How should a waiting list for treatment be managed?

Testing the Timing: Time Factor in Radiation Treatment for Head and Neck Cancers

OPINION STATEMENT

Overall radiation treatment time has long been recognized as an important factor in head and neck tumor control. The concern of tumor growth in waiting time either before starting radiotherapy or during treatment is substantial given its negative impact on clinical outcome. There is an overwhelming evidence that increasing the time to initiate treatment increases the tumor burden and worsens the prognosis. This effect is more pronounced especially in patients with an early stage cancer disease. Delay in treatment initiation is contributed by both health care- and patient-related factors. Health care-related factors include advancement in diagnostic modalities and transfer of patient to academic health care centers accompanied by delayed referrals and long-awaited appointments. Patient-related factors include delayed reporting time and socioeconomic factors. An efficient transition of care along with access of cancer care modalities to community health care centers will not only improve the quality of care in secondary health care centers but also help decrease the patient burden in tertiary centers. A quick and well-structured multidisciplinary appointment program is fundamental in shortening the time required for patient referrals, thus increasing the optimal survival time for Head and Neck cancer patients with early initiation of treatment.

Waiting time to radiotherapy as a prognostic factor for glioblastoma patients in a scenario of medical disparities

Objective To evaluate the effect of waiting time (WT) to radiotherapy (RT) on overall survival (OS) of glioblastoma (GBM) patients as a reliable prognostic variable in Brazil, a scenario of medical disparities. Method Retrospective study of 115 GBM patients from two different health-care institutions (one public and one private) in Brazil who underwent post-operative RT. Results Median WT to RT was 6 weeks (range, 1.3-17.6). The median OS for WT ≤ 6 weeks was 13.5 months (95%CI , 9.1-17.9) and for WT > 6 weeks was 14.2 months (95%CI, 11.2-17.2) (HR 1.165, 95%CI 0.770-1.762; p = 0.470). In the multivariate analysis, the variables associated with survival were KPS (p < 0.001), extent of resection (p = 0.009) and the adjuvant treatment (p = 0.001). The KPS interacted with WT to RT (HR 0.128, 95%CI 0.034-0.476; p = 0.002), showing that the benefit of KPS on OS depends on the WT to RT. Conclusion No prognostic impact of WT to RT could be detected on the OS. Although there are no data to ensure that delays to RT are tolerable, we may reassure patients that the time-length to initiate treatment does not seem to influence the control of the disease, particularly in face of other prognostic factors.

Managing waiting time for radiotherapy: A single machine unit experience

Waiting time (WT) for radiotherapy (RT) is a significant clinical problem. This paper examines various strategies for managing WT for patients treated with radical and palliative intent in the new setting of a rural single machine unit in Australia. Cohorts of patients undergoing both radical and palliative RT in Bendigo had their WT prospectively recorded. Matched cohorts from the hub centre (Peter MacCallum Cancer Centre, Melbourne) treated with palliative intent were also collated. Strategies implemented included a devoted priority meeting, palliative points system, and reallocation of appointment times. The audit was to continue until best practice guidelines were bettered. Three cohorts of patients were compared. There is a significant trend for increasing numbers of patients treated per month since the centre opened (P < 0.0001). The ratio of palliative to radical intent patients remained stable between 46 and 52%. Mean WT for palliative RT reduced from 25 days in the first cohort to 7 days in the final cohort (P < 0.0005). Waiting time for palliative RT was initially longer at Bendigo than the hub centre (P < 0.0005), but by the final cohort there was a non-significant difference favouring the Bendigo cohort (P = 0.26). Waiting time for radical treatment also improved throughout the three cohorts in Bendigo (P < 0.0005). A number of new strategies have successfully resulted in the abolition of lengthy WT for RT in Bendigo despite the increasing demand for the RT service.

Clinical Prioritisation for Curative Radiotherapy: A Local Waiting List Initiative

AIMS

Waiting time for radiotherapy is a major problem in radiation oncology practice. The aim of this paper is to present the experience of the Peter MacCallum Cancer Centre in trialling a number of strategies to reduce patient waiting times.

MATERIALS AND METHODS

All patients starting megavoltage radiotherapy with curative intent in three separate 1-week blocks had their waiting times recorded. The cohorts were each 8 weeks apart and were before (September), during (November) and after (January) the introduction of a priority points system.

RESULTS

Median waiting time was 35 days in September, 42 days in November and 31 days in January. The number of extremely long waits (>90 days) decreased to 1 by January. Significantly more patients were pre-booked for treatment in January (27/51) compared with September (17/65; P = 0.003) and November (12/65; P < 0.001). Pre-booked patients had shorter waiting times compared with patients who was not pre-booked (P < 0.0001). Difficulties at one particular treating location contributed to the longer median waiting times in November. Although there had no significant difference in waiting time in non-breast unit patients between the three cohorts, there was a decrease in waiting times in breast unit patients, especially between November and January (P = 0.0008). There was no significant increase in delay to starting treatment in other treating units, resulting in more equitable access across all units.

CONCLUSIONS

A combination of encouraging pre-booking and the introduction of a priority points system has led to a decrease in waiting times, especially among breast unit patients.

Waiting for radiation therapy: does it matter?

The aim of this study was to determine the direct and indirect effects of prolonged waiting times for radiation therapy. We used the Medline, CancerNet and EMBASE databases to search the international research using the keywords radiotherapy, waiting times and delay. The negative effects of prolonged waiting times for radiation therapy can be broken down into direct and indirect effects. Direct effects include tumour control factors and patient factors. Indirect effects include changes in referral patterns and change in management of tumours. The precise effects of prolonged waiting times for radiation therapy are difficult to define. Evidence exists for some tumour sites, such as postoperative head and neck, small-cell lung cancer and high-grade cerebral gliomas, that tumour control might be adversely affected. The effect on other tumour sites is less established. Patient factors are likely to be consistent across all tumour types and indirect effects are hard to quantify.

How do waiting times affect radiation dose fractionation schedules?

The purpose of the present paper was to evaluate the changing patterns of dose prescription at the Queensland Radium Institute from 1995 to 1998 inclusive. Data were analysed from the treatment files collected on each patient and these were compared with data on delay time. There has been an increased use of shorter fractionation schedules in the period studied. Paradoxically, radical treatments have become longer. The average number of fractions for all patients was 17.4 and for palliative treatments it was 7.4. The monthly delay varied from 0 to 22 working days and the average was 7 working days. When fraction number was compared to treatment delay, there was a negative linear correlation (R = -0.25). The correlation was stronger (R = -0.467) when palliative treatments were compared, indicating that clinicians were more prepared to alter palliative treatments in the presence of a treatment delay than curative ones.

Patterns of fractionation for palliation of bone metastases

The patterns of fractionation used to treat bone metastases in a single centre in two time periods were determined. Clinical audit was carried out, for two periods of 6 months each, in 1988 and 1993. Data recorded included patient, tumour and treatment variables. Palliation of bone metastases represented 40% and 44% of palliative treatment courses, and 20% and 21% of total treatment courses, respectively, in both time periods. Shorter treatment schedules were used in 1993 compared to 1988, with mean fraction numbers of 6.4 versus 8.5, respectively (P < 0.001). This reduction in fraction numbers occurred for the common primary tumour sites. In 1993, the mean numbers of fractions used were related to the primary tumour site (7.8 fractions for breast, 5.0 for lung, 6.7 for melanoma, 6.4 for other primary sites, P = 0.05), the treatment site (7.3 fractions for weight-bearing bones, 4.0 for non-weight-bearing bones, P < 0.001) and patient address (6.0 fractions for city postcode vs 7.8 for country postcode, P = 0.02). Treating radiation oncologist (5.7-7.8 mean fractions, P = 0.06) and patient age (P = 0.56) were not significant factors for the number of fractions used. It is likely that there were multiple causes for a reduction in the number of fractions used to treat bone metastases, including the results of clinical trials and increasing pressure to optimize the use of scarce resources. However, patients who may have a better prognosis (breast primary) and those with metastases in weight-bearing bones continued to receive longer treatment schedules.

Waiting times for radiotherapy treatment: not all that mysterious and certainly preventable

A computer program which deals specifically with the analysis and prediction of waiting times for radiotherapy has been written for a standard spreadsheet. The model has several components. The basic module calculates waiting times on the basis of any mismatch between the number of patients referred for treatment each week and the number of patients who can actually start treatment in that week. Monte Carlo simulations are used to estimate the 95% confidence intervals on projected waiting times. Another module calculates projected waiting times for three different categories of treatment, given user specified parameters, including financial data. The trade-offs between financial profit and waiting times for treatment are demonstrated. This shows quite explicitly the human consequences (in terms of delay in starting treatment) of decreasing, for fiscal reasons, resources allocated to certain categories of patients. The advantage of this type of analysis is that it clearly shows that waiting lists do not always arise through mismanagement of resources, they arise because resources are inadequate.