PubMed · 9560708
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J de St Georges. 1997. Scheduling stress-free emergencies.. https://pubmed.ncbi.nlm.nih.gov/9560708/
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PURPOSE: The purpose of this study was to evaluate the performance of different radiotherapy workload models using a prospectively collected dataset of patient and treatment information from a single center. METHODS AND MATERIALS: Information about all individual radiotherapy treatments was collected for 2 weeks from the three linear accelerators (linacs) in our department. This information included diagnosis code, treatment site, treatment unit, treatment time, fields per fraction, technique, beam type, blocks, wedges, junctions, port films, and Eastern Cooperative Oncology Group (ECOG) performance status. We evaluated the accuracy and precision of the original and revised basic treatment equivalent (BTE) model, the simple and complex Addenbrooke models, the equivalent simple treatment visit (ESTV) model, fields per hour, and two local standards of workload measurement. RESULTS: Data were collected for 2 weeks in June 2001. During this time, 151 patients were treated with 857 fractions. The revised BTE model performed better than the other models with a mean [observed -predicted] of 2.62 (2.44-2.80). It estimated 88.0% of treatment times within 5 min, which is similar to the previously reported accuracy of the model. CONCLUSION: The revised BTE model had similar accuracy and precision for data collected in our center as it did for the original dataset and performed the best of the models assessed. This model would have uses for patient scheduling, and describing workloads and case complexity.
Delay of care is a persistent and undesirable feature of current health care systems. Although delay seems to be inevitable and linked to resource limitations, it often is neither. Rather, it is usually the result of unplanned, irrational scheduling and resource allocation. Application of queuing theory and principles of industrial engineering, adapted appropriately to clinical settings, can reduce delay substantially, even in small practices, without requiring additional resources. One model, sometimes referred to as advanced access, has increasingly been shown to reduce waiting times in primary care. The core principle of advanced access is that patients calling to schedule a physician visit are offered an appointment the same day. Advanced access is not sustainable if patient demand for appointments is permanently greater than physician capacity to offer appointments. Six elements of advanced access are important in its application balancing supply and demand, reducing backlog, reducing the variety of appointment types, developing contingency plans for unusual circumstances, working to adjust demand profiles, and increasing the availability of bottleneck resources. Although these principles are powerful, they are counter to deeply held beliefs and established practices in health care organizations. Adopting these principles requires strong leadership investment and support.
There is often a considerable delay from initial tumour diagnosis to the start of radiotherapy treatment, which may be due to factors such as waiting lists and referral delays. This paper uses widely published models and clinical parameters to calculate the effect of delays in treatment on local tumour control for four different types of tumour-squamous cell carcinoma (head and neck), breast, cervix and prostate. The Poisson model for tumour control probability (TCP), an exponential function for tumour growth and the linear quadratic model of cell kill are used to calculate the change in TCP for delays between diagnosis and treatment of up to 100 days. Typical values of the clinical parameters have been taken from the literature; these include alpha and beta, sigma(alpha), tumour size at diagnosis, pre-treatment doubling time, delay in onset of accelerated repopulation and doubling time during treatment. It is acknowledged that there are limitations in the reliability of these data for predicting absolute values of tumour control, but models are still useful for predicting how changes in treatment parameters are likely to affect the outcome. It is shown that for fast-growing tumours a delay of 1-2 months can have a significant adverse effect on the outcome, whereas for slow-growing tumours such as Ca prostate a delay of a few months does not significantly reduce the probability of tumour control. These calculations show the importance of ensuring that delays from diagnosis through to treatment are minimized, especially for patients with rapidly proliferating tumours.