Search

57 Background: BMT survivors are underrepresented in survivorship research, yet are at high risk for complications. Practice guidelines are vague, non-BMT clinicians are inadequately informed, and communication between BMT and non-BMT clinicians is inconsistent. Our objective is to develop EHR-integrated survivorship care planning that is user-centered, supports non-BMT clinician and survivor needs, but does not adversely impact clinical workflow. Methods: A multidisciplinary team of clinicians (primary care, oncology, BMT), engineers, and EHR analysts used a systems engineering approach to identify barriers and facilitators to BMT survivorship care planning. The team identified patient data categories to include in BMT survivorship care plan (SCP) templates, as well as examined tasks, technology, workflows and individual roles and responsibilities necessary to support care planning. Results: Facilitators include: potential for EHR to discretely capture individual diagnosis and treatment data to create accurate SCPs addressing survivor and primary care information needs. Barriers are: lack of EHR inter-operability which prevents sharing of patient data outside the BMT center’s EHR system, reliance on manual entry of critical data elements into the SCP (i.e. majority of the 88 patient data categories, identified by the team, are non-discrete in the EHR), inefficient or absent survivorship workflows, lack of resources (including time, dedicated clinical staff, space, SCP content), poorly defined roles and responsibilities for survivorship care provision, and lack of evidence-based BMT survivorship guidelines. Conclusions: Work system barriers impede use of the EHR to support survivorship care planning. Steps to overcome barriers: design discrete fields in the EHR to support patient-level data capture, re-engineer existing workflows to support survivorship care planning, obtain BMT program consensus on SCP content, and evaluate user-centeredness of SCPs. This research has the potential to improve feasibility and sustainability of survivorship care planning activities, resulting in improved communication and care coordination for BMT survivors.

e14014Background: High-risk neuroblastoma (NBL) patients (pts) enrolled in a COG phase III clinical trial (ANBL0032) were randomized to isotretinoin (RA) alone or RA + immunotherapy (IM): dinutuxim...

9583 Background: Clinical information, “-omic” datasets, and tissue samples are becoming more difficult to harmonize and manage for advanced data mining. We believe that clinical research data can be centralized and provide direct access to sample availability and associated data from a variety of information stores. Methods: We obtained a standardized set of anonymized patient data from the International Neuroblastoma Risk Group. The cohort consists of more than 11,000 children diagnosed worldwide between 1974 and 2002. The data consist of 34 metrics, such as age at diagnosis, stage of tumor, and other clinical and biological markers. We instantiated the dataset into a Postgres database, and using the Django web framework, created a data model for rapid development of tools and views and built a front-end interface for generating complex queries. To test the feasibility of accessing information on disparate and geographically distinct data samples, we have a formal agreement with the Children's Oncology Group Tumor Bank at The Research Institute at Nationwide Children's Hospital. Based on query results, we consume the Tumor Bank tissue inventory data through a web-facing application programming interface. The end-user is presented only with the number of patients who match their query search terms and for whom tissue samples are available. Results: We have completed our initial implementation and have agreements for collaboration with other international consortium groups. We have created a paradigm for statisticians to securely update and add data, and a verification system checks for internal validity and provides a report of the transaction. Our system can initiate queries and accept results in a variety of standards-compliant formats, and will be available in demonstration form by May 2012. Conclusions: Querying patient data while interrogating external sources allows researchers to observe which ancillary data and samples are available and to quickly download data or request any samples. While designed around a neuroblastoma dataset, our system can be applied to a variety of clinical scenarios and will be made available through an open-source license.