A clinical decision and economic analysis model of cancer pain management

Am J Manag Care. 2003 Oct;9(10):651-64.


Objective: To design a model that educates clinical decision makers and healthcare professionals about the burden of cancer pain in their individual populations, and that assists them in weighing the effectiveness and cost of different cancer pain management strategies.

Study design: Tailored cost-effectiveness analysis using an evidence-based decision analytic model.

Methods: The spreadsheet-based model compares 3 strategies: (1) guideline-based care (GBC), (2) oncology-based care (OBC), and (3) usual care (UC). The model calculates the likelihood of cancer pain in a healthcare population, how effectively that pain is managed, and the average monthly cost of medications plus procedural interventions. Model inputs were derived from published US population demographics, cancer registry data, high-quality studies of cancer pain management, standard reimbursement schedules, and expert opinion. The model permits users to tailor population demographics, strategy effectiveness, and resource costs.

Results: Of 100 000 patients with typical US demographics, approximately 508 (0.51%) will have cancer and 205 (0.20%) will suffer from cancer pain. After 1 month, the percentage of cancer pain patients with effective pain management and the cost of each strategy were estimated as follows: (1) GBC, 80% and dollar 579; (2) OBC, 55% and dollar 466; and (3) UC, 30% and dollar 315. Compared with OBC, GBC had an incremental cost-effectiveness ratio of dollar 452 per additional patient relieved of cancer pain. Compared with UC, OBC had an incremental cost-effectiveness ratio of dollar 601 per additional patient relieved of cancer pain.

Conclusion: Guideline-based cancer pain management leads to improved pain control with modest increases in resource use.

Publication types

  • Comparative Study
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cost of Illness
  • Cost-Benefit Analysis
  • Decision Support Systems, Clinical*
  • Decision Support Techniques
  • Drug Costs / statistics & numerical data
  • Health Care Costs / statistics & numerical data
  • Humans
  • Models, Econometric*
  • Neoplasms / complications*
  • Neoplasms / economics
  • Neoplasms / physiopathology
  • Pain / etiology
  • Pain Management*
  • Palliative Care / classification
  • Palliative Care / economics
  • Palliative Care / methods*
  • Practice Guidelines as Topic
  • Probability
  • United States