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BACKGROUND: Medication reconciliation (MedRec) in hospitals is an important tool to enhance the continuity of care, but completing MedRec is challenging. AIM: The aim of this study was to investigate whether queueing theory could be used to compare various interventions to optimise the MedRec process to ultimately reduce the number of patients discharged prior to MedRec being completed. Queueing theory, the mathematical study of waiting lines or queues, has not been previously applied in hospital pharmacies but enables comparisons without interfering with the baseline workflow. METHOD: Possible interventions to enhance the MedRec process (replacing in-person conversations with telephone conversations, reallocating pharmacy technicians (PTs) or adjusting their working schedule) were compared in a computer experiment. The primary outcome was the percentage of patients with an incomplete discharge MedRec. Due to the COVID-19 pandemic, it was possible to add a real-life post hoc intervention (PTs starting their shift later) to the theoretical interventions. Descriptive analysis was performed. RESULTS: The queueing model showed that the number of patients with an incomplete discharge MedRec decreased from 37.2% in the original scenario to approximately 16% when the PTs started their shift 2 h earlier and 1 PT was reassigned to prepare the discharge MedRec. The number increased with the real-life post hoc intervention (PTs starting later), which matches a decrease in the computer experiment when started earlier. CONCLUSION: Using queueing theory in a computer experiment could identify the most promising theoretical intervention to decrease the percentage of patients discharged prior to MedRec being completed.
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Multi-disciplinary planning in health care is an emerging research field that applies to many health care areas with similar underlying planning characteristics. We provide a review of the literature and describe cross-relations between different applications. We identify multiple fields to classify the literature upon. These fields relate to the system characteristics, decision characteristics, and applicability. The relevant papers for each of these fields are discussed, which provides a broad and thorough overview of the present research, and guides readers towards identifying the applicable literature for their research based on the characteristics of their problem. Furthermore, we disclose research gaps and present open challenges for further research.
RESUMO
BACKGROUND: Pathology departments face a growing volume of more and more complex testing in an era where healthcare costs tend to explode and short turnaround times (TATs) are expected. In contrast, the histopathology workforce tends to shrink, so histopathology employees experience high workload during their shifts. This points to the need for efficient planning of activities in the histopathology laboratory, to ensure an equal division of workload and low TATs, at minimum costs. METHODS: The histopathology laboratory of a large academic hospital in The Netherlands was analysed using mathematical modelling. Data were collected from the Laboratory Management System to determine laboratory TATs and workload performance during regular working hours. A mixed integer linear programme (MILP) was developed to model the histopathology processes and to measure the expected performance of possible interventions in terms of TATs and spread of workload. RESULTS: The MILP model predicted that tissue processing at specific moments during the day, combined with earlier starting shifts, can result in up to 25% decrease of TATs, and a more equally spread workload over the day. CONCLUSIONS: Mathematical modelling can help to optimally organise the workload in the histopathology laboratory by predicting the performance of possible interventions before actual implementation. The interventions that were predicted by the model to have the highest performance have been implemented in the histopathology laboratory of University Medical Center Utrecht. Further research should be executed to collect empirical evidence and evaluate the actual impact on TAT, quality of work and employee stress levels.