experience (1). The main contributors to patient waiting times are inadequate appointment duration, staff experience level, patient late arrival and machine.
S564 ESTRO 36 _______________________________________________________________________________________________
The aim of the study was to evaluate the effectiveness of the new workflow in terms of reducing errors. Material and Methods Since April 2016, a paperless workflow has been introduced for each area of the pathway including; referral, data capture at CT, planning information and treatment information up to the last fraction. A focus group was formed to investigate the options available for recording the required information at all stages. These included using an electronic referral and booking form, dynamic documents for recording treatment setup details, electronic journals for recording actions and histories throughout the treatment and toxicity scoring. All checks required on before, during and after treatments were assigned as tasks or checklists and these were made into a standardised automated protocol.All errors at our centre are recorded electronically on a centralised incidence reporting system. The numbers of error occurrences that happened 3 months before and after the introduction of the process were analysed. Results In total, there were 51 and 49 radiotherapy related incidents recorded before and after the introduction of the paperless workflow respectively. The number of incidents related to transcription errors decreased from 29% (15/51) to 16% (8/49) since the paperless change. It’s noted that there was a small rise in reported incidences in other areas of the pathway due to a change in work procedure. Conclusion It’s suggested the number of transcription errors was minimised through the adoption of the paperless workflow. It’s also proved to be beneficial to have a centralised electronic incident reporting system to monitor and review incidents in a radiotherapy department, in order to streamline and optimise existing patient pathways. PO-1023 Reducing waiting room times - A 5 year review of an in-house KPI tool A. Wallis1, D. Moretti1 1 Liverpool Hospital, Radiation Oncology, Liverpool, Australia Purpose or Objective Patient waiting times has a significant impact in a patient’s overall satisfaction of their healthcare experience (1). The main contributors to patient waiting times are inadequate appointment duration, staff experience level, patient late arrival and machine breakdowns (1). Literature on radiation oncology productivity is dominated by variation and validation of the basic treatment equivalent (BTE) model (2). However, the technological advancements in imaging and treatment modalities such as intensity modulated radiation therapy (IMRT), image guided radiotherapy (IGRT), volumetric RT (VMAT) and Tomotherapy have changed the landscape of RT and its productivity measures (4). In 2011, the management team at Liverpool and Macarthur Cancer Therapy Centres (LMCTC) introduced an in-house key performance indicator (KPI) tool to measure the performance of the treatment machines. The catalyst for the design and implementation of the tool was the introduction of the New South Wales (NSW) Performance Measures report of 2010 (3). The main objective of the tool was to capture each individual patient's appointment time to ensure adequate and individualised patient appointment scheduling. It was hypothesised that the introduction of this tool would reduce the waiting room time for patients. Material and Methods In 2010, Mosaiq 2.0X was installed in LMCTC. This version allowed the extraction of time stamps in a reporting tool (Crystal reports version 11). Standardisation of the treatment processes improved the robustness of patient
data and allowed accurate extraction of time stamps in Mosaiq. This data were then imported into Microsoft Excel on a weekly basis for visual display of the KPIs. The tool was launched in October of 2010 for a trial period of two months and has been in use in the department since its introduction. Results During the period of October to December 2010, the department recorded that 56% of patients were treated on time. Since the tool was introduced and actioned in 2011, the department has recorded an average of 71.2% (range 69-76%) of patients treated on time. These results are encouraging considering the number of attendances to the department has increased over the 5 year period (Fig 1). The percentage of patients arriving late to their appointment is 8% (range 7.0-9.1) (Table 1). The average waiting room time for a patient is 3.5 minutes (range 2.3 – 4.5 minutes).
Conclusion The development of an in-house KPI tool has reduced waiting time for patients at LMCTC. Since the introduction of the tool we have increased the number of patients treated on time from 56% to 71.2% over the past 5 years. This is despite the increasing patient attendances and changes in technology and complexity. Interestingly, despite improvements from hospital management to improve parking and access to the departments, 8% of patients do not arrive on time for their appointment. PO-1024 Effectiveness of couch coordinate constraints to reduce error rates in radiation therapy delivery O. Nairz1, N. Breitkreutz1 1 MVZ InnMed, Strahlentherapie, Oberaudorf, Germany Purpose or Objective “Movement from reference marks” is one of the most error-prone steps in the radiation therapy process. The use of indexed immobilization devices and constrained absolute, patient specific couch coordinates is generally considered to be an efficient tool to reduce the risk of radiation therapy errors (RTE) during treatment delivery. In the light of implementing a quantitative risk assessment we analyzed table coordinates of patients treated in our department. We investigated the effectiveness of tolerance values to lower the incidence of both wrong movements from reference marks and irradiation of the wrong patient or isocenter. Material and Methods Actual table values of patients in treatment position during a period of 18 months were extracted from the records of the verification system. Patient setups were
S565 ESTRO 36 _______________________________________________________________________________________________
divided into four groups: thermoplastic head masks, patient specific indexed whole body vacuum cushions, doubly indexed non-patient-specific immobilization – i.e. indexed knee/feet rests together with indexed head or head and arm rests – and not indexed at all. For the definition of the tolerance windows we required that at most one in forty setups should provoke an interlock. Furthermore movements between reference marks and target points have to be large enough to violate the tolerances with a high probability if they are not performed. We required this probability to be 99%. Results For all four subgroups feasible interlock thresholds can be defined. Especially for patient specific immobilization devices they can be set very tightly. For thermoplastic masks the limits are below plus or minus one centimeter in all three directions and for the vacuum cushions the largest tolerance value, which is in the longitudinal direction, amounts to not more than +/- 2.5 cm. But even ample tolerances, as we find for non-indexed immobilizations, should be implemented since they help to decrease the risk of irradiating the wrong patient or isocenter significantly. Also the obtained minimum shifts from the reference marks are feasible and can easily be adopted in routine setup. Conclusion Tolerances to table coordinates help to detect shifts which are not applied at all or not in all directions. They also prove to be efficient in discriminating between two different isocenters or patients. The values presented here are both dependent on the immobilization devices used and on the patient collective. Therefore each department has to examine the applicability of the values in its setting. Especially if other technical means, e.g. surface scanning or RFID technology, are not available, indexed immobilization devices and couch coordinate tolerances can serve as a simple and effective method to reduce the risk of RTEs in treatment delivery. PO-1025 Development of a in-house KPI tool A. Wallis1, D. Moretti1 1 Liverpool Hospital, Radiation Oncology, Liverpool, Australia Purpose or Objective Health informatics and data mining have enabled the analysis of operational performance and assist managers in making informed decisions in their departments (1). In 2010 the New South Wales Government in Australia requested that all departments, both public and private, were required to report on the percentage of patients treated within 10 minutes of their scheduled appointment time. At the time, the Liverpool and Macarthur Cancer Therapy Centres (LMCTC) did not have a tool which could measure the patient’s waiting time. This was the catalyst for developing an in-house tool to measure the patient’s waiting time as well as a number of other key performance indicators (KPIs). The purpose of this abstract is to present how an in-house tool can be developed and established within a department to measure departmental KPIs such as individual patient appointment times, patient waiting time, machine utilisation and the impact of changing techniques and technology. Material and Methods In 2010, Mosaiq 2.0X was installed in LMCTC. This version allowed the extraction of time stamps into a reporting tool (Crystal Reports V11). Definition of a patient's appointment required the standardisation of the treatment processes. This ensured improved robustness of patient data and allowed accurate extraction of time stamps in Mosaiq. The data from the reporting tool is imported into Microsoft Excel 2013 on a weekly basis for visual display and actioning on the KPIs.
Results A weekly in-house KPI tool which compares machine utilisation and performance, completion of QA tasks and individual patient appointments has been utilised at LMCTC since 2011. The tool has enabled staff to monitor patient appointment duration on a daily basis and allows direct comparison with the patient’s scheduled time. A traffic light system has been developed to allow easy visualisation of patient appointments requiring adjustment (Fig 1). A buffer time which is -12% and + 8% of the scheduled appointment time is applied to allow easy visualisation of appointments requiring action. Based on the results and traffic light display, each patient’s appointments are adjusted for the following week, resulting in a machine schedule made up of individualised patient appointments. Queue times are compared with scheduled patient appointments to review the timeliness of patients attending their appointment. The tool was designed and released in October of 2010 for a trial period of two months and has been in use in the department since its introduction.
Figure 1: Traffic light system which compared individual appointment duration against the scheduled appointment time. Conclusion The development of an in-house KPI tool has many advantages for a radiation oncology department. Individual appointment times can be recorded and adjusted to ensure adequate time is allocated for an individual’s needs. Ensuring adequate scheduling results in reducing patient waiting times and stress for treatment staff. It also displays machine utilisation and overall performance.
