Available online at ScienceDirect. Procedia Manufacturing 3 (2015 )

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1 Available online at ScienceDirect Procedia Manufacturing 3 (2015 ) th International Conference on Applied Human Factors and Ergonomics (AHFE 2015) and the Affiliated Conferences, AHFE 2015 Human factors in the design of medical simulation tools Norah AlRomi * Software Engineering Department, Prince Sultan University, Riyadh, Saudi Arabia Abstract This paper describes the human factor design issues relevant to medical simulation systems. Decision making in medical domains is an increasingly complex task that involves a number of stakeholders, sub-specialties and technologies. Medical simulation creates a lifelike situation for individuals to practice decision-making and procedural activities in a safe environment for the patients and professionals where it involves simulated human patients, emergency response and simulated animation. Evidence suggests that medical simulation improves the effectiveness, safety, and efficiency in health care services. Moreover, it has been shown to consistently deliver significant value to the organization, staff, or students in decision-making. Although medical simulation provided ideal approaches for addressing healthcare issues, the number of successful software implementation and development is relatively small compared with other established engineering disciplines, such as the manufacturing industry. Software quality models in particular offer the opportunity to systematically assess the level of compliance of software systems with industry standards. In addition, applying software quality models increase the customer satisfaction and decrease the quality cost Published The Authors. by Elsevier Published B.V. by This Elsevier is an open B.V. access article under the CC BY-NC-ND license Peer-review ( under responsibility of AHFE Conference. Peer-review under responsibility of AHFE Conference Keywords:Software quality models; Decision support systems; Medical simulation; Quality assurance factors; Software engineering 1. Introduction Human factors in the design of medical simulation are an emerging field of research in software engineering. The design and development of medical simulation systems and tools often involves a good understanding of the functional requirements of the systems in addition to the best practices and standards in the domains relevant to the * Corresponding author. address:norah.r.it@gmail.com Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of AHFE Conference doi: /j.promfg

2 Norah AlRomi / Procedia Manufacturing 3 (2015 ) applied context. Medical simulation systems are considered as Decision Support Systems since the assess in the decision making process. However, the knowledge that underlies the medical decision support systems, either knowledge-based or model-based, is unfortunately scattered throughout the literature. In this research a systematic literature review of published work in medical simulation and software quality models computing in a way that helps software engineers in understanding the research directions and best practices for designing and developing medical simulation tools. 2. Literature review Medical simulation is the utilization of technology related to education, training, and management in medical contexts [1]. Simulation creates a lifelike situation for individuals to practice decision-making and procedural activities in a safe environment for the patients and professionals. For example, simulation can provide scenarios in which it involves simulated human patients in clinical procedures or surgery [2][3]. Another simulation example in medical contexts is in emergency response systems at the emergency department Medical simulation categorization Medical simulation has been reported in the literature as categorizations in different ways. For example, complexity of visualization, platforms, and contexts of use [4]. In [1], Barjis et al. categorized medical simulation tools into four categories as depicted in the following. Clinical and training simulation: Is a training technique for physicians, medical students, nurses, and other healthcare professionals, which is used to study, and analyze the behavior of diseases, including biological processes in human body [1]. Example of this category include haptic device (e.g. robotic arm or endoscope simulation) [2][3]. Operational simulation:is a technique for modeling a process and is used for capturing, and analyzing health care operations, patient flow, service delivery, and scheduling, healthcare business and optimization design [1]. Example of this category include the patient flow at the emergency department at a hospital [5]. Managerial simulation: Is a type of interactive training and feedback and is used as a tool for managerial purposes, decision-making, strategic planning, and policy implementation. Example of this category include comprehensive management planning for healthcare processes, staffing, equipment and buildings as shown in the following figure [6]. Educational simulation: It is a training and educational technique, where virtual and physical objects are extensively used to help a learner explore, navigate or obtain more information about the environment [1]. Example of this category include haptic device [2]. Brailsford (2007) classifies medical simulation models into three groups [7]: Models of the human body that include biological processes. Models for modeling patient flow in the clinic, ward, department, or hospital. Models for strategies that are used for strategic planning of the organization. Kathleen R. Rosen MD have classified medical simulation into five types including [8]: Standardized patients. Human patient simulation. Virtual reality. Task trainers. Software-based simulation.

3 290 Norah AlRomi / Procedia Manufacturing 3 ( 2015 ) Researchers have varied in categorizing medical simulation, where each author has a different name for the same category. The following table highlights the mapping. Table 1. Mapping simulation terms to categories set by different authors. Barjas Brailsford Kathleen R. Rosen MD Clinical Human body Standardized patients, Human patients and Task trainer. Operational Patient flow Virtual reality. Educational Human body Standardized patients, Human patients and Task trainer. Managerial Strategies Software-based simulation. 3. Medical simulation challenges Medical simulation is a multi-disciplinary and complex field [1]. It is a theoretical and practical domain that focuses on multi-methods, multi-paradigms, multi-modeling and multi-disciplines [1]. One of the main challenges in medical simulation is the need for medical simulators that truly apply multi-modeling to present visual, auditory, haptic, and olfactory displays[9]. And based on that, the correlation of the different displays is the challenge that lies here because their should be proper relation between user perceives sensory cues and to user interactions [1][9] Medical simulation values and benefits that are produced to improve clinical, operational and management processes are clear and easy to perceive nowadays [1]. And although medical simulation is now more known and prepared in the healthcare industry, it is still a sophisticated and a highly technical tool for non-technical user s comprehension. This challenge triggers user resistance, which is considered as barrier to a successful simulation implementation in healthcare. This barrier exists in with the fact that detailed simulation such as medical simulation, requires tremendous time and effort[9]. In addition, evidence has shown that Human-Simulator Interfaces are often critical. In order to avoid errors, poor training, and to provide a complete solution for different cases in medical simulation (e.g. applying a surgery on a patient, or training for healthcare practices), a medical simulator designed interface should replicate that of the real world and easily used[9]. Thus, the physician's, student or any healthcare practitioner should touch instruments that provide the same look and feel as their real world counterparts. These tool s requirements can be challenging, especially if the simulator addresses open surgery where the degrees of freedom and the numbers of instruments are significantly larger than in minimally-invasive surgery. As a conclusion for the previous challenge, user acceptance is an important matter in healthcare simulation. A simulation model can only provide good and accurate results based on the input data, although the data collection is a challenge in healthcare simulation. In healthcare, often the medical simulation tool developers lack sufficient input data for their simulation models, which leads to delivering rather approximate results. Data collection is challenge due to not available useful formats for historic data; data collection should take place over a long span of time; meeting with healthcare professionals for gathering data collection and verification purpose is also a hard task due to their busy schedules [1]. The input data need to be complete, accurate and real. The entered data play a big role in assessing health professional in decision making since they are considered as decision support systems. In order to provide ideal data collection it may require, integrating simulation models with the organization information systems (IS) to support the daily operation. Validation and verification in medical simulation is a subject of extensive research. It is considered as a real devil because without applying profound verification and validation, it would be risky, if not disastrous, to make any decisions or forecasts based on the model outcomes. To overcome this challenge, innovative modeling approaches, model validation, especially for complex models should be used. And in order to enhance model verification an approach such as emerging approach of Collaborative, Participative, Interactive Modeling [1], or by applying CPI Modeling, in which models are designed collaboratively with participation of the users using the medical simulation

4 Norah AlRomi / Procedia Manufacturing 3 (2015 ) tool and business owners [1]. Moreover, validation quite differs from the verification process. A significant research challenge may increase when developing a valid simulation model, designing valid experiments based on the model, and carrying out a rigorous analysis of the experiments. The cost is a major issue for medical modeling and simulation. Although the popular perception may be that the medical enterprise is well funded, the reality, especially in medical education, is quite the opposite. The cost of simulators must be significantly reduced if they are to become commonly-available tools within the medical school curriculum. 4. Human factors in medical simulation Although medical simulation provided ideal approaches for addressing healthcare issues, the number of successful implementation and development is relatively small compared with the manufacturing industry [7]. Modern hospitals and clinics are encountering high levels of competition in both domestic and global markets. In addition, patients, physicians, medical students and trainees are increasingly demanding for more quality in health care services delivered with a reasonable cost. Notably, research in [10] has suggested that medical Simulation models needs to be structured in a way that it optimizes the safety and quality of health care systems [10]. On the other hand, implementing qualified simulation systems may increase the complexity of system and negatively influences the efficiency of these systems. This consequently led to the emergence of software solutions that focused more on basic science education with simulation than less clinical training. Issues prevalent in the literature that have been cited as relevant for medical simulation systems are: Complexity and multiple interactions associated with healthcare systems [11]. High cost of simulation tools [12]. Medical errors [13]. Lack of reliable data and relevant tools [14]. Usability problems and lack of a user-friendly interface. Human error plays a crucial role in the safety of medical simulation tools. Human errors can frequently be traced back to deficiencies in the design of the human-machine interface as been highlighted in the literature review[14]. If the system and interface design was not designed with human capabilities and by considering the limitations of the cognitive, perception and physical human factors, physician, operators and healthcare providers are being placed in situations where the demands imposed on them are unrealistic from a psychological point of view[15]. Subsequently, the result will be an inevitable error. The discipline of human factors, or ergonomics, deals with the highlighted medical simulation issues and challenges by designing interfaces that take into account human capabilities and limitations. The lack of attention to human factors during the design phase seriously jeopardizes the human safety. Following design principles related to medical simulation may decrease human errors and lead to a better understanding for the tools. As an example of human factors design principles that can be adopted include [15]: User should be provided with prompt feedback after each action. Make the functions of the various controls clear and obvious. Displayed messages should be easy to understand. Minimize the load on the users memory as much as possible. Increase efficiency by provide users with shortcuts. Provide clearly marked exits for the user to leave the system if medical simulation tool had an interface. 5. Conclusion Although medical simulation provided ideal approaches for addressing healthcare issues, the number of successful implementation and development is relatively small compared with the manufacturing industry. Modern hospitals and clinics are encountering high levels of competition in both domestic and global markets In this

5 292 Norah AlRomi / Procedia Manufacturing 3 ( 2015 ) research we have focused on the human factors in the design of medical simulation systems and tools in order to overcome the highlighted challenges and issues in the literature. References [1]J. Barjis, Healthcare Simulation and its Potential Areas and Future Trends, SCS M&S Mag., vol. 1, no. January, pp. 1 6, [2]T. R. Coles, D. Meglan, and N. W. John, The role of haptics in medical training simulators: A survey of the state of the art, IEEE Trans. Haptics, vol. 4, no. 1, pp , [3]E. Samur, L. Flaction, and H. Bleuler, Design and evaluation of a novel haptic interface for endoscopic simulation, IEEE Trans. Haptics, vol. 5, no. 4, pp , [4]C. C. Chen, J. S. Daponte, and M. D. Fox, Fractal feature analysis and classification in medical imaging., IEEE Trans. Med. Imaging, vol. 8, no. 2, pp , [5]X. Wang, X. Shen, and X. Liu, Improving patient flow at hospital emergency services A simulation study, in Icsssm11, 2011, pp [6]I. W. Gibson and B. L. Lease, An approach to hospital planning and design using discrete event simulation, in 2007 Winter Simulation Conference, 2007, pp [7]S. C. Brailsford, TUTORIAL: ADVANCES AND CHALLENGES IN HEALTHCARE SIMULATION MODELING, in Proceedings of the 2007 Winter Simulation Conference, 2007, pp [8]K. R. Rosen, The history of medical simulation, J. Crit. Care, vol. 23, no. 2, pp , [9]R. Bowen Loftin, Grand Challenges in Medical Modeling and Simulation. pp , [10]S. B. Issenberg, W. C. McGaghie, I. R. Hart, J. W. Mayer, J. M. Felner, E. R. Petrusa, R. A. Waugh, D. D. Brown, R. R. Safford, I. H. Gessner, D. L. Gordon, and G. A. Ewy, Simulation technology for health care professional skills training and assessment., JAMA, vol. 282, no. 9, pp , [11]P. N. Lowe and M. W. Chen, System of systems complexity: Modeling and simulation issues, Simul. Interoperability Stand. Organ. - SISO Eur. Simul. Interoperability Work. EURO SIW 2008, no. 2, pp , [12]B. Mielczarek and J. Uzialko-Mydlikowska, Application of computer simulation modeling in the health care sector: a survey, Simulation, vol. 88, no. 2, pp , [13]R. Blaser, M. Schnabel, D. Mann, P. Jancke, K. Kuhn, and R. Lenz, Potential prevention of medical errors in casualty surgery by using information technology, in Proceedings of the ACM Symposium on Applied Computing, 2004, pp [14]T. Eldabi, Implementation issues of modeling healthcare problems: Misconceptions and lessons, Proc. - Winter Simul. Conf., pp , [15]L. Lin, R. Isla, K. Doniz, H. Harkness, K. J. Vicente, and D. J. Doyle, Applying human factors to the design of medical equipment: patientcontrolled analgesia., J. Clin. Monit. Comput., vol. 14, no. 4, pp , 1998.