Pharmacy contributions to improving care and sustaining change in a comprehensive cancer center.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 150-150
Author(s):  
Megan Brown ◽  
David L. Crosby ◽  
Megan H. Howe ◽  
Konstantin H. Dragnev
Keyword(s):  
Performance Indicator ◽  
Project Work ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Drug Preparation ◽  
Start Time ◽  
Process Measures ◽  
Efficient Treatment ◽  
Delivery Of Care ◽  
Time Performance

150 Background: Delivery of care in an outpatient hematology/oncology clinic is complex and requires the coordination of numerous services for timely and efficient treatment of patients. The Norris Cotton Cancer Center in Lebanon, NH, began a comprehensive assessment of clinic and infusion operations in 2012 and developed a system-wide initiative to address inconsistent performance in infusion, clinic and pharmacy services. Methods: Cancer center staff and leadership were trained on lean six-sigma methodology to initiate system-wide improvements through targeted project work. We collaborated with quality improvement experts and our analytics team to obtain and analyze data from our EMR. Then we established process measures for all operational areas and a key performance indicator (KPI) for hem/onc infusion: “on time performance.” The pharmacy contributed to on-time performance by focusing improvement initiatives on turn-around time (TAT) for clinical reviews and order verification, TAT for drug preparation, and initiating a process for pre-making chemotherapy. Results: "On-time performance” was initially defined as the percentage of patients whose first medication began within 45 minutes of their scheduled infusion start time. The baseline of 63% in 2012 was improved to 86% in 2018. Through sustained process improvement work and concerted efforts to further improve the patient experience and utilization of resources, an interdisciplinary team targeted a 15 minute on-time performance measurement; this baseline of 28% was improved to 47% in less than a year. In 2012 the pharmacy set a goal of reviewing and verifying orders within 30 minutes, and completing drug preparation within 45 minutes of order verification. The baseline percentage of orders reviewed within 30 minutes was 47% and this increased to 93% in 2018. Our baseline percentage of doses prepared within 45 minutes was 37% and the current median time for drug preparation across all orders is 45 minutes. The pharmacy currently pre-makes approximately 11% of doses. Conclusions: The cancer center pharmacy increased efficiency and improved on-time performance in delivery of care by using data-driven continuous improvement methods.

scholarly journals Easier Said Than Done: Keys to Successful Implementation of the Distress Assessment and Response Tool (DART) Program

2016 ◽  
Vol 12 (5) ◽  
pp. e513-e526 ◽  
Author(s):  
Madeline Li ◽  
Alyssa Macedo ◽  
Sean Crawford ◽  
Sabira Bagha ◽  
Yvonne W. Leung ◽  
...  
Keyword(s):  
Cancer Care ◽  
Large Scale ◽  
Performance Indicator ◽  
Evaluation Framework ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Successful Implementation ◽  
Psychosocial Needs ◽  
Electronic Screening ◽  
Screening For Distress

Purpose: Systematic screening for distress in oncology clinics has gained increasing acceptance as a means to improve cancer care, but its implementation poses enormous challenges. We describe the development and implementation of the Distress Assessment and Response Tool (DART) program in a large urban comprehensive cancer center. Method: DART is an electronic screening tool used to detect physical and emotional distress and practical concerns and is linked to triaged interprofessional collaborative care pathways. The implementation of DART depended on clinician education, technological innovation, transparent communication, and an evaluation framework based on principles of change management and quality improvement. Results: There have been 364,378 DART surveys completed since 2010, with a sustained screening rate of > 70% for the past 3 years. High staff satisfaction, increased perception of teamwork, greater clinical attention to the psychosocial needs of patients, patient-clinician communication, and patient satisfaction with care were demonstrated without a resultant increase in referrals to specialized psychosocial services. DART is now a standard of care for all patients attending the cancer center and a quality performance indicator for the organization. Conclusion: Key factors in the success of DART implementation were the adoption of a programmatic approach, strong institutional commitment, and a primary focus on clinic-based response. We have demonstrated that large-scale routine screening for distress in a cancer center is achievable and has the potential to enhance the cancer care experience for both patients and staff.

scholarly journals Definition and Coordination of Roles and Responsibilities Among Cancer Center Clinic and Research Personnel

2020 ◽  
Vol 16 (1) ◽  
pp. e64-e74
Author(s):  
Simon J. Craddock Lee ◽  
Torsten Reimer ◽  
Sandra Garcia ◽  
Erin L. Williams ◽  
Mary West ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Physician Attitudes ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Research Staff ◽  
Research Personnel ◽  
Delivery Of Care ◽  
Roles And Responsibilities ◽  
Clinic Staff ◽  
Staff Members

PURPOSE: Effective enrollment and treatment of patients in cancer clinical trials require definition and coordination of roles and responsibilities among clinic and research personnel. MATERIALS AND METHODS: We developed a survey that incorporated modified components of the Survey of Physician Attitudes Regarding the Care of Cancer Survivors. Surveys were administered to clinic nursing staff and research personnel at a National Cancer Institute–designated comprehensive cancer center. Results were analyzed using χ2-tests, t tests, and analyses of variance. RESULTS: Surveys were completed by 105 staff members (n = 50 research staff, n = 55 clinic staff; 61% response rate). Research staff were more likely to feel that they had the skills to answer questions, convey information, and provide education for patients on trials (all P < .05). Both clinic and research staff reported receipt of communication about responsibilities in fewer than 30% of cases, although research staff reported provision of such information in more than 60% of cases. Among 20 tasks related to care of patients in trials, no single preferred model of responsibility assignment was selected by the majority of clinic staff for nine tasks (45%) or by research staff for three tasks (15%). Uncertainty about which team coordinates care was reported by three times as many clinic staff as research staff ( P = .01). There was also substantial variation in the preferred model for delivery of care to patients in trials ( P < .05). CONCLUSION: Knowledge, attitudes, and perception of care and responsibilities for patients on clinical trials differ between and among clinic and research personnel. Additional research about how these findings affect efficiency and quality of care on clinical trials is needed.

Reducing infusion wait time: Redesigning medication order verification workflow at a comprehensive cancer center.

2018 ◽  
Vol 36 (30_suppl) ◽  
pp. 129-129
Author(s):  
Michael D Kearney ◽  
Rachel A Wolfberg ◽  
Mark Sudol ◽  
Shital Shukla ◽  
Barbara Fine ◽  
...  
Keyword(s):  
Process Improvement ◽  
Patient Flow ◽  
Wait Time ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Drug Preparation ◽  
Medication Order ◽  
Throughput Time ◽  
Reduced Order ◽  
Dana Farber Cancer Institute

129 Background: Many factors contribute to long wait times for cancer patients on the day of their infusion. At Dana-Farber Cancer Institute (DFCI), a contributing factor is patient flow between exam and infusion. Order verification affects patient flow and begins when the following two criteria are met: provider signed an order and the patient’s scheduled infusion appointment arrives. Patients often check-in to infusion before their scheduled infusion appointment. Order verification has three sequential steps: nurse verification, pharmacist 1 verify (V1), and pharmacist 2 verify (V2). Methods: A team of pharmacists, nurses, providers, and process improvement leads designed a pilot in which V1 moved before nurse verification, concurrent with patient check-in to infusion. Further, V1 began as soon as an order was signed; the pharmacist did not wait for a patient’s scheduled infusion appointment. Nurse verification and V2 occurred in sequence after V1. Timestamp data were extracted from Epic and analyzed via Tableau to assess reduction in verification throughput, defined as time between infusion check-in and V2. Fourteen providers and one pharmacist joined a 6-week pilot to adopt the redesigned workflow beginning 4/23/18. Results: At baseline, time between check-in and V2 was consistent for pilot and non-pilot orders. During the pilot, time between check-in and V2 was shorter for pilot orders, showing a sustained decrease of approximately 10 minutes. The table below provides time in minutes between infusion check-in and V2 for pilot and non-pilot orders at baseline (3/12/18-4/20/18) and following workflow redesign (4/23/18-6/1/18). Conclusions: Implementing the pilot workflow reduced order verification throughput time and enabled drug preparation to begin sooner. Expanding this workflow to all medication orders can decrease infusion wait time at DFCI.[Table: see text]

Measuring quality with the Quality Oncology Practice Initiative (QOPI) at a university comprehensive cancer center

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 6535-6535
Author(s):  
D. W. Blayney ◽  
G. Miela ◽  
D. Markstrom ◽  
D. Hanauer ◽  
K. McNiff ◽  
...  
Keyword(s):  
Data Transfer ◽  
Electronic Record ◽  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Free Text ◽  
Tumor Type ◽  
Large Hospital ◽  
Process Measures ◽  
Search Tool ◽  
Oncology Practice

6535 Background: QOPI is a tool which measures adherence to both guidelines and to process for use in quality improvement. QOPI was designed and pilot tested predominately in physician office practices [Neuss et al, JCO 23:6233]. We tested QOPI at UMCCC, a large, hospital-based, academic cancer center, where medical documents are stored in a free-text electronic record, in a paper-based hospital record, and in clinic charts. In 2006, 136 physicians and 58 mid-level practitioners provided 66,699 clinic visits and oversaw 37,500 infusion visits. Methods: The tumor registry selected consecutive cases of breast (BrCa), lung, and colorectal cancer (CRC), and lymphoma (L), diagnosed between minus 6 and minus 30 months from day 0 of each QOPI round. Forty charts with each tumor type and 40 deceased patient records were identified for each round. A clinical pharmacist accessed the pharmacy database to obtain drug data, and the tumor registrar abstracted the three other chart formats, using an electronic text search tool [Hanauer et al Proc ASCO 2006 abs 6080] for the electronic record. De-identified data were submitted to a secure, ASCO-hosted server, for analysis. Results: In the Spring 2006 round, 163 charts yielding 1641 measures required 148 hours of abstractor effort, for an average of 54 minutes (mins) per chart and 5.4 mins per measure. In the Fall, 2006 round, 236 charts yielding 2334 measures required 256.5 hours of effort for 65 mins per chart and 6.6 mins per measure. For comparison, a survey of QOPI practices for the Fall round yielded 29 mins per chart. UMCCC treatment (Rx) and process measures are shown in the table . Conclusions: Abstracting for QOPI measures, using abstractors not involved in patient care, is labor intensive but feasible at a large cancer center. QOPI has allowed UMCCC to assess concordance with guidelines and other quality measures, provides comparison data to other practices, and identifies processes for improvement. The evolution of QOPI will include data transfer from electronic records. [Table: see text] [Table: see text]

ASCO-GU 2019: Fortgeschrittenes Nierenzellkarzinom

2019 ◽  
Vol 10 (02) ◽  
pp. 75-76
Author(s):  
Ine Schmale
Keyword(s):  
Comprehensive Cancer Center ◽  
Cancer Center ◽  
Washington Dc

Das Armamentarium zur Behandlung des Nierenzellkarzinoms (RCC) hat sich um effektive Therapien erweitert, durch die der Therapiealgorithmus komplett umgestellt werden musste. Prof. Michael B. Atkins vom Georgetown-Lombardi Comprehensive Cancer Center, Washington DC/USA, und Prof. Daniel Y. C. Heng vom Tom Baker Cancer Center, Calgary/Kanada, teilten beim ASCO-GU ihre Einschätzung zur optimalen Behandlung des Nierenzellkarzinoms in der Erst- und Zweitlinientherapie für das Jahr 2019.

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