bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reducing wait time (WT) in the infusion center (IC).
Authors: Foster Gail , Lopez Lianne , Aschian Angela , Chan Betty , Inofuentes Martha , Truax Zoraida , Golingay Sally , Lynch Gwendoyn , Matsuura Peggy , Barzi Afsaneh 
Published: February 2017

249 Background: Lengthy WT is the primary source of dis-satisfaction with IC services. We created a patient (PT) flow diagram outlining each step beginning from PT check-in to discharge and plotted the impact of multiple disciplines on the WT. A team of nurses, pharmacists, physicians, and administrators, analyzed the flow and identified ways to improve efficiencies. Methods: First attempt was to improve communications between pharmacy and nursing, coordinating the preparation of medications with patient’s check-in time; use of a simple clipboard facilitated this task and improved the efficiency of the system. Next, the team worked on re-routing administration of non-intravenous medications to clinic nurses. Administration of all non-intravenous medications, not requiring prolonged monitoring, was shifted to clinic nurses. Hospital policies reviewed and in-services regarding specific medication, monitoring, and administration techniques were conducted prior to this transition. Our scheduling template was ignorant of the capacity and time frames available for each PT, therefore allowing more PT than available slots. With help from information technology, the scheduling template was redesigned to include constraints that would allow scheduling to the maximum of available slots. Our center does not have an emergency room. Therefore, on a daily basis an average of 5-7% of PT load is related to urgent treatments such as blood transfusion, hydration, antibiotic therapy, etc. To proactively anticipate this volume, a virtual bed was assigned to treat only urgent cases leaving one bed open at all times. A nursing staffing plan was also developed to staff these urgent cases. Lastly, physicians were trained to turn in the chemotherapy orders ahead of the scheduled time and to be prompt in responding to pharmacy and nursing staff for order clarification. Results: We selected time from check-in to IC to start of chemotherapy as our outcome measures. Prior to implementation of changes the average WT was 3 hours (H) & 26 minutes (M). As a result of this project the WT was reduced to an average of 2 H & 46 M in the 2nd quarter and remained stable in the 3rdquarter despite a 20% increase in the volumes. Conclusions: Delivery of timely PT care is possible through multidisciplinary efforts.
Assessing the cost of cancer care delivery using a time-driven, activity-based costing software.
Authors: Incalcaterra James , Guzman Alexis B. , Huang Yu-Ting , DelValle-Garza Monica , Kolom Christian C. , Zhao Xin , Mutua Danson N , Dhingra Krishan , Feeley Thomas W. 
Published: February 2017

25 Background: Poor costing systems and measurement have led to cross-subsidies and cost-shifting in health care. A large academic cancer center has adopted Robert Kaplan’s bottom-up cost accounting methodology called time-driven, activity-based costing (TDABC). TDABC in health care has been proven to be an effective cost accounting tool to measure and improve care delivery by standardizing and creating transparency around patient care processes. The project aims to process map and identify event triggers associated with each process map, use a software applications to compute the costs and resource capacities. Methods: Information technology and financial subject-matter experts integrated clinical, resource, and financial data from the institution’s enterprise information warehouse, general ledger, resource and asset management systems into the software application. Clinical business managers, nurse managers and other clinical content experts helped identify patient-level care processes. Results: The institution deployed a project team to integrate data from the institution’s enterprise information warehouse and aid in the process mapping across three multidisciplinary care centers. The team was able to successfully cost both direct and overhead costs associated with 69 head and neck, 18 endocrine, and 15-20 proton therapy patient-level processes over 7 different business department within 7 months. The resource capacity analysis was the most difficult to analyze due of the lack of transparency around resource’s clinical, administrative, and research responsibilities. Dashboards are currently being developed to help assess changes in patient care processes, cost or resource utilization. Conclusions: This methodology can be used across all health care organizations in all countries to analyze the true cost of care delivery.
Using a collaborative approach for rapid deployment of advance care planning in the outpatient oncology setting.
Authors: Severson Jane Alcyne , Smerage Jeffrey B. , Lindsay Jamie , Petersen Laura , Swetz Keith Mark , Kamal Arif 
Published: February 2017

255 Background: The Michigan Oncology Quality Consortium (MOQC) is a quality collaborative supported by Blue Cross Blue Shield of Michigan with the goal of improving cancer care in Michigan by using data gathered as part of the national Quality Oncology Practice Initiative (QOPI) program. In spring 2014, MOQC launched a statewide collaborative focused on advance care planning (ACP) with the following aim: to provide training and resources on how to engage patients in a discussion of goals, preferences, and priorities for care. Methods: The collaborative was comprised of three half day learning sessionsthat included an overview of legal and documentation requirements, and facilitator-lead role play with patient actors. Lean resources (templates, workflows) to support standard work in the clinic were disseminated. Eight oncology practices participated ranging in size from 1-25 physicians. ASCO QOPI data were analyzed pre and post collaborative in spring 2014 and spring 2015. Results: Improvement in QOPI performance from spring 2014 to 2015 on “hospice enrollment, palliative care referral/services, or documented discussion” (73.8% to 77.6%) and “for patients not referred, hospice or palliative care discussed within the last 2 months of life” (17.7% to 23.9%) was noted for participating practices (n = 8). Collaborative participants also compared favorably to all other MOQC practices (n = 47) in spring 2015 on “hospice enrollment, palliative care referral or documented discussion” (77.6% vs. 66.2%) (p < 0.001). Conclusions: Use of a collaborative education modelthat includes facilitator-lead role play and dissemination of lean resources to create standard work may be an efficient and effective approach to improve discussion and documentation of advance care planning.
Quality and value of subspecialty reinterpretation of outside thoracic CT scans of patients referred to a tertiary cancer center.
Authors: Carter Brett W. , Erasmus Jeremy J. , Truong Mylene , Munden Reginald F. , Shepard Jo-Anne O. , Clarke Ryan Kristopher , Steele Joseph Rodgers 
Published: February 2017

265 Background: At tertiary cancer centers, physicians frequently request reinterpretation of imaging studies performed at outside institutions. The purposes of this study were to determine the quality of outside computed tomography (CT) scans of the chest and compare the accuracy of accompanying radiology reports from outside institutions and our multidisciplinary cancer center. Methods: Two thoracic radiologists graded the quality of 59 outside chest CT scans and generated independent reports for 52 of the scans. A third thoracic radiologist scored the outside reports and reinterpretations for quality. Fisher’s exact tests were used to compare the frequency with which crucial items appeared in outside reports and reinterpretations. Next, two outside thoracic radiologists identified discrepancies between outside reports and reinterpretations (first radiologist) and determined whether the outside report or reinterpretation was more accurate in each case (second radiologist). Finally, the impact of discrepancies on management was evaluated, largely based on NCCN guidelines. Results: Of the 59 outside CT scans, 35 (59%) were of poor quality. Reinterpretations were more likely than outside reports to include information about lymph nodes, adrenal and liver metastasis, tumor nodules, and tumor texture. In 19 of 52 cases (37%), discrepancies were identified between outside reports and reinterpretations. In 17 of these cases, the reinterpretation was superior; in 2 cases, the reinterpretation and outside report were of equal quality. Among these 17 cases, reinterpretation allowed staging in nine cases that could not be staged with information from the outside reports; resulted in upstaging without management change in one case and upstaging with management change in four cases among the five cases with staging information present in both sets of reports; and revealed a significant omission (2 cases) or error (1 case) that changed management in three cases. In total, reinterpretation resulted in significant changes to 16 of 52 (31%) of CT scans. Conclusions: Subspecialty reinterpretation of chest CT scans can substantially improve clinical management.
Initiating a lymphedema screening program for breast cancer patients: Determining variability as a first step towards standardization.
Authors: Shaitelman Simona Flora , Ferguson Susan E , DeSnyder Sarah Marie , Kheirkhah Parviz , Lilly Susan , Travis Marigold , Johnson Theresa , Alvarez Ana , Rohe Duke , Buchholz Thomas A. , Bedrosian Isabelle 
Published: February 2017

267 Background: Breast cancer-related lymphedema (BCRL) is a known morbidity from treatment, and yet published estimates of its incidence are highly variable. Typically, a single volume measurement is recorded and the degree of intra-rater variability undefined. A threshold 5% limb volume change over time is used to define “mild lymphedema.” Therefore, small variations in volume measurements could substantially lead to over or under-diagnosis of BRCL. Gage R&R is a measurement systems analysis tool that quantifies the amount of variability induced in measurements by the measurement system itself and compares this to the total variability observed. The objective of this study was to define the degree of variability in perometer arm volume measurements and factors contributing to this. Methods: A medical assistant (MA) was trained by a certified lymphedema therapist (CLT) to perform perometer-based lymphedema screening in the MD Anderson Nellie B. Connally Breast Center. Three measurements of each arm were taken and intra-rater variability against the mean calculated. Gage R&R testing was performed by the MA and CLT, to determine the degree of repeatability and reproducibility variation. As a part of this analysis, 30 patients had both their left and right arm volume measured 3 times by each measurer. Results: Over 6 months, 565 breast cancer patients had preoperative BCRL screening by the MA, with an overall intra-rater variability of 2.2%. Among the 30 patients in the Gage R&R study, median body mass index (BMI) was 27.7 (range 19.7-38.3), median height 1.61m (range 1.52-1.73m), intrarater variability of the MA was 2.07% and of the CLT was 1.21%. Gage R&R accounted for 4.77% of total variability, 1.26% repeatability and 3.51% reproducibility. Regression analysis demonstrated that patient BMI and height impacted variability in measurements (R2= 16.27). Conclusions: Intra-rater variability for perometer arm measurements diminishes with experience. Gage R&R variability is acceptable per industry standards, but there is room for improvement. Our findings suggest a role for targeted interventions to minimize variability in the diagnosis of BCRL based on patient characteristics.
Adopting QOPI metrics into a continuous plan for learning across a five hospital network.
Authors: Denny Diane , Dunn Kathleen , Hepler Marjorie , Allen Debra Kay , Markman Maurie 
Published: February 2017

268 Background: Cancer Treatment Centers of America, Inc. (CTCA) is a national network of five hospitals that specialize in the treatment of patients fighting complex or advanced stage cancer. Utilizing metrics from the Quality Oncology Practice Initiative (QOPI), CTCA developed a “QOPI related continuous measures plan” from those areas of opportunity which fell greater than 10% below the QOPI aggregate score based upon its centers initial data abstraction efforts and findings. A total of 20 QOPI related measures were selected. The plan endorsed by clinical leaders and administration targeted at 50% improvement in these measures within 18 months. Methods: A crosswalk of metrics including patient inclusions and exclusions was developed for the continuous measures plan with data abstraction efforts based upon a review of new patients regardless of analytic or non-analytic status. As they progress through their treatment course, some measures are reported quarterly while others are reported semi-annually given volumes. A composite target of 71.2% was identified using T2 methodology, halving the distance between desired level of performance and the lowest facility baseline. Multiple educational efforts and strategies were deployed including efforts to redesign elements in the EHR. Results: The results are made visible in a number of different forums including a quality dashboard housing quarterly data and a monthly operations review process. A composite score in which data is rolled up from all 20 measures is reported as a key performance indicator across all five centers. Baseline composite through current performance is shown below by facility and in total. Conclusions: The QOPI related continuous measures plan is an evolving performance improvement process providing clinicians with an achievable goal for improving patient care. Team members anticipate surpassing the target through current and planned interventions. [Table: see text]
ASCO PRO workgroup update: Patient-reported outcome measures as a quality indicator.
Authors: Stover Angela M. , Chiang Anne C. , Basch Ethan M. 
Published: February 2017

276 Background: Although patient questionnaires are commonly used to assess healthcare experiences (e.g., satisfaction with care), patient-reported outcome (PRO) measures assessing symptoms and physical functioning have not conventionally been used for quality assessment. More typically, quality of care is measured with administrative data, such as hospital readmission rates. There is an opportunity to advance the science of quality measurement by integrating a patient-centered approach. Methods: ASCO established a multi-stakeholder PRO Workgroup (including extensive patient input) to develop and test PRO-based performance measures (PRO-PMs). The ASCO PRO Workgroup outlined three initial PRO-PMs to test for patients receiving moderately/highly emetogenic chemotherapy: 1) process: proportion self-reporting symptoms at, or within 2 weeks, of last visit; 2) outcome: proportion experiencing moderate/high levels of nausea; and 3) outcome: proportion reporting moderate/high pain. We will give an update on progress of the ASCO PRO Workgroup and outline next steps needed to implement PRO measures as a quality metric. Results: Three key methodological advancements are needed before PRO measures can be used as a quality metric. First, areas of cancer care delivery need to be identified that are important to patients when considering quality of care and are amenable to performance evaluation with PRO measures (e.g., symptom control). Second, existing PRO questionnaires need to be systematically identified and evaluated for potential quality use. The review should evaluate psychometric properties, validity and reliability, and feasibility for clinical use. Third, testing is needed in representative practice settings to iteratively refine: 1) logistical strategy for systematically collecting this information at the practice level; 2) approaches for minimizing missing data, particularly from underserved populations; and 3) adjustments for patient characteristics (case-mix). Conclusions: Methodological advancements are necessary before PRO measures can be implemented as a quality metric. PRO measures have the potential to provide quality assessments that are useful to patients making health care decisions.
NCI Community Cancer Center Program (NCCCP): Disparities in time between cancer diagnosis and treatment initiation.
Authors: Teixeira-Poit Stephanie , Spain Pamela , Halpern Michael T. , Castro Kathleen M. , Prabhu Das Irene , Adjei Brenda A. , Clauser Steven 
Published: February 2017

279 Background: The National Cancer Institute Community Cancer Centers Program (NCCCP) was designed to improve care and reduce cancer disparities at community hospitals. This study examined the number of days between diagnosis and treatment for five National Quality Forum-endorsed cancer quality measures. Methods: A retrospective analysis of data from patients diagnosed and receiving breast or colon cancer treatment at 12 NCCCP sites was performed. We examined time to treatment for cancer quality measures, stratified by concordant and non-concordant patients. We compared patients diagnosed before (2006 – 2007) vs. during (2008-2013) the NCCCP period. Results: Concordant Cases. Time to treatment was significantly greater in the NCCCP vs. pre-NCCCP periods for three measures. Compared to pre-the NCCCP period, time to treatment in the NCCCP period was longer by 26 days for women with breast cancer receiving hormonal therapy (HT). Time to HT increased significantly among most patient and hospital subgroups (race, age, insurance, hospital size). Black, Medicaid, and/or uninsured patients generally had longer-than-average time to treatment for all measures when receiving guideline-concordant care. Non-Concordant Cases. Among women with breast cancer who received treatment outside the measure window, most eligible for multi-agent chemotherapy received treatment within one month after the cut-off. In contrast, a majority eligible for other cancer quality measures received treatment more than one after the cut-off. Among women who were non-concordant for HT, those with Medicare or private insurance were significantly more likely to begin HT within one month after the cut-off than were those with Medicaid. Conclusions: More than half of patients non-concordant on cancer quality measures received treatment more than one month after the treatment window. However, many were non-concordant because they were just outside the time window to be considered concordant (< 30 days), particularly Medicaid patients. Rapid reporting of quality indicators could inform targeted efforts to improve care coordination and concordance rates, particularly among certain patient subgroups.
Assessing the impact of restricted follow-up and small sample sizes on survival estimations in prostate cancer using registry data.
Authors: Shah Dhvani , Paly Victoria , Briggs Andrew , Sidhu Manpreet , Ma Esprit , Bonthapally Vijayveer 
Published: February 2017

294 Background: Economic evaluations in oncology aim to assess the value of new therapies in the long term based on clinical trial data that often have restricted follow-up times (< 5 years) and small sample sizes (< 500 patients). This requires the use of extrapolation assumptions on long-term survival that go beyond the observed data. In this analysis, differences in survival extrapolation methods are tested in samples of sizes and follow-up reflecting typical clinical trials against a background of known survival in prostate cancer from a US based cancer registry. Methods: Data from the National Cancer Institute's Surveillance Epidemiology and End Results (SEER) registry on long-term survival in patients with stage IV prostate cancer were employed. The data set comprised those patients diagnosed between 1988 and 2003, with follow-up data available until 2012. Additional survival for those who received surgery (compared to those who did not), was estimated based on extrapolations using standard parametric statistical models (exponential, Weibull, log-logistic, log-normal, Gamma) fitted to the observed data. Survival analyses were run for 5 sample size scenarios (n = 27,670, 1000, 500, 200, 50) and 6 follow-up scenarios (follow-up years = 25, 20, 10, 5, 2, 1) yielding 30 combination scenarios. Performance of the methods was tested relative to the maximum follow-up, maximum sample size scenario (i.e. reference case) from the SEER registry. Results: Log-logistic and log-normal models were associated with flat tails which led to inflated survival estimations. For scenarios with smaller sizes, gamma models often did not converge. Exponential models were the most frequently reported as best model fit (in approximately 50% of scenarios). Also, gains in OS were consistent when exponential models were selected, and closely matched gain in OS from the reference case. Conclusions: Since clinical trials in oncology are often associated with small patient sample sizes and restricted follow-up, selecting an exponential model may lead to the most consistent and stable results based on the experiment constructed here. Further research should confirm these results for other types of cancer.
Time trends in opioid use in cancer and noncancer patients: Observations from administrative data.
Authors: Barbera Lisa Catherine , DeAngelis Carlo , Earle Craig , Atzema Clare , Dudgeon Deborah Jane , Gomes Tara , Howell Doris , Husain Amna , O'Brien Mary Ann , Seow Hsien , Sussman Jonathan , Sutradhar Rinku , Chu Anna , Liu Ying 
Published: February 2017

298 Background: Opioid prescribing has been increasingly scrutinized in the non-cancer patient population due to concerns with morbidity, mortality and diversion. Resulting regulatory changes have decreased prescribing. As an unintended consequence, we hypothesized that cancer patients might be similarly impacted. Methods: All Ontario residents ≥ 65 years are eligible for government paid pharmacare. For each year from 2004 to 2013, Ontarians ≥ 65 years were stratified into 3 groups: no cancer history, cancer diagnosis > 5 years ago and cancer diagnosis ≤ 5 years ago. We evaluated time trends in 2 outcomes: (1) opioid prescription rate = total number of pharmacare claims / total population of ≥ 65 year olds, and (2) mean daily opioid dose (in morphine equivalents) = sum of all patient’s mean daily opioid doses over their first 90 days of opioid therapy in each year / total patients with an opioid prescription in that year. Results: Ontario’s population of ≥ 65 year olds increased each year with 2.1 million residents in 2013 (5% recent cancer, 10% remote cancer). Demographics were similar by year for each group. Across all years, overall opioid prescription rates were highest for those with a recent cancer and lowest for those with no cancer history. Overall prescription rates increased over time by 23%, 14% and 10% in the non-cancer, remote cancer and recent cancer groups respectively, primarily due to increases in long acting opioids and immediate release single agents. Decreases were seen in long acting oxycodone and fentanyl prescriptions in all cancer groups. The mean daily opioid dose increased for patients receiving long acting oxycodone and fentanyl but was relatively stable for other drug classes. With the exception of immediate release single agents, the mean daily dose was similar between cancer groups regardless of drug class. Conclusions: Regulatory measures have succeeded in decreasing prescription rates in some but not all drug classes among Ontarians aged ≥ 65. Changes over time in both prescription rates and mean daily opioid dose were similar for all 3 groups, suggesting that factors influencing prescribing are affecting cancer and non-cancer patients similarly, possibly to the detriment of some cancer patients.