Value of information of a clinical prediction rule: informing the efficient use of healthcare and health research resources.

Research paper by Sonia S Singh, Bohdan B Nosyk, Huiying H Sun, James Malcolm JM Christenson, Grant G Innes, Aslam Hayat AH Anis

Indexed on: 26 Jan '08Published on: 26 Jan '08Published in: International journal of technology assessment in health care


The aim of this study was to estimate the potential cost-effectiveness and expected value of perfect information of a recently derived clinical prediction rule for patients presenting to emergency departments with chest discomfort.A decision analytic model was constructed to compare the Early Disposition Prediction Rule (EDPR) with the current standard of care. Results were used to calculate the potential cost-effectiveness of the EDPR, as well as the Value of Information in conducting further research. Study subjects were adults presenting with chest discomfort to two urban emergency departments in Vancouver, British Columbia, Canada. The clinical prediction rule identifies patients who are eligible for early discharge within 3 hours of presentation to the emergency department. The outcome measure used was inappropriate emergency department discharge of patients with acute coronary syndrome (ACS).The incremental cost-effectiveness ratio of the EDPR in comparison to usual care was (negative) $2,999 per inappropriate ACS discharge prevented, indicating a potential cost-savings in introducing the intervention. The expected value of perfect information was $16.3 million in the first year of implementation, suggesting a high benefit from conducting further research to validate the decision rule.The EDPR is likely to be cost-effective; however, given the high degree of uncertainty in the estimates of costs and patient outcomes, further research is required to inform the decision to implement the intervention. The potential health and monetary benefits of this clinical prediction rule outweigh the costs of doing further research.