The impact of treatment complexity and computer-control delivery technology on treatment delivery errors

Abstract Purpose: To analyze treatment delivery errors for three-dimensional (3D) conformal therapy performed at various levels of treatment delivery automation and complexity, ranging from manual field setup to virtually complete computer-controlled treatment delivery using a computer-controlled conformal radiotherapy system (CCRS). Methods and Materials: All treatment delivery errors which occurred in our department during a 15-month period were analyzed. Approximately 34,000 treatment sessions (114,000 individual treatment segments [ports]) on four treatment machines were studied. All treatment delivery errors logged by treatment therapists or quality assurance reviews (152 in all) were analyzed. Machines “M1” and “M2” were operated in a standard manual setup mode, with no record and verify system (R/V). MLC machines “M3” and “M4” treated patients under the control of the CCRS system, which (1) downloads the treatment delivery plan from the planning system; (2) performs some (or all) of the machine set up and treatment delivery for each field; (3) monitors treatment delivery; (4) records all treatment parameters; and (5) notes exceptions to the electronically-prescribed plan. Complete external computer control is not available on M3; threfore, it uses as many CCRS features as possible, while M4 operates completely under CCRS control and performs semi-automated and automated multi-segment intensity modulated treatments. Analysis of treatment complexity was based on numbers of fields, individual segments, nonaxial and noncoplanar plans, multisegment intensity modulation, and pseudoisocentric treatments studied for a 6-month period (505 patients) concurrent with the period in which the delivery errors were obtained. Treatment delivery time was obtained from the computerized scheduling system (for manual treatments) or from CCRS system logs. Treatment therapists rotate among the machines; therefore, this analysis does not depend on fixed therapist staff on particular machines. Results: The overall reported error rate (all treatments, machines) was 0.13% per segment, or 0.44% per treatment session. The rate (per machine) depended on automation and plan complexity. The error rates per segment for machines M1 through M4 were 0.16%, 0.27%, 0.12%, 0.05%, respectively, while plan complexity increased from M1 up to machine M4. Machine M4 (the most complex plans and automation) had the lowest error rate. The error rate decreased with increasing automation in spite of increasing plan complexity, while for the manual machines, the error rate increased with complexity. Note that the real error rates on the two manual machines are likely to be higher than shown here (due to unnoticed and/or unreported errors), while (particularly on M4) virtually all random treatment delivery errors were noted by the CCRS system and related QA checks (including routine checks of machine and table readouts for each treatment). Treatment delivery times averaged from 14 min to 23 min per plan, and depended on the number of segments/plan, although this analysis is complicated by other factors. Conclusion: Use of a sophisticated computer-controlled delivery system for routine patient treatments with complex 3D conformal plans has led to a decrease in treatment delivery errors, while at the same time allowing delivery of increasingly complex and sophisticated conformal plans with little increase in treatment time. With renewed vigilance for the possibility of systematic problems, it is clear that use of complete and integrated computer-controlled delivery systems can provide improvements in treatment delivery, since more complex plans can be delivered with fewer errors, and without increasing treatment time.