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Quality Improvement Initiative for Addressing Pain in Patients Undergoing Radiation Therapy
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Volume 96 Number 2S Supplement 2016 Purpose/Objective(s): Quality assurance (QA) is a critical peer review process in radiation therapy that has been shown to detect errors that can be corrected prior to delivery of the first treatment. This is an important but complex process involving input from a multi-disciplinary team consisting of medical physicists, radiation oncologists, and treatment planners. Highly automated treatment planning for breast radiotherapy (RT) has been the standard of care at this institution since 2004. Automated approaches have led to improvements in planning efficiency and process standardization with a reduction in planning related errors. On average, 1,000 radical breast plans and 300 boost plans are reviewed yearly. Given the large volume of cases, the current challenge was to develop a process to improve efficiency whilst maintaining safety. An automated QA framework was developed to better prioritize time for complex cases presented at QA rounds. Materials/Methods: As a first step in this planned multistep prospective approach to improving QA efficiency, all RT plans for weekly QA rounds were reviewed and assigned complexity scores. A 4-point categorical scoring system was utilized. The scoring system assesses and categorizes the overall complexity of breast RT plans, with separate clinical and planning scores reflecting standard (score Z 0) to high complexity cases (score Z 3). The scoring quantifies (1) the level of clinical complexity based on the discussion at rounds and (2) the technical and planning complexity. Scores were attributed for all cases discussed from July 2014 to February 2015. Automated ranking of clinical complexity was assessed using the normalized discounted cumulative gain. This metric assigns a perfect score of 1 if the predicted algorithm ranked patients exactly based on recorded clinical complexity. Rank learning was performed using the Rank SVM algorithm with comprehensive features drawn from the images, regions of interest (ROI), and the RT plan. Examples include ROI dose volume histograms, shape features, beam geometry descriptors, and monitoring units. Results: The initial results have focused on predicting the clinical complexity score in order to prioritize the ranking of cases for QA rounds. The clinical complexity scores of 375 plans were reviewed using the normalized discounted cumulative gain grouped into 30 study dates. Three possible orderings were devised (1) random ordering as a base case (2) 4 field, 2 field, followed by boost plans, and (3) the algorithm’s predicted ordering. The average results over all study dates were as follows (1) random: 0.61, (2) 4 field, 2 field followed by boost plans: 0.77, and (3) learned algorithm: 0.82. Conclusion: The automated QA algorithm’s predicted ordering has led to a superior ranking of complex clinical cases. Complex plans are inherently at a higher risk for error. The ability of this automated QA process to rank and prioritize complex cases will ultimately help to focus the QA rounds, improve efficiency, and reduce the potential for error. Author Disclosure: K. Rock: None. A.S. Barry: None. C. McIntosh: None. T. Purdie: None. C.A. Koch: None.
1164 Quality Improvement Initiative for Addressing Pain in Patients Undergoing Radiation Therapy A.L. Holtzman,1 J.P. Williams,1 D.F. Hutchinson,1 C.G. Morris,2 and A.R. Yeung1; 1Department of Radiation Oncology, University of Florida, Gainesville, FL, 2Department of Radiation Oncology, University of Florida, Jacksonville, FL Purpose/Objective(s): Pain management during radiotherapy (RT) is a prevalent issue affecting oncology patients and is an important metric used to assess quality of care. By establishing a standardized intervention to better address pain during on-treatment visits (OTVs), we hope to lower patient-reported pain scores and enhance the overall care in patients undergoing RT. Materials/Methods: Between June 2015 and February 2016, 93 consecutive patients were enrolled on an IRB-approved quality improvement initiative. Thirty-five patients were evaluated prospectively following the intervention and 58 were retrospectively reviewed to provide a control group for assessing outcome. The intervention was multifaceted: (1) a pain
management in-service was performed by a resident physician with the nursing staff; (2) educational material was created for patients and reviewed with them during the initial nurse teaching session; and (3) any patient with a pain score of 5 or greater at an OTV was seen by a clinic nurse within 2 days to determine if any further action was needed. Pain was assessed on a weekly basis during RT using the patient-reported Defense and Veterans Pain Rating Scale (DVPRS). Study aims were a 30% reduction in pain scores above 5 or greater and to increase the number of pain scores documented during every on-treatment visit (OTV) to over 90%. A Fisher exact test was used for statistical comparison. Results: Of the 58 retrospective and 35 prospective patients enrolled on the study intervention, 67% (n Z 39) and 66% (n Z 23) were treated with curative intent, respectively. The median patient age at enrollment was 65 (range, 27-89) and 68 years (range, 38-80 years), accordingly. Primary disease sites included lung (72% and 63%), gynecologic (22% and 31%), and other (6% and 6%) in the two groups, respectively. Before the intervention, average DVPRS score at initial consultation was 2 (range, 0-9) and 97% of patients had pain scores documented during subsequent OTVs. Twenty-five percent (44/176) of patient-reported OTV DVPRS scores measured 5 or greater. Following the implementation of the intervention, average DVPRS score at initial consultation was 3 (range, 0-9) and 100% of DVPRS scores were documented during OTVs. Twelve percent (10/83) of patient-reported DVPRS scores were 5 or greater representing a 50% reduction (P < 0.05). The number of patients with 2 or more pain scores greater than or equal to 5 was reduced from 20% (11/55) prior to the intervention to 7% (2/30) after the intervention (P Z 0.13). Conclusion: Actively involving nursing staff in the initial education and then follow-up of patients led to a 50% reduction in patient-reported DVPRS scores of 5 or greater reported during weekly OTVs. Author Disclosure: A.L. Holtzman: None. J.P. Williams: None. D.F. Hutchinson: None. C.G. Morris: None. A.R. Yeung: None.
1165 Potential Failure Modes for Magnetic ResonanceeOnly Treatment Planning in the Pelvis C.K. Glide-Hurst, B.M. Miller, J.P. Kim, M.S.U. Siddiqui, and B. Movsas; Henry Ford Health System, Detroit, MI Purpose/Objective(s): To devise and apply a failure mode and effects analysis (FMEA) to conventional multi-modality (MR-simulation in conjunction with CT-simulation) and MR-only (MR-simulation as the primary radiation treatment planning (RTP) modality) workflows for pelvis external beam RTP. Materials/Methods: A multi-disciplinary 9 member team developed process maps, identified potential failure modes (FMs) and causes, and assigned numerical values to the Occurrence (O), Severity (S), and Detectability (D) parameters. Risk priority numbers (RPNs) were calculated based on the product of O, S, and D as a surrogate metric of relative patient risk. An alternative metric, a 3-digit composite number (SOD) was computed to elucidate high-severity FMs for each workflow. Results: Five major process maps were shared by both workflows, mostly within the MR imaging domain. Synthetic CT generation from MRI datasets introduced 3 major subprocesses and propagated 46 unique FMs, 15 with RPNs >100. In contrast, image fusion and target delineation subprocesses using the conventional workflow added 9 and 10 FMs, respectively, with 6 RPNs >100. FMs with the highest RPNs for MR-only RTP were inaccurate target/organ delineation (inadequate training, RPN Z 240) and patient- and system-level distortion corrections not properly managed (RPN Z 210 and 168, respectively). The dose calculation subprocess had RPNs ranging from 140-192 due to inaccuracies introduced by bone segmentation in MR datasets. For conventional RTP, highest RPNs occurred for poor image fusion quality and interpretation of multi-modality information confounding delineation. The highest SODs were related to changes in target location due to bladder/rectal filling conditions for both conventional (961, RPN Z 54) and MR-only (822, RPN Z 32) workflows (Table 1).
Volume 96 Number 2S Supplement 2016 Purpose/Objective(s): Quality assurance (QA) is a critical peer review process in radiation therapy that has been shown to detect errors that can be corrected prior to delivery of the first treatment. This is an important but complex process involving input from a multi-disciplinary team consisting of medical physicists, radiation oncologists, and treatment planners. Highly automated treatment planning for breast radiotherapy (RT) has been the standard of care at this institution since 2004. Automated approaches have led to improvements in planning efficiency and process standardization with a reduction in planning related errors. On average, 1,000 radical breast plans and 300 boost plans are reviewed yearly. Given the large volume of cases, the current challenge was to develop a process to improve efficiency whilst maintaining safety. An automated QA framework was developed to better prioritize time for complex cases presented at QA rounds. Materials/Methods: As a first step in this planned multistep prospective approach to improving QA efficiency, all RT plans for weekly QA rounds were reviewed and assigned complexity scores. A 4-point categorical scoring system was utilized. The scoring system assesses and categorizes the overall complexity of breast RT plans, with separate clinical and planning scores reflecting standard (score Z 0) to high complexity cases (score Z 3). The scoring quantifies (1) the level of clinical complexity based on the discussion at rounds and (2) the technical and planning complexity. Scores were attributed for all cases discussed from July 2014 to February 2015. Automated ranking of clinical complexity was assessed using the normalized discounted cumulative gain. This metric assigns a perfect score of 1 if the predicted algorithm ranked patients exactly based on recorded clinical complexity. Rank learning was performed using the Rank SVM algorithm with comprehensive features drawn from the images, regions of interest (ROI), and the RT plan. Examples include ROI dose volume histograms, shape features, beam geometry descriptors, and monitoring units. Results: The initial results have focused on predicting the clinical complexity score in order to prioritize the ranking of cases for QA rounds. The clinical complexity scores of 375 plans were reviewed using the normalized discounted cumulative gain grouped into 30 study dates. Three possible orderings were devised (1) random ordering as a base case (2) 4 field, 2 field, followed by boost plans, and (3) the algorithm’s predicted ordering. The average results over all study dates were as follows (1) random: 0.61, (2) 4 field, 2 field followed by boost plans: 0.77, and (3) learned algorithm: 0.82. Conclusion: The automated QA algorithm’s predicted ordering has led to a superior ranking of complex clinical cases. Complex plans are inherently at a higher risk for error. The ability of this automated QA process to rank and prioritize complex cases will ultimately help to focus the QA rounds, improve efficiency, and reduce the potential for error. Author Disclosure: K. Rock: None. A.S. Barry: None. C. McIntosh: None. T. Purdie: None. C.A. Koch: None.
1164 Quality Improvement Initiative for Addressing Pain in Patients Undergoing Radiation Therapy A.L. Holtzman,1 J.P. Williams,1 D.F. Hutchinson,1 C.G. Morris,2 and A.R. Yeung1; 1Department of Radiation Oncology, University of Florida, Gainesville, FL, 2Department of Radiation Oncology, University of Florida, Jacksonville, FL Purpose/Objective(s): Pain management during radiotherapy (RT) is a prevalent issue affecting oncology patients and is an important metric used to assess quality of care. By establishing a standardized intervention to better address pain during on-treatment visits (OTVs), we hope to lower patient-reported pain scores and enhance the overall care in patients undergoing RT. Materials/Methods: Between June 2015 and February 2016, 93 consecutive patients were enrolled on an IRB-approved quality improvement initiative. Thirty-five patients were evaluated prospectively following the intervention and 58 were retrospectively reviewed to provide a control group for assessing outcome. The intervention was multifaceted: (1) a pain
management in-service was performed by a resident physician with the nursing staff; (2) educational material was created for patients and reviewed with them during the initial nurse teaching session; and (3) any patient with a pain score of 5 or greater at an OTV was seen by a clinic nurse within 2 days to determine if any further action was needed. Pain was assessed on a weekly basis during RT using the patient-reported Defense and Veterans Pain Rating Scale (DVPRS). Study aims were a 30% reduction in pain scores above 5 or greater and to increase the number of pain scores documented during every on-treatment visit (OTV) to over 90%. A Fisher exact test was used for statistical comparison. Results: Of the 58 retrospective and 35 prospective patients enrolled on the study intervention, 67% (n Z 39) and 66% (n Z 23) were treated with curative intent, respectively. The median patient age at enrollment was 65 (range, 27-89) and 68 years (range, 38-80 years), accordingly. Primary disease sites included lung (72% and 63%), gynecologic (22% and 31%), and other (6% and 6%) in the two groups, respectively. Before the intervention, average DVPRS score at initial consultation was 2 (range, 0-9) and 97% of patients had pain scores documented during subsequent OTVs. Twenty-five percent (44/176) of patient-reported OTV DVPRS scores measured 5 or greater. Following the implementation of the intervention, average DVPRS score at initial consultation was 3 (range, 0-9) and 100% of DVPRS scores were documented during OTVs. Twelve percent (10/83) of patient-reported DVPRS scores were 5 or greater representing a 50% reduction (P < 0.05). The number of patients with 2 or more pain scores greater than or equal to 5 was reduced from 20% (11/55) prior to the intervention to 7% (2/30) after the intervention (P Z 0.13). Conclusion: Actively involving nursing staff in the initial education and then follow-up of patients led to a 50% reduction in patient-reported DVPRS scores of 5 or greater reported during weekly OTVs. Author Disclosure: A.L. Holtzman: None. J.P. Williams: None. D.F. Hutchinson: None. C.G. Morris: None. A.R. Yeung: None.
1165 Potential Failure Modes for Magnetic ResonanceeOnly Treatment Planning in the Pelvis C.K. Glide-Hurst, B.M. Miller, J.P. Kim, M.S.U. Siddiqui, and B. Movsas; Henry Ford Health System, Detroit, MI Purpose/Objective(s): To devise and apply a failure mode and effects analysis (FMEA) to conventional multi-modality (MR-simulation in conjunction with CT-simulation) and MR-only (MR-simulation as the primary radiation treatment planning (RTP) modality) workflows for pelvis external beam RTP. Materials/Methods: A multi-disciplinary 9 member team developed process maps, identified potential failure modes (FMs) and causes, and assigned numerical values to the Occurrence (O), Severity (S), and Detectability (D) parameters. Risk priority numbers (RPNs) were calculated based on the product of O, S, and D as a surrogate metric of relative patient risk. An alternative metric, a 3-digit composite number (SOD) was computed to elucidate high-severity FMs for each workflow. Results: Five major process maps were shared by both workflows, mostly within the MR imaging domain. Synthetic CT generation from MRI datasets introduced 3 major subprocesses and propagated 46 unique FMs, 15 with RPNs >100. In contrast, image fusion and target delineation subprocesses using the conventional workflow added 9 and 10 FMs, respectively, with 6 RPNs >100. FMs with the highest RPNs for MR-only RTP were inaccurate target/organ delineation (inadequate training, RPN Z 240) and patient- and system-level distortion corrections not properly managed (RPN Z 210 and 168, respectively). The dose calculation subprocess had RPNs ranging from 140-192 due to inaccuracies introduced by bone segmentation in MR datasets. For conventional RTP, highest RPNs occurred for poor image fusion quality and interpretation of multi-modality information confounding delineation. The highest SODs were related to changes in target location due to bladder/rectal filling conditions for both conventional (961, RPN Z 54) and MR-only (822, RPN Z 32) workflows (Table 1).