INTELLIGENT KNOWLEDGE BASE FOR CANCER PAIN TREATMENT

描述：这项快速通道SBIR赠款建议确定为癌症疼痛治疗建立一个计算机化决策支持系统的可行性。研究人员先前的NCI研究开发并测试了一种药理学算法方法，该方法基于AHCPR癌症疼痛管理指南，在门诊环境中进行。药理学算法利用疗效与毒性的决策点将综合评估数据纳入治疗建议流程图。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算法项目显示，在一项对照试验中，随机进入算法过程的患者在统计上显著降低了疼痛强度。第二阶段项目将建立在嵌入工作流应用程序的算法评估组件的基础上，以启动疼痛管理知识库决策支持系统的快速原型。该系统将针对以RO1钢笔和铅笔算法格式处理的实际病例进行测试。一个临床专家小组将以盲目的方式对治疗建议进行评分。将在临床肿瘤学环境中进行Beta测试，重点是遵守原始算法指南和用户可接受性。

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 II project will build on the assessment components of the algorithm imbedded in a workflow application to launch rapid prototyping of a knowledgebase decision support system for pain management. The system will be tested against actual cases treated in the RO1 pen and pencil algorithm format. A panel of clinical experts will rate the treatment recommendations in a blinded fashion. Beta testing in a clinical oncology setting will be performed focusing on adherence to the original algorithm guidelines and user acceptability.