Model-based systems engineering approaches to disaster preparedness in rural healthcare systems: a scoping review **Registration:** Open Science Framework **Authors:** Tom Berg, PhD, MBA; College of Nursing, University of Tennessee, Knoxville; tberg1@utk.edu; Roles: concept and protocol development, writing, oversight, corresponding author, guarantor of review; 1200 Volunteer Blvd, Knoxville, TN 37996 Kelsi Marino; University of Tennessee, Knoxville; kmarino1@vols.utk.edu; Roles: screening, data extraction, writing Kristina Kintziger, PhD, MPH; College of Public Health, University of Nebraska Medical Center; kkintziger@unmc.edu; Roles: protocol development, screening, data extraction & management, writing, guarantor of review **Support:** University of Tennessee, Knoxville, Undergraduate Summer Research Award, provided stipend and professional development opportunities for KM. **Introduction** *Rationale* Disasters and other emergency events have complex effects on human systems, particularly if the events are severe or prolonged. When these types of events happen in rural communities, the resources of the local public health, healthcare, and emergency response organizations can be quickly depleted or overwhelmed. Planning for emergencies can help to mitigate their impact. Model-based systems engineering (MBSE) methods, including computer simulations, can provide insight on how best to prepare for these events and to explore the effects of varying approaches and resource utilization. In order to best apply these methods for improving disaster management in rural settings, a synthesis of the current body of evidence in this field is needed. The objective of this scoping review is to provide a descriptive overview of the application of computer simulation based on MBSE approaches to disaster preparedness and response for rural healthcare systems. The scoping review methodology is an appropriate choice for this objective, as this type of review is focused on generating an overview of the current body of literature on a given topic, providing a summary of relevant results, and identify gaps for future research. There is no critical appraisal of the literature in a scoping review (Arksey & O’Malley, 2005). This is actually a benefit of applying the scoping review methodology to this research objective, as we expect the MBSE approaches, disasters, rural settings, and disaster contexts found in the literature to be heterogeneous. Further, a scoping review allows us to have an expansive focus and still use a comprehensive and rigorous approach to address the topic. *Objectives* The main objective of this scoping review is to provide a descriptive overview of the application of computer simulation methods, based on MBSE approaches, to disaster management for rural healthcare systems. - Population: rural healthcare settings and systems - Context: disasters, both natural (e.g., geophysical, meteorological, hydrological, climatological, biological) and technological (e.g., industrial or transport accident, other man-made disasters) - Methods/concepts: MBSE methods (e.g., discrete event models, agent-based models, system dynamics models, computer simulation) - Outcomes: disaster-related resilience, preparedness, and response; morbidity and mortality; economic outcomes *Methods* This scoping review will use the framework proposed by Arskey and O’Malley (2005). We will also use more recent guidelines for scoping reviews, particularly the PRISMA extension for scoping reviews (PRISMA-ScR) by Tricco et al. (2018). Arksey and O”Malley propose a six stage framework for conducting scoping reviews, including: - Identifying the research question(s) - Identifying relevant studies - Selecting studies for inclusion - Charting the data - Summarizing and reporting The optional sixth stage – Consultation with stakeholders – will not be included due to time and resource limitations. Data management will be conducted using a combination of Microsoft Excel, EndNote reference software, and potentially, NVivo for charting the data. *Identifying the Research Question(s)* We conducted pilot or exploratory searches on our topic to develop this proposal. This helped us to further refine our inclusion and exclusion criteria, search limits and terms, populations of interest, etc. We decided not to apply search limits – in terms of publication types, years of publication, rural settings, disaster types – in order to ensure that we obtained the most comprehensive body of evidence to understand the use of MBSE approaches to improve disaster management in rural healthcare settings. Based on our preliminary work, we propose the following research questions: - What types of MBSE approaches are used in disaster management in rural settings? - What are the types of disasters that have been addressed with these methods? - In what healthcare settings have these methods been used? - In what aspects of the disaster management cycle are these methods applied? - What measures are used to assess the effectiveness of the application of these methods? That is, what are the primary and secondary outcomes assessed? - What has been the real-world impact of these methods in disaster management policy and planning? - What are the strengths and limitations on the use of these methods in disaster management for rural healthcare settings? *Identifying Relevant Studies* We will conduct searches in the following information sources/electronic databases: Web of Science, CINAHL, Scopus, ACM Digital Library, Engineering Village, Business Source Complete, and PubMed. Reference lists of included studies will be checked to ensure we have identified all relevant articles for consideration. Our search strategy includes the following eligibility criteria: - Types of publications: journal articles, conference proceedings, books/chapters, gray literature - Time: any - Language: English - Setting: rural healthcare, any type - Types of methods: computer simulation, agent-based models, system dynamics models, discrete event models, multi-method simulation, computational models, network models/simulation, Bayesian models, artificial intelligence, machine learning - Context: disasters, any type We consulted with several academic librarians in both the health sciences and engineering disciplines to choose these electronic databases and limits. We will exclude magazine chapters, newspaper articles, newsletters, book reviews, as we prefer to include mostly peer-reviewed or otherwise refereed sources of information. The academic librarians also helped us to refine our keywords. After initial pilot testing, we determined that the use of “emergency” as a substitute for disaster was inappropriate, as most of the results related to emergency were related to emergency rooms, departments, or medical professionals, rather than disaster contexts. We further expanded our MBSE terms to include US and UK spellings or relevant words. The full proposed search terms for Web of Science is provided in Appendix 1. *Selecting Studies for Inclusion* Our selection process involves two reviewers through all steps of the selection process, with a 3rd available to resolve disagreements. Searches will be conducted in each database separately and compiled using EndNote. Duplicates will be deleted based on available information. The initial set of potentially eligible studies will be reviewed by KK and KM through separate title/abstract and full text screening steps. The full text will be retrieved for the final list of included studies. Disagreements will be discussed first by the two reviewers, and if a consensus is not reached, TB will serve as the arbitrator. We will report the results of study selection based on PRISMA-ScR standards. Inter-rater reliability will be calculated for each step of the process. *Charting the Data* Based on our preliminary searches, we have proposed to extract the following information from our included studies. - Authors - Title - Journal - Year of publication - Geographic context of study (e.g., city, state, country) - Study objectives/research questions - Type of study - Type of MBSE approach used - Healthcare setting (e.g., clinic, hospital) - Disaster context (e.g., tornado, flood, pandemic) - Disaster management cycle context (e.g., preparedness, response, recovery, mitigation) - Data sources and types, if any, used in the study - Method of evaluation and validation - Parameters included in the model (e.g., supply chain, human resources, patient volume) - Outcomes assessed (e.g., morbidity, mortality) - Impact (e.g., direct uses in or application to policy or planning) - Strengths - Limitations The data extraction process will be pilot tested by two reviewers on a sample of included studies (i.e., 10%, but no less than 5). This will ensure that the data extraction and coding is consistent and help us to make necessary revisions to the process. Issues will be discussed by the entire team. For full data extraction, two reviewers (KK and KM) will independently extract the data from each included study. Data will then be compared and discrepancies will be discussed and resolved, with arbitration, if necessary by TB. If data is missing after extraction is complete from a given study, we will attempt to contact the corresponding author to complete the record. If after two attempts, we have not been successful, we will attempt to reach out to the next most likely contact (first, senior author) for missing information. *Summarizing and Reporting* Methods of data extraction and charting have been described. We will conduct a qualitative analysis of this data to identify themes in the relevant included literature to better understand the uses and implications of MBSE approaches to improve disaster management in rural healthcare settings. This will allow us to determine the most frequently used methods, and the effectiveness of computer simulation approaches in improving disaster preparedness and response in rural settings. We will be able to identify key challenges to their use, as well as important gaps in the literature where additional research is needed. We will present the results in aggregate, using both text and visual formats, as needed. *Ethics* As we are not collecting primary data or using secondary data to conduct human research, nor requesting data of any kind from included study authors, but rather are synthesizing evidence from the published literature, Institutional Review Board consideration was not needed. *Dissemination* We expect to submit at least one abstract to a local (university) conference and at least one to a national/international conference for presentation. Further, we anticipate submitting the results of this scoping review for publication to a scientific, peer-reviewed journal. **References** Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol 2005;8:19–32. Tricco AC, Lillie E, Zarin W, et al. PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation. Ann Intern Med 2018;169:467-73. **Appendix 1** *Web of Science Search Terms* - Disaster–related terms: ALL=(disaster OR catastrophe OR “humanitarian operation*” OR “emergency management” OR “disaster management” OR “disaster planning” OR “pandemic” OR “epidemic” OR “outbreak” OR “natural disaster” OR pollution OR “agricultur* disaster” OR “famine” OR “agriculture* pests” OR “agriculture* diseas*” OR “environment* disaster” OR “environment* emergency” OR wildfire OR “wild fire” OR “wild land fire” OR “industrial disaster” OR “man-made disaster” OR terroris* OR war OR “political unrest” OR “industrial emerg*” OR “man made disaster” OR “man-made emergency” OR “mass violence”) - Rural terms: ALL=(rural OR non-urban OR frontier) - Healthcare terms: ALL=(healthcare OR “health care” OR “healthcare system” OR "health care system") - MBSE terms: ALL=("decision science” OR “decision support system*” OR “multi-method model*” OR “multimethod model*” OR “multi-method simulation” OR “multi method simulation” OR “hybrid model*” OR “hybrid simulation” OR “discrete event simulation” OR “discrete event model*” OR “agent based model*” OR “agent-based model*” OR “system* dynamic*” OR “system* model*” OR “system* theory” OR “system* thinking” OR “computer model*” OR “computer simulation” OR “digital twin” OR “multi-level analys*” OR “multilevel analys*” OR “system* analys*” OR simulation OR “computational model*” OR “computational simulation” OR “equation-based model*” OR “equation based model*” OR “neural net*” OR “network simulation” OR “Bayesian model*” OR “Bayesian network*” OR “Monte-Carlo” OR “Monte Carlo” OR “Markov chain*” OR "quantitative model*" OR “artificial intelligence” OR “machine learning” OR “predictive analytics” OR “predictive model*”) - Outcome terms: ALL=(resilience OR preparedness OR “disaster preparedness” OR “disaster response” OR “emergency preparedness” OR “emergency response” OR effectiveness OR mortality OR morbidity OR injury OR "economic losses" OR damage OR destruction OR death OR "lives lost" OR “disability-adjusted life years” OR “disability adjusted life years” OR “community function*”) Combined all terms listed above with: #1 AND #2 AND #3 AND #4 AND #5