- Open Access
Documentation framework for healthcare simulation quality improvement activities
© The Author(s) 2017
- Received: 30 April 2017
- Accepted: 11 October 2017
- Published: 17 October 2017
- Quality improvement
- Latent errors
- Latent threats
- In situ simulation
- Systems testing
- Facility testing
- Healthcare failure mode effects analysis
- Point of care
Medical simulation methodology is increasingly being utilised beyond the traditions of education to evaluate patient care workflows, processes, and systems within the health context. A literature review of healthcare facility testing showed that individual clinical departments and singular patient flow processes had been tested under a variety of simulated conditions, such as virtual environments, table top exercises, and live simulation exercises. Each method demonstrated strengths and weaknesses in finding active or latent system failures [1–4]. With the building of our new healthcare facility, it was decided that live (physical) testing of the environment using a medical simulation methodology was the best approach to bridge the gap from architectural plans, to real-world efficient and effective patient care, and for orientation and training of teams to their new environment [4–7]. Although the hospital had yet to open, testing the systems under immersive simulated conditions at the point of care delivery effectively replicated real-world workflows and systems [1, 6]. Within Australia, two new hospitals reported using medical simulation to test specific clinical scenarios and patient flow journeys prior to service delivery. Unfortunately in both instances, testing beyond the first round did not occur due to funding and human resource limitations. This led to considerable staff workarounds, rectification of process errors after commencement of patient care, and unfavourable media reports [8, 9].
This paper will provide an example of an approach to identify latent system issues using live medical simulation and the development of an associated documentation framework. The documentation framework aims to help structure medical simulation scenarios specifically designed for quality improvement activities, and to capture and report findings of system deficits identified in the simulations, to key decision-makers.
Our metropolitan mixed public and private healthcare organisation built a satellite health service outside the capital city specialising in day oncology and day surgery, with 64 short stay surgical and medical inpatient beds. Two significant dilemmas were apparent: over half of the staff were new to the main organisation and no onsite critical care support was available. Additionally, the new facility was adapting existing processes from the main central facility, where services were not similar. A serious potential risk to patient safety was noted. Organisational priorities for opening included efficient and effective staff training and systems designed to ensure patient safety in concert with excellent patient experience. Testing of a whole healthcare facility is a large undertaking. It was decided that testing needed to occur over multiple iterations, allowing for system improvements to be made and retested. The final testing cycle was a 24-h live simulation activity. A critical part of the activity was data collection, which led to the development and utilisation of two new tools: the Simulation-based Quality Improvement Tool (SQIOT) and the Healthcare Failure Modes Effects Analysis (HFMEA) Summary Report.
Four phases of implementation over the life cycle of the project
Planning and preparation Simulation centre
a) SQIOT drafted as scenario template and for data collection during iterative simulations
b) HFMEA Report Summary drafted to present collated and risk-rated data from the simulations to decision-makers.
c) Scenario development and instrument testing undertaken within the simulation centre: face validity.
Key action Phase 1: testing of documentation tools and scenarios within the simulation centre
Oct. 1, 2
Oct. 6, 7
Test 1: ‘In-hospital’ simulation testing of systems and workflows with hospital leaders and senior clinicians.
Identification of required amendments to systems and workflows. Scenarios refined based on revised processes.
Test 2: ‘In-hospital’ simulation testing of systems and workflows with hospital leaders and senior clinicians repeated.
Identification of further required amendments to systems and workflows.
Scenarios refined based on revised processes for Phase 3.
Key action Phase 2: Testing of documentation tools, scenarios and processes/systems/workflows within the hospital.
‘24-h’ simulation in situ simulation testing with hospital staff and simulated patients.
Key action Phase 3: testing of processes/systems/workflows within the hospital over a consecutive 24-h period.
‘Post-simulation’ staff survey conducted by hospital leadership: 100% (n = 112) participating staff confident to open hospital.
Report Summary (HFMEA) submitted: Phases 2 and 3 data highlighting improvements made and further recommendations for refinements to processes and workflows. Hospital opened with commencement of full service delivery.
Simulation activity Phase 1: planning and preparation
Identify existing documentation tools designed to test healthcare systems and workflows using live simulation methodology
Identify existing documentation tools designed to report outcomes of the systems and workflow simulations to key stakeholders
Design appropriate in situ simulation scenarios to test systems and workflows
How to document the simulations as a system and workflow test in comparison to traditional educational simulations?
What would be the best method to collect and collate the large data sets in the context of concurrent, iterative simulations?
In what format should the data be presented to the organisation’s decision-makers to ensure that staff would be prepared and oriented, and that the systems and processes are safe?
The Plan-Do-Study-Act (PDSA) methodology provides a structure for cyclical or iterative testing of changes to a system or process, to improve quality and effectiveness [11, 16–18], and therefore ideally suited the iterative nature of our activity. There was little in the literature about using the PDSA framework for quality improvement within medical simulation [6, 7, 19, 20], and there was no evidence of a standard template for running medical simulation scenarios specifically to test clinical and non-clinical processes. As a result, the MEST developed the SQIOT.
Simulation-based Quality Improvement Observation Tool (SQIOT) Design
Usually, PDSA Quality Improvement (QI) activities evaluate existing clinical processes when a process change has been implemented and needs to be assessed. The PDSA process aims to identify a distinct relationship between an intentional change in a process, and any variation (positive or negative), to the intended outcome . A complexity with this activity was that the majority of existing procedures, patient flow, staff workflow, and emergency response processes were new and untested. This meant that the more focused use of the PDSA methodology could not be utilised. Instead, each process or system was initially assessed as a whole, with all strengths and weaknesses documented, and system amendments then made for the subsequent testing cycles. The benefit of the PDSA methodology was that it provided the required flexibility for testing within this context [10, 11].
The ‘Plan’ component in the template was divided into a number of elements. The front pages were derived from the organisations’ simulation scenario template. It outlined simulation objectives; key scenario information orientating people to time, place, situation, and alignment to organisational strategy; and national safety standards . The larger corporate organisation’s five strategic priorities for its business were Safety, Efficiency, Future, Quality, and Experience . Alignment with the organisational strategy was essential for leadership engagement, support in the form of human and financial resourcing, and their commitment to support the required actions when failures were identified. Additionally, alignment to the Australian National Safety and Health Quality Service Standards (NSHQSS)  assisted the organisation in the attainment of robust evidence for hospital accreditation. The ‘Plan Phase’ within the tool documented each step of a drafted individual process or workflow, which acted as the basis for scenario script.
The ‘Do’ component was the execution of the simulation activity, wherein the observers of the simulation identified if a specific step in the process was or was not achieved by circling ‘yes’ or ‘no’. Data was also captured through free text including refinements required to the simulation. Data was collected in real time, with observers following the simulated process and workflow being tested; video review was not used.
The ‘Study’ component of the method allowed data to be captured from multiple sources and formats, both qualitative and quantitative in nature. This enabled precise recognition of system failure, at what point it occurred in the process, and often captured possible solutions to the identified error. Qualitative data included direct observation of the process testing, staff experience within the simulated process/workflow, and participant and simulated patient feedback during the debrief. Comparison studies of actual events to the written processes/workflows and to their anticipated outcomes occurred throughout the activity. Examples of quantitative data collected included emergency team response times, amount of time between unexpected patient presentation at the reception desk to ambulance arrival, and the length of time for ambulance transport from the regional hospital to the central health service.
Simulation activity Phases 2 and 3: simulation-based quality improvement tool
Conduct the simulations
Utilise and revise the SQIOT
Collate and rate risks derived from collected data using the HFMEA Summary Report
Report results to organisational decision-makers
Changes made to SQIOT during testing
Amendments made to SQIOT
Phase of testing
The document went through multiple versions upon commencement of simulations within the simulation centre and as the hospital processes and workflows were being redeveloped and amended.
Section: do number of steps in a process reduced
Amended after Phase 2
Initially, the SQIOT form was too prescriptive as it outlined every single expected step in a care episode or process. As a result, the ‘Do’ section of the SQIOT was predominately not completed by observers during Phase 2.
addition of ‘Not Applicable’
Amended after Phase 2
Data collectors expressed confusion when completing the tool because a number of prescribed equipment or processes were not in place. What was needed was an option of ‘N/A’ (not applicable) in addition to ‘Yes or No’.
increased space for free text
Amended after Phase 2
Observers were a mixture of clinical experts, simulation team members, hospital leaders, service support staff, and the embedded simulated patients. After test one, observers requested additional free text space. The key benefit of adding more open space for comments was that observers could document their view, thus ensuring the process was evaluated from multiple perspectives. Later, during data analysis, it was possible to identify themes from the varied observer groups.
Phase 3, an intense 24-h event, occurred 3 weeks after Phase 2 and involved hospital staff from every service and department rostered over three shifts. Phase 3 consisted of 24 simulations, with trained community members participating as simulated patients (n = 12), hospital staff (n = 70), third party providers (n = 14) (e.g. ambulance, fire services, funeral directors), and MEST members (n = 7). All systems and departments operated in synchrony under live simulated conditions with both mannequin-based and simulated patient scenarios. Hospital staff were surveyed after each simulation activity via a validated tool to ascertain their confidence level in the system/process being tested . At the completion of the 24 h, the hospital leadership, via a staff survey, sought the opinion of the staff on hospital readiness. In Phases 2 and 3 of testing, 67 SQIOT forms were completed.
Simulation activity Phases 2 and 3: HFMEA Summary Report (Act)
All documented data from each test and phase captured on the SQIOT was collated and presented in a format that highlighted the risks, identified potential system impacts, and allowed for decision-makers to prioritise future actions. HFMEA is normally used to identify causes and effects of failure modes in systems and processes before a significant event or near miss occurs [13, 14, 25, 26]. In this study, the HFMEA was used for simulated patient care activities in the real hospital environment. Health Failure Modes, Effects Analysis, and traditional Failure Mode Effects Analysis provide numerical ranking for the identified failures or risks based on severity and probability [13, 23, 25, 26]. Numerical ranking scales vary, e.g.1–16 [13, 14, 26], 1–10 , 1–5 , or 1–4  typically with 1 being ‘no’ or ‘minor impact’ through to the largest number meaning catastrophic harm (e.g. permanent patient harm) [25, 26]. The methodology permits a specific process to be broken down into its elements and analysed for actual and potential weaknesses or failures within the process or system (failure modes) [13, 26]. Because this study generated a large amount of information, simplification was needed. The solution was (1) the risk ranking scale was comprised of a colour coding system (refer to Fig. 3 for the severity ranking matrix; (2) identified concerns/barriers were organised in accordance with the organisation’s five strategic priorities ; and (3) each identified failure mode was analysed to identify the likely cause. Once a failure mode was identified and reported, the clinical and non-clinical directors then assigned individual accountability to rectify identified concerns within specified time frames. This report format eased the burden on decision-makers to identify, prioritise action, and apply remedies to failure modes prior to errors reoccurring [12, 25].
Examples of identified failure modes illustrate actual or potential failure modes
Failure mode (identified barrier)
Position of A/C power cord to cancer care treatment chairs demonstrated significant falls risk to the patient and staff. Also inhibitive of efficient staff workflow
Each treatment pod had additional power point installed and TV’s repositioned in each pod
No standing orders for adrenaline administration in an emergency (no resuscitation team onsite)
Organisational Resuscitation Committee endorsed standing orders for the hospital for all registered nurses to administer adrenaline in a medical emergency
Imminent birth presenting to reception (no emergency department)
No wheelchair or trolley at front reception to move patient to another location
No neonatal resuscitation equipment available
Unknown process—unknown if oxytocic medications are/will be available from pharmacy
Required equipment purchased and staff orientated to its location and function
Pharmacy consulted and process implemented
Adult (chest pain) and a paediatric (asthma) presenting to front reception
With one administration staff on front desk, cannot initiate required phone calls at the desk to get help and attend to patient
Insufficient emergency equipment located on reception level of the hospital
Support services assistant and security currently not on medical emergency page (security: external company)
Staff knowledge in management of a paediatric emergency or first aid measures (no paediatric inpatients for this hospital)
Staff assist button for front reception
Mobile phone/deck phone at reception
External security company to be added to the emergency pager list
Emergency equipment purchased for reception:
- Oxygen and masks (adult and paediatric)
- Vital signs monitor
- IV cannulation
Paediatric education plan and resuscitation training planned and commenced
Volume of overhead emergency buzzer in inpatient areas. Staff in single rooms with door closed cannot hear staff assist or code blue call bells
Company contacted and volume increased
In the literature, there is a paucity of evidence of using PDSA QI as a methodology in healthcare simulation testing alongside HFMEA reporting. The methodology used in this study produced results that satisfied leadership and led to an uneventful opening of the satellite hospital with no patient or staff harm, and a grateful community.
The iterative amendments to the tool throughout Phases 1–3 allowed for continued improvements in data collection. Small changes such as reducing the number of steps documented in a process led to more data being collected and more meaningful data. This improvement was thought to be attributed to (1) reducing cognitive load for the data collectors, (2) better characterising of the ‘real-life’ processes, and (3) highlighting the salient parts of the process.
Observers were trained how to use the tool, but did not have an opportunity to practice using it in action prior to the commencement of Phase 2, Test 1. It seems likely that data collection would have been strengthened by observer training. Nevertheless, useful data was collected with each iteration and, as might be expected, the data usefulness improved as observers became more experienced, and the instruments designed with flexibility in mind were improved as the multi-phased testing process was implemented.
Although this activity was not testing a single system or process, it required the design of a report that could capture the actual and potential failure modes of multiple individual processes and systems. To do this effectively, the second template (HFMEA Summary Report) was designed. This documentation methodology proved beneficial in two ways. First, it provided organisational decision-maker’s precise and actionable information on identified latent failures with sufficient information to make informed decisions based on priorities and resourcing. Second, it served as positive reinforcement for middle management and front line staff that their concerns were being taken seriously by the senior managers and that their suggestions for improvement were acted upon. It reinforced a positive safety culture for which all staff felt empowered and invested in the quality and safety of their new hospital.
Normally, HFMEA identifies the points of failure from a process perspective. This study went a step further through the inclusion of feedback from the simulated patients as their role as consumers. A limitation of testing a system or process under simulated conditions is the validity of ‘guessing’ or making assumptions about the patient perspective as a recipient of care within that system. To help ameliorate this problem and strengthen the testing of the system and its interdependencies, local consumers who would be using the health service were trained as simulated patients. The simulated patient participated in the associated simulation activity debrief, and their thoughts and opinions were captured. This provided reassurance that a system or process was safe and efficient, while at the same time resulted in a positive patient experience. The idea was to combine safe and reliable processes with a patient-centred approach. Simulation and HFMEA used collaboratively permitted unique and precise prioritising failure modes. Without this comprehensive approach, failure modes might have remained unnoticed or unrecognised [14, 25, 29, 30].
The hospital leadership and Commissioning Steering Committee team realised the value of the simulation activity. This realisation led them to make accurate and well-informed decisions regarding operational readiness. They were so confident in the simulation-based testing process that a delay in the highly publicised opening date was among the possible options. Patient safety and staff preparedness was paramount. The simulation testing and the detailed reporting of the activities added strength to the decision-making of the hospital leadership team and provided reassurance to the hospital executive council, Commissioning Steering Committee, and the hospital board members.
The simulation methodology utilised for the testing of health systems and processes provides a unique lens through which staff at all levels can observe, assess, and evaluate [1, 5–7]. The advantage of simulation as a quality improvement methodology is that one cannot accurately predict system performance without testing prior to ‘going live’. While we may intuitively agree with the truth of that statement, we and our health service were extremely pleased to see how much true value was received via the simulation-based testing and the associated methodology provided in this report. Smart, dedicated professionals created the physical layout, thoughtfully designed the systems, and placed highly trained professionals within these designs. The natural motivation of all these professionals was patient and staff safety, system effectiveness, and patient satisfaction. Despite all these talents and dedication, there were still a number of weaknesses and errors, a few of which with a pretty high likelihood of resulting in a costly error remained in the system. The PDSA methodology utilised within this iterative and increasingly complex simulation activity allowed incremental and substantial positive changes to occur. While the process does use a number of resources, it is believed to have resulted in a greater likelihood in producing more positive and widespread sustainable change in the organisation, than ‘one off’ change implementation [10, 31]. This study demonstrated that in this circumstance, an iterative PDSA quality improvement methodology was an effective framework for structuring simulation-based healthcare system testing. The PDSA and HFMEA frameworks collectively allowed rapid data collection regarding individual systems or processes, and a larger ecosystem working in synchrony during a 24-h simulation event. This allowed visibility of the interrelated parts and gaps within the coordination between individuals, departments, and systems.
The validity of the conclusions is fundamentally dependent on the accuracy of the simulation scenarios. The future use of this type of simulation testing needs to include how to portray clinical process authentically enough to elicit appropriate care delivery and staff behaviours and attitudes that would naturally occur during service delivery. In this activity, we believe that the quality of the simulations were sound to start with and got better with each iteration. Scenarios were designed and tested during Phase 1 within the simulation centre. Additionally, the simulations were run four times within the real hospital environment (Phases 2–3) with continuous revalidation of scenario content. Although there were multiple observers of the simulation activities, there may have been interactions or potential system weaknesses missed simply due to observers’ natural inability to see and hear all. Consideration for future activities should be capturing video of the simulations to undertake a second review and to enable inter-rater reliability studies of the Observation Simulation Quality Improvement Observation Tool.
The inclusion of feedback and suggestions from frontline staff ultimately led to better processes and less workarounds, as the staff helped to shape and design the system in which they would function. A secondary outcome was that before the doors of the health service opened, there was a positive simulation culture throughout the organisation, and recognition of how simulation methodology can support a health service beyond education and training.
The authors of the paper would like to acknowledge Mater Education’s contribution, in particular Mater Education’s Practice Improvement Centre Simulation Team, Pauline Lyon, Katherine Jackman, education coordinators, and Mater Private Hospital Springfield staff for their support of this project.
Funding for all aspects of this project was provided by the Mater Education.
Availability of data and materials
The datasets used and analysed during the current study are available from the corresponding author upon reasonable request.
The conception, co-ordination of the study design, analysis of data and writing and preparation of the manuscript was led by MB. RD, CM, DB, and RS contributed to the study design and writing and critical revisions of the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
The study approval was from Mater Ethics and Governance Committee (HREC/15/MHS/79).
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Alder M, Mobley B, Eppich W, Lappe M, Green M, Mangold K. Use of simulation to test systems and prepare staff for a new hospital transition. J Patient Saf. 2015; doi:https://doi.org/10.1097/PTS.0000000000000184.
- Kerner R, Gallo K, Cassara M, D’Angelo J, Egan A, Simmons J. Simulation for operational readiness in a new freestanding emergency department. Simul Healthc. 2016; doi:https://doi.org/10.1097/SIH.0000000000000180.
- Bender J. In situ simulation for systems testing in newly constructed perinatal facilities. Semin Perinatol. 2011; doi:https://doi.org/10.1053/j.semperi.2011.01.007.
- Ventre K, Barry J, Davis D, Baiamonte V, Wentworth A, Pietras M, Coughlin L, Barley G. Using in situ simulation to evaluate operational readiness of a children’s hospital-based obstetrics unit. Simul Healthc. 2014;9:102–11.View ArticlePubMedGoogle Scholar
- Preston P, Lopez C, Corbett N. How to integrate findings from simulation exercises to improve obstetrics care in the institution. Semin Perinatol. 2011; doi:https://doi.org/10.1053/j.semperi.2011.01.008.
- Gardner A, Ahmed R, George R, Frey J. In situ simulation to assess workplace attitudes and effectiveness in a new facility. Simul Healthc. 2013; doi:https://doi.org/10.1097/SIH.0b013e31829f7347.
- Yager P, Collins C, Blais C, O’Connor K, Donovan P, Martinez M, Cummings B, Hartnick C, Noviski N. Quality improvement utilizing in-situ simulation for a dual-hospital pediatric code response team. J Pediatr Otorhinolaryngol. 2016;88:42–6.View ArticleGoogle Scholar
- Queensland Health, Queensland Government. Lady Cilento Children’s Hospital review operational and building. 2015. https://publications.qld.gov.au/dataset/lady-cilento-children-s-hospital-commissioning-review/resource/2cd8b21c-34eb-4b84-a96a-06454b3cfc6d Licensed under Creative Commons Attribution. Accessed December 2016.
- Parliament of Western Australia, Perth. More than bricks and mortar: the report of the inquiry into the organisational response within the Department of Health to the challenges associated with commissioning the Fiona Stanley Hospital. Education and Health Standing Committee. 2014; Report 2. Accessed December 2016.Google Scholar
- Taylor M, McNicholas C, Nicolay C, Darzi A, Bell D, Reed J. Systematic review of the application of the plan-do-study-act method to improve quality in healthcare. BMJ Qual Saf. 2014;23:290–8.View ArticlePubMedGoogle Scholar
- Science of improvement: testing changes. Institute for Healthcare Improvement. http://www.ihi.org/resources/Pages/HowtoImprove/ScienceofImprovementTestingChanges.aspx. Accessed 4 May 2016.
- Speroff T, O’Connor G. Study designs for PDSA quality improvement research. QMHC. 2004;13:17–32.PubMedGoogle Scholar
- DeRosier J, Stalhandske E, Bagian JP, Nudell T. Using healthcare failure mode and effect analysis: the VA National Center for patient safety’s prospective risk analysis system. Jt Comm J Qual Improv. 2002;28:248–67. 209PubMedGoogle Scholar
- Nielsen D, Dieckmann P, Mohr M, Mitchell A, Ostergaard D. Augmenting health care failure mode effects analysis with simulation. Simul Healthc. 2014;9:48–55.View ArticlePubMedGoogle Scholar
- Birnbach D, Nevo I, Scheinman S, Fitzpatrick M, Shekhter I, Lombard J. Patient safety begins with proper planning: a quantitative method to improve hospital design. Qual Saf Health Care. 2010; doi:https://doi.org/10.1136/qshc.2008.031013.
- Speroff T, O’Connor G. Study design for PDSA quality improvement research. QMHC. 2004;13:17–32.PubMedGoogle Scholar
- Deming W. Out of the crisis. Cambridge, MA: Cambridge University Press; 1986.Google Scholar
- Nakayama D, Bushey T, Hubbard I, Cole D, Brown A, Grant T, Shaker I. Using a plan-do-study-act cycle to introduce a new OR service line. AORN. 2010; https://doi.org/10.1016/j.aorn.2010.01.018.
- Murphy J. Using plan do study act to transform a simulation center. Clinical Simulation in Nursing. 2013; doi:https://doi.org/10.1016/j.ecns.2012.03.002.
- Ozekcin L, Tuite P, Willner K, Hravnak M. Early identification of patient physiologic deterioration by acute care nurses. Clin Nurs Spec. 2015:166–73.Google Scholar
- Australian Commission on Safety and Quality in Health Care, National Safety and Quality Health Service Standards. Sydney; 2012.Google Scholar
- Exceptional every time strategy document. 2016. http://www.exceptional.org.au/. Accessed December 2016.
- Cornwall A, Jewkes R. What is participatory research? Soc Sci Med. 2010. Mar;70(5):794.View ArticleGoogle Scholar
- Franklin A, Burns P, Lee C. Psychometric testing on the NLN Student Satisfaction and Self-Confidence in Learning, Simulation Design Scale, and Educational Practices Questionnaire using a sample of pre-licensure novice nurses. Nurse Educ Today. 2014; doi:https://doi.org/10.1016/j.nedt.2014.06.011.
- Davis S, Riley W, Gurses A, Miller K, Hansen H. Failure modes and effects analysis based on in-situ simulations: a methodology to improve understanding of risks and failures. 2008. Available at: http://www.ncbi.nlm.nih.gov/books/NBK43662/pdf/advances-davis_60.pdf.Accessed 4 July 2016.
- Ashley L, Dexter R, Marshall F, McKenzie B, Ryan M, Armitage G. Improving the safety of chemotherapy administration: an oncology nurse-led failure mode and effects analysis. Oncol Nurs Forum. 2011;38:e436–44.View ArticlePubMedGoogle Scholar
- Spath P. Home study program: using failure mode and effects analysis to improve patient safety. AORN. 2003;78:16–37.View ArticleGoogle Scholar
- Paparella S. Failure modes and effects analysis: a useful tool for risk identification and injury prevention. J Emerg Nursing. 2007; doi: https://doi.org/10.1016/j.jen.2007.03.009.
- Guise J, Mladenovic J. In situ simulation: identification of system issues. Semin Perinatol. 2013; doi: https://doi.org/10.1053/j.semperi.2013.02.007.
- Geis G, Pio B, Pendergrass T, Moyer M, Patterson M. Simulation to assess the safety of new healthcare teams and new facilities. Simul Healthc. 2011; https://doi.org/10.1097/SIH.0b013e3182dff30.
- Donnelly P, Kirk P. Use the PDSA model for effective change management. Educ Prim Care 2015; doi:10..1080/14739879.2015.11494356.Google Scholar