In this paper, we present the design and implementation of the integrated proactive surveillance system for prostate cancer (PASS-PC). The integrated PASS-PC is a multi-institutional web-based system aimed at collecting a variety of data on prostate cancer patients in a standardized and efficient way. The integrated PASS-PC was commissioned by the Prostate Cancer Foundation (PCF) and built through the joint of efforts by a group of experts in medical oncology, genetics, pathology, nutrition, and cancer research informatics. Their main goal is facilitating the efficient and uniform collection of critical demographic, lifestyle, nutritional, dietary and clinical information to be used in developing new strategies in diagnosing, preventing and treating prostate cancer.The integrated PASS-PC is designed based on common industry standards – a three tiered architecture and a Service- Oriented Architecture (SOA). It utilizes open source software and programming languages such as HTML, PHP, CSS, JQuery, Drupal and MySQL. We also use a commercial database management system – Oracle 11g. The integrated PASS-PC project uses a “confederation model” that encourages participation of any interested center, irrespective of its size or location. The integrated PASS-PC utilizes a standardized approach to data collection and reporting, and uses extensive validation procedures to prevent entering erroneous data. The integrated PASS-PC controlled vocabulary is harmonized with the National Cancer Institute (NCI) Thesaurus. Currently, two cancer centers in the USA are participating in the integrated PASS-PC project.The final system has three main components: 1. National Prostate Surveillance Network (NPSN) website; 2. NPSN myConnect portal; 3. Proactive Surveillance System for Prostate Cancer (PASS-PC). PASS-PC is a cancer Biomedical Informatics Grid (caBIG) compatible product. The integrated PASS-PC provides a foundation for collaborative prostate cancer research. It has been built to meet the short term goal of gathering prostate cancer related data, but also with the prerequisites in place for future evolution into a cancer research informatics platform. In the future this will be vital for successful prostate cancer studies, care and treatment.
e17507 Background: Clinical trial sponsors have strong scientific, financial, and regulatory interests in rapidly activating studies at participating sites. Academic medical centers have difficulty activating trials within a few weeks of sponsor agreement because, among other inefficiencies, they engage the necessary committee reviews, regulatory approvals, contracting, and budgeting in serial fashion. Incremental revisions in such workflows do not result in strong improvements. Methods: We redesigned our institutional workflow to complete clinical trial activation tasks within six weeks. Historical procedures were replaced rather than scrutinized. A high level leadership committee was required to change and integrate procedures across the medical center, and engage sponsors to improve their turnaround times. A web-based collaborative workflow tracking tool was created to help coordinate the necessary tasks and measure performance. Six clinical trials from the Cancer Center portfolio were used to test and improve the new workflow. Results: Clinical trial activation redesign took one year. For the six studies used as tests of change, the activation times were 49, 54, 78, 58, 62, and 32 days. Times in excess of 6 weeks were largely due to sponsor delays. Conclusions: Considerable effort is required to significantly alter a complex workflow like clinical trial activation. Appropriate priorities, leadership, staffing, and tools are required. Markedly shortened study activation for a small series of cancer trials taught our academic medical center lessons that will be useful for improving the process for all clinical trials, and will make us a better partner for pharmaceutical and academic sponsors as well as for investigator initiated research. [Table: see text]
collections of stem cells and autologous therapeutic cells rose by 23% to an all time high of 106 collections in one month. The average number of individual patients per week rose from 8.7 (standard deviation 2.4) to 11.2 (standard deviation 1.8). Nursing safety standards were not exceeded, and management involvement in resolving scheduling conflicts dropped from two requests per week to one in two months. Conclusion: By changing apheresis scheduling method to day one scheduling only, we were able to increase throughput without having to make any additional adjustments to resources. The clearer format resulted in increased staff satisfaction with the scheduling process. This change represented a paradigm shift from previous scheduling models, and has yielded a major improvement in use of resources. Additionally, it provides a dynamic tool to support evaluation of facility utilization to meet future demand.
staff, leadership. 4. Rejuvenation of shared governance at the unit level. Results: To determine the influence of the ANM role, a staff engagement survey was re-administered and compared to the 2010 results. The results showed a dramatic increase in staff engagement and satisfaction, as evidenced in the attached table. In addition, since the ANM role was established, nurse certification has increased by 120% and nurses pursuing higher education has increased 700%, supporting the premise that the ANM role had a positive influence in professional nursing development. Discussion: The ANM role had a positive influence on staff engagement, satisfaction, and professional growth. Effective motivational strategies combined with a functional management structure cultivates an environment where the highest level of quality care can be delivered.
Background: To ensure the highest degree of accuracy for the CIBMTR (Center for International Blood and Marrow Transplant Research) data, the Penn State Milton S. Hershey Medical Center Bone Marrow Transplant Program has created Powerforms integrated into the EMR (Electronic Medical Record) for GVHD (Graft versus Host Disease) and PS (Performance Status) Assessment. In 2008, the data manager was able to change the GVHD and PS Assessment from a paper based method to an Electronic method. Methods: The Bone Marrow Transplant Data Manager collects GVHD Assessment for all Allogeneic Transplant patients, and Assessments for patients on the research track at the time periods of Day 100, six months, and yearly post-transplant. The assessments are collected in the EMR. The Attending Physicians then review, modify if needed, and sign the assessment forms. These forms are used as source documentation during CIBMTR Data Submission Audits. Results: Our results show a decrease in errors found during a CIBMT audit. The NMDP Audit in 2004 showed an overall error rate (OER) of 1.2% and a Critical Error Rate (CER) of 2.8%. PS errors were 47.9% of the CER, and GVHD errors were 14.6% of the CER. In 2008, the audit showed a 1.7% OER and a 3.8% CER. PS errors were 24.5% of the CER, and GVHD were 3.8% of the CER. In 2012, the audit showed a 0.7% OER and a 0.8% CER. PS errors were 4.3% of CER and GVHD were 4.3% of CER. Conclusion: Implementing the collection of GVHD and PS assessment using the EMR has decreased the error rate found on the CIBMTR Audit. The continuation of this method of data collection can further reduce the error rate.
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