31J Contemp Med Sci | Vol. 8, No. 1, January-February 2022: 31–37 Original Teaching Clinical Decision-Making Skills to Undergraduate Nursing Students via Web-based Virtual Patients during the COVID-19 Pandemic: A New Approach to The CyberPatientTM Simulator Toktam Masoumian Hosseini1, Soleiman Ahmady1,2* , Samuel Edelbring3 1Department of Medical Education, Virtual School of Medical Education & Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran. 2Research Affiliated Faculty at Department of LIME, Karolinska Institute, Solna, Sweden. 3Department of Learning, Informatics, Management and Ethics, Karolinska Institute, Stockholm, Sweden. *Correspondence to: Soleiman Ahmady (E-mail: soleiman.ahmady@gmail.com) (Submitted: 11 December 2021 – Revised version received: 28 December 2021 – Accepted: 19 January 2022 – Published online: 26 February 2022) Abstract Objective: The aim of this study was to investigate the effects of using a virtual patient simulator on the acquisition of clinical decision- making skills in nursing students during the pandemic COVID-19. Methods: This was a quasi-experimental study with a pretest-posttest design. Following the case-based learning strategy, the educational intervention was designed and implemented in five steps (pre-activities, introduction, scenario briefing, web-based clinical scenarios, presentation and de-briefing). We assessed clinical decision-making skills of nursing students before the intervention, after the intervention, and 1 months later, with Clinical Decision-Making questionnaire. In this study SPSS software version 23.0 was used to analyze the data and significance level was considered P < 0.05. Results: Clinical decision-making skills of nursing students was compared before (48/04 ± 12/77) and immediately after training (91/49 ± 7/66) using paired tests, and a statistically significant difference was found (P = 0/009). Also, before the intervention, most students were thinking analytically (63/80%) and making clinical decisions, while after the intervention, most students had an analytic-intuitive model of clinical decision-making (63/80%). Conclusion: The study showed that the decision-making skills of nursing students were significantly improved by virtual patient simulations. The educational intervention and simulator used in this study can be integrated into undergraduate nursing student education curricula to help them acquire clinical decision-making skills. Keywords: Nursing students, clinical decision-making, simulation education, virtual simulation, skill ISSN 2413-0516 Introduction One of the most important aspects of nursing education is teaching clinical decision-making skills.1 Because of the com- plexity of the legal and professional issues they face today, nursing students must acquire sufficient skills to make clinical decisions during their education.2 Furthermore, the ability to diagnose and treat patients quickly and accurately is not a skill that can only be learned theoretically.3 Currently, after training courses and before students enter the practical stage and deal directly with patients, they teach them how to diagnose dis- ease quickly, make the right decision, take action accordingly, and deal with patients professionally.4–5 For this purpose, con- sidering the achievements of technology, it is recommended to do planning so that it can be integrated into the clinical educa- tion curriculum.6 In this regard, technology has been used in nursing education for years to enhance teaching and learning.7 In the meanwhile, due to the increasing incidence and prevalence of COVID-19 worldwide and in Iran since the beginning of the twentieth century, all clinical rotations and training opportunities have been suspended to reduce the burden of the disease.8–9 A major challenge in the current situ- ation is how to continue medical education when there is no direct contact with patients.9 In order to solve this challenge, we need to pay more attention to the capabilities of technology in clinical education. The use of simulation-based learning platforms began before COVID-19, but the pandemic has also led to an increase in the demand for and use of alternate, inno- vative methods for training nurses.10 Several research studies on integrating technology into clinical education suggest that simulation-based learning can help nursing students develop their clinical decision-making skills in complex situations.11 These patients are based on a scientifically problem-based design and structure that mimics the various stages of dealing with real patients, beginning with the initial interview and continuing through treatment.12 Real emergency patients need to be treated immediately. In these situations, patient care is a priority that cannot be postponed for the student’s presence. In this situa- tion, the student is in a passive and observational role and does not make decisions.13 While by working with a virtual patient, the student has the opportunity to work with severe, abnormal, and critical patients. The student has the opportu- nity to work with patients who have complex physical and mental conditions, make mistakes, then receive feedback and reapply the correct method. During virtual patient simula- tions, time and urgency constraints are controlled. In other words, time pressure can be removed when the student needs to concentrate.14 Research has shown that simulation-based education not only improves students’ mental abilities, including their clin- ical reasoning skills, but also positively affects their attitudes.15 In addition, this method has a significant impact on students’ acquisition of skills in the application of techniques. As a http://orcid.org/0000-0003-0551-6068 32 J Contemp Med Sci | Vol. 8, No. 1, January-February 2022: 31–37 Teaching Clinical Decision-Making Skills to Undergraduate Nursing Students Original T.M. Hosseini et al. result, nursing educators are increasingly incorporating vir- tual simulations into their curricula.16 In the field of clinical education, there are many simulated environments designed to enhance students’ scientific and practical skills and prepare them for real-life situations.17 Despite the numerous technology-based programs for clinical education, there are few computer programs that can simulate the realism of the doctor-patient relationship.15 One of the simulators currently being used in clinical education is the CP-platform developed by the Department of Surgery at the College of British Columbia. CP has managed to accom- plish this task and make this long-standing dream of instruc- tors and students a reality.18 The philosophy behind CP-platform is that traditional medical education starts with etiology and pathogenesis and then moves on to signs and symptoms of the disease. But in real cases, the patient goes to the doctor with the chief com- plaint, and the doctor has to think backward after examining the signs and symptoms to find out the cause and pathogenesis of the disease. This difference has led to many challenges being encountered by trainees, doctors, and novice nurses in attempting to apply theoretical knowledge in practice. This is one of the reasons why medical and nursing schools have moved to a problem-solving, case-based curriculum. These changes brought a number of challenges and issues including patient availability, the need to increase the number of clinical faculty, and ultimately an increase in the cost of medical edu- cation. Consequently, the centralized use of computers may be the solution to the challenges associated with a problem- or case-based curriculum.19 The CP interactive learning system is based on problem solving and clinical decision making. In the system, students gather information with menus that include laboratory results, clinical examination of the patient, and diagnosis and treat- ment of the patient’s condition. Students can also select cases of increasing difficulty so they can work on more advanced aspects of diagnosis and treatment for that particular condition each time.19 The results of the study conducted by Farahmand et al. (2020) show that the use of CP virtual patient simulator has been effective in enhancing students’ history taking skill, increasing the effectiveness of clinical education and reducing costs.20 There is limited evidence that simulation can be an effec- tive teaching and learning tool for clinical decision making. However, no study to date has measured the impact of cyber- patient simulation on students’ clinical decision-making skills. Therefore, the present study investigated the impact of using this virtual patient simulator on the acquisition of clinical decision-making skills by nursing students at Shahid Beheshti University of Medical Sciences (SBMU) in Iran. Theoretical Framework The National League for Nursing Jeffries Simulation Theory21 was combined with International Nursing Association for Clinical Simulation and Learning (INACSL) Standards of Best Practice (SOBP)22 to form the theoretical basis for this study. Simulation activities are designed, implemented, and evaluated using concepts laid out in Jeffries’ NLN theory. This theory focuses on context, background, design, simulation experiences, facilitators and educators, participants, and outcomes. As outlined in the INACSL SOBP, a valid and rigorous simulation experience requires well-defined and content-aligned outcomes, qualified faculty, adequate student preparation, theory-based debriefing, and curriculum integration.21,23 Materials and Methods Study Design and Setting This was a quasi-experimental study with a pretest-posttest design. It was conducted between 2020 and 2021 to evaluate the effectiveness of the CP training programme on the clinical decision-making skills of nursing students at SBMU. Study Participants and Sampling All third-year nursing students who had completed their clerk- ship at the time of the study (n = 58) were selected by census method. The inclusion criteria for the present study were interest in participating in the research and completion of a course in medical-surgical nursing. Exclusion criteria for par- ticipants included refusal to proceed with the research, failure to attend an educational session, and failure to complete the research instruments in the second phase of data collection. Ethical Consideration Informed consent was obtained from all participants. They were assured that their personal information would be kept confidential and only general statistics and data would be published. This project was conducted in the form of PhD stu- dent thesis and approved by the Ethics Committee of Shahid Beheshti University of Medical Sciences (Ethics Code: IR.SBMU.SME.REC.1400.044) Address: https://ethics. research.ac.ir/EthicsProposalView.php?id = 213812. Intervention In a briefing session, students learned how to use the cyberpa- tient system and what is expected of them during the training process. The screen interface components and navigation boxes were described. Students were given 10 minutes to familiarize themselves with the software and completed the demographic information questionnaire and Clinical Decision-Making (CDM) instrument during this session. Based on the educational strategy of case-based learning, the educational intervention was designed and implemented in five steps (pre-activities, introduction, scenario briefing, Web-based clinical scenarios, presentation and de-briefing) (Figure 1). 1. Pre-activities In accordance with the clinical course plan for cardiology dis- eases in nursing students’ internships, eight cardiovascular disease case studies were selected from the CP case library (Figure 2-1). The cases were placed in the virtual classroom dashboard in the CP system for students to access. The educa- tional intervention was conducted in the university clinical laboratory for eight weeks. Students participated in a clinical exercise each week under the supervision of a clinical pro- fessor and worked on a clinical case. For each case, the fol- lowing steps were completed in order. 2. Introduction A clinical instructor discussed the educational goals to be achieved after working with the virtual patient (Figure 2-2). https://ethics.research.ac.ir/EthicsProposalView.php?id=213812 https://ethics.research.ac.ir/EthicsProposalView.php?id=213812 33J Contemp Med Sci | Vol. 8, No. 1, January-February 2022: 31–37 T.M. Hosseini et al. Original Teaching Clinical Decision-Making Skills to Undergraduate Nursing Students He also asked questions to inquire about the all-encompassing prior knowledge relevant to the case. 3. Scenario Briefing Students began working with the virtual patient after the clin- ical professor briefly introduced the case. 4. Clinical Scenarios Presentation We scheduled and conducted a one-hour session with the vir- tual patient each time, beginning with the students logging into the simulator (https://app.cyberpatient.ca) until the system provided feedback (Figure 1). Users accessed the system using their assigned usernames and passwords. Once the student logged in, the virtual patient became available. After interviewing, examining the virtual patient’s body sys- tems, gathering information, and reviewing lab test requests related to the patient, the student moved on to the diagnosis phase and selected an appropriate treatment based on their diagnosis. This phase ended with the simulator providing feedback to the student on his actions during the history taking, physical examination, diagnosis, and treatment phases. By selecting the case on the screen, the virtual patient was presented in this simulated environment. Text, images, videos, and animations were used in an interactive virtual patient experience. This system allowed the student to enter a question and ask it to a virtual patient. The software then searched for the keyword in the question and returned an answer with voice and dialogue from the virtual patient (Figure 2-3). By clicking on the left side of the mouse, the user could explore the systems related to the patient’s problem. In the toolbar of the software, there were a number of tools for monitoring and examining patients. If needed, the student could take a blood sample from a virtual patient, insert a nasal cannula, and take the patient’s temperature and blood pressure with a thermometer and blood pressure monitor. Students were able to see step-by-step how to perform each of the required actions on the virtual patient (Figure 2-4). Using the mouse, the student selected the part of the vir- tual patient to be examined and could then perform inspec- tion, palpation, percussion, and auscultation. The physical examinations were simulated with software that allowed the user to listen to the lungs and decide for themselves whether the sounds were normal or not. The student was expected to make a possible diagnosis based on the data collected. Once the diagnosis was made, the student had to create a treatment plan for the patient. This system allows the student to pre- scribe medication for the patient. When prescribing the med- ication, the student should determine the name, method of administration, dosage, and number of daily doses. The student should create a follow-up plan and make Fig. 1 Cycle of simulation-based learning activities in the CP-based intervention. The clinical scenario presentation step was performed on the CyberPatient platform. In this figure you can see the main components of the platform. From the case dashboard (case encounter), the user can follow any path. It is also possible to return to the case presentation from the Diagnosis, Therapy or Follow-up sections. 34 J Contemp Med Sci | Vol. 8, No. 1, January-February 2022: 31–37 Teaching Clinical Decision-Making Skills to Undergraduate Nursing Students Original T.M. Hosseini et al. recommendations to improve the patient’s lifestyle and diet after selecting the appropriate treatment for the patient. Using the CyberPatient simulator, the student could calculate the cost-effectiveness of diagnostic, therapeutic, and follow-up interventions. The software could also record the time spent on each case, the number of errors made and the immediate feedback received. 5. Debriefing The students took part in a 90-minute debriefing session in the conference hall of Mofid Hospital in Tehran 24 hours after working with each virtual patient. The clinical professor had attended a debriefing training workshop to be able to conduct debriefing sessions and had the necessary expertise for that. In this study, the 3D model was used for debriefing, and all debriefing sessions were in compliance with INACSL standards. Data Collection Tool and Technique In this study, a demographic information questionnaire and Clinical Decision-Making (CDM) instrument by Lauri and Salantera (2002) were used to collect data. This 24-item instru- ment was divided into four subscales, each containing six items corresponding to the four steps of the decision-making process. The CDM uses a 5-point Likert scale. The even-numbered items reflect decision making in situations with unpredictable outcomes, such as “I make assumptions about impending care problems when I first meet the patient.” The odd items reflected decision making in structured tasks or in situations where there is sufficient time to gather information or plan activities, e.g., “Based on my preliminary information, I specify all the items I want to monitor and ask the patient about. According to the instructions, the total sum of the scores was interpreted.” Responses are graded from never (1), rarely (2), sometimes (3), often (4), and almost always (5). Scores range from 120–24, with scores ranging from five (always) to one (never) for sen- tences with a positive semantic load and vice versa for expres- sions with a negative semantic load. Reversed expressions in this questionnaire are: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, and 23 (one = always, five = never). A score of 67 or less indicates sys- tematic analytic decision making, a score between 68 and 78 indicates analytic intuitive decision making, and a score above 78 indicates intuitive interpretive clinical decision making.22 Internal Consistency Reliability The reliability of the translated questionnaire was confirmed in the Javadi study (2008), and the internal correlation of Cronbach’s alpha was reported to be 0.75.24 The reliability of this instrument was re-assessed by Karimi Naghandar et al. (2013) and α = 0.85 was reported.25 In the present study, the reliability of this tool was evaluated by using the test-retest method on 20 people. Cronbach’s alpha coefficient of 0.86 was calculated and reported. Analysis SPSS software version 23.0 was used to analyze the data and examine the effects of using the simulator on students’ clinical decision-making skills. Shapirovilk and Kolmogorov-Spironov tests were used to examine the natural distribution of the quan- titative variables. Descriptive statistics such as mean, standard deviation, and frequency distribution were used to describe the characteristics of the participants. Paired t-tests were used to compare the clinical decision-making score in the two phases before and after the intervention and to compare the score immediately after the intervention and one month after the intervention. Significance level was considered P < 0.05. Results Fifty-eight students in their third year of nursing studies par- ticipated in this study. The majority of participants were single (87.3%) and female (81.7%). The mean age of the participants was 21 ± 4.5 years. The majority of participants (86.2%) had never experienced virtual training or simulation in clinical education. Clinical decision-making skills of nursing students was compared before (48/04 ± 12/77) and immediately after training (76/49 ± 7/66) using paired tests, and a statistically significant difference was found (P = 0/009). Students used analytic clinical decision-making before the intervention and intuitive clinical decision-making after the intervention. A sta- tistical difference was also observed in clinical decision- making skills before and after one month of follow-up (P = 0/001). Comparison of clinical decision-making ability immediately after training with that after one month of follow-up (73/06 ± 4/9) also revealed no statistically signifi- cant difference (P = 0/235) (Table 1). Fig. 2 Screenshots of the CyberPatient Software. 35J Contemp Med Sci | Vol. 8, No. 1, January-February 2022: 31–37 T.M. Hosseini et al. Original Teaching Clinical Decision-Making Skills to Undergraduate Nursing Students In addition, the results showed that 24.14% of nursing students used analytic-intuitive reasoning before the interven- tion. 70.69% of the respondents used analytical decision making while 5.17% used intuitive decision making. After the implementation of educational intervention based on virtual patient simulator, 25.86% students showed intuitive thinking, 63.80% showed analytical-intuitive thinking and 10.34% showed analytical thinking. Thus, it can be said that before the intervention, most students were thinking analytically and making clinical decisions, while after the intervention, most students had an analytic-intuitive model of clinical decision- making (Table 2). Discussion Accordingly, the current study, designed to examine the effects of virtual patient simulators on nursing students’ clinical decision-making skills, showed a significant increase in their ability to make clinical decisions. In addition, the results showed that the durability of acquired clinical decision-making skills was significantly higher after one month of the interven- tion than before the intervention. Numerous studies have examined how virtual patients and simulators affect students’ clinical decision-making skills. As shown in a study by Nibbelink et al. (2018), computer soft- ware can provide unlimited opportunities for nursing student education by providing a safe and realistic environment in which they can practice clinical decision making and practical skills without potential risk to the patient.26 Consistent with the findings of the present study, Roh (2013) et al. conducted a comparative study to assess nurses’ self-efficacy in cardiopulmonary resuscitation decision making using computer simulations and mannequin simula- tions. The study showed that computer simulations had a greater impact on nurses’ clinical decision making.27 Endacott’s (2012) study assessed nurses’ clinical deci- sion-making skills using standardized patients and manne- quins in an OSCE test. According to the study, the use of standardized patient simulation methods helped to strengthen nurses’ clinical decision-making skills more effectively than mannequins. In addition, simulation and informal feedback were used to improve clinical decision making in emergency situations.28 After working with each virtual patient, we debriefed and then drew conclusions from each case. We then provided feedback to the student on how to improve their per- formance on the case. As previously mentioned, 5.17% of the subjects used intu- itive reasoning before the intervention and 25.86% after the intervention, which indicates that the use of the CP virtual patient simulator improved the use of intuitive clinical rea- soning by nursing students. Non-analytical or intuitive rea- soning occurs unconsciously and fast, it happens in the moment without much effort, and it doesn’t require much energy. Pattern recognition is equivalent to analytical rea- soning. In daily life, we do things that happen automatically over time without thinking about them. To understand these behaviors, we form cognitive structures in our minds called pattern recognition.29 Pattern recognition in medicine is divided into diseases. Based on pattern recognition theory, due to repeated expo- sure, an organized set of medical information imprints itself in their memory so that they use it when making decisions and solving problems with subsequent patients. Therefore, when experienced students are confronted with new disease cases that are similar to diseases they have previously encountered, they can quickly recall a structured network of relevant infor- mation and thus spend less time finding solutions (Okoli, 2018).30 A CP simulator, as mentioned earlier, provides a suc- cessful solution to learning pattern recognition for any disease through continuous practice on a standard virtual patient. The nursing student, supported by his cognitive structures, can make a correct clinical decision in a situation similar to the virtual case. In the present study, it was found that 70.69% of the sub- jects used analytical thinking before the intervention which decreased to 10.34% after the intervention. The study found that the number of students who used analytical-intuitive thinking increased after the intervention (63.80%). When a nursing student uses analytic or hypothetico-deductive rea- soning, he or she attempts to make a possible diagnosis by establishing a causal relationship between signs and symp- toms. It is a trial and error-based method. Students and novice nurses usually use this approach to solve clinical problems based on the pathophysiology of disease. The reason is that their information is insufficiently structured and they lack clinical experience in dealing with different types of patients.31 Therefore, it can be said that clinical reasoning is a spec- trum that includes analytical reasoning or deductive hypoth- eses on one side and intuitive or non-analytical reasoning on the other. In general, the nursing student moves from analyt- ical thinking on this side of the spectrum to intuitive thinking on the other side of the spectrum, gaining experience and Table 2. Classification of nursing students according to three clinical decision models before and after the intervention Decision-Making Model Range of Score Number Before intervention Percent (%) Before intervention Number Immediately after the intervention Percent (%) Immediately after the intervention Intuitive decision making Above 78 3 5/17 15 25/86 Analytical-intuitive decision making Between 78–68 14 24/14 37 63/80 Analytical decision making Under 68 41 70/69 6 10/34 Table 1. A Comparison of the scores in Pre-test and Post-tests (1 and 2) and the scores in Post-tests (1 and 2) in the CP-based intervention Group Pre-test Post-test 1 Post-test 2 CP-based training 48/04 ± 12/77 76/49 ± 7/66 73/06 ± 4/09 Paired test Pretest- Posttest 1 Pretest- Posttest 2 Posttest1- Posttest 2 P value P = 0/009 P = 0/001 P = 0/235 36 J Contemp Med Sci | Vol. 8, No. 1, January-February 2022: 31–37 Teaching Clinical Decision-Making Skills to Undergraduate Nursing Students Original T.M. Hosseini et al. practice and repeating the clinical decision-making position. In the middle of this spectrum is analytic-intuitive reasoning. Similarly, in study conducted by Şahin (2021), most of the participants reported that virtual patient simulation enabled them to apply theoretical knowledge in practice. Based on these results, simulation-based training and the use of visual technologies can facilitate the acquisition of nursing skills.32 The results of this study are in agreement with Parker’s study (2014). Parker’s study found that nurses’ decision-making pro- gressed from intuitive to analytical through education.33 Sur- gical students were taught surgical skills using virtual patients in Shariati’s study (2008). Based on the results of this study, educating students based on virtual patients helps them to design a mental framework or pattern recognition to taking history of the patient. Thus, the student can use this same pat- tern recognition to taking history from almost all patients with similar complaints.34 Limitation and Recommendation This study shows that using simulation to teach clinical skills is critical for a successful curriculum. The fact is that imple- menting such a curriculum is not always easy. Incorporating simulations into the curriculum remains a challenge, but it is important to note that they are most successful when they become a natural part of the curriculum rather than a separate component. The results of this study may not suggest that the improvement in students’ clinical decision-making skills is simply due to working with the CP virtual patient simulation platform as a teaching tool, because it was used in a compre- hensive clinical course with planned educational activities. On the other hand, building knowledge and acquiring process skills are also complicated. The other limitations of this study include the small number of research units, implementation of this intervention in a group setting, lack of a control group to compare outcomes, and implementation in only one heart disease rota- tion. The educational intervention and simulator used in this study can be integrated into undergraduate nursing curricula to help students acquire clinical decision-making skills. In addition, it is proposed to evaluate the impact of using the CP virtual patient simulator and the educational design of this study on nursing students’ critical thinking skills. Conclusion The review of the results shows that nursing students’ clinical decision-making skills can be improved in a controlled envi- ronment using the CP simulator. Students can practice their skills in a safe environment without harming patients. The use of CP-based clinical simulation is a great way for nursing stu- dents to combine, relate, and ultimately apply their theoretical knowledge to nursing practice. Acknowledgment We would like to thank the nursing students and faculty mem- bers of the Shahid Beheshti University of Medical Sciences (SBMU) for their participation in this project. Conflicting Interest Disclosure CP is a business education product developed by the Univer- sity of British Columbia / Vancouver Coastal Health (UBC / VCH) and Spin-off Company UBC / VCH. The University of British Columbia and the Virtual University of Medical Sciences, Tehran, Iran, have signed a Memorandum of Under- standing granting all Iranian medical universities free access to this platform. Due to this probable conflict of interest, the study was organized and conducted by a research committee with no conflict of interest at Shahid Beheshti University of Medical Sciences.  References 1. 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