 rewards a decision that is in line with the mapped real processes and punishes ones with deviation, which is a typical behavioristic approach that, when applied in a game with immediate feedback, will have a motivational trigger effect. Thus, regarding the measurement of learning engagement, several proxy attributes were used to describe how active participants were in the serious game ( Figure 2). First, the number of addressed tasks was measured. The players address tasks by initiating an investigation of patient profiles, allocating resources, and coordinating with other lead nurses. The activity of the player was measured based on how many tasks were initiated and in how many tasks the player was involved. Second, we measured the time spent on the task. The rationale was that this would sufficiently indicate the focus of the player in carrying out their task. Increased time performing operational tasks represents a moderate relationship with achievement. Third, we measured the number of finished tasks. This is a learner-generated metric on the number of tasks correctly answered. The measurements of concentration are useful for understanding participants' cognitive load in learning and for user behavior modeling analysis. Figure 2 describes the relationship of the 3 measures used for user engagement and motivation to the 3 types of game activities and explains which psychological needs they serve. This impacts the game flow and the cognitive workload and immersiveness the player feels while playing and triggers the training of procedural knowledge.

Scoring-Based Mechanic for Logistical Aspects
As described in the introduction, an important factor that supports the learning process while playing is the inbuilt feedback the player receives during gameplay. Consequently, the game mechanic needs to be meaningful for the player (ie, based on the inbuilt feedback). The player needs to understand if the decision or process execution they just performed in the game is correct. As described in the "Game Scenario Description and Narrative" section, the narrative comprises the core of the learning process and focuses on acquiring procedural knowledge. This process is triggered by events. The focus is on improving the flow and resource usage. This needs to be reflected in the feedback and scoring mechanism both to support the player in performing the task while playing and to measure if the player made the right decisions. A possibility of doing this is to use the same metrics in the game as used for measuring logistics performance, which are typically related to time, quality, and costs. A good basis for this is the level 1 metric in the supply chain operations reference model [36], which can be used for both rewarding a desired or correct decision or for penalizing a wrong execution.
The relevant indicators in each process of the gameplay used for scoring the player performance included production throughput, activity executions, resource management, and production resilience. For production throughput, a point is rewarded when a patient is discharged from the emergency department. Activity executions, based on a comparison of an individual resource plan in the gameplay and the reality, are performed for each simulated incoming patient in order to impose penalty points for any delays of production. This scoring mechanism contributes to process quality and on one side shows deviation from the expected procedure and clearly indicates if medical activities as registered in the patient log are carried out in the right modules. For resource management, penalty points are imposed for the absence of the right staff in corresponding activities. The requirements of doctors and nurses refer to the estimation from subject matter experts based on the operational practice. This is also related to process quality and is implemented in order to point out the importance of planning resources (in this case, the medical staff with the right qualifications). Production resilience is represented by the availability of doctors, as they are the core competent medical employee in almost all kinds of medical care. This scoring mechanism is introduced to ensure that the players pay attention to the aspects of flexibility and adaptability to react to unexpected events (eg, getting more patients than planned, a critical person getting sick, or a device not functioning). It is related to the risk appetite an organization needs to have.
These 4 different mechanisms are implemented in the different processes in the gameplay. The implementation and how it is applied in the game is shown in Table 1, which considers the production and resource characteristics of pediatric emergency medicine. Provided that the positive and negative feedback framework is psychologically beneficial for systematically effective employee performances in operational settings, as demonstrated by Losada and Heaphy [37], the optimal ratio of 6:1 for the distribution of positive and negative feedback is used for the calibration of the order of magnitude for each role in emergency medicine delivery.

Game-Based Learning Practices
The players in the pilot study evaluating the game effect were recruited from master's students participating in relevant courses from the Department of Biomedical Engineering and Health Systems. A total of 36 students were recruited for the sandbox game and web-based multiplayer serious game in 2019 and 2020, respectively. The participants received a briefing about the roles, resources, and rules of the game.

Learning Behavior Analysis Based on In-Game Data
In-game behavior data were collected. As an initial point of activating the behavior chain, the player was informed of any changes to the point system. All logistical aspects were considered. The behavior coding was then performed in accordance with Table 2. All selected behaviors shown in Table  2 are based on previous studies, with necessary adjustments according to the content of the serious game and the research questions in this study. The data were automatically exported from the multiplayer serious game application and manually judged based on tape recordings of the playing of the board game. Then, the raw data set was created by transcribing in-game behaviors into learning engagement attributes. The logistical performance dependent variable was computed as the number of tokens during a participant's entire training session.

Gamification Acceptance Evaluation Based on Questionnaire
After playing, each player was asked to answer the Core and Social Presence Modules of the Game Experience Questionnaire (GEQ) validated from a previous study to obtain insights into the experience of gamification and gaming with a digital game practice and multiplayer board game [38]. The main goal of the questionnaire was to evaluate whether the game mechanics were accepted and whether the gamified processes were easy to play and understand. The dimensions were competence, sensory and imaginative immersion, flow, tension or annoyance, challenge, negative affect, and positive affect. The answers to these questions were based on a 6-point Likert scale, with 0 meaning "not at all" and 4 meaning "extremely." In addition, the participants answered a questionnaire that featured 3 dimensions of social presence based on a 6-point Likert scale, with 0 meaning "not at all" and 4 meaning "extremely." These dimensions were psychological involvement -empathy, psychological involvement -negative feelings, and behavioral involvement.

First Case Study: Sandbox Game
The first case study was based on an analog sandbox game ( Figure 3). The game requires participation from 5 module nurses, simulating 5 modules, and 1 individual management nurse, playing the triage place in the ED. The team under the control of the management role is composed of physicians and nurses who take care of all activities. The ED receives a predefined number of incoming patients for triage each round based on actual patient flow data from a large pediatric ED, and decisions must be made by the triage nurse regarding whether the patient is referred elsewhere or stays at the ED.
The patients who stay at the ED are prioritized and then cared for by the simulated teams of doctors and nurses who work at the workstations or modules. The session lasts 12 rounds, simulating 6 hours in real time. Each patient has a unique profile for prioritization and activities, requiring human resources that are allocated by the person responsible for the module. The observation rooms and patient prioritization are decided by the module nurses and triage nurse, with urgent patients indicated on cards sent to the red module as an exception.

Second Case Study: Multiplayer Online Serious Game
The second case study took place via teleconference, with the same participants playing a serious game programmed in Unity (Unity Technologies). An online gaming lobby was available before each session from the built-in UNet service. The players were prompted by the workstation indicators if there were tasks assigned to their specific modules. Resource management occurred through the digital protocol when the players entered the working areas ( Figure 4). In addition, the players could navigate the simulated emergency department and interact with nonplayer characters, such as patients, doctors, and nurses. Unlike the sandbox game in the first case study, the multiplayer online serious game automatically calculated and presented the player's score. It is important to note that the actual game mechanics were identical between the analog and digital game.

Ethical Approval and Considerations
The study sought ethical approval from Stockholm's regional board of ethical approval, which resulted in the decision that the use and processing of any data in this study do not require ethical approval (case No. 2019-0744).
Participation in the games did not affect students' grades and great care was taken to ask for students' voluntary participation, apart from any mandatory learning activities. The examiner of the courses involved did not attend the gameplay sessions and was not informed about the results to avoid potential cross contamination.

Participants
The data set of the serious game consisted of 5263 rows of raw data-containing information. The players' average age was 25.72 (SD 6.64) years. The students spent a total of 178 hours interacting with the digital environment. The students in the sample were from management and engineering backgrounds.

Player Questionnaire
In the feedback from both cases, all respondents indicated that the game realized in either modality was easy to play and understand, as Figure 5 illustrates. The Core Module items displayed high values for positive affect, challenge, and flow and low values for tension and negative affect. The assessment of the game experience revealed a significant score difference between either tension or negative affect and any of the remaining dimensions. The social presence value reported scores varying between 0.5 and 3 across 3 dimensions. Empathy and behavior involvement scored higher in the board game practice. No component-specific