PubMed · 10186107
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
1998. Better reporting, forms and procedures reduce medication errors.. https://pubmed.ncbi.nlm.nih.gov/10186107/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Radiotherapy workload is poorly represented by simple parameters of patients, fractions or fields treated because these do not contain any measure of treatment complexity. However, complexity is increasing and there is an urgent need to quantify this. We have evaluated the basic treatment equivalent (BTE) model as a measure of radiotherapy workload and complexity. Radiotherapy treatment times, from the patient entering to exiting the treatment room maze, were measured for 1298 treatment sessions on 269 patients. The data were used to assess the original model and derive three new models for predicting treatment duration. The most complicated, the 'Addenbrooke's complex model', contained two additional predictor variables, including 'site/technique', in a linear additive form. Before the study, the department used a standard treatment appointment time of 10 minutes. However, 50% of the measured treatments took longer than 10 minutes, (mean 10.9). Summed over the working day, this discrepancy indicates that a standard 10-minute appointment is a poor basis for scheduling radiotherapy. The original BTE model was effective in predicting treatment times, although this was improved by refinement of the model. The Addenbrooke's complex model correctly predicted 70% of treatment times to within 2 minutes (55% for the original BTE model), 80% to within 2.5 minutes and 95% to within 4.7 minutes. The percentage of the variation in observed times accounted for by the model is 59.4%. The models can represent radiotherapy complexity, can improve scheduling on linear accelerators, and are likely to be applicable to other departments. They are thus tools to assess the impact of changes in complexity from new techniques, trial protocols (e.g. the Medical Research Council prostate radiotherapy trial RTO1), and possible time saving from advanced technology such as multileaf collimators (MLCs) or automated machine set-up. The replacement of manually-lifted shielding blocks by MLCs should save 1.1-1.5 minutes for a three- or four-field pelvic plan (i.e. 12%-13%). The models could also be used to aid planning for future linear accelerator provision and for costing radiotherapy treatment.
Random fluctuations in demand make it impossible to see all patients in a very short time scale unless capacity exceeds the mean demand. We describe a model to estimate the capacity levels required as a function of mean demand. Random fluctuations were assumed to follow a Poisson distribution. A Monte Carlo analysis was used to model variations in length of waiting times. To see patients without a waiting list the capacity must exceed mean demand by an amount proportional to the square root of the mean; if capacity equals mean demand, then actual demand will exceed capacity almost half the time. The smaller the mean demand, the greater the percentage increase in capacity that is required. Thus, subdivision of numbers, for subspecialization or fast-tracking, demands greater overall capacity. When multiple serial steps are required, each step must have spare capacity if a waiting list is to be avoided. When capacity is only slightly greater than mean demand, random fluctuations mean that targets can be met for long stretches of time, but these are interspersed with periods when the waiting list rises substantially. Allowing a small waiting time (2-4 weeks) considerably reduces the excess capacity required. Targets such as the 2-week wait for cancer referrals can be achieved only if resource levels are set to give considerably more patient slots per week than mean demand. The level of spare capacity required depends on the level of demand and the maximum waiting time permitted. Without surplus capacity, waiting targets cannot be met. To meet the 2-week waiting target, capacity must exceed mean demand by two patient slots per week for 99% success, or by one slot per week for 90% success.
Healthy Public Policy is one of the key health promotion actions. Advancement of Healthy Public Policy requires that the health consequences of policy should be correctly foreseen and that the policy process should be influenced so that those health consequences are considered. Health Impact Assessment is an approach that could assist in meeting both requirements. Policies often produce health impacts by multiple indirect routes, which makes prediction difficult. Prediction in Health Impact Assessment may be based on epidemiological models or on sociological disciplines. Health Impact Assessment must be based on an understanding of, and aim to add value to, the policy-making process. It must therefore conform to policy-making timetables, present information in a form that is policy relevant and fit the administrative structures of policy makers. Health Impact Assessment may be used to inform health advocacy but is distinct from it. There is a danger that Health Impact Assessment could be misunderstood as health imperialism.