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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. Nairz
1
, N. Breitkreutz
1
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
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. Wallis
1
, D. Moretti
1
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,