This research addresses the fluctuating demand and high variability in healthcare systems. These system’s variations need to be considered whilst at the same time making efficient use of the systems’ resources. Patient dependency fluctuation, which makes determining the level of adequate staffing highly complex, is among the variations addressed. Dealing with variability is found to be a key feature in the design, planning and evaluation of healthcare systems. Healthcare providers are facing increasing challenges resulting from an aging population, higher patient expectancies, a shortage of healthcare professionals, as well as increasing costs and reduced funding. Despite the accentuated need for effective healthcare systems and efficient use of resources, many healthcare organisations are inadequately designed and, moreover, poorly managed. Hospital systems consist of complex interrelations between relatively small units, each of which is sensitive to stochastic variations in demand. In addition to this aspect of the system view, a critical resource for the patients’ wellbeing and survival is the staffing level of nurses. This puts the planning and scheduling of human resources as one of the system’s foremost aims. Current tools for staffing and personnel planning in healthcare organisations do not take into consideration the workload variations that result from the variable nature of patient dependency levels. The work presents the empirical findings of a number of case studies conducted at a regional hospital in Sweden. Principles and practical suggestions for the robust system design of inpatient wards using Discrete Event Simulation (DES) have been identified. Although DES techniques have, in principle, all the features for modelling the variation and stochastic nature of systems, DES has not been previously used for workload studies of inpatient wards. The main contribution of this work is therefore how a combination of DES and the data of Patient Classification Systems (PCSs) can be used to model workload variations and, subsequently, plan the nurse staffing requirements in systems with high variability. The work presented gives step by step guidance in how the analysis and subsequent modelling of an inpatient ward should be carried out. It defines a novel modelling methodology for patient dependency variations and length of stay modelling of a patient’s dependency progression, including an adaptation to the ward’s discharge figures. The modelling approach opens a novel way of analysing and evaluating the system design of inpatient wards.

The main subject matter of this thesis concerns radiotherapy patient scheduling subproblems formulated as four separate shop scheduling problem models (i.e. hybrid flowshop, flowshop, mixed shop and multiple identical parallel machine scheduling problems) based on the characteristics of the intricate real-life treatment processes observed at the Arden Cancer Centre in Coventry, UK. Insight into these processes was gained by developing and using a novel discrete-event simulation (DES) model of the four units of the radiotherapy department. By typifying the subproblems as well-known scheduling problem models, it was intended that methods amenable to them such as heuristics be used in the study. Four novel constructive heuristics based on priority dispatching rules and strategies adapted from some established algorithms have been developed and implemented using the C++ programming language. Further, these heuristics were incorporated into the DES model to create schedules of appointments for the patients generated daily. The effectiveness and efficiency of the constructive heuristics have been tested using the following performance criteria: minimising i) average waiting time to the start of treatment, and ii) average percentage of patients late for their treatment, and iii) the amount of overtime slots used for the patients received in a given period of time. The coordinated constructive heuristics and the DES model have also been tested using possible alternative pathways patients can follow in the treatment unit. The aim of these tests was to compare the efficiency of the radiotherapy department’s current pathway to other possible pathways. Further, strategies for using maximum allowed breaches of targeted due dates, reserved slots for critical treatments and overtime slots was also included in the heuristics. The results of several tests showed that the heuristics created schedules of appointments whose average waiting times for emergency, palliative and radical treatments improved by about 50%, 34% and 41%, respectively, compared to the historical data. However, their major slack was evidenced by the fact that about 13% of the patients needing palliative treatment were expected to be late for treatment compared to about 1% of those requiring radical treatment.

<p dir="ltr">Nurse managers recognize clinical judgment as a core competency of practice readiness (Boyer et al., 2019; Harrisona et al., 2020). Reflection is vital for novice nurses’ clinical judgment development (Tanner, 2006), enhancing their ability to deliver safe care. Despite its importance, nurse educators lack the ability to measure reflection in simulation debriefings. To address this gap, the Student-Centered Reflection Scale (SCRS) was created to assess reflection during simulation debriefings. This study aimed to conduct item testing and evaluate the psychometric properties of the SCRS to measure the presence of reflection during simulation debriefings.</p><p dir="ltr">Four sequential psychometric studies conducted initial item testing and evaluated the SCRS’s reliability and validity. The first two studies, a content validity study with 11 experts and pretesting study with 16 undergraduate nursing students, provided feedback for item revision and evidence of validity. Then an exploratory factor analysis (EFA) and item analysis study, involving 92 undergraduate nursing students from a Midwest public baccalaureate nursing school, was completed. Finally, a confirmatory factor analysis (CFA) and convergent validity study were completed using the SCRS and Groningen Reflective Ability Scale (GRAS; Aukes et al., 2007) in a convenience sample of 218 nonoverlapping undergraduate nursing students from the same educational institution.</p><p dir="ltr">The EFA revealed 20 items with four factors or subscales: (a) comparison of previous knowledge (four items); (b) analysis of biases, beliefs, and consideration of moral and ethical criteria (five items); (c) analyzing the scenario (eight items); and (d) challenging assumptions and considering a change in practice (three items). These subscales explained 82.78% of the variance and demonstrated acceptable item loadings ranging from .50–.83 and inter-item reliability ranging from .219–.664. The CFA demonstrated acceptable global fit (RMSEA = .071, CFI = .914, TLI = .900) and component fit. The SCRS also demonstrated reliability (α = .92) and convergent validity (<i>r </i>= .496) with the GRAS.</p><p dir="ltr">In conclusion, the SCRS demonstrated adequate reliability and validity with this sample. The SCRS is a resource for educators to evaluate and foster reflective skills in undergraduate nursing students during simulation debriefings, thereby promoting nursing students’ overall practice preparedness.<br></p>

Nursing faculty are utilizing high-fidelity patient simulation (HPS) with debriefing to help engage nursing students in making competent clinical decisions. This quasi-experimental study examined the use of HPS with debriefing and students’ ability to make nursing care decisions using standardized exams. The experimental group received debriefing after HPS and the control group did not receive debriefing after HPS. The pre- and post-test assessed participants’ ability to make clinical care decisions. The analysis of the pre-test and post-test HESI scores showed that there was no significant difference between the two groups.