INTELLIGENT KNOWLEDGE BASE FOR CANCER PAIN TREATMENT

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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.