 studies selected for inclusion are shown in Table 1 in alphabetical list ordered by first author.

Table 1.

Included studies in alphabetical list order by first author.

Citation Title Sample/Setting Findings Quotations with the Intersection of the Triad
Count %
Ammenwerth et al. (2012) Simulation studies for the evaluation of health information technologies: experiences and results 50 total simulation runs – 10 doctors, 5 patients

  • Simulation studies helpful to test newly developed health IT systems that are not sufficiently mature to enter field evaluation

  • Simulation studies are time-consuming and require appropriate training of participants along with time and cost resources

 1 1.89%
Anderson (2009) The need for organisational change in patient safety initiatives 4 interventions simulated including computerised physician order entry, decision support systems, and a clinical pharmacist on hospital rounds

  • Simulated interventions indicated a mismatch between patient safety goals and hospital actions to decrease the risk of potential medication errors

  • In addition to information technology and clinical pharmacist rounds, integration of root cause analysis to address the reported error in the simulation model predicted reduction of approximately 70% in the error rate over time

 3 5.66%
Aronowitz (2017) Using objective structured clinical examination (OSCE) as education in advanced practice registered nursing education OSCE implementations conducted at 2 different sites

  • OSCEs can be utilised for education and examination purposes

  • Starting OSCEs with basic scenarios and gradually increasing complexity during program is described as the best method of implementing OSCEs

 1 1.89%
Ben-Assuli OZ et al. (2016) Cost-effectiveness evaluation of EHR: Simulation of an abdominal aortic aneurysm in the emergency department 26 trials conducted with 26 physicians

  • The results of simulating a case of abdominal aortic aneurysm indicated EHR utilisation in the emergency department increases the quality adjusted life years of the patient and improves medical decision-making

  • This simulation also indicated that EHR utilisation also contributes in making cost-effective decisions for this particular case

 2 3.77%
Borycki et al. (2013) Usability methods for ensuring health information technology safety: evidence-based approaches-Contribution of the IMIA working group health informatics for patient safety This paper discusses 8 different phases to test the usability of IT system through clinical simulation and naturalistic testing discussed

  • An analogous phased approach important to test HIT system before implementation

  • Clinical simulation testing required in the context of the real use of systems in different healthcare settings

 8 15.09%
Carlson et al. (2011) The impact of a diagnostic reminder system on student clinical reasoning during simulated case studies 20 fourth-year medical students

  • Students reported the diagnostic reminder system software relatively simple to learn and indicated that the software helped contemplates diagnostic options that were not originally considered

  • The software was considered to be valuable in the case of simulated scenarios

 2 3.77%
Carney et al. (2014) Using computational modelling to assess the impact of clinical decision support on cancer screening improvement strategies within the community health centres 44 community health centres

  • The authors observed significant-simulated disparities in organisational learning over time between community health centres that began the simulation with high and low clinical decision support capacity

 3 5.66%
Gonzalez Bernaldo de Quiros et al. (2017) Representation of people’s decisions on health information systems This paper includes interdisciplinary discussions of teams that were involved in the Hospital Italiano de Buenos Aires (HIBA), a university hospital’s health information system (HIS) implementation process

  • The interdisciplinary teams discussed HIS facing the need and challenge to reflect social human processes, included in constructivist and complexity theories (frameworks of human sciences to comprehend human learning and socio-cultural changes)

  • Utilisation of various system simulation tools is proposed to explore the functionality of the system and to evaluate the impact of different interventions

 1 1.89%
Denham CRC et al. (2013) Safe use of Electronic Health Records and Health Information Technology systems: Trust but verify Results from a poll of health care leaders from 76 organisations worldwide discussed along with results of national deployment of Texas Medical Institute of Technology’s electronic health record computerised prescriber order entry (TMIT EHR-CPOE) Flight Simulator verification test

  • HIT hazards were of greatest concern out of 30 hazards presented

  • Electronic health record-computer prescriber order entry (EHR-CPOE) simulation helped identify hazards in HIT on a national scale

 5 9.43%
Eddy DMS (2012) A simulation shows limited savings from meeting quality targets under the Medicare shared savings program Simulated Medicare patient population

  • Findings indicate that improvement in diabetes quality measures would reduce Medicare costs by a very low margin

  • For accountable care organisations, it will be important to improve utilisation of information technology and care coordination in order to achieve greater savings

 1 1.89%
Gold et al. (2015) Integrating the Electronic Health Record into high-fidelity interprofessional intensive care unit simulations 3 teams (2 nurses, 1 critical care fellow, and 1 ICU resident)

  • Integration of EHR into high-fidelity team-based simulations helped identify multiple safety issues (alert fatigue, negative effect on interprofessional communication, and issues related to selective data collection) directly related to the EHR

  • Integration of EHR into high-fidelity team-based simulation helped establish an infrastructure to focus on educational strategies and to implement informatics solutions to resolve identified safety issues

 1 1.89%
Haun et al. (2017) Veteran’s preferences for exchanging information using veterans affairs health information technologies: Focus group results and modelling simulations 2 rounds of focus group interviews- a single cohort of 47 veterans and 1 female caregiver (included simulation modelling activities and a self-administered survey)

  • Secure Messaging, Appointment Reminders, and RX Refill were the most commonly used resources by participating veterans

  • A strong preference for standardised, integrated, and synchronised user-friendly interface designs was expressed

  • Findings from this study influenced the design of the My HealtheVet patient portal and prioritisation of approaches that promote integration between VA HIT platforms

 1 1.89%
Heskin et al. (2019) A systematic review of the educational effectiveness of simulation used in open surgery 6 studies reviewed

  • Two studies indicated simulator to be as good as an animal model of much higher fidelity

  • Future studies needed to provide higher evidence for simulator’s educational value

 1 1.89%
March et al. (2013) Use of simulation to assess electronic health record safety in the intensive care unit: a pilot study 38 participants including 9 interns, 10 residents, and 19 fellows

  • Regardless of the level of training, average users of the EHR have a poor rate of recognising abnormal patterns in patient condition or medical errors

  • Simulation training provides an organised mechanism to both restructure and redesign EHR education

 2 3.77%
McGaghie et al. (2011) Does simulation-based medical education with deliberate practice yield better results than traditional clinical education? A meta-analytic comparative review of the evidence 14 studies reviewed

  • Compared the efficacy of traditional clinical education towards skill acquirement objectives vs simulation-based medical education with deliberate practice

  • Results indicated that for the purpose of achieving specific clinical skill acquirement goals, simulation-based medical education with deliberate practice is more efficient than traditional clinical medical education

 2 3.77%
Nelson (2003) Using simulation to design and integrate technology for safer and more efficient practice environment Sample size is not discussed

  • A Patient Safety Simulation Laboratory (PSSL) concept introduced along with a description of the laboratory at the VA Hospital in Tampa, Florida

 1 1.89%
Parush et al. (2017) Can teamwork and situational awareness (SA) in ED resuscitations by improved with a technological cognitive aid? Design and a pilot study of a team simulation display

  • Interviewed 18 resuscitation team members to discuss their roles, objectives, patterns, and communication

  • Observed 30 video recordings of simulated resuscitation training of residents

  • Observed 12 live resuscitations to document the type, nature of teams, and information exchange content

  • Compared to no situation display, resuscitation teamwork, as evaluated by the Clinical Teamwork Scale (CTS), was overall better with the situational display

  • The study implied during a resuscitation event, better teamwork and communication can be facilitated with a situational display

 2 3.77%
Pennathur et al. (2010) Development of a simulation environment to study emergency department information technology Simulation of an emergency department whiteboard developed, sample size is not discussed

  • Created a patient tracking system display simulator that could investigate the impact of display parameters and ED operations on user performance

 4 7.55%
Qayumi et al. (2014) Status of simulation in health care education: an international survey 42 simulation centres worldwide

  • Results indicate that despite major interest and enthusiasm in the healthcare community, utilisation of simulation in healthcare education is restricted to specific areas

  • Some institutions do not include simulation education in their budgets

 3 5.66%
Schaaf et al. (2014) Analysis and prediction of effects of the Manchester Triage System on patient waiting times in an emergency department by means of agent-based simulation 10 simulation runs to analyse how a triage nurse and different factors impact the patient waiting times

  • The simulation model provided an important contribution to the implementation of standardiseda procedure for investigating and monitoring emergencies

  • The simulation model also allowed exact mapping of the process flow and the waiting times of the emergency department

 2 3.77%
Stocker et al. (2012) Impact of an embedded simulation team training programme in paediatric intensive care unit: a prospective, single-centre, longitudinal study 219 multidisciplinary health care professionals – simulation sessions and anonymous evaluation questions

  • Repeated simulation training exposure was indicated as being effective to crisis resource management training, compared to single, isolated exposure

 2 3.77%
Stusser RJD (2013) Quality and cost improvement of healthcare via complementary measurement and diagnosis of patient general health outcome using electronic health record data: Research rationale and design This paper discusses the rationale and design of a research program for a balanced assessment and diagnostic clinical decision support system of the fluctuating health status of the patient using EHR

  • Clinical simulation and optimisation of general health CDSS/EHR trials conducted

 1 1.89%
Wallner et al. (2016) Cost effectiveness and value of information analyses of islet cell transplantation in the management of ‘unstable’ type 1 diabetes mellitus The population was a hypothetical cohort of patients who met the transplantation inclusion criteria and other characteristics defined by the authors

  • Compared to the conventional therapy, current transplantation technology provides significant improvements in health outcomes for highly selected patients with ‘unstable’ type I diabetes

 1 1.89%
Wiig SG et al. (2014) Safer@home-Simulation and training: the study protocol of a qualitative action research design Qualitative action research design -to be conducted in close collaboration with a multidisciplinary team consisting of members from two Norwegian municipalities, in addition to clinical education and simulation experts, service user (patient) representatives

  • Study protocol involves developing and investigating simulation-based telecare training programmes to help the use of videocall technology in elderly home care

 3 5.66%
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ATLAS.ti 8.2, a qualitative data analysis tool, was used to code and categorise the final 24 studies. All studies were loaded into ATLAS. ti as full-text documents named by first author and article title. Qualitative data analysis software is appropriate for this type of analysis as it provides an opportunity to utilise a cyclical and iterative approach to data analysis that would have been difficult using a spreadsheet application (Friese, 2014). Additionally, the ability for inter-coder reliability is built into the software creating a more transparent means than traditional coding methods for multiple coders to collaborate and compare codes (Friese, 2014). All documents were coded by two researchers. The process began by first reading each paper to understand the context as it related to finding the intersection of health informatics, healthcare quality and safety, and healthcare simulation. Coding began with one researcher developing the coding structure. The coding phase included three different sub-phases:

  1. Material extraction (open coding)

  2. Themes extraction (axial coding)

  3. Relationship generation (selective coding).

When the first researcher finished coding, the ATLAS. ti project bundle was emailed to the second researcher for coding. During coding by the second researcher, no new categories were added. The data were reviewed a minimum of three times by the two researchers responsible for coding.

Material extraction (open coding) involved breaking down each paper to code passages, or quotations. As this process matured, logical categories were formed. Open coding resulted in a large amount of codes and categories and subcategories. Throughout this process, the codes, categories, and subcategories were continually compared to one another for opportunities for combining codes, categories, or subcategories (similarities) and the need to separate codes, categories, or subcategories (differences) (Glaser & Strauss, 2017). Following the open coding phase, axial coding was conducted. The axial coding sub-phase focused on extracting themes or categories. This sub-phase also involved generating subcategories based on relationships between each categories through refinement or combination techniques (Strauss & Corbin, 1998). This process yielded fewer, more focused themes or categories and subcategories. Finally, relationship generation, or selective coding was conducted to result in understanding the intersection of health informatics, quality and safety, and simulation. We acknowledge that the process described is typical for developing a grounded theory (Glaser & Strauss, 2017; Strauss & Corbin, 1998); however this process was the most efficient process for being able to narrow the codes and themes and expose the relationships between health informatics, healthcare quality and safety, and healthcare simulation. Finally, we utilised the network feature in ATLAS.ti to validate that each paper did, in fact, belong to each overarching category: health informatics AND healthcare quality and safety AND healthcare simulation. For the network analysis, the three simulation codes illustrated in Figure 2 were merged into one code and named “simulation.” The three simulation codes corresponded to the three simulation definitions (technique, education, and activity) described earlier.

Figure 2.

Figure 2.

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ATLAS.ti coding structure.

Through the coding and category generation process, additional categories were needed. For example, an implications category was added as we felt that it was relevant to the findings. An introduction category was also added. This category assisted with writing the introduction to this manuscript and the quotations coded in this category did not necessarily factor into the results presented. Studies that did not fit into a category were coded into a miscellaneous category. The final coding structure is illustrated in Figure 2. In this illustration, the term “grounded” refers to the frequency of the code relative to the code category.

Examples of interdependence of the triad and potential implications of this interdependence are discussed in the following sections.

3. Findings

Our search resulted in 24 papers that we concluded should be at the intersection of health informatics AND healthcare quality and safety AND healthcare simulation. In order to demonstrate and emphasise the intersection of the triad, first, we will deconstruct the triad. The deconstruction of the triad involves discussing how many sections from the reviewed literature were coded under different components of the triad. For example, one of the studies mentioned using a simulated Medicare patient population to test the effect of an intervention on health and cost outcomes, hence, this particular section in that paper was coded under the “Simulation Computerized/Technique” category (Borycki et al., 2013). The deconstruction component is followed by the results relative to the intersection of health informatics, healthcare quality and safety, and healthcare simulation.

In the 24 papers reviewed, health informatics had 77 coded quotations, healthcare quality and safety had 77 coded quotations, and healthcare simulation had 101 coded quotations. Healthcare simulation was coded into three subcategories: computerised (n = 34), device (n = 43), and education (n = 40) for 117 total codes between the three categories. Because quotations could be coded under multiple subcategories, the total healthcare simulation codes (n = 101) is fewer than the individual group healthcare simulation codes (n = 117). For example, one of the studies discusses the importance of developing healthcare simulation activities and utilising them to improve user education as well as test educational techniques and EHR redesign (March et al., 2013). This particular quotation was coded under both device and education.

Not all the papers reviewed described a clear intersection of the triad. For example, one of the papers used a healthcare simulation model to compare differences in cost-effectiveness and clinical outcomes for islet cell transplantation technology and conventional therapy for type I diabetes patients (Wallner, Shapiro, Senior, & McCabe, 2016). Although the integration of healthcare simulation and health informatics is clearly described, healthcare quality and safety outcomes were implied (Wallner et al., 2016). On the other hand, another paper clearly described a need for clinical simulations to identify technology-induced errors, indicating a clear intersection of the triad (Borycki et al., 2013).

Out of 24 papers, a total of seven papers had three or more quotations that indicated an interdependence between the components of the triad, which accounted for more than half (54.71%) of the total quotations with the intersection of the triad. The remainder of the papers had two or fewer quotations that indicated an interdependence between the components of the triad (Table 1).

The next section elaborates the findings at the intersection of health informatics, healthcare quality and safety, and healthcare simulation as defined earlier in this paper. Creating a network in ATLAS. ti helps to visualise this intersection by showing the relationships between the categories and each paper (see Figure 3.)

Figure 3.

Figure 3.

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Network diagram showing that each paper was coded in all three categories: health informatics AND simulation AND quality and safety.

3.1. The intersection of health informatics, healthcare quality and safety, and healthcare simulation

Findings from this qualitative review of the literature suggest that healthcare simulation is used as a vehicle to improve healthcare quality and safety; health informatics facilitates healthcare quality and safety; and healthcare simulation tests and trains health informatics systems.

For example, healthcare simulation led to an improvement in recognition of patient safety issues such as medication errors (Anderson, 2009), as well as communication errors, and data gathering/cognitive errors (Gold et al., 2015). Healthcare simulation also improved nontechnical skills such as teamwork and communication (Gold et al., 2015; Gonzalez Bernaldo de Quiros et al., 2017; Stocker et al., 2012). Additionally, simulation education and testing increased healthcare confidence (Aronowitz 2017; Heskin et al., 2019). Compared to traditional clinical medical education, simulation-based medical education is considered to be superior in the acquisition of complex medical skills such as cardiac life support, laparoscopic surgery, and cardiac auscultation, etc. (McGaghie et al., 2011).

Computer-based simulations, employed by computational modelling, were shown to be used to investigate and analyse complex systems and describe critical associations with respect to clinical decision support outcomes and cancer screening improvements. These type of simulations can imitate and forecast real-world behaviour under different assumptions, conditions, and situations (Carney et al., 2014; Parush et al., 2017). Computerised simulations were also shown as a means to conduct random controlled trials (RCTs) without involving real patients, saving cost and time (Qayumi et al., 2014; Stusser RJD, 2013).

Another important finding was the use of simulation for testing and teaching healthcare software applications, such as the EHRs (Anderson, 2009; Gold et al., 2015), electronic medication management systems, electronic patient tracking tools, and clinical decision support systems (Borycki et al., 2013; Carlson et al., 2011; Denham CRC et al., 2013; March et al., 2013; Nelson, 2003). These healthcare software applications are used in clinical environments and linked to healthcare quality and safety. For example, one study discussed the strategy for root cause analysis during simulation testing of the new IT system to help identify medication errors and a large decrease in the error rate over time (Anderson, 2009). Simulations are not always about training people. Simulations are considered as a useful approach to: detect technology-induced errors (Ammenwerth et al., 2012; Ben-Assuli OZ, Sagi, Ironi, & Leshno, 2016; March et al., 2013; Wallner et al., 2016), verify quality and improve HIT systems (Denham CRC et al., 2013), provide empirical evidence regarding how an HIT system may impact information seeking, decision-making, workflow (Borycki et al., 2013), and diagnostic reasoning (Carlson et al., 2011).

4. Discussion

The primary goal of this paper was to increase awareness on the emerging triad of health informatics, healthcare quality and safety, and healthcare simulation and to set an agenda for further work in this area. Figure 4 summarises different components of the triad discussed in the 24 studies reviewed by illustrating the 12 themes relative to each components of the triad. For example, papers focusing on health informatics discussed healthcare quality and safety and healthcare simulation in terms of electronic health records (EHR) (Ben-Assuli OZ et al., 2016; Borycki et al., 2013), predictive analytics (Carlson et al., 2011), outcomes (Ammenwerth et al., 2012; Stusser RJD, 2013), and computer modelling (Carney et al., 2014). Similarly, papers focusing on healthcare quality and safety, discussed health informatics and healthcare simulation in terms of risk reduction (Anderson, 2009), improved confidence (Aronowitz 2017), teamwork (Parush et al., 2017; Stocker et al., 2012), and skills (Heskin et al., 2019; Stocker et al., 2012). Some themes were combined resulting in the 10 themes illustrated.

Figure 4.

Figure 4.

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Illustration of health informatics, quality & safety, and simulation themes.

Several key points can be gleaned from the literature that lies at the intersection of health informatics, healthcare quality and safety, and healthcare simulation. First, it is important to note that Gaba (Gaba, 2004) posits that simulation is “a technique, not technology … ” yet the literature suggests a stronger relationship between simulation and technology (Ben-Assuli OZ et al., 2016; Wallner et al., 2016). For example, Wallner et al. (2016) used a computerised simulation model to test the difference in outcomes of two different treatments for type I diabetes (Wallner et al., 2016). Similarly, Ben-Assuli OZ et al. (2016) tested the cost-effectiveness of EHR by simulating an abdominal aortic aneurysm (Ben-Assuli OZ et al., 2016). It is our position that simulation will be used to test various therapeutic modalities that consider patient-specific factors to result in safer care delivery.

Second, in terms of implications for research, the interdependence of the triad of health informatics, healthcare quality and safety, and healthcare simulation has been illuminated. There is an underabundance of peer-reviewed publications, which is indicative of the lack of awareness that exists or the value in the triad of health informatics, healthcare quality and safety, and healthcare simulation. The future of research in this intersection, is well-positioned to focus on developing full-scale integration of healthcare simulation into undergraduate and graduate programme curricula (Qayumi et al., 2014), adding simulation to test HIT systems in healthcare organisations (Parush et al., 2017), and simulation techniques to conduct randomised controlled trials without involving live patients (Coiera et al., 2007; Stusser RJD, 2013).

In summary, we feel that advancements are needed in medical and consumer education. Decision-makers from education should consider increasing simulation hours in the curriculum in place of more resource-intensive clinical placement hours. Doing so is aligned with the literature to provide a risk-free environment for competence at any level (Anderson, 2009; Gold et al., 2015; McGaghie et al., 2011). In addition to training students and healthcare providers, simulation education strategies may be used to train patients or their caretakers on home healthrelated equipment (Wiig SG et al., 2014). For health system decision-makers, using simulation as a mechanism to test new health IT systems used in and around patient care may provide a different level of knowledge to inform purchases (Parush et al., 2017). Simulation as a technology for modelling could also benefit healthcare researchers by providing the ability to model RCTs, thus conserving human, fiscal, and time resources (Coiera et al., 2007; Stusser RJD, 2013).

5. Conclusion

This position paper investigated the triad of health informatics, healthcare quality and safety, and healthcare simulation and is intended to bring to light the intersection of these three traditionally separate disciplines in healthcare. Synthesis of the literature described how organisations can take advantage of the interdependence of health informatics, healthcare quality and safety, and healthcare simulation across a broader variety of healthcare environments, including teamwork, communication, and complex system relationships. Recognition of this interdependence could be helpful to each discipline individually (health informatics, healthcare quality and safety, or healthcare simulation) or collectively in areas where all three exist. Lastly, we make a case for thinking of health informatics, healthcare quality and safety, and healthcare simulation as an interdependent triad that, when considered together, hold promise for a variety of healthcare organisations to realise greater gains in healthcare quality and patient safety.

Disclosure statement

No potential conflict of interest was reported by the authors.

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