INTELLIGENT KNOWLEDGE BASE FOR CANCER PAIN TREATMENT

产品说明：这个快速通道SBIR补助金建议确定建立一个计算机化的决策支持系统用于癌症疼痛治疗的可行性。 研究者之前的NCI研究开发并测试了一种基于AHCPR癌症疼痛管理指南的门诊药理学算法方法。 药理学算法利用疗效与毒性决策点将综合评估数据汇集到治疗建议流程图中。 根据疼痛的严重程度和副作用指标，重新评估参数指导后续护理。 RO1算法项目证明，在对照试验期间，随机分配至算法过程的患者的疼痛强度在统计学上显著降低。 第一阶段项目将把“纸和笔”算法的评估组成部分转换为医疗保健工作流程软件应用程序。 工作流程中嵌入的评估元素将根据原始算法研究的实际数据集进行数据建模。 将评估工作流程模型的可行性和功能限制。 长期目标将是利用优化的评估工作流程应用程序，推出一个快速原型的完整算法，以知识为基础的决策支持系统。

DESCRIPTION: This FAST-TRACK SBIR grant proposes to determine the feasibility of building a computerized decision support system for cancer pain treatment. The investigator's previous NCI research developed and tested a pharmacologic algorithm method, based on the AHCPR Guideline for Cancer Pain Management, in the outpatient setting. The pharmacologic algorithm utilizes an efficacy versus toxicity decision point to funnel comprehensive assessment data into a treatment recommendations flow chart. Re-assessment parameters guide follow-up care dependent upon the severity of pain and side effect indicators. The RO1 algorithm project demonstrated a statistically significant reduction in pain intensity for patients randomized to the algorithm process during a controlled trial. The Phase I project will convert the assessment components of the "paper and pencil" algorithm into a health care workflow software application. The assessment elements, embedded in the workflow will undergo data modeling against actual datasets from the original algorithm study. Feasibility and feature limitations to the workflow model will be assessed. The long term objective will be to utilize the optimized assessment workflow application to launch a rapid prototyping of the complete algorithm into a knowledge-based decision support system.