Difference between revisions of "Cost-effectiveness analysis"

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ICER = CostB – CostA / EffectivenessB – EffectivenessA
ICER = CostB – CostA / EffectivenessB – EffectivenessA
In medical informatics, CEA involves such comparisons as the cost of system (e.g., drug alert) implementation with the cost of treating the adverse events that occur in the absence of the system.
In medical informatics, CEA involves such comparisons as the cost of system (e.g., drug alert) implementation with the cost of treating the adverse events that occur in the absence of the system. See also, [[Cost-Benefit Analysis]].

Revision as of 03:26, 3 March 2007


Cost-effectiveness analysis (CEA) seeks to quantify the benefits of a medical intervention through the use of a clinical outcome such as life expectancy, cost per life saved, or the number of adverse events (e.g., anaphylactic reaction) avoided. In CEA, the interventions being compared must be measured using the same measurement of cost (dollars) and the same measurement of outcome (a clinical outcome). The standard metric is the incremental cost-effectiveness ratio (ICER), the ratio of net costs to net benefits in dollars per unit outcome. Expressed as an equation by Friedman and Wyatt (1),

ICER = CostB – CostA / EffectivenessB – EffectivenessA

In medical informatics, CEA involves such comparisons as the cost of system (e.g., drug alert) implementation with the cost of treating the adverse events that occur in the absence of the system. See also, Cost-Benefit Analysis.


Cost-effectiveness analysis has for decades been a part of corporations’ decision-making process with regard to mergers and acquisitions, information technology purchases, and other activities with a quantifiable financial impact. Within health care, CEA has become more common during the past 40 years as new technologies, medical devices, and medications made it possible to treat medical conditions for which prognosis once was poor (one cannot compare the costs of treatment versus non-treatment in the absence of medical therapies) (2). CEA has commonly been practiced by insurance administrators when deciding which procedures and treatments to cover, but it can be applied to many other initiatives. CEA was one component used in developing the Oregon Health Plan (3,4), which has been described as denying coverage of low-priority services rather than of low-priority people (5).

Principle Use

Cost-effectiveness analysis can be used to assess the economic relationships among many components of patient care and medical practice. Some common uses include:

Classes of drugs – Several researcher in psychiatry have reported that atypical antipsychotic medications are cost-effective in general use among all schizophrenia patients, but Polsky and colleagues found no such advantage in a CEA comparing second-generation antipsychotics with first-generation antipsychotics (6).

Medical conditions – Zethraeus and associates reviewed a CEA model for osteoporosis prevention and treatment (7).

Screening programs – CEA has been widely used in the development of cancer screening programs (8).

Clinical research applications – Identifying and qualifying clinically meaningful biomarkers for new drug development can be difficult. Williams and colleagues describe how CEA can be used to qualify biomarkers when physiological processes are not well understood (9).

Medical devices – Drug-eluting stents have been developed as an alternative to bare metal stents for percutaneous coronary interventions on the premise that the release of medications offers additional cardiovascular benefits. Brophy and Erickson (10) used CEA to determine the incremental cost-effectiveness of replacing bare-metal stents with drug-eluting stents.


Cost-effectiveness analysis facilitates the comparison of multiple options that may have few common characteristics. It permits organizations to estimate the financial and other consequences of taking different courses of action, thereby supporting prudent use and administration of resources.


Cost-effectiveness analysis reduces a potentially complex decision that may involve multidimensional factors to a single ratio that compares the costs of multiple options. The CEA equation may force the analyst to place a dollar value on aspects of health care that may not lend themselves well to quantification. Decision-makers may reject the findings of CEA studies if they cannot agree with the underlying assumptions (e.g., additional days of survival) or the dollar values attached to components used in the calculation. CEA may be used as a basis for health care decision-making when patients and health care providers would be better served by open discussion at the societal level (11).

Examples in Informatics

Cost-effectiveness analysis has many applications in medical informatics. Eisenstein and colleagues used CEA to demonstrate that information interventions redirecting 10% of low-severity emergency room visits to outpatient care providers results in monthly savings of $12,523 to Durham County, North Carolina (12).

University of Toronto researchers used CEA to compare an automated medical documentation system to the system being used at a Canadian hospital (13). The existing hospital system created a gap between patient discharge and the entering of a final discharge note into patients’ records, which created problems for some patients. The analysis indicated that the automated documentation system had higher costs than the existing system but offered greater benefits.

Battistotti and associates analyzed the cost-effectiveness of a system that sends instant messages to remind patients of upcoming appointments (14). They reported a drop in the rate of missed appointments, which averaged 7-8% prior to system implementation, and noted that the system would pay for itself and generate cost savings.


(1) Friedman CP, Wyatt JC. (2006) Evaluation methods in biomedical informatics, 2nd ed. Springer Science+Business Media: New York, p. 315-6.

(2) Primer on cost-effectiveness analysis. Effective Clinical Practice. Sept/Oct 2000. Accessed online March 2, 2007 at http://www.acponline.org/journals/ecp/sepoct00/primer.htm.

(3) Hadorn DC. Setting health care priorities in Oregon: cost-effectiveness meets the rule of rescue. JAMA. 1991;265:2218-25.

(4) Klevit HD, Bates AC, Castanares T, Kirk EP, Sipes-Metzler PR, Wopat R. Prioritization of health care services: a progress report by the Oregon Health Services Commission. Archives of Internal Medicine. 1991;151:912-6.

(5) Lamb EJ. Rationing of medical care: rules of rescue, cost-effectiveness, and the Oregon plan. American Journal of Obstetrics and Gynecology. 2004;190:1636-41.

(6) Polsky D, Doshi JA, Bauer MS, Glick HA. Clinical trial-based cost-effectiveness analyses of antipsychotic use. American Journal of Psychiatry. 2006;163(12):2047-56.

(7) Zethraeus N, Borgstrom F, Strom O, Kanis JA, Jonsson B. Cost-effectiveness of the treatment and prevention of osteoporosis – a review of the literature and a reference model. Osteoporosis International. 2007;18(1):9-23.

(8) Knudsen AB, McMahon PM, Gazelle GS. Use of modeling to evaluate the cost-effectiveness of cancer screening programs. Journal of Clinical Oncology. 2007;25(2):203-8.

(9) Williams SA, Slavin DE, Wagner JA, Webster CJ. A cost-effectiveness approach to the qualification and acceptance of biomarkers. Nature Reviews. Drug Discovery. 2006;5(11):897-902.

(10) Brophy JM, Erickson LJ. Cost-effectiveness of drug-eluting coronary stents in Quebec, Canada. International Journal of Technology Assessment in Health Care. 2005;21(3):326-33.

(11) Sulmasy DP. Cancer care, money, and the value of life: whose justice? Which rationality? Journal of Clinical Oncology. 2007;25(2):217-22.

(12) Eisenstein EL, Anstrom KJ, Macri JM, Crosslin DR, Johnson FS, Kawamoto K, Lobach DF. Assessing the potential economic value of health information technology interventions in a community-based health network. AMIA Annual Symposium Proceedings. 2005;:221-5.

(13) Kopach R, Sadat S, Gallaway ID, Geiger G, Ungar WJ, Coyte PC. Cost-effectiveness analysis of medical documentation alternatives. International Journal of Technology Assessment in Health Care. 2005;21(1):126-31.

(14) Battistotti A, Quaglini S, Cuoco E. Reducing dropouts in outpatient care through an SMS-based system. Studies in Health Technology and Informatics. 2006;124:935-40.