Wu Jonn, Ho Cheryl, Laskin Janessa, Gavin David, Mak Paul, Duncan Keith, French John, McGahan Colleen, Reid Sherry, Chia Stephen, Cheung Heidi
BC Cancer Agency, Vancouver, British Columbia.
Stud Health Technol Inform. 2013;183:98-103.
Understanding the impact of treatment policies on patient outcomes is essential in improving all aspects of patient care. The BC Cancer Agency is a provincial program that provides cancer care on a population basis for 4.5 million residents. The Lung and Head & Neck Tumour Groups planned to create a generic yet comprehensive software infrastructure that could be used by all Tumour Groups: the Outcomes and Surveillance Integration System (OaSIS). The primary goal was the development of an integrated database that will amalgamate existing provincial data warehouses of varying datasets and provide the infrastructure to support additional routes of data entry, including clinicians from multiple-disciplines, quality of life and survivorship data from patients, and three dimensional dosimetric information archived from the radiotherapy planning and delivery systems. The primary goal is to be able to capture any data point related to patient characteristics, disease factors, treatment details and survivorship, from the point of diagnosis onwards. Through existing and novel data-mining techniques, OaSIS will support unique population based research activities by promoting collaborative interactions between the research centre, clinical activities at the cancer treatment centres and other institutions. This will also facilitate initiatives to improve patient outcomes, decision support in achieving operational efficiencies and an environment that supports knowledge generation.
了解治疗政策对患者治疗结果的影响对于改善患者护理的各个方面至关重要。不列颠哥伦比亚癌症机构是一个省级项目,为450万居民提供全面的癌症护理。肺癌和头颈肿瘤小组计划创建一个通用但全面的软件基础设施,供所有肿瘤小组使用:结果与监测整合系统(OaSIS)。主要目标是开发一个综合数据库,该数据库将整合现有的省级不同数据集的数据仓库,并提供基础设施以支持额外的数据录入途径,包括多学科临床医生的数据、患者的生活质量和生存数据,以及从放射治疗计划和交付系统存档的三维剂量信息。主要目标是能够从诊断开始就捕捉与患者特征、疾病因素、治疗细节和生存相关的任何数据点。通过现有的和新颖的数据挖掘技术,OaSIS将通过促进研究中心、癌症治疗中心的临床活动和其他机构之间的协作互动,支持基于人群的独特研究活动。这也将促进改善患者治疗结果的举措、实现运营效率的决策支持以及支持知识生成的环境。