bluuebunny/crossref_metadata_2025_split huggingface dataset:
Use of pregabalin for preemptive analgesia in an enhanced recovery after surgery program.
Authors: Cain Katherine , Hoang Nhi , Iniesta Maria , Pitcher Brandelyn , Corzo Camila , Meyer Larissa , Lasala Javier , Ramirez Pedro T 
Published: September 2018

265 Background: A key component of any Enhanced Recovery After Surgery (ERAS) program is standardized multimodal analgesia to minimize opioid use. Our program utilizes acetaminophen, nonsteroidal anti-inflammatory agents and gabapentanoids perioperatively. The primary aim of our study was to evaluate pregabalin use after surgery and impact on total morphine equivalent daily dose (MEDD). Methods: Data was collected retrospectively on consecutive ERAS patients who underwent open gynecologic surgery from 3/4/2016 through 2/22/2018. We excluded any patient identified as a chronic opioid user. Standard preoperative medications included 300 mg tramadol ER, 75 mg pregabalin, and 400 mg celecoxib per institutional guidelines. Standard postoperative pain regimen included scheduled oral acetaminophen, ibuprofen and pregabalin unless contraindicated with rescue oral oxycodone and IV hydromorphone as needed. Pregabalin was ordered 75 mg twice daily for 4 doses. Recommended exceptions to pregabalin included hypersensitivity or age greater than 65 years. Median and range were calculated to summarize continuous variables. Spearman’s rank was used to test the correlation between post-operative pregabalin dose and MEDD. Results: A total of 533 patients were included for analysis. Twelve patients were excluded for chronic opioid use. 38% of patients received all four post-operative pregabalin doses (207/533). 32.8% of patients received no pregabalin (175/533). The fourth dose was the most common missed dose (274/533, 51.4%). The top reasons dose 4 was missed included not being ordered (58.8%) and patient already discharged (12.4%). There were 202 patients older than 65 years, the majority of these patients received no post-operative pregabalin (123/202, 60.9%). There was no difference in the amount of MEDD between those who received all pregabalin doses compared to those who received none (90.75 mg [4-663.5 mg] vs 82.5 mg [4-634.5 mg]; p = 0.269). Conclusions: Pregabalin for preemptive analgesia in an ERAS program does not decrease MEDD.
Representativeness of oncology clinical trials in Alberta.
Authors: Karim Safiya , Xu Yuan , Kong Shiying , Cheung Winson Y. 
Published: September 2018

27 Background: The Institute of Medicine encourages broad participation by patients in clinical trials to “efficiently provide practice-changing evidence”. Enrolment of a representative sample of the general oncology population in clinical trials is essential to ensure that outcomes are generalizable and to ensure equity and social justice. Our aims were to 1) determine the characteristics of all cancer patients compared to those enrolled on a clinical trial and 2) to determine factors and outcomes associated with clinical trial participation. Methods: We assembled a large cohort of patients diagnosed with all solid malignancies between 2004 and 2016 in a large Canadian province. We collected information on age, sex, tumor type, Charlson comorbidity index (CCI), year of diagnosis, treatment type (surgery, chemo, radiation, and immunotherapy). Logistic regression was used to identify factors associated with clinical trial participation. Cox regression models were constructed to determine overall survival (OS). Results: We identified 146,294 patients with a cancer diagnosis, of which 43% were men and the mean age at diagnosis was 61 years (SD 15.6). Approximately 3% (4,364/146,294) of all patients were enrolled in a clinical trial. Baseline characteristics showed that clinical trial patients were younger (mean age 58 years vs 61 years), had fewer comorbidities (CCI 0 = 83% vs. 73%) and more often male (45% vs 43%). Compared to all patients, clinical trial patients were more likely to be younger (OR 0.98, p < 0.001), male (OR 1.21, 95% CI 1.10-1.32 p < 0.001), and to have a diagnosis of brain (OR 3.8, 95% CI 2.86-5.05, p < 0.001), kidney (OR 2.61, 95% CI 1.93-3.52) or breast cancer (OR 2.38, 95% CI 1.84-3.08). Compared to all patients and those not treated on clinical trial, patients on clinical trial had improved OS (HR 0.81, 95% CI 0.77-0.85, p < 0.001). Conclusions: Only a small proportion of real world patients are treated within a clinical trial. Characteristics of clinical trials patients are significantly different from all patients with cancer. More representative clinical trials are needed in order to reflect the real world cancer population.
National trends in end of life care for veterans with advanced cancer.
Authors: Smith Claire Elizabeth Powers , Coke Pat , Kluger Monica , Kamal Arif , Kelley Michael J. 
Published: September 2018

3 Background: It is imperative to provide quality end of life (EOL) care for cancer patients. This entails minimizing aggressive measures at the EOL. Although rates of hospice utilization within the Veteran’s Health Administration have improved, chemotherapy administration and intensive care unit (ICU) admission at the EOL, indicators of aggressive care, are not clearly declining over recent years. Methods: We identified 32,665 veterans diagnosed with stage IV lung, colorectal, or pancreatic cancer who died between 2009-2016 using VA cancer registry and Corporate Data Warehouse data through a novel EOL Dashboard Tool, which has been validated at multiple VA sites. This EOL tool reports three indicators; incidence of chemotherapy use in the last 14 days of life, ICU admission in the last 30 days of life and hospice admission or consult. Change over time, 2009-2016, was assessed using a repeated measures one-way ANOVA with post hoc test for linear trend of time for individual cancers and two-way ANOVA for all cancers combined. Results: Chemotherapy use in the last 14 days of life declined from 6.8% in 2009 to 4.4% in 2016 (p < 0.05). ICU admission in the last 30 days did not change significantly, from 13.3% in 2009 to 14.7% in 2016. The exception was stage IV lung cancer patients in whom ICU admissions increased from 12.9% to 16.2% (p = 0.01). Patients utilizing hospice services increased from 32.4% to 52.6% (p < 0.01). When combined for all years in an unadjusted analysis by VA regional network (VISN), chemotherapy use ranged geographically from 4.2% to 8.1% and for ICU admission from 8.4% to 18.0%. Conclusions: While chemotherapy administration at the EOL is declining for veterans with stage IV cancer, ICU admissions are unchanged and becoming more common in stage IV lung cancer despite increasing hospice utilization. Compared to prior Medicare reports, veterans have similar rates of EOL chemotherapy use and fewer EOL ICU admissions, adding to a growing body of literature showing that despite veterans having poorer health and utilizing more medical resources, the VA performs at or above non-veteran health care institutions on end of life cancer care. There is notable geographic variation in aggressive EOL care.
Analyzing pathways data to reduce unwarranted variation, understand practice patterns, and update pathways.
Authors: Hall Julia Cooper , Hamilton Joanna M. , Jackman David Michael , Tremonti Carole Kathleen , Greenberg Teresa L. , Hayne Victoria , Stein Leah A. , Jacobson Joseph O. 
Published: September 2018

300 Background: A clinical pathways program requires regular updates to pathways content, real-time decision support, and data collection and analysis of decisions made. A goal of Dana-Farber Pathways (DFP) is to analyze data to reduce unwarranted variation and inform granular, warranted variation. Methods: Each of our 31 medical oncology pathways is created and updated by DF experts (clinicians and scientists). DFP is implemented through a web-based portal that provides real-time decision support. DFP captures on- and off- pathway treatment decisions and reasons for off-pathway decisions; this is shared with each disease group monthly. For each interval review meeting we assess on-pathway rates and decisions for each node in the pathway. Low on-pathway rates prompt discussion about possible action, including provider education or pathway modification. Results: In 2017, 7,460 total treatment decisions were collected through DFP; 78% were on-pathway. We have clinical reasoning for 90% of the off-pathway decisions. Off-pathway analysis has been used in several important ways: 1. Catalyzing change: we detected early adoption of immunotherapy in small cell lung cancer and discussed the relevant data. The pathway was updated to adopt this class of treatment. 2. Understanding unexpected events: we identified a recent etoposide shortage and discussed alternate recommendations. They were added to the pathway in case of future shortages. 3. Provider education: we detected a consistently low on-pathway rate in one location, mostly driven by a specific provider. This provided a mechanism to discuss practice patterns and provide targeted education. Conclusions: Off-pathway analysis provides insight into user variation, fosters and supports peer coaching, and supports the creation of dynamic, up-to-date pathways.[Table: see text]
Creating an end-of-life dashboard with administrative data.
Authors: Wong Tracy , Mezheritsky Irina , Jagels Barbara , Loggers Elizabeth Trice 
Published: September 2018

304 Background: The care given at the End of Life (EoL) is a topic of great interest for cancer patients, oncologists and insurers. This focus has been guided by a desire to achieve the highest quality of life (QoL) near the end of life while simultaneously decreasing costs of what is deemed by many as futile life-sustaining efforts. While EoL measures are reported by hospitals and clinics to external quality programs, the ability for a single center to sustainably measure these data proves burdensome, thus limiting the ability to assess and monitor for the purposes of improvement. Seattle Cancer Care Alliance (SCCA), a single comprehensive cancer center, set out to create regular, accessible and actionable EoL reporting. Methods: This project was broken into 5 phases: (1) Determining rationale for reporting and defining the audience(s). (2) Defining the patient population and measures of interest. (3) Identifying data sources and ensuring data quality. (4) Developing the report and building a data visualization. (5) Sharing report with stakeholders to seek input on definitions visual display. Results: An EoL Metrics Dashboard was created and made available to all SCCA clinicians and staff. The dashboard provides rates for 10 EoL metrics: chemotherapy at 7/14/30 days before death, radiation treatment at 7/14/30 days before death, emergency department (ED) visit within 30 days of death (DoD), ED plus inpatient admission within 30 DoD, intensive care unit (ICU) stay within 30 (DoD), and death in the ICU. The denominator is defined as decedents with ≤3 visits at SCCA within 12 months of death. Data is displayed monthly, rolling 16-month with trendlines for EoL events. Data is viewable by EoL event, cancer type, sex, race, age at death and payor. There is also a permission-based view containing patient-level data. Conclusions: With clear data definitions and dedicated analytic resources it is feasible to create an automated EoL report using administrative data. The largest limitation is that the data available to a hospital or clinic is only inclusive of care within their facility. Also, while this report is useful to detect trends, additional analysis is required at the patient level to determine whether an event was avoidable or did not align with the patient’s wishes.
Harnessing the electronic health record to optimize oral anticancer therapy monitoring and follow-up.
Authors: Jeffers Kate , Walde Amy 
Published: September 2018

305 Background: UCHealth pursued and became certified as a Quality Oncology Program from the Quality Oncology Practice Initiative (QOPI) through the American Society of Clinical Oncology (ASCO) in 2015. During our gap analysis to prepare for certification, we identified a deficit in our education, monitoring, and follow up relating to oral anti-cancer therapies. Although we used QOPI as a benchmark, we sought to improve patient care and safety in the realm of oral anti-cancer therapies. Methods: UCHealth has implemented policies surrounding oral chemotherapy to include patient education, consent, use of the EHR for ordering, and patient monitoring of adherence and toxicity through development of a flowsheet. This includes the use of Best Practice Alerts (BPA) to trigger staff to evaluate adherence and compliance, smart texts to pull data into progress notes, silent BPAs to remind staff to call patients within 10 days of starting oral chemotherapy, and weekly reporting of staff compliance with assessing patient adherence. Results: EHR changes were implemented in May, 2015 with reporting of staff compliance beginning in September. At that time, UCHealth was only monitoring adherence and toxicity in 24% of patients on an oral anti-cancer therapy. Through continued quality improvement projects, staff education, and optimization of clinical decision support tools, UCHealth consistently monitors adherence and toxicity in over 85% of patients on an oral anti-cancer therapy. Results have been monitored for over two years with continued improvements seen. Conclusions: Using the reporting data, we are able to identify quality improvement projects to include discrete data such as individual staff member compliance. Continuous refinements of the BPA and report have occurred as a result of discrete data analysis by a multi-disciplinary committee.
Improving efficiency of exam room use at a comprehensive cancer center.
Authors: Yakowec Jing Jing Wang , Regan Emily , Wagner Andrew J. , Herold Christina Isabella 
Published: September 2018

307 Background: Efficient use of exam rooms is crucial to be able to accommodate increasing patient volumes. One workflow approach that takes patients into an exam room only when the provider is ready can free up space for other patient-provider pairs (co-rooming), in contrast to rooming the patient before knowing the provider’s readiness status (sequential-rooming). We conducted a pilot to implement co-rooming on the breast cancer patient floor at Dana-Farber Cancer Institute (DFCI) and to assess whether patient time alone in exam rooms is reduced. Methods: As part of the planning for the co-rooming pilot, root cause analysis, current state sequential-rooming workflow mapping, and proposed future state co-rooming workflow mapping were performed. In the pilot group, clinic assistants roomed a patient only if the provider verbally expressed readiness within 10 minutes. The real-time locating system (RTLS) database was leveraged to generate baseline and pilot data. For 5 providers, patient time alone in an exam room on 5 and 3 consecutive baseline (2/17/17 – 3/17/17) and pilot (3/24/17 – 4/7/17) Friday mornings, respectively, were analyzed using the XbarS statistical process control chart method. Results: At baseline, the mean patient time alone in an exam room was 14 minutes (range 0-86). During pilot mornings, the mean patient time alone was reduced to 4 minutes (range 0-20), which was statistically significantly lower than the baseline mornings. The table below provides the XbarS chart data for the mean time alone +/- 3-sigma. Conclusions: In our pilot, patient time alone in an exam room was significantly decreased by changing from a sequential-rooming process to a co-rooming process, with minimal disruption to workflow. Co-rooming can reduce inefficient exam room use. [Table: see text]
Using electronic medical record system to improve compliance with national guidelines for comprehensive distress screening in cancer patients.
Authors: Pandya Chintan , Sabatka Sandra , Kettinger Michelle , Alongi Alexander , Hamel Lauren M. , Guancial Elizabeth A. , Dougherty David W. 
Published: September 2018

309 Background: Psychosocial distress screening (DS) and management is associated with improved quality of life and outcomes in cancer patients and is required for accreditation by the American College of Surgeons Commission on Cancer. Comprehensive distress screening (CDS) consists of routine distress screening, evaluation, referral to appropriate psychosocial services, and follow-up to ensure adequate care. Electronic medical record (EMR) systems can be leveraged to facilitate and document CDS as part of clinical care and to evaluate the CDS process as a quality standard. The aim of this study is to develop and implement an EMR-based tool to document and evaluate the CDS process as part of routine oncology care. Methods: An EMR-based tool with structured data fields is developed for social workers to document risk factors for distress, assessment, management plan including psychosocial service referrals, and time spent delivering care following DS using the NCCN distress thermometer (DT). Evaluation of CDS process is done in cancer patients who have documented psychosocial care in the EMR-system from 1/2017-5/2018. Results: During the study period, 1327 cancer patients underwent 2480 distress screening evaluations. The average distress score was 3.2 (median = 2) on the DT scale of 0-10, with 855 (64%), 326 (25%), and 146 (11%) patients reporting on average mild (0-3), moderate (4-6), and severe (7-10) distress respectively. 400/1327 (30%) patients accounted for 1177 documented social work contact/visits, of which financial (40%) and emotional (15%) were the most common concerns. 89% (1047) of the visits had follow-up plans and 77% of encounters resulted in referrals, of which financial support (26%) and pharmacy assistance (22%) were the most common referral services. The average time spent on each psychosocial care visit was reported to be 21 minutes. Conclusions: EMR-based forms with structured data fields can be used to document and promote improved adherence to national guidelines for CDS as part of routine oncology care by facilitating data collection. Such tools can be leveraged to capture relevant data on impact of CDS on social work resource utilization.
The Washington State Community Cancer Care Report: A multi-stakeholder effort to characterize quality of care and costs for Washington State oncology practices.
Authors: Ramsey Scott David , Kreizenbeck Karma L. , Panattoni Laura Elizabeth , Fedorenko Catherine R. , Li Li , Sun Qin , Barger Sarah , Lyman Gary H. 
Published: September 2018

31 Background: Although many quality measures exist in oncology, there have been few efforts to prioritize, measure, and report quality and costs for an entire region. Here, we report the results of a multi-year, multi-stakeholder effort aimed at public reporting of nationally recognized quality metrics for oncology practices in Washington State. Methods: Quality metrics were selected from nationally-recognized measures through a structured process involving oncology providers, health insurance leaders, patient advocates, and policy experts. Cancer registry records from 2014 to 2016 were linked with claims data from two commercial insurers and Medicare, representing approximately 70% of cancer patients in the state. Patients were assigned to oncology clinics using claims data; 27 clinics were large enough for inclusion in at least one measure in the report. Thirteen metrics were combined to produce one clinic-level quality score per measure. Each quality score was mapped to total episode cost per patient. Results were adjusted to account for clinic size, cancer characteristics (e.g. stage), demographics, and comorbidity where appropriate. Results: Conclusions: The Community Cancer Care in Washington State: Quality and Cost Report 2018 is the first publicly accessible report showing clinic-level quality measures linked to cost in oncology. Its ultimate goal is to improve care and lower costs by spurring collaboration, research and innovation.[Table: see text]
Improving quality care through IT/analytics for team-based care interventions.
Authors: Rodgers Georgina T. , Lindberg Jacob 
Published: September 2018

316 Background: Team based care is essential to achieve coordinated, high-quality care. We recognized a responsibility to identify new treatment patients to provided targeted, team based interventions. Previous attempts at identifying new patients were difficult, manual, and delayed. Our aim was to provide personnel with the appropriate means to identify new patients for services such as patient education, financial navigation, social work assessments and outreach calls for care coordination. As a result of multiple barriers and challenges, only 51% of our new patients were being identified. This project fit well in the larger context of the transformation of health care delivery through our participation in the Oncology Care Model, which places an emphasis on team based care. Methods: We used the Plan-Do-Study-Act continuous improvement model to develop an automated data set to identify new treatment patients across the oncology service line and leveraged our Information Technology (IT) Analytics team. A report was established based utilizing logic to identify the first appointment scheduled within the department and a secondary tab to look back at new patients within a three week time frame. Information is housed within a business intelligence software system that is easy for end users to see and understand. The initial report was validated for accuracy at an identification rate of 93% and the 3 week “look back” report gives the ability to identify the remaining patients. Results: We have seen marked improvements metrics as a result of the utilization of this report. Our Financial Navigators have been able to improve out of pocket estimate calls to 98% in April of 2018. Social Work initial patient assessments have improved by 40%, post chemotherapy outreach calls from RN Care Coordinators have improved 12%, and we have seen a 20% increase in the number of documented oncology patient education encounters. Conclusions: This project demonstrates the value of using IT/Data Analytics for continuous quality improvement projects related to patient centered team based care. The cross collaboration of stakeholders in data analysis should be explored as a mechanism to transform care through key services and interventions.